[caffe] 01/07: Imported Upstream version 1.0.0~rc3+20160715-g42cd785
Zhou Mo
cdluminate-guest at moszumanska.debian.org
Mon Aug 1 03:25:34 UTC 2016
This is an automated email from the git hooks/post-receive script.
cdluminate-guest pushed a commit to branch master
in repository caffe.
commit 4322debdfb2115279b4fb44b51701b68935d546e
Author: Zhou Mo <cdluminate at gmail.com>
Date: Mon Aug 1 02:17:45 2016 +0000
Imported Upstream version 1.0.0~rc3+20160715-g42cd785
---
.travis.yml | 58 +-
CMakeLists.txt | 6 +-
Makefile | 23 +-
Makefile.config.example | 1 +
cmake/Modules/FindMKL.cmake | 2 +-
cmake/ProtoBuf.cmake | 2 +-
cmake/Targets.cmake | 19 +-
cmake/Utils.cmake | 5 +-
data/cifar10/get_cifar10.sh | 2 +-
data/ilsvrc12/get_ilsvrc_aux.sh | 2 +-
data/mnist/get_mnist.sh | 25 +-
docker/Makefile | 50 +
docker/README.md | 52 +
docker/standalone/cpu/Dockerfile | 43 +
docker/standalone/gpu/Dockerfile | 43 +
docker/templates/Dockerfile.template | 42 +
docs/installation.md | 35 +-
docs/multigpu.md | 2 +-
examples/00-classification.ipynb | 13031 +------------------
examples/01-learning-lenet.ipynb | 4680 +------
examples/02-brewing-logreg.ipynb | 5771 --------
examples/02-fine-tuning.ipynb | 1175 ++
examples/03-fine-tuning.ipynb | 947 --
examples/brewing-logreg.ipynb | 1164 ++
examples/cifar10/cifar10_full.prototxt | 11 +-
examples/cifar10/cifar10_quick.prototxt | 11 +-
examples/cifar10/convert_cifar_data.cpp | 2 +
examples/cifar10/create_cifar10.sh | 1 +
examples/cifar10/train_full.sh | 7 +-
examples/cifar10/train_full_sigmoid.sh | 3 +-
examples/cifar10/train_full_sigmoid_bn.sh | 3 +-
examples/cifar10/train_quick.sh | 5 +-
examples/cpp_classification/classification.cpp | 2 +-
examples/cpp_classification/readme.md | 2 +-
examples/finetune_flickr_style/readme.md | 10 +-
.../hdf5_classification/nonlinear_solver.prototxt | 15 -
examples/hdf5_classification/solver.prototxt | 15 -
examples/imagenet/create_imagenet.sh | 1 +
examples/imagenet/resume_training.sh | 4 +-
examples/imagenet/train_caffenet.sh | 3 +-
examples/images/cat gray.jpg | Bin 0 -> 92726 bytes
examples/mnist/convert_mnist_data.cpp | 89 +-
examples/mnist/create_mnist.sh | 1 +
examples/mnist/lenet.prototxt | 11 +-
examples/mnist/lenet_auto_solver.prototxt | 6 +-
examples/mnist/readme.md | 2 +-
examples/mnist/train_lenet.sh | 3 +-
examples/mnist/train_lenet_adam.sh | 3 +-
examples/mnist/train_lenet_consolidated.sh | 3 +-
examples/mnist/train_lenet_docker.sh | 119 +
examples/mnist/train_lenet_rmsprop.sh | 4 +-
examples/mnist/train_mnist_autoencoder.sh | 3 +-
examples/mnist/train_mnist_autoencoder_adadelta.sh | 3 +-
examples/mnist/train_mnist_autoencoder_adagrad.sh | 3 +-
examples/mnist/train_mnist_autoencoder_nesterov.sh | 3 +-
examples/net_surgery.ipynb | 56 +-
.../net_surgery/bvlc_caffenet_full_conv.prototxt | 15 +-
examples/net_surgery/conv.prototxt | 11 +-
examples/pascal-multilabel-with-datalayer.ipynb | 479 +
.../pycaffe/layers/pascal_multilabel_datalayers.py | 216 +
examples/pycaffe/tools.py | 121 +
examples/siamese/create_mnist_siamese.sh | 1 +
examples/siamese/mnist_siamese.prototxt | 13 +-
examples/siamese/train_mnist_siamese.sh | 3 +-
include/caffe/common.hpp | 5 +
include/caffe/layers/crop_layer.hpp | 76 +
include/caffe/layers/cudnn_relu_layer.hpp | 1 +
include/caffe/layers/cudnn_sigmoid_layer.hpp | 1 +
include/caffe/layers/cudnn_tanh_layer.hpp | 1 +
include/caffe/layers/inner_product_layer.hpp | 1 +
include/caffe/layers/input_layer.hpp | 44 +
include/caffe/layers/lstm_layer.hpp | 154 +
include/caffe/layers/parameter_layer.hpp | 45 +
include/caffe/layers/python_layer.hpp | 1 +
include/caffe/layers/recurrent_layer.hpp | 187 +
include/caffe/layers/rnn_layer.hpp | 47 +
include/caffe/net.hpp | 27 +-
include/caffe/parallel.hpp | 5 +-
include/caffe/util/cudnn.hpp | 24 +-
include/caffe/util/db_lmdb.hpp | 13 +-
include/caffe/util/upgrade_proto.hpp | 6 +
models/bvlc_alexnet/deploy.prototxt | 11 +-
models/bvlc_googlenet/deploy.prototxt | 11 +-
models/bvlc_reference_caffenet/deploy.prototxt | 11 +-
.../bvlc_reference_rcnn_ilsvrc13/deploy.prototxt | 11 +-
models/finetune_flickr_style/deploy.prototxt | 11 +-
python/CMakeLists.txt | 22 +-
python/caffe/__init__.py | 2 +-
python/caffe/_caffe.cpp | 100 +-
python/caffe/classifier.py | 1 +
python/caffe/coord_map.py | 185 +
python/caffe/detector.py | 2 +-
python/caffe/draw.py | 34 +-
python/caffe/io.py | 7 +-
python/caffe/net_spec.py | 8 +-
python/caffe/pycaffe.py | 94 +-
python/caffe/test/test_coord_map.py | 192 +
python/caffe/test/test_io.py | 15 +
python/caffe/test/test_net.py | 266 +-
python/caffe/test/test_python_layer.py | 26 +
python/draw_net.py | 15 +-
scripts/cpp_lint.py | 2 +-
scripts/download_model_binary.py | 2 +-
scripts/travis/build.sh | 13 +
scripts/travis/configure-cmake.sh | 32 +
scripts/travis/configure-make.sh | 36 +
scripts/travis/configure.sh | 11 +
scripts/travis/defaults.sh | 10 +
scripts/travis/install-deps.sh | 110 +
scripts/travis/install-python-deps.sh | 14 +
scripts/travis/setup-venv.sh | 18 +
scripts/travis/test.sh | 19 +
scripts/travis/travis_build_and_test.sh | 54 -
scripts/travis/travis_install.sh | 99 -
scripts/travis/travis_setup_makefile_config.sh | 31 -
src/caffe/blob.cpp | 4 +-
src/caffe/common.cpp | 42 +
src/caffe/layers/crop_layer.cpp | 143 +
src/caffe/layers/crop_layer.cu | 115 +
src/caffe/layers/cudnn_conv_layer.cu | 12 +-
src/caffe/layers/cudnn_relu_layer.cpp | 1 +
src/caffe/layers/cudnn_relu_layer.cu | 23 +-
src/caffe/layers/cudnn_sigmoid_layer.cpp | 2 +
src/caffe/layers/cudnn_sigmoid_layer.cu | 23 +-
src/caffe/layers/cudnn_tanh_layer.cpp | 1 +
src/caffe/layers/cudnn_tanh_layer.cu | 23 +-
src/caffe/layers/dropout_layer.cpp | 4 +-
src/caffe/layers/exp_layer.cpp | 3 +-
src/caffe/layers/image_data_layer.cpp | 11 +-
src/caffe/layers/inner_product_layer.cpp | 42 +-
src/caffe/layers/inner_product_layer.cu | 31 +-
src/caffe/layers/input_layer.cpp | 27 +
src/caffe/layers/lstm_layer.cpp | 244 +
src/caffe/layers/lstm_unit_layer.cpp | 131 +
src/caffe/layers/lstm_unit_layer.cu | 154 +
src/caffe/layers/memory_data_layer.cpp | 2 +-
src/caffe/layers/parameter_layer.cpp | 8 +
src/caffe/layers/recurrent_layer.cpp | 295 +
src/caffe/layers/recurrent_layer.cu | 44 +
src/caffe/layers/rnn_layer.cpp | 236 +
src/caffe/layers/window_data_layer.cpp | 3 +
src/caffe/net.cpp | 126 +-
src/caffe/parallel.cpp | 20 +-
src/caffe/proto/caffe.proto | 79 +-
src/caffe/solver.cpp | 8 +-
src/caffe/test/test_blob.cpp | 8 +
src/caffe/test/test_caffe_main.cpp | 4 +-
src/caffe/test/test_crop_layer.cpp | 283 +
src/caffe/test/test_data_transformer.cpp | 1 -
src/caffe/test/test_embed_layer.cpp | 2 +-
src/caffe/test/test_gradient_based_solver.cpp | 8 +-
src/caffe/test/test_image_data_layer.cpp | 44 +-
src/caffe/test/test_inner_product_layer.cpp | 240 +
src/caffe/test/test_lstm_layer.cpp | 288 +
src/caffe/test/test_net.cpp | 308 +-
src/caffe/test/test_neuron_layer.cpp | 20 +
src/caffe/test/test_rnn_layer.cpp | 217 +
src/caffe/test/test_split_layer.cpp | 61 -
src/caffe/util/db_lmdb.cpp | 73 +-
src/caffe/util/insert_splits.cpp | 21 +-
src/caffe/util/upgrade_proto.cpp | 64 +-
tools/caffe.cpp | 61 +-
tools/convert_imageset.cpp | 9 +-
tools/extra/parse_log.py | 10 +-
tools/extra/plot_training_log.py.example | 26 +-
tools/extract_features.cpp | 5 +-
tools/upgrade_net_proto_binary.cpp | 5 +-
tools/upgrade_net_proto_text.cpp | 8 +-
tools/upgrade_solver_proto_text.cpp | 3 +-
169 files changed, 9521 insertions(+), 24661 deletions(-)
diff --git a/.travis.yml b/.travis.yml
index 4dc7ed7..3297954 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,40 +1,52 @@
-# Use a build matrix to do two builds in parallel:
-# one using CMake, and one using make.
+dist: trusty
+sudo: required
+
+language: cpp
+compiler: gcc
+
env:
+ global:
+ - NUM_THREADS=4
matrix:
- - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true
- - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3
- - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true
- - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true
- - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false
- - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3
+ # Use a build matrix to test many builds in parallel
+ # envvar defaults:
+ # WITH_CMAKE: false
+ # WITH_PYTHON3: false
+ # WITH_IO: true
+ # WITH_CUDA: false
+ # WITH_CUDNN: false
+ - BUILD_NAME="default-make"
+# - BUILD_NAME="python3-make" WITH_PYTHON3=true
+ - BUILD_NAME="no-io-make" WITH_IO=false
+ - BUILD_NAME="cuda-make" WITH_CUDA=true
+ - BUILD_NAME="cudnn-make" WITH_CUDA=true WITH_CUDNN=true
-language: cpp
+ - BUILD_NAME="default-cmake" WITH_CMAKE=true
+ - BUILD_NAME="python3-cmake" WITH_CMAKE=true WITH_PYTHON3=true
+ - BUILD_NAME="no-io-cmake" WITH_CMAKE=true WITH_IO=false
+ - BUILD_NAME="cuda-cmake" WITH_CMAKE=true WITH_CUDA=true
+ - BUILD_NAME="cudnn-cmake" WITH_CMAKE=true WITH_CUDA=true WITH_CUDNN=true
-# Cache Ubuntu apt packages.
cache:
apt: true
directories:
- - /home/travis/miniconda
- - /home/travis/miniconda2
- - /home/travis/miniconda3
-
-compiler: gcc
+ - ~/protobuf3
before_install:
- - export NUM_THREADS=4
- - export SCRIPTS=./scripts/travis
- - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION"
+ - source ./scripts/travis/defaults.sh
install:
- - sudo -E $SCRIPTS/travis_install.sh
+ - sudo -E ./scripts/travis/install-deps.sh
+ - ./scripts/travis/setup-venv.sh ~/venv
+ - source ~/venv/bin/activate
+ - ./scripts/travis/install-python-deps.sh
before_script:
- - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib
- - export PATH=$CONDA_DIR/bin:$PATH
- - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
+ - ./scripts/travis/configure.sh
-script: $SCRIPTS/travis_build_and_test.sh
+script:
+ - ./scripts/travis/build.sh
+ - ./scripts/travis/test.sh
notifications:
# Emails are sent to the committer's git-configured email address by default,
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 32cc42a..da7142c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,8 +10,8 @@ endif()
project(Caffe C CXX)
# ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.0.0-rc3")
-set(CAFFE_TARGET_SOVERSION "1.0.0-rc3")
+set(CAFFE_TARGET_VERSION "1.0.0-rc3" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc3" CACHE STRING "Caffe soname version")
add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
# ---[ Using cmake scripts and modules
@@ -47,6 +47,8 @@ if(UNIX OR APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
endif()
+caffe_set_caffe_link()
+
if(USE_libstdcpp)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
diff --git a/Makefile b/Makefile
index 598d28d..403e00a 100644
--- a/Makefile
+++ b/Makefile
@@ -248,6 +248,8 @@ ifeq ($(UNAME), Linux)
LINUX := 1
else ifeq ($(UNAME), Darwin)
OSX := 1
+ OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .)
+ OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)
endif
# Linux
@@ -270,22 +272,29 @@ endif
ifeq ($(OSX), 1)
CXX := /usr/bin/clang++
ifneq ($(CPU_ONLY), 1)
- CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+ CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | tr -d '[a-z ]')
ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
CXXFLAGS += -stdlib=libstdc++
LINKFLAGS += -stdlib=libstdc++
endif
# clang throws this warning for cuda headers
WARNINGS += -Wno-unneeded-internal-declaration
+ # 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+)
+ OSX_10_OR_LATER := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true)
+ OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true)
+ ifeq ($(OSX_10_OR_LATER),true)
+ ifeq ($(OSX_10_5_OR_LATER),true)
+ LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR)
+ endif
+ endif
endif
# gtest needs to use its own tuple to not conflict with clang
COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
# boost::thread is called boost_thread-mt to mark multithreading on OS X
LIBRARIES += boost_thread-mt
# we need to explicitly ask for the rpath to be obeyed
- DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
ORIGIN := @loader_path
- VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
+ VERSIONFLAGS += -Wl,-install_name, at rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
else
ORIGIN := \$$ORIGIN
endif
@@ -355,9 +364,9 @@ ifeq ($(BLAS), mkl)
# MKL
LIBRARIES += mkl_rt
COMMON_FLAGS += -DUSE_MKL
- MKL_DIR ?= /opt/intel/mkl
- BLAS_INCLUDE ?= $(MKL_DIR)/include
- BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
+ MKLROOT ?= /opt/intel/mkl
+ BLAS_INCLUDE ?= $(MKLROOT)/include
+ BLAS_LIB ?= $(MKLROOT)/lib $(MKLROOT)/lib/intel64
else ifeq ($(BLAS), open)
# OpenBLAS
LIBRARIES += openblas
@@ -552,7 +561,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
$(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
@ echo LD -o $@
- $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+ $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS)
@ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT); ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
$(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
diff --git a/Makefile.config.example b/Makefile.config.example
index 8fd49c9..07bed63 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -98,6 +98,7 @@ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
+# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index d2012db..5ab93b2 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -20,7 +20,7 @@ caffe_option(MKL_MULTI_THREADED "Use multi-threading" ON IF NOT MKL_USE_SINGL
# ---[ Root folders
set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKLROOT} ${INTEL_ROOT}/mkl
DOC "Folder contains MKL")
# ---[ Find include dir
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index fc799bd..73f647f 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -23,7 +23,7 @@ endif()
# place where to generate protobuf sources
set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
-include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
+include_directories("${PROJECT_BINARY_DIR}/include")
set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index 2401f25..a796d00 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -1,16 +1,17 @@
################################################################################################
# Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
# some objects which are not addressed directly but are registered via static constructors
-if(BUILD_SHARED_LIBS)
- set(Caffe_LINK caffe)
-else()
- if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
- set(Caffe_LINK -Wl,-force_load caffe)
- elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
- set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+macro(caffe_set_caffe_link)
+ if(BUILD_SHARED_LIBS)
+ set(Caffe_LINK caffe)
+ else()
+ if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+ set(Caffe_LINK -Wl,-force_load caffe)
+ elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+ set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+ endif()
endif()
-endif()
-
+endmacro()
################################################################################################
# Convenient command to setup source group for IDEs that support this feature (VS, XCode)
# Usage:
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index a1bde1a..653de5f 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -346,10 +346,11 @@ function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_va
elseif(lib MATCHES "^-l.*")
list(APPEND libflags ${lib})
elseif(IS_ABSOLUTE ${lib})
- get_filename_component(name_we ${lib} NAME_WE)
get_filename_component(folder ${lib} PATH)
+ get_filename_component(filename ${lib} NAME)
+ string(REGEX REPLACE "\\.[^.]*$" "" filename_without_shortest_ext ${filename})
- string(REGEX MATCH "^lib(.*)" __match ${name_we})
+ string(REGEX MATCH "^lib(.*)" __match ${filename_without_shortest_ext})
list(APPEND libflags -l${CMAKE_MATCH_1})
list(APPEND folders ${folder})
else()
diff --git a/data/cifar10/get_cifar10.sh b/data/cifar10/get_cifar10.sh
index 623c848..423f109 100755
--- a/data/cifar10/get_cifar10.sh
+++ b/data/cifar10/get_cifar10.sh
@@ -2,7 +2,7 @@
# This scripts downloads the CIFAR10 (binary version) data and unzips it.
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
echo "Downloading..."
diff --git a/data/ilsvrc12/get_ilsvrc_aux.sh b/data/ilsvrc12/get_ilsvrc_aux.sh
index 90935f2..dc0d0a7 100755
--- a/data/ilsvrc12/get_ilsvrc_aux.sh
+++ b/data/ilsvrc12/get_ilsvrc_aux.sh
@@ -8,7 +8,7 @@
# - the training splits with labels
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
echo "Downloading..."
diff --git a/data/mnist/get_mnist.sh b/data/mnist/get_mnist.sh
index 8eb6aee..ecadffa 100755
--- a/data/mnist/get_mnist.sh
+++ b/data/mnist/get_mnist.sh
@@ -2,23 +2,14 @@
# This scripts downloads the mnist data and unzips it.
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
echo "Downloading..."
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
-
-echo "Unzipping..."
-
-gunzip train-images-idx3-ubyte.gz
-gunzip train-labels-idx1-ubyte.gz
-gunzip t10k-images-idx3-ubyte.gz
-gunzip t10k-labels-idx1-ubyte.gz
-
-# Creation is split out because leveldb sometimes causes segfault
-# and needs to be re-created.
-
-echo "Done."
+for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
+do
+ if [ ! -e $fname ]; then
+ wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
+ gunzip ${fname}.gz
+ fi
+done
diff --git a/docker/Makefile b/docker/Makefile
new file mode 100644
index 0000000..3a6575b
--- /dev/null
+++ b/docker/Makefile
@@ -0,0 +1,50 @@
+# A makefile to build the docker images for caffe.
+# Two caffe images will be built:
+# caffe:cpu --> A CPU-only build of caffe.
+# caffe:gpu --> A GPU-enabled build using the latest CUDA and CUDNN versions.
+
+DOCKER ?= docker
+
+all: docker_files standalone
+
+.PHONY: standalone devel
+
+standalone: cpu_standalone gpu_standalone
+
+
+cpu_standalone: standalone/cpu/Dockerfile
+ $(DOCKER) build -t caffe:cpu standalone/cpu
+
+gpu_standalone: standalone/gpu/Dockerfile
+ $(DOCKER) build -t caffe:gpu standalone/gpu
+
+docker_files: standalone_files
+
+standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile
+
+FROM_GPU = "nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04"
+FROM_CPU = "ubuntu:14.04"
+GPU_CMAKE_ARGS = -DUSE_CUDNN=1
+CPU_CMAKE_ARGS = -DCPU_ONLY=1
+
+# A make macro to select the CPU or GPU base image.
+define from_image
+$(if $(strip $(findstring gpu,$@)),$(FROM_GPU),$(FROM_CPU))
+endef
+
+# A make macro to select the CPU or GPU build args.
+define build_args
+$(if $(strip $(findstring gpu,$@)),$(GPU_CMAKE_ARGS),$(CPU_CMAKE_ARGS))
+endef
+
+# A make macro to construct the CPU or GPU Dockerfile from the template
+define create_docker_file
+ @echo creating $@
+ @echo "FROM "$(from_image) > $@
+ @cat $^ | sed 's/$${CMAKE_ARGS}/$(build_args)/' >> $@
+endef
+
+
+standalone/%/Dockerfile: templates/Dockerfile.template
+ $(create_docker_file)
+
diff --git a/docker/README.md b/docker/README.md
new file mode 100644
index 0000000..fdab641
--- /dev/null
+++ b/docker/README.md
@@ -0,0 +1,52 @@
+# Caffe standalone Dockerfiles.
+
+The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running:
+
+```
+docker build -t caffe:cpu standalone/cpu
+```
+for example. (Here `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail).
+
+Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned.
+
+# Running Caffe using the docker image
+
+In order to test the Caffe image, run:
+```
+docker run -ti caffe:cpu caffe --version
+```
+which should show a message like:
+```
+libdc1394 error: Failed to initialize libdc1394
+caffe version 1.0.0-rc3
+```
+
+One can also build and run the Caffe tests in the image using:
+```
+docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"
+```
+
+In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running:
+```
+docker run -ti --volume=$(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
+```
+will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume=` Docker flag).
+
+Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used:
+```
+docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
+```
+where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option.
+
+# Other use-cases
+
+Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe).
+
+Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running:
+```
+docker run -ti caffe:cpu python
+```
+(`ipython` is also available in the container).
+
+Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py`
+
diff --git a/docker/standalone/cpu/Dockerfile b/docker/standalone/cpu/Dockerfile
new file mode 100644
index 0000000..4fef25a
--- /dev/null
+++ b/docker/standalone/cpu/Dockerfile
@@ -0,0 +1,43 @@
+FROM ubuntu:14.04
+MAINTAINER caffe-maint at googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ cmake \
+ git \
+ wget \
+ libatlas-base-dev \
+ libboost-all-dev \
+ libgflags-dev \
+ libgoogle-glog-dev \
+ libhdf5-serial-dev \
+ libleveldb-dev \
+ liblmdb-dev \
+ libopencv-dev \
+ libprotobuf-dev \
+ libsnappy-dev \
+ protobuf-compiler \
+ python-dev \
+ python-numpy \
+ python-pip \
+ python-scipy && \
+ rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+ for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+ mkdir build && cd build && \
+ cmake -DCPU_ONLY=1 .. && \
+ make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/standalone/gpu/Dockerfile b/docker/standalone/gpu/Dockerfile
new file mode 100644
index 0000000..daf6a72
--- /dev/null
+++ b/docker/standalone/gpu/Dockerfile
@@ -0,0 +1,43 @@
+FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
+MAINTAINER caffe-maint at googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ cmake \
+ git \
+ wget \
+ libatlas-base-dev \
+ libboost-all-dev \
+ libgflags-dev \
+ libgoogle-glog-dev \
+ libhdf5-serial-dev \
+ libleveldb-dev \
+ liblmdb-dev \
+ libopencv-dev \
+ libprotobuf-dev \
+ libsnappy-dev \
+ protobuf-compiler \
+ python-dev \
+ python-numpy \
+ python-pip \
+ python-scipy && \
+ rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+ for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+ mkdir build && cd build && \
+ cmake -DUSE_CUDNN=1 .. && \
+ make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/templates/Dockerfile.template b/docker/templates/Dockerfile.template
new file mode 100644
index 0000000..8834f05
--- /dev/null
+++ b/docker/templates/Dockerfile.template
@@ -0,0 +1,42 @@
+MAINTAINER caffe-maint at googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ cmake \
+ git \
+ wget \
+ libatlas-base-dev \
+ libboost-all-dev \
+ libgflags-dev \
+ libgoogle-glog-dev \
+ libhdf5-serial-dev \
+ libleveldb-dev \
+ liblmdb-dev \
+ libopencv-dev \
+ libprotobuf-dev \
+ libsnappy-dev \
+ protobuf-compiler \
+ python-dev \
+ python-numpy \
+ python-pip \
+ python-scipy && \
+ rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+ for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+ mkdir build && cd build && \
+ cmake ${CMAKE_ARGS} .. && \
+ make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docs/installation.md b/docs/installation.md
index ef781e8..4aac7c4 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -5,13 +5,23 @@ title: Installation
# Installation
Prior to installing, have a glance through this guide and take note of the details for your platform.
-We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
-The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
+We install and run Caffe on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS.
+The official Makefile and `Makefile.config` build are complemented by a [community CMake build](#cmake-build).
+
+**Step-by-step Instructions**:
+
+- [Docker setup](https://github.com/BVLC/caffe/tree/master/docker) *out-of-the-box brewing*
+- [Ubuntu installation](install_apt.html) *the standard platform*
+- [OS X installation](install_osx.html)
+- [RHEL / CentOS / Fedora installation](install_yum.html)
+- [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Microsoft*
+- [OpenCL](https://github.com/BVLC/caffe/tree/opencl) *see the OpenCL branch led by Fabian Tschopp*
+
+**Overview**:
- [Prerequisites](#prerequisites)
- [Compilation](#compilation)
- [Hardware](#hardware)
-- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
When updating Caffe, it's best to `make clean` before re-compiling.
@@ -20,7 +30,7 @@ When updating Caffe, it's best to `make clean` before re-compiling.
Caffe has several dependencies:
* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
- * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+ * library version 7+ and the latest driver version are recommended, but 6.* is fine too
* 5.5, and 5.0 are compatible but considered legacy
* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
* [Boost](http://www.boost.org/) >= 1.55
@@ -30,14 +40,14 @@ Optional dependencies:
* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v3)
+* cuDNN for GPU acceleration (v5)
Pycaffe and Matcaffe interfaces have their own natural needs.
* For Python Caffe: `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
* For MATLAB Caffe: MATLAB with the `mex` compiler.
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v5; older versions are supported in older Caffe.
**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
@@ -54,7 +64,8 @@ There are several implementations of this library. The choice is yours:
* [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
* [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
1. Install MKL.
- 2. Set `BLAS := mkl` in `Makefile.config`
+ 2. Set up MKL environment (Details: [Linux](https://software.intel.com/en-us/node/528499), [OS X](https://software.intel.com/en-us/node/528659)). Example: *source /opt/intel/mkl/bin/mklvars.sh intel64*
+ 3. Set `BLAS := mkl` in `Makefile.config`
* [OpenBLAS](http://www.openblas.net/): free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
1. Install OpenBLAS
2. Set `BLAS := open` in `Makefile.config`
@@ -81,10 +92,6 @@ Install MATLAB, and make sure that its `mex` is in your `$PATH`.
*Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
-#### Windows
-
-There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
-
## Compilation
Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
@@ -112,7 +119,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
-### Compilation with CMake
+### CMake Build
In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
The basic steps are as follows:
@@ -128,9 +135,9 @@ See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
## Hardware
-**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
+**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with Titan Xs, K80s, GTX 980s, K40s, K20s, Titans, and GTX 770s including models at ImageNet/ILSVRC scale. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
-**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.
+**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capability >= 3.0.
Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
diff --git a/docs/multigpu.md b/docs/multigpu.md
index 01cfb89..d91acef 100644
--- a/docs/multigpu.md
+++ b/docs/multigpu.md
@@ -17,7 +17,7 @@ updated model, 0\-\>2, and then 0\-\>1, 2\-\>3.
For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
-Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
+Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
"nvidia-smi topo -m" will show you the connectivity matrix. You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.
diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
index 89b7dd3..1950f08 100644
--- a/examples/00-classification.ipynb
+++ b/examples/00-classification.ipynb
@@ -4,54 +4,72 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Instant Recognition with Caffe\n",
+ "# Classification: Instant Recognition with Caffe\n",
"\n",
- "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n",
+ "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n",
"\n",
- "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)"
+ "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+ "### 1. Setup\n",
+ "\n",
+ "* First, set up Python, `numpy`, and `matplotlib`."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
+ "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
+ "# display plots in this notebook\n",
"%matplotlib inline\n",
"\n",
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
+ "# set display defaults\n",
+ "plt.rcParams['figure.figsize'] = (10, 10) # large images\n",
+ "plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels\n",
+ "plt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Load `caffe`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# The caffe module needs to be on the Python path;\n",
+ "# we'll add it here explicitly.\n",
"import sys\n",
+ "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
"sys.path.insert(0, caffe_root + 'python')\n",
"\n",
"import caffe\n",
- "\n",
- "plt.rcParams['figure.figsize'] = (10, 10)\n",
- "plt.rcParams['image.interpolation'] = 'nearest'\n",
- "plt.rcParams['image.cmap'] = 'gray'\n",
- "\n",
- "import os\n",
- "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
- " print(\"Downloading pre-trained CaffeNet model...\")\n",
- " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+ "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+ "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)."
]
},
{
@@ -60,50 +78,65 @@
"metadata": {
"collapsed": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CaffeNet found.\n"
+ ]
+ }
+ ],
"source": [
- "caffe.set_mode_cpu()\n",
- "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
- " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
- " caffe.TEST)\n",
- "\n",
- "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
- "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
- "transformer.set_transpose('data', (2,0,1))\n",
- "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
- "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n",
- "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB"
+ "import os\n",
+ "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+ " print 'CaffeNet found.'\n",
+ "else:\n",
+ " print 'Downloading pre-trained CaffeNet model...'\n",
+ " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)"
+ "### 2. Load net and set up input preprocessing\n",
+ "\n",
+ "* Set Caffe to CPU mode and load the net from disk."
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
- "# set net to batch size of 50\n",
- "net.blobs['data'].reshape(50,3,227,227)"
+ "caffe.set_mode_cpu()\n",
+ "\n",
+ "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n",
+ "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+ "\n",
+ "net = caffe.Net(model_def, # defines the structure of the model\n",
+ " model_weights, # contains the trained weights\n",
+ " caffe.TEST) # use test mode (e.g., don't perform dropout)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Feed in the image (with some preprocessing) and classify with a forward pass."
+ "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n",
+ "\n",
+ " Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n",
+ " \n",
+ " As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {
"collapsed": false
},
@@ -112,21 +145,54 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Predicted class is #281.\n"
+ "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n"
]
}
],
"source": [
- "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
- "out = net.forward()\n",
- "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))"
+ "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n",
+ "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+ "mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values\n",
+ "print 'mean-subtracted values:', zip('BGR', mu)\n",
+ "\n",
+ "# create transformer for the input called 'data'\n",
+ "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+ "\n",
+ "transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension\n",
+ "transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel\n",
+ "transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]\n",
+ "transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3. CPU classification\n",
+ "\n",
+ "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# set the size of the input (we can skip this if we're happy\n",
+ "# with the default; we can also change it later, e.g., for different batch sizes)\n",
+ "net.blobs['data'].reshape(50, # batch size\n",
+ " 3, # 3-channel (BGR) images\n",
+ " 227, 227) # image size is 227x227"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "What did the input look like?"
+ "* Load an image (that comes with Caffe) and perform the preprocessing we've set up."
]
},
{
@@ -139,7 +205,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f665b02ae90>"
+ "<matplotlib.image.AxesImage at 0x7f09693a8c90>"
]
},
"execution_count": 7,
@@ -148,2843 +214,9 @@
},
{
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAHDCAYAAADvDfQIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvcuvbUly3veLyMy1z7m3urrZZDfJ5kMkRdrQA4YgyBY8\n8MwDAx4Y8MTQ0CNPPPc/Y8/9F1iwYcCGAUOQH5DtiWRYoiXwoRbJZndX1b3n7LUyIzyIyFzrVFfT\nNqF2ScZOgmTds89Ze61cmZERX3zxhbg7j/EYj/EYj/EYj/EYj/H/fujXfQOP8RiP8RiP8RiP8Rj/\nso6HI/UYj/EYj/EYj/EYj/HnHA9H6jEe4zEe4zEe4zEe4885Ho7UYzzGYzzGYzzGYzzGn3M8HKnH\neIzHeIzHeIzHeIw/53g4Uo/xGI/xGI/xGI/xGH/O8TNxpETk3xGRfyAi/4eI/Cc/i+94jMd4jMd4\njMd4 [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f66a46f8a10>"
+ "<matplotlib.figure.Figure at 0x7f09b97eab50>"
]
},
"metadata": {},
@@ -2992,14 +224,16 @@
}
],
"source": [
- "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+ "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n",
+ "transformed_image = transformer.preprocess('data', image)\n",
+ "plt.imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Adorable, but was our classification correct?"
+ "* Adorable! Let's classify it!"
]
},
{
@@ -3013,31 +247,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
- " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
- " 'n02127052 lynx, catamount']\n"
+ "predicted class is: 281\n"
]
}
],
"source": [
- "# load labels\n",
- "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
- "try:\n",
- " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
- "except:\n",
- " !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
- " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+ "# copy the image data into the memory allocated for the net\n",
+ "net.blobs['data'].data[...] = transformed_image\n",
+ "\n",
+ "### perform classification\n",
+ "output = net.forward()\n",
+ "\n",
+ "output_prob = output['prob'][0] # the output probability vector for the first image in the batch\n",
"\n",
- "# sort top k predictions from softmax output\n",
- "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
- "print labels[top_k]"
+ "print 'predicted class is:', output_prob.argmax()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Indeed! But how long did it take?"
+ "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..."
]
},
{
@@ -3051,26 +281,31 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1 loops, best of 3: 7.14 s per loop\n"
+ "output label: n02123045 tabby, tabby cat\n"
]
}
],
"source": [
- "# CPU mode\n",
- "net.forward() # call once for allocation\n",
- "%timeit net.forward()"
+ "# load ImageNet labels\n",
+ "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+ "if not os.path.exists(labels_file):\n",
+ " !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+ " \n",
+ "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n",
+ "\n",
+ "print 'output label:', labels[output_prob.argmax()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "That's a while, even for a batch size of 50 images. Let's switch to GPU mode."
+ "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {
"collapsed": false
},
@@ -3079,8706 +314,244 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "10 loops, best of 3: 90.9 ms per loop\n"
+ "probabilities and labels:\n"
]
- }
- ],
- "source": [
- "# GPU mode\n",
- "caffe.set_device(0)\n",
- "caffe.set_mode_gpu()\n",
- "net.forward() # call once for allocation\n",
- "%timeit net.forward()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Much better. Now let's look at the net in more detail.\n",
- "\n",
- "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[('data', (50, 3, 227, 227)),\n",
- " ('conv1', (50, 96, 55, 55)),\n",
- " ('pool1', (50, 96, 27, 27)),\n",
- " ('norm1', (50, 96, 27, 27)),\n",
- " ('conv2', (50, 256, 27, 27)),\n",
- " ('pool2', (50, 256, 13, 13)),\n",
- " ('norm2', (50, 256, 13, 13)),\n",
- " ('conv3', (50, 384, 13, 13)),\n",
- " ('conv4', (50, 384, 13, 13)),\n",
- " ('conv5', (50, 256, 13, 13)),\n",
- " ('pool5', (50, 256, 6, 6)),\n",
- " ('fc6', (50, 4096)),\n",
- " ('fc7', (50, 4096)),\n",
- " ('fc8', (50, 1000)),\n",
- " ('prob', (50, 1000))]"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "[(k, v.data.shape) for k, v in net.blobs.items()]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
+ },
{
"data": {
"text/plain": [
- "[('conv1', (96, 3, 11, 11)),\n",
- " ('conv2', (256, 48, 5, 5)),\n",
- " ('conv3', (384, 256, 3, 3)),\n",
- " ('conv4', (384, 192, 3, 3)),\n",
- " ('conv5', (256, 192, 3, 3)),\n",
- " ('fc6', (4096, 9216)),\n",
- " ('fc7', (4096, 4096)),\n",
- " ('fc8', (1000, 4096))]"
+ "[(0.31243637, 'n02123045 tabby, tabby cat'),\n",
+ " (0.2379719, 'n02123159 tiger cat'),\n",
+ " (0.12387239, 'n02124075 Egyptian cat'),\n",
+ " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n",
+ " (0.070957087, 'n02127052 lynx, catamount')]"
]
},
- "execution_count": 26,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "[(k, v[0].data.shape) for k, v in net.params.items()]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Helper functions for visualization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
- "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
- "def vis_square(data, padsize=1, padval=0):\n",
- " data -= data.min()\n",
- " data /= data.max()\n",
- " \n",
- " # force the number of filters to be square\n",
- " n = int(np.ceil(np.sqrt(data.shape[0])))\n",
- " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
- " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
- " \n",
- " # tile the filters into an image\n",
- " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
- " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
- " \n",
- " plt.imshow(data)"
+ "# sort top five predictions from softmax output\n",
+ "top_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest items\n",
+ "\n",
+ "print 'probabilities and labels:'\n",
+ "zip(output_prob[top_inds], labels[top_inds])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The input image"
+ "* We see that less confident predictions are sensible."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The first layer filters, `conv1`"
+ "### 4. Switching to GPU mode\n",
+ "\n",
+ "* Let's see how long classification took, and compare it to GPU mode."
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ffaf3877710>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 loop, best of 3: 1.42 s per loop\n"
+ ]
}
],
"source": [
- "# the parameters are a list of [weights, biases]\n",
- "filters = net.params['conv1'][0].data\n",
- "vis_square(filters.transpose(0, 2, 3, 1))"
+ "%timeit net.forward()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+ "* That's a while, even for a batch of 50 images. Let's switch to GPU mode."
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01f952d0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10 loops, best of 3: 70.2 ms per loop\n"
+ ]
}
],
"source": [
- "feat = net.blobs['conv1'].data[0, :36]\n",
- "vis_square(feat, padval=1)"
+ "caffe.set_device(0) # if we have multiple GPUs, pick the first one\n",
+ "caffe.set_mode_gpu()\n",
+ "net.forward() # run once before timing to set up memory\n",
+ "%timeit net.forward()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The second layer filters, `conv2`\n",
- "\n",
- "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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- "RK5CYII=\n"
- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01f42290>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "filters = net.params['conv2'][0].data\n",
- "vis_square(filters[:48].reshape(48**2, 5, 5))"
+ "* That should be much faster!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+ "### 5. Examining intermediate output\n",
+ "\n",
+ "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n",
+ "\n",
+ "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n",
+ "\n",
+ "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n",
+ "\n",
+ " The activations are exposed as an `OrderedDict`, `net.blobs`."
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01f42310>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "data\t(50, 3, 227, 227)\n",
+ "conv1\t(50, 96, 55, 55)\n",
+ "pool1\t(50, 96, 27, 27)\n",
+ "norm1\t(50, 96, 27, 27)\n",
+ "conv2\t(50, 256, 27, 27)\n",
+ "pool2\t(50, 256, 13, 13)\n",
+ "norm2\t(50, 256, 13, 13)\n",
+ "conv3\t(50, 384, 13, 13)\n",
+ "conv4\t(50, 384, 13, 13)\n",
+ "conv5\t(50, 256, 13, 13)\n",
+ "pool5\t(50, 256, 6, 6)\n",
+ "fc6\t(50, 4096)\n",
+ "fc7\t(50, 4096)\n",
+ "fc8\t(50, 1000)\n",
+ "prob\t(50, 1000)\n"
+ ]
}
],
"source": [
- "feat = net.blobs['conv2'].data[0, :36]\n",
- "vis_square(feat, padval=1)"
+ "# for each layer, show the output shape\n",
+ "for layer_name, blob in net.blobs.iteritems():\n",
+ " print layer_name + '\\t' + str(blob.data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The third layer output, `conv3` (rectified, all 384 channels)"
+ "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n",
+ "\n",
+ " The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)."
]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01dfb3d0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conv1\t(96, 3, 11, 11) (96,)\n",
+ "conv2\t(256, 48, 5, 5) (256,)\n",
+ "conv3\t(384, 256, 3, 3) (384,)\n",
+ "conv4\t(384, 192, 3, 3) (384,)\n",
+ "conv5\t(256, 192, 3, 3) (256,)\n",
+ "fc6\t(4096, 9216) (4096,)\n",
+ "fc7\t(4096, 4096) (4096,)\n",
+ "fc8\t(1000, 4096) (1000,)\n"
+ ]
}
],
"source": [
- "feat = net.blobs['conv3'].data[0]\n",
- "vis_square(feat, padval=0.5)"
+ "for layer_name, param in net.params.iteritems():\n",
+ " print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The fourth layer output, `conv4` (rectified, all 384 channels)"
+ "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps."
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 15,
"metadata": {
"collapsed": false
},
- "outputs": [
- {
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01cf9110>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "feat = net.blobs['conv4'].data[0]\n",
- "vis_square(feat, padval=0.5)"
+ "def vis_square(data):\n",
+ " \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n",
+ " and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
+ " \n",
+ " # normalize data for display\n",
+ " data = (data - data.min()) / (data.max() - data.min())\n",
+ " \n",
+ " # force the number of filters to be square\n",
+ " n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+ " padding = (((0, n ** 2 - data.shape[0]),\n",
+ " (0, 1), (0, 1)) # add some space between filters\n",
+ " + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n",
+ " data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n",
+ " \n",
+ " # tile the filters into an image\n",
+ " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+ " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+ " \n",
+ " plt.imshow(data); plt.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The fifth layer output, `conv5` (rectified, all 256 channels)"
+ "* First we'll look at the first layer filters, `conv1`"
]
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJNCAYAAAARaCA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmQbdd5nrf2dOaeu++Mi4sZIAiCIgnKFCWKkkmJiRWX\nklhlp+xyVSouy3Ek2dZEABxAkAQoUaKHuOKqxLbsKs+uisqSIkWWLFOkbHOeQZCYL+489O3x9Jn2\nlB+Mf3zf+6L6aMtuVyrv82+tWmefPay99u4+z3pXVNd1EEIIIYQQfzji/9I7IIQQQgjx/0X0EiWE\nEEII0QC9RAkhhBBCNEAvUUIIIYQQDdBLlBBCCCFEA/QSJYQQQgjRAL1ECSGEEEI0QC9RQgghhBAN\n0EuUEEIIIUQD0qP+wvc99vMQkR61OracZPC5Ok6wLrF1eT6FNsWU1I0ntqLENuztMk3tfv7yJ34Z\n2nzo [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01c2f0d0>"
+ "<matplotlib.figure.Figure at 0x7f09692b9ed0>"
]
},
"metadata": {},
@@ -11786,334 +559,30 @@
}
],
"source": [
- "feat = net.blobs['conv5'].data[0]\n",
- "vis_square(feat, padval=0.5)"
+ "# the parameters are a list of [weights, biases]\n",
+ "filters = net.params['conv1'][0].data\n",
+ "vis_square(filters.transpose(0, 2, 3, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The fifth layer after pooling, `pool5`"
+ "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsnWd5Nnyvedh77cnb23biKY5xppI4sRNCEigmFNLS\nMpSWqQWaFlGplTipkHrQg6JKPekkVepZUfVBUaqoFQFBCxQKgRA5NJMz4STO4Nixt2Nv73HtNa/1\nH6z/utf1Ps+93vXunfzf5pee62Tb652e+bmf655SvV5PAgICAgICAgICNo70VhcgICAgICAgIOD/\nrwiCVEBAQEBAQEDAJhEEqYCAgICAgICATSIIUgEBAQEBAQEBm0QQpAICAgICAgICNokgSAUEBAQE\nBAQEbBJBkAoICAgICAgI2CSCIBUQEBAQEBAQsEkEQSogICAgICAgYJMIglRAQEBAQEBAwCaR3YqP\nplKp [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01bbe590>"
+ "<matplotlib.figure.Figure at 0x7f09692c7350>"
]
},
"metadata": {},
@@ -12121,416 +590,29 @@
}
],
"source": [
- "feat = net.blobs['pool5'].data[0]\n",
- "vis_square(feat, padval=1)"
+ "feat = net.blobs['conv1'].data[0, :36]\n",
+ "vis_square(feat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The first fully connected layer, `fc6` (rectified)\n",
- "\n",
- "We show the output values and the histogram of the positive values"
+ "* The fifth layer after pooling, `pool5`"
]
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJMCAYAAADaNPObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3UtsHed1OPB7RfFNSRSp98NWHCdWbDexHQdJ0wRBijYN\n2gJtV+0m6KpAgXbRoECALrrsOosuuyjaTdAuUgRdGC0cGAlcN02c1DXk+Blb0Yt6kBQpUiRFUsx/\n0aBo9R398+mbuXOHV7/fzgffzBzOnXvv0fjMud2f/exnHQAAHsyeficAALAbKaIAAAooogAACiii\nAAAKKKIAAAooogAACiiiAAAKKKIAAArsbfqAe/bsSaZ7jo+PJ+vW1taSWLfbTWK5w0KjddH+mlAl\nl5mZmax10fnb2NioNZe6DUoun/zkJ7PW/fCHP+x5LnVrSy79ymPv3vQjc2trq9ZcomNsb29nbVv3\nedmz [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01ae5a90>"
+ "<matplotlib.figure.Figure at 0x7f09691fce50>"
]
},
"metadata": {},
@@ -12538,361 +620,31 @@
}
],
"source": [
- "feat = net.blobs['fc6'].data[0]\n",
- "plt.subplot(2, 1, 1)\n",
- "plt.plot(feat.flat)\n",
- "plt.subplot(2, 1, 2)\n",
- "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+ "feat = net.blobs['pool5'].data[0]\n",
+ "vis_square(feat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The second fully connected layer, `fc7` (rectified)"
+ "* The first fully connected layer, `fc6` (rectified)\n",
+ "\n",
+ " We show the output values and the histogram of the positive values"
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJPCAYAAACgtar/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XncHFWd7/Hvj8WLKwwjBlRmcFS8LjMD6IBeXB4UHEAH\ncZzrFWeUwXV8KeCGgqIEFQFX3McFMSKyCIowgiQCD4sIYUkgEDCABBIhT4iEhBCWhJz7R3eTTqe6\nu5Zzqk5Vfd6vV17pp7v6nFOntl+fc+qUOecEAAAAfzapugAAAABNQ4AFAADgGQEWAACAZwRYAAAA\nnhFgAQAAeEaABQAA4FmqAMvMtjKzM83sZjObb2a7mdnWZjbLzBaY2Uwz2yp0YQEAAOogbQvWNySd\n55x7oaR/kHSLpMMlzXLO7Sjpwu7fAAAArWfjJho1sy0lzXHO/d3A+7dIeo1zbsrMtpU06Zz73+GK\nCgAA [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb01953f90>"
+ "<matplotlib.figure.Figure at 0x7f096914f6d0>"
]
},
"metadata": {},
@@ -12900,7 +652,7 @@
}
],
"source": [
- "feat = net.blobs['fc7'].data[0]\n",
+ "feat = net.blobs['fc6'].data[0]\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(feat.flat)\n",
"plt.subplot(2, 1, 2)\n",
@@ -12911,12 +663,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The final probability output, `prob`"
+ "* The final probability output, `prob`"
]
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 20,
"metadata": {
"collapsed": false
},
@@ -12924,191 +676,18 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x7ffb01f12a50>]"
+ "[<matplotlib.lines.Line2D at 0x7f09587dfb50>]"
]
},
- "execution_count": 38,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAADICAYAAAC6TEOmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGzVJREFUeJzt3X2MXPV97/HPh12c4AdCHgiktiMTYh4chSb0yvheQjKN\nKHJpGkdBKkEhVCnlIvU6yb2qIje3Uu5at1Xa6qJLUxTq5LoNAW5olRjqSDgGGqZCCWCbEh53wRti\nsI1tzJO54Kddz/f+cc6wZ2dn58x6Z3bOWb9fkjXn6Tf7G/u3M/7M93fOcUQIAAAAAFB+J/W6AwAA\nAACAziDgAQAAAMAsQcADAAAAgFmCgAcAAAAAswQBDwAAAABmCQIeAAAAAMwSuQHP9krbQ7a3217T\nZP8q24/ZftT2I7Y/ndm3w/bj6b4tne48AAAAAGCMW90Hz3afpGckXSppt6Stkq6KiMHMMfMi4q10\n+aOS [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7ffb019b9dd0>"
+ "<matplotlib.figure.Figure at 0x7f0969270290>"
]
},
"metadata": {},
@@ -13117,6 +696,7 @@
],
"source": [
"feat = net.blobs['prob'].data[0]\n",
+ "plt.figure(figsize=(15, 3))\n",
"plt.plot(feat.flat)"
]
},
@@ -13124,38 +704,51 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's see the top 5 predicted labels."
+ "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6. Try your own image\n",
+ "\n",
+ "Now we'll grab an image from the web and classify it using the steps above.\n",
+ "\n",
+ "* Try setting `my_image_url` to any JPEG image URL."
]
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {
"collapsed": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
- " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
- " 'n02127052 lynx, catamount']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# load labels\n",
- "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
- "try:\n",
- " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
- "except:\n",
- " !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
- " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+ "# download an image\n",
+ "my_image_url = \"...\" # paste your URL here\n",
+ "# for example:\n",
+ "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n",
+ "!wget -O image.jpg $my_image_url\n",
+ "\n",
+ "# transform it and copy it into the net\n",
+ "image = caffe.io.load_image('image.jpg')\n",
+ "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n",
+ "\n",
+ "# perform classification\n",
+ "net.forward()\n",
+ "\n",
+ "# obtain the output probabilities\n",
+ "output_prob = net.blobs['prob'].data[0]\n",
+ "\n",
+ "# sort top five predictions from softmax output\n",
+ "top_inds = output_prob.argsort()[::-1][:5]\n",
+ "\n",
+ "plt.imshow(image)\n",
"\n",
- "# sort top k predictions from softmax output\n",
- "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
- "print labels[top_k]"
+ "print 'probabilities and labels:'\n",
+ "zip(output_prob[top_inds], labels[top_inds])"
]
}
],
@@ -13178,7 +771,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.9"
+ "version": "2.7.10"
},
"priority": 1
},
diff --git a/examples/01-learning-lenet.ipynb b/examples/01-learning-lenet.ipynb
index 3562c7a..1c32826 100644
--- a/examples/01-learning-lenet.ipynb
+++ b/examples/01-learning-lenet.ipynb
@@ -4,12 +4,26 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Python solving with LeNet\n",
+ "# Solving in Python with LeNet\n",
"\n",
"In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline."
+ ]
+ },
+ {
"cell_type": "code",
"execution_count": 1,
"metadata": {
@@ -17,8 +31,15 @@
},
"outputs": [],
"source": [
- "import os\n",
- "os.chdir('..')"
+ "from pylab import *\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe."
]
},
{
@@ -29,19 +50,18 @@
},
"outputs": [],
"source": [
- "import sys\n",
- "sys.path.insert(0, './python')\n",
- "import caffe\n",
+ "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
"\n",
- "from pylab import *\n",
- "%matplotlib inline"
+ "import sys\n",
+ "sys.path.insert(0, caffe_root + 'python')\n",
+ "import caffe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
+ "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)."
]
},
{
@@ -56,72 +76,36 @@
"output_type": "stream",
"text": [
"Downloading...\n",
- "--2015-06-30 14:41:56-- http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
- "Resolving yann.lecun.com... 128.122.47.89\n",
- "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 9912422 (9.5M) [application/x-gzip]\n",
- "Saving to: 'train-images-idx3-ubyte.gz'\n",
- "\n",
- "train-images-idx3-u 100%[=====================>] 9.45M 146KB/s in 57s \n",
- "\n",
- "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
- "\n",
- "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
- "Resolving yann.lecun.com... 128.122.47.89\n",
- "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 28881 (28K) [application/x-gzip]\n",
- "Saving to: 'train-labels-idx1-ubyte.gz'\n",
- "\n",
- "train-labels-idx1-u 100%[=====================>] 28.20K 107KB/s in 0.3s \n",
- "\n",
- "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
- "\n",
- "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
- "Resolving yann.lecun.com... 128.122.47.89\n",
- "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 1648877 (1.6M) [application/x-gzip]\n",
- "Saving to: 't10k-images-idx3-ubyte.gz'\n",
- "\n",
- "t10k-images-idx3-ub 100%[=====================>] 1.57M 205KB/s in 8.2s \n",
- "\n",
- "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
- "\n",
- "--2015-06-30 14:43:02-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
- "Resolving yann.lecun.com... 128.122.47.89\n",
- "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 4542 (4.4K) [application/x-gzip]\n",
- "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
- "\n",
- "t10k-labels-idx1-ub 100%[=====================>] 4.44K 26.9KB/s in 0.2s \n",
- "\n",
- "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
- "\n",
- "Unzipping...\n",
- "Done.\n",
"Creating lmdb...\n",
"Done.\n"
]
}
],
"source": [
- "# Download and prepare data\n",
+ "# run scripts from caffe root\n",
+ "import os\n",
+ "os.chdir(caffe_root)\n",
+ "# Download data\n",
"!data/mnist/get_mnist.sh\n",
- "!examples/mnist/create_mnist.sh"
+ "# Prepare data\n",
+ "!examples/mnist/create_mnist.sh\n",
+ "# back to examples\n",
+ "os.chdir('examples')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We need two external files to help out:\n",
- "* the net prototxt, defining the architecture and pointing to the train/test data\n",
- "* the solver prototxt, defining the learning parameters\n",
+ "### 2. Creating the net \n",
+ "\n",
+ "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n",
"\n",
- "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+ "We'll need two external files to help out:\n",
+ "* the net `prototxt`, defining the architecture and pointing to the train/test data\n",
+ "* the solver `prototxt`, defining the learning parameters\n",
+ "\n",
+ "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
"\n",
"This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
]
@@ -134,36 +118,38 @@
},
"outputs": [],
"source": [
- "from caffe import layers as L\n",
- "from caffe import params as P\n",
+ "from caffe import layers as L, params as P\n",
"\n",
"def lenet(lmdb, batch_size):\n",
" # our version of LeNet: a series of linear and simple nonlinear transformations\n",
" n = caffe.NetSpec()\n",
+ " \n",
" n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
" transform_param=dict(scale=1./255), ntop=2)\n",
+ " \n",
" n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
" n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
" n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
" n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
- " n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
- " n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
- " n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
- " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+ " n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+ " n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+ " n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+ " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n",
+ " \n",
" return n.to_proto()\n",
" \n",
- "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
- " f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
+ "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+ " f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n",
" \n",
- "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
- " f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
+ "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+ " f.write(str(lenet('mnist/mnist_test_lmdb', 100)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
+ "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
]
},
{
@@ -186,7 +172,7 @@
" scale: 0.00392156862745\r\n",
" }\r\n",
" data_param {\r\n",
- " source: \"examples/mnist/mnist_train_lmdb\"\r\n",
+ " source: \"mnist/mnist_train_lmdb\"\r\n",
" batch_size: 64\r\n",
" backend: LMDB\r\n",
" }\r\n",
@@ -240,10 +226,10 @@
" }\r\n",
"}\r\n",
"layer {\r\n",
- " name: \"ip1\"\r\n",
+ " name: \"fc1\"\r\n",
" type: \"InnerProduct\"\r\n",
" bottom: \"pool2\"\r\n",
- " top: \"ip1\"\r\n",
+ " top: \"fc1\"\r\n",
" inner_product_param {\r\n",
" num_output: 500\r\n",
" weight_filler {\r\n",
@@ -254,14 +240,14 @@
"layer {\r\n",
" name: \"relu1\"\r\n",
" type: \"ReLU\"\r\n",
- " bottom: \"ip1\"\r\n",
- " top: \"ip1\"\r\n",
+ " bottom: \"fc1\"\r\n",
+ " top: \"fc1\"\r\n",
"}\r\n",
"layer {\r\n",
- " name: \"ip2\"\r\n",
+ " name: \"score\"\r\n",
" type: \"InnerProduct\"\r\n",
- " bottom: \"ip1\"\r\n",
- " top: \"ip2\"\r\n",
+ " bottom: \"fc1\"\r\n",
+ " top: \"score\"\r\n",
" inner_product_param {\r\n",
" num_output: 10\r\n",
" weight_filler {\r\n",
@@ -272,7 +258,7 @@
"layer {\r\n",
" name: \"loss\"\r\n",
" type: \"SoftmaxWithLoss\"\r\n",
- " bottom: \"ip2\"\r\n",
+ " bottom: \"score\"\r\n",
" bottom: \"label\"\r\n",
" top: \"loss\"\r\n",
"}\r\n"
@@ -280,14 +266,14 @@
}
],
"source": [
- "!cat examples/mnist/lenet_auto_train.prototxt"
+ "!cat mnist/lenet_auto_train.prototxt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+ "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule."
]
},
{
@@ -302,8 +288,8 @@
"output_type": "stream",
"text": [
"# The train/test net protocol buffer definition\r\n",
- "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
- "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
+ "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n",
+ "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n",
"# test_iter specifies how many forward passes the test should carry out.\r\n",
"# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
"# covering the full 10,000 testing images.\r\n",
@@ -324,39 +310,44 @@
"max_iter: 10000\r\n",
"# snapshot intermediate results\r\n",
"snapshot: 5000\r\n",
- "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
+ "snapshot_prefix: \"mnist/lenet\"\r\n"
]
}
],
"source": [
- "!cat examples/mnist/lenet_auto_solver.prototxt"
+ "!cat mnist/lenet_auto_solver.prototxt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
+ "### 3. Loading and checking the solver\n",
+ "\n",
+ "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
- "collapsed": true
+ "collapsed": false
},
"outputs": [],
"source": [
"caffe.set_device(0)\n",
"caffe.set_mode_gpu()\n",
- "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
+ "\n",
+ "### load the solver and create train and test nets\n",
+ "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+ "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+ "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
]
},
{
@@ -376,8 +367,8 @@
" ('pool1', (64, 20, 12, 12)),\n",
" ('conv2', (64, 50, 8, 8)),\n",
" ('pool2', (64, 50, 4, 4)),\n",
- " ('ip1', (64, 500)),\n",
- " ('ip2', (64, 10)),\n",
+ " ('fc1', (64, 500)),\n",
+ " ('score', (64, 10)),\n",
" ('loss', ())]"
]
},
@@ -403,8 +394,8 @@
"text/plain": [
"[('conv1', (20, 1, 5, 5)),\n",
" ('conv2', (50, 20, 5, 5)),\n",
- " ('ip1', (500, 800)),\n",
- " ('ip2', (10, 500))]"
+ " ('fc1', (500, 800)),\n",
+ " ('score', (10, 500))]"
]
},
"execution_count": 9,
@@ -413,7 +404,7 @@
}
],
"source": [
- "# just print the weight sizes (not biases)\n",
+ "# just print the weight sizes (we'll omit the biases)\n",
"[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
]
},
@@ -421,7 +412,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+ "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
]
},
{
@@ -434,7 +425,7 @@
{
"data": {
"text/plain": [
- "{'loss': array(2.301163673400879, dtype=float32)}"
+ "{'loss': array(2.365971088409424, dtype=float32)}"
]
},
"execution_count": 10,
@@ -458,216 +449,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 5. 0. 4. 1. 9. 2. 1. 3.]\n"
+ "train labels: [ 5. 0. 4. 1. 9. 2. 1. 3.]\n"
]
},
{
"data": {
- "image/png": [
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- "JBizrwIJxCYJgQTc9wfcW+D0KiOwK+8ngkBcJN2jo3N/95zf+f5+PyFJEioqKioqzz6arW6AioqK\n",
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- "OxvXLBUVFRWVe4nWYLuAoTV/D68eU1FRUVGJEXFRvu6RfhQhhBrzrqKiohIFkiSJ+x2PdoY9AmSt\n",
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- "amtrqaqqwuFw0NLSwq1bt2hpaWFsbIxwOLzVzVRReWZITk7m2LFj7Nmzh/z8fLxeL2fOnOHs2bPM\n",
- "zMywuLi41U3ccp7WYAPc906wUQgh0Gq1aLVa5Vh8fDwulwuj0UhcXBwmkwm3243P5yM/P5+MjAyS\n",
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAABKCAYAAACfHW4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztndlXW1ea9n+S0CwBkpAQCMw8jwZscOzYwWMcO6mVpFJd\nXV2u7rrpm/4D+rb/g77o1b1WX1X1Rd9UkkplqtixcUxsPGCbMWDMPAo0MWhAE6DvIuucgsQZbCMB\n/Z3fWtzIWGwdnf2cvd/3ed8tSyQSSEhISEjsf+R7PQAJCQkJiZ+HJNgSEhISBwRJsCUkJCQOCJJg\nS0hISBwQJMGWkJCQOCBIgi0hISFxQJAEW0JCQuKAIAm2hISExAFBEmwJCQmJA4Ik2BISEhIHhLRk\nvbFMJpNq3iUkJCRegEQiIXvW69IKW0JCQuKAIAm2hISExAFBEmwJCQmJA0LSYtj/vyOXy1EoFKhU\nKpRK [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f7939901710>"
+ "<matplotlib.figure.Figure at 0x7f51a65ee690>"
]
},
"metadata": {},
@@ -676,8 +465,8 @@
],
"source": [
"# we use a little trick to tile the first eight images\n",
- "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
- "print solver.net.blobs['label'].data[:8]"
+ "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+ "print 'train labels:', solver.net.blobs['label'].data[:8]"
]
},
{
@@ -691,204 +480,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 7. 2. 1. 0. 4. 1. 4. 9.]\n"
+ "test labels: [ 7. 2. 1. 0. 4. 1. 4. 9.]\n"
]
},
{
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- "AAAASUVORK5CYII=\n"
- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAABKCAYAAACfHW4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztndlTm/d+/1/aN4SEECCQ2MS+L8Y4OMHGS+I4mbTHMznt\nmbbT02WmnXOu+zf0ttOr3nQ6016kaZxOTtOck9ixseMFbGyIzb6DAIFYBQjtEvpd5Pc88XZibCME\np89rxjdGiI8ePc/7+/18vp9FlkgkkJCQkJA4/MhTbYCEhISExN6QBFtCQkLiiCAJtoSEhMQRQRJs\nCQkJiSOCJNgSEhISRwRJsCUkJCSOCJJgS0hISBwRJMGWkJCQOCJIgi0hISFxRJAEW0JCQuKIoEzW\nG8tkMqnmXUJCQuI1SCQSshf9v7TDlpCQkDgiSIItISEhcURIWkjkDx2tVktxcTF6vR61Wo3RaCQr\nKwuL [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f7939901490>"
+ "<matplotlib.figure.Figure at 0x7f51a4030a50>"
]
},
"metadata": {},
@@ -896,17 +495,19 @@
}
],
"source": [
- "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
- "print solver.test_nets[0].blobs['label'].data[:8]"
+ "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+ "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "### 4. Stepping the solver\n",
+ "\n",
"Both train and test nets seem to be loading data, and to have correct labels.\n",
"\n",
- "Let's take one step of (minibatch) SGD and see what happens."
+ "* Let's take one step of (minibatch) SGD and see what happens."
]
},
{
@@ -937,7 +538,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f79383819d0>"
+ "(-0.5, 24.5, 19.5, -0.5)"
]
},
"execution_count": 14,
@@ -946,439 +547,9 @@
},
{
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAD7CAYAAADkSGhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztne1y5DpsROkdb5JnzMPlNVO7Htv5kcJeuN0NNKixx+sr\nVKlEURI/QOAQlOTxw+vr6zrllFNO+Q7y494NOOWUU065lZxAO+WUU76NnEA75ZRTvo2cQDvllFO+\njZxAO+WUU76NnEA75ZRTvo08flTB//3f/02/B3l9fV0vLy9v9iwvn4v7qm2ttR4eHt5sVd6PHz/e\nnVP5Oa9L5/ZX6ZeXlz/9yv3DvNhHHVFPpNkxbpfLpTy+Xq/r+fnZ2jOdqe0WosrBsWf2EPv/+q//\nerP953/+57u82F5eXtbz8/OfMYo07vOYOvY50VvVZybX6/XNFmPFjrOtov3gPusf95g38VXm61Ue\nk//5 [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f7939901850>"
+ "<matplotlib.figure.Figure at 0x7f519c0eab90>"
]
},
"metadata": {},
@@ -1387,13 +558,15 @@
],
"source": [
"imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
- " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
+ " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "### 5. Writing a custom training loop\n",
+ "\n",
"Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
"Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
"* logging will continue to happen as normal\n",
@@ -1424,8 +597,8 @@
"Iteration 125 testing...\n",
"Iteration 150 testing...\n",
"Iteration 175 testing...\n",
- "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
- "Wall time: 15.7 s\n"
+ "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n",
+ "Wall time: 14.4 s\n"
]
}
],
@@ -1448,7 +621,7 @@
" # store the output on the first test batch\n",
" # (start the forward pass at conv1 to avoid loading new data)\n",
" solver.test_nets[0].forward(start='conv1')\n",
- " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
+ " output[it] = solver.test_nets[0].blobs['score'].data[:8]\n",
" \n",
" # run a full test every so often\n",
" # (Caffe can also do this for us and write to a log, but we show here\n",
@@ -1458,7 +631,7 @@
" correct = 0\n",
" for test_it in range(100):\n",
" solver.test_nets[0].forward()\n",
- " correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
+ " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
" == solver.test_nets[0].blobs['label'].data)\n",
" test_acc[it // test_interval] = correct / 1e4"
]
@@ -1467,7 +640,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's plot the train loss and test accuracy."
+ "* Let's plot the train loss and test accuracy."
]
},
{
@@ -1480,7 +653,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.text.Text at 0x7f793878f490>"
+ "<matplotlib.text.Text at 0x7f5199b33610>"
]
},
"execution_count": 16,
@@ -1489,331 +662,9 @@
},
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEZCAYAAADR8/HkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYFOW1h98DCMgmIELY90UUlKiogDqiMUQjehNv1CTG\nqNdrYlzijcaYFbObxMQYlxg1Jhq3xC0mkRhAR0VxQQFRdpB9Z9gX2c7941TZNT3VPTUz3dPTzXmf\np5/u6q7l6+rq71fnfOc7R1QVx3Ecx6lvGhW6AY7jOM6BiQuQ4ziOUxBcgBzHcZyC4ALkOI7jFAQX\nIMdxHKcguAA5juM4BcEFyHEcxykILkBO3hCRbSKyNXjsF5EdkeULa7G/chG5LMF6rYJjP1e7lhcf\nIvJ5EVkSfO+nRaRdlnVHiMibIrJFRGaIyMgM6/0x+N365K/lzoGMC5CTN1S1laq2VtXWwBLg0+Gy\nqj5a [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f79387025d0>"
+ "<matplotlib.figure.Figure at 0x7f519bf16690>"
]
},
"metadata": {},
@@ -1827,7 +678,8 @@
"ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
"ax1.set_xlabel('iteration')\n",
"ax1.set_ylabel('train loss')\n",
- "ax2.set_ylabel('test accuracy')"
+ "ax2.set_ylabel('test accuracy')\n",
+ "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))"
]
},
{
@@ -1836,7 +688,7 @@
"source": [
"The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
"\n",
- "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+ "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
]
},
{
@@ -1849,109 +701,9 @@
"outputs": [
{
"data": {
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f7938233050>"
+ "<matplotlib.figure.Figure at 0x7f51999dd210>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f793821a310>"
+ "<matplotlib.figure.Figure at 0x7f519994c650>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7f79380a2550>"
+ "<matplotlib.figure.Figure at 0x7f51997d3ad0>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792bd10b10>"
+ "<matplotlib.figure.Figure at 0x7f519bee6f50>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792bca2290>"
+ "<matplotlib.figure.Figure at 0x7f519c0b0c10>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792bb3b910>"
+ "<matplotlib.figure.Figure at 0x7f519969d110>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792ba9ddd0>"
+ "<matplotlib.figure.Figure at 0x7f5199603550>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b9b9550>"
+ "<matplotlib.figure.Figure at 0x7f5199591d10>"
]
},
"metadata": {},
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGLBJREFUeJztnVtsY+tVx3+ft+/29i224yQzk+lp+9AHpNOX8lAq+lBV\np0Jq4YWqEgKVUvEABQESbXmgBV5KJSoED0ioLeKmFgQqKi/QVgKpPHA5qKcXzqVnTjOTjJ2L49jx\n/f7xYK9vtj3JnLHjTOxk/6WtOJ5k5zv1v+tb31r/9d9Ka40LF7PAc9ULcLF6cEnjYma4pHExM1zS\nuJgZLmlczAyXNC5mxtykUUq9oJR6VSn1ulLqk4tclIvlhpqnTqOUsoDXgPcBeeB/gI9orV9Z7PJc\nLCPmjTTvAu5pre9rrXvAV4EPLW5ZLpYZ3jl/bwvYc3z/EPhx5w8opdxS84pDa63Oen/eSOMS4gZj\nXtLk [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b97ec50>"
+ "<matplotlib.figure.Figure at 0x7f51994fb990>"
]
},
"metadata": {},
@@ -3429,99 +851,9 @@
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- ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAACbCAYAAACkuQVhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE5dJREFUeJzt3XuwXWV5x/HfL8lJcpJDEkJCbDiHhlJthNGKFSHBG9V2\nqKNoW6vSVq3t2OmolabqiMy0f7Wj1emIDtPOWCkq3tqqRZzWC62gSGICmAshxAty1ZJA7ic3cnn6\nx96Jh+ScnPfJynv23vH7mclkr7Wfvda717vWypO11n4fR4QAAABw8iZ1ugEAAAC9joQKAACgIRIq\nAACAhkioAAAAGiKhAgAAaIiECgAAoKEpnVy5bcZsAAAAPSMiPNr8qgmV7SskXSdpsqRPRMQ/HBuz\nbNmy4z63YsUKLVmy5GnzJk0qv5hmj/pdx5QZi+vw4cMdj605dtip2BZ33XWXLr744qLY0Rw6dKg4\n9uDB [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b91c3d0>"
+ "<matplotlib.figure.Figure at 0x7f519948af90>"
]
},
"metadata": {},
@@ -3544,7 +876,7 @@
"source": [
"We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
"\n",
- "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+ "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
]
},
{
@@ -3557,109 +889,9 @@
"outputs": [
{
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\nUSBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\niXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\njWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\nZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\nswtg [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b65d950>"
+ "<matplotlib.figure.Figure at 0x7f51991d5790>"
]
},
"metadata": {},
@@ -3667,81 +899,9 @@
},
{
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b4bf3d0>"
+ "<matplotlib.figure.Figure at 0x7f51990357d0>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b44f290>"
+ "<matplotlib.figure.Figure at 0x7f5198fc5750>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b2b79d0>"
+ "<matplotlib.figure.Figure at 0x7f5198e2cfd0>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b1419d0>"
+ "<matplotlib.figure.Figure at 0x7f51994cc7d0>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792b127d90>"
+ "<matplotlib.figure.Figure at 0x7f5199160590>"
]
},
"metadata": {},
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792af4a550>"
+ "<matplotlib.figure.Figure at 0x7f519c085f10>"
]
},
"metadata": {},
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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAACbCAYAAACkuQVhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEaNJREFUeJzt3X+QXWV9x/HPJ5vdbJIlyUD4UZKYhBYoYbCGAgMYIVTb\noYxB2lqRtkptx05HrSlVRmSm/YNpRyvTER2nnbFQFKzaFi3qtCApDYiIRCAhkCAk/Gj40SRAyY8l\n2WST/faPezcsm/3xPDl59twb3q+ZDPfc/d7zPPc+59z75fx4vo4IAQAA4NBNqrsDAAAA7Y6ECgAA\noCISKgAAgIpIqAAAACoioQIAAKiIhAoAAKCiyXU2bps5GwAAQNuICI/0fNGEyvbFkm6Q1CHpxoj4\n2+Exxx9//EGv6+3tVU9Pz/B15bSb1c+BgYG2im2VucNG63NfX5+6u7uTYnPWO5L9+/cnx5bsR86Y\nlIoF [...]
"text/plain": [
- "<matplotlib.figure.Figure at 0x7d792aed8c90>"
+ "<matplotlib.figure.Figure at 0x7f519998c390>"
]
},
"metadata": {},
@@ -4941,123 +1029,9 @@
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"metadata": {},
@@ -5166,6 +1057,207 @@
" xlabel('iteration')\n",
" ylabel('label')"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6. Experiment with architecture and optimization\n",
+ "\n",
+ "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n",
+ "\n",
+ "- Define new architectures for comparison\n",
+ "- Tune optimization by setting `base_lr` and the like or simply training longer\n",
+ "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n",
+ "\n",
+ "Feel free to explore these directions by editing the all-in-one example that follows.\n",
+ "Look for \"`EDIT HERE`\" comments for suggested choice points.\n",
+ "\n",
+ "By default this defines a simple linear classifier as a baseline.\n",
+ "\n",
+ "In case your coffee hasn't kicked in and you'd like inspiration, try out\n",
+ "\n",
+ "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n",
+ "2. Stack more fully connected and nonlinear layers\n",
+ "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n",
+ "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n",
+ "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Iteration 0 testing...\n",
+ "Iteration 25 testing...\n",
+ "Iteration 50 testing...\n",
+ "Iteration 75 testing...\n",
+ "Iteration 100 testing...\n",
+ "Iteration 125 testing...\n",
+ "Iteration 150 testing...\n",
+ "Iteration 175 testing...\n",
+ "Iteration 200 testing...\n",
+ "Iteration 225 testing...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7f5199af9f50>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEZCAYAAADR8/HkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXecXGX1/98nnfTee4EUCAZCgAASegApXxsdFPiJBftX\nEcEvAWwoKiJVBQERgtJEpSNLDaSHEFIJIdkkpG7aJiHt/P449zJ3Zqfc2Z3Z3dk979drXju3P3N3\n5vncU57ziKriOI7jOLVNk7pugOM4jtM4cQFyHMdx6gQXIMdxHKdOcAFyHMdx6gQXIMdxHKdOcAFy\nHMdx6gQXIMdxHKdOcAFq5IjI+SIyXUS2isgqEXlaRI6q4TknichfC9XGHNfaFrR9q4jsE5HtkeXz\nqnG+MhG5LMZ+bYNrP129lpcewXflw+BzPyEinbLsOyq4l5tEZIWIXJuy/SsiskRENovItJp+55zS\nxAWo [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f5198f46a10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "train_net_path = 'mnist/custom_auto_train.prototxt'\n",
+ "test_net_path = 'mnist/custom_auto_test.prototxt'\n",
+ "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n",
+ "\n",
+ "### define net\n",
+ "def custom_net(lmdb, batch_size):\n",
+ " # define your own net!\n",
+ " n = caffe.NetSpec()\n",
+ " \n",
+ " # keep this data layer for all networks\n",
+ " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+ " transform_param=dict(scale=1./255), ntop=2)\n",
+ " \n",
+ " # EDIT HERE to try different networks\n",
+ " # this single layer defines a simple linear classifier\n",
+ " # (in particular this defines a multiway logistic regression)\n",
+ " n.score = L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n",
+ " \n",
+ " # EDIT HERE this is the LeNet variant we have already tried\n",
+ " # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+ " # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+ " # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+ " # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+ " # n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+ " # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n",
+ " # n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+ " # n.score = L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n",
+ " \n",
+ " # keep this loss layer for all networks\n",
+ " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n",
+ " \n",
+ " return n.to_proto()\n",
+ "\n",
+ "with open(train_net_path, 'w') as f:\n",
+ " f.write(str(custom_net('mnist/mnist_train_lmdb', 64))) \n",
+ "with open(test_net_path, 'w') as f:\n",
+ " f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n",
+ "\n",
+ "### define solver\n",
+ "from caffe.proto import caffe_pb2\n",
+ "s = caffe_pb2.SolverParameter()\n",
+ "\n",
+ "# Set a seed for reproducible experiments:\n",
+ "# this controls for randomization in training.\n",
+ "s.random_seed = 0xCAFFE\n",
+ "\n",
+ "# Specify locations of the train and (maybe) test networks.\n",
+ "s.train_net = train_net_path\n",
+ "s.test_net.append(test_net_path)\n",
+ "s.test_interval = 500 # Test after every 500 training iterations.\n",
+ "s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+ "\n",
+ "s.max_iter = 10000 # no. of times to update the net (training iterations)\n",
+ " \n",
+ "# EDIT HERE to try different solvers\n",
+ "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n",
+ "s.type = \"SGD\"\n",
+ "\n",
+ "# Set the initial learning rate for SGD.\n",
+ "s.base_lr = 0.01 # EDIT HERE to try different learning rates\n",
+ "# Set momentum to accelerate learning by\n",
+ "# taking weighted average of current and previous updates.\n",
+ "s.momentum = 0.9\n",
+ "# Set weight decay to regularize and prevent overfitting\n",
+ "s.weight_decay = 5e-4\n",
+ "\n",
+ "# Set `lr_policy` to define how the learning rate changes during training.\n",
+ "# This is the same policy as our default LeNet.\n",
+ "s.lr_policy = 'inv'\n",
+ "s.gamma = 0.0001\n",
+ "s.power = 0.75\n",
+ "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n",
+ "# `fixed` is the simplest policy that keeps the learning rate constant.\n",
+ "# s.lr_policy = 'fixed'\n",
+ "\n",
+ "# Display the current training loss and accuracy every 1000 iterations.\n",
+ "s.display = 1000\n",
+ "\n",
+ "# Snapshots are files used to store networks we've trained.\n",
+ "# We'll snapshot every 5K iterations -- twice during training.\n",
+ "s.snapshot = 5000\n",
+ "s.snapshot_prefix = 'mnist/custom_net'\n",
+ "\n",
+ "# Train on the GPU\n",
+ "s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+ "\n",
+ "# Write the solver to a temporary file and return its filename.\n",
+ "with open(solver_config_path, 'w') as f:\n",
+ " f.write(str(s))\n",
+ "\n",
+ "### load the solver and create train and test nets\n",
+ "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+ "solver = caffe.get_solver(solver_config_path)\n",
+ "\n",
+ "### solve\n",
+ "niter = 250 # EDIT HERE increase to train for longer\n",
+ "test_interval = niter / 10\n",
+ "# losses will also be stored in the log\n",
+ "train_loss = zeros(niter)\n",
+ "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+ "\n",
+ "# the main solver loop\n",
+ "for it in range(niter):\n",
+ " solver.step(1) # SGD by Caffe\n",
+ " \n",
+ " # store the train loss\n",
+ " train_loss[it] = solver.net.blobs['loss'].data\n",
+ " \n",
+ " # run a full test every so often\n",
+ " # (Caffe can also do this for us and write to a log, but we show here\n",
+ " # how to do it directly in Python, where more complicated things are easier.)\n",
+ " if it % test_interval == 0:\n",
+ " print 'Iteration', it, 'testing...'\n",
+ " correct = 0\n",
+ " for test_it in range(100):\n",
+ " solver.test_nets[0].forward()\n",
+ " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
+ " == solver.test_nets[0].blobs['label'].data)\n",
+ " test_acc[it // test_interval] = correct / 1e4\n",
+ "\n",
+ "_, ax1 = subplots()\n",
+ "ax2 = ax1.twinx()\n",
+ "ax1.plot(arange(niter), train_loss)\n",
+ "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+ "ax1.set_xlabel('iteration')\n",
+ "ax1.set_ylabel('train loss')\n",
+ "ax2.set_ylabel('test accuracy')\n",
+ "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))"
+ ]
}
],
"metadata": {
@@ -5187,7 +1279,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.9"
+ "version": "2.7.10"
},
"priority": 2
},
diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb
deleted file mode 100644
index d36871f..0000000
--- a/examples/02-brewing-logreg.ipynb
+++ /dev/null
@@ -1,5771 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Brewing Logistic Regression then Going Deeper\n",
- "\n",
- "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "import os\n",
- "os.chdir('..')\n",
- "\n",
- "import sys\n",
- "sys.path.insert(0, './python')\n",
- "import caffe\n",
- "\n",
- "\n",
- "import os\n",
- "import h5py\n",
- "import shutil\n",
- "import tempfile\n",
- "\n",
- "import sklearn\n",
- "import sklearn.datasets\n",
- "import sklearn.linear_model\n",
- "\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x11dbd0090>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "X, y = sklearn.datasets.make_classification(\n",
- " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
- " n_clusters_per_class=2, hypercube=False, random_state=0\n",
- ")\n",
- "\n",
- "# Split into train and test\n",
- "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
- "\n",
- "# Visualize sample of the data\n",
- "ind = np.random.permutation(X.shape[0])[:1000]\n",
- "df = pd.DataFrame(X[ind])\n",
- "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy: 0.783\n",
- "Accuracy: 0.783\n",
- "Accuracy: 0.783\n",
- "Accuracy: 0.783\n",
- "1 loops, best of 3: 508 ms per loop\n"
- ]
- }
- ],
- "source": [
- "%%timeit\n",
- "# Train and test the scikit-learn SGD logistic regression.\n",
- "clf = sklearn.linear_model.SGDClassifier(\n",
- " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
- "\n",
- "clf.fit(X, y)\n",
- "yt_pred = clf.predict(Xt)\n",
- "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Save the dataset to HDF5 for loading in Caffe."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Write out the data to HDF5 files in a temp directory.\n",
- "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
- "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
- "if not os.path.exists(dirname):\n",
- " os.makedirs(dirname)\n",
- "\n",
- "train_filename = os.path.join(dirname, 'train.h5')\n",
- "test_filename = os.path.join(dirname, 'test.h5')\n",
- "\n",
- "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
- "# To show this off, we'll list the same data file twice.\n",
- "with h5py.File(train_filename, 'w') as f:\n",
- " f['data'] = X\n",
- " f['label'] = y.astype(np.float32)\n",
- "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
- " f.write(train_filename + '\\n')\n",
- " f.write(train_filename + '\\n')\n",
- " \n",
- "# HDF5 is pretty efficient, but can be further compressed.\n",
- "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
- "with h5py.File(test_filename, 'w') as f:\n",
- " f.create_dataset('data', data=Xt, **comp_kwargs)\n",
- " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
- "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
- " f.write(test_filename + '\\n')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from caffe import layers as L\n",
- "from caffe import params as P\n",
- "\n",
- "def logreg(hdf5, batch_size):\n",
- " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
- " n = caffe.NetSpec()\n",
- " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
- " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
- " n.accuracy = L.Accuracy(n.ip1, n.label)\n",
- " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
- " return n.to_proto()\n",
- " \n",
- "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n",
- " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
- " \n",
- "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n",
- " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Time to learn and evaluate our Caffeinated logistic regression in Python."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy: 0.782\n",
- "Accuracy: 0.782\n",
- "Accuracy: 0.782\n",
- "Accuracy: 0.782\n",
- "1 loops, best of 3: 287 ms per loop\n"
- ]
- }
- ],
- "source": [
- "%%timeit\n",
- "caffe.set_mode_cpu()\n",
- "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n",
- "solver.solve()\n",
- "\n",
- "accuracy = 0\n",
- "batch_size = solver.test_nets[0].blobs['data'].num\n",
- "test_iters = int(len(Xt) / batch_size)\n",
- "for i in range(test_iters):\n",
- " solver.test_nets[0].forward()\n",
- " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
- "accuracy /= test_iters\n",
- "\n",
- "print(\"Accuracy: {:.3f}\".format(accuracy))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Do the same through the command line interface for detailed output on the model and solving."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n",
- "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n",
- "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
- "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
- "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
- "test_iter: 250\n",
- "test_interval: 1000\n",
- "base_lr: 0.01\n",
- "display: 1000\n",
- "max_iter: 10000\n",
- "lr_policy: \"step\"\n",
- "gamma: 0.1\n",
- "momentum: 0.9\n",
- "weight_decay: 0.0005\n",
- "stepsize: 5000\n",
- "snapshot: 10000\n",
- "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
- "solver_mode: CPU\n",
- "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
- "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n",
- "state {\n",
- " phase: TRAIN\n",
- "}\n",
- "layer {\n",
- " name: \"data\"\n",
- " type: \"HDF5Data\"\n",
- " top: \"data\"\n",
- " top: \"label\"\n",
- " hdf5_data_param {\n",
- " source: \"examples/hdf5_classification/data/train.txt\"\n",
- " batch_size: 10\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"ip1\"\n",
- " type: \"InnerProduct\"\n",
- " bottom: \"data\"\n",
- " top: \"ip1\"\n",
- " inner_product_param {\n",
- " num_output: 2\n",
- " weight_filler {\n",
- " type: \"xavier\"\n",
- " }\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"accuracy\"\n",
- " type: \"Accuracy\"\n",
- " bottom: \"ip1\"\n",
- " bottom: \"label\"\n",
- " top: \"accuracy\"\n",
- "}\n",
- "layer {\n",
- " name: \"loss\"\n",
- " type: \"SoftmaxWithLoss\"\n",
- " bottom: \"ip1\"\n",
- " bottom: \"label\"\n",
- " top: \"loss\"\n",
- "}\n",
- "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n",
- "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n",
- "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n",
- "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n",
- "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n",
- "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
- "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
- "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
- "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
- "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
- "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n",
- "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
- "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
- "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n",
- "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
- "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n",
- "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n",
- "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n",
- "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n",
- "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
- "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
- "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
- "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
- "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
- "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
- "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
- "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n",
- "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
- "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
- "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n",
- "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n",
- "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n",
- "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
- "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
- "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n",
- "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n",
- "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:32.652724 2013098752 net.cpp:122] with loss weight 1\n",
- "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n",
- "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
- "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
- "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n",
- "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
- "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n",
- "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n",
- "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n",
- "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
- "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n",
- "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n",
- "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
- "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n",
- "state {\n",
- " phase: TEST\n",
- "}\n",
- "layer {\n",
- " name: \"data\"\n",
- " type: \"HDF5Data\"\n",
- " top: \"data\"\n",
- " top: \"label\"\n",
- " hdf5_data_param {\n",
- " source: \"examples/hdf5_classification/data/test.txt\"\n",
- " batch_size: 10\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"ip1\"\n",
- " type: \"InnerProduct\"\n",
- " bottom: \"data\"\n",
- " top: \"ip1\"\n",
- " inner_product_param {\n",
- " num_output: 2\n",
- " weight_filler {\n",
- " type: \"xavier\"\n",
- " }\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"accuracy\"\n",
- " type: \"Accuracy\"\n",
- " bottom: \"ip1\"\n",
- " bottom: \"label\"\n",
- " top: \"accuracy\"\n",
- "}\n",
- "layer {\n",
- " name: \"loss\"\n",
- " type: \"SoftmaxWithLoss\"\n",
- " bottom: \"ip1\"\n",
- " bottom: \"label\"\n",
- " top: \"loss\"\n",
- "}\n",
- "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n",
- "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n",
- "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n",
- "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n",
- "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n",
- "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
- "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
- "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
- "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
- "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
- "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n",
- "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
- "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
- "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n",
- "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
- "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n",
- "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n",
- "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n",
- "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n",
- "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
- "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
- "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
- "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
- "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
- "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
- "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
- "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n",
- "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
- "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
- "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n",
- "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n",
- "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n",
- "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
- "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
- "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n",
- "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n",
- "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:32.655422 2013098752 net.cpp:122] with loss weight 1\n",
- "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n",
- "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
- "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
- "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n",
- "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
- "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n",
- "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n",
- "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n",
- "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
- "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n",
- "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n",
- "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n",
- "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n",
- "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
- "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
- "I0318 00:58:32.683643 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.3736\n",
- "I0318 00:58:32.683686 2013098752 solver.cpp:315] Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n",
- "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n",
- "I0318 00:58:32.683861 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.3\n",
- "I0318 00:58:32.683871 2013098752 solver.cpp:204] Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n",
- "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
- "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
- "I0318 00:58:32.701917 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n",
- "I0318 00:58:32.701961 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
- "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n",
- "I0318 00:58:32.702029 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
- "I0318 00:58:32.702041 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
- "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
- "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
- "I0318 00:58:32.721529 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n",
- "I0318 00:58:32.721562 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
- "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n",
- "I0318 00:58:32.721603 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
- "I0318 00:58:32.721613 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
- "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
- "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
- "I0318 00:58:32.743494 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.77\n",
- "I0318 00:58:32.743544 2013098752 solver.cpp:315] Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n",
- "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n",
- "I0318 00:58:32.744231 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n",
- "I0318 00:58:32.744249 2013098752 solver.cpp:204] Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n",
- "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
- "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
- "I0318 00:58:32.769110 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n",
- "I0318 00:58:32.769170 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
- "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n",
- "I0318 00:58:32.769242 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
- "I0318 00:58:32.769255 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
- "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
- "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
- "I0318 00:58:32.788722 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n",
- "I0318 00:58:32.788751 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
- "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n",
- "I0318 00:58:32.788833 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
- "I0318 00:58:32.788846 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
- "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
- "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
- "I0318 00:58:32.808061 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7856\n",
- "I0318 00:58:32.808112 2013098752 solver.cpp:315] Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n",
- "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n",
- "I0318 00:58:32.808753 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:32.808773 2013098752 solver.cpp:204] Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n",
- "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
- "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
- "I0318 00:58:32.831614 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n",
- "I0318 00:58:32.831657 2013098752 solver.cpp:315] Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n",
- "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n",
- "I0318 00:58:32.831728 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n",
- "I0318 00:58:32.831745 2013098752 solver.cpp:204] Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n",
- "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
- "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
- "I0318 00:58:32.852712 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7796\n",
- "I0318 00:58:32.852748 2013098752 solver.cpp:315] Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n",
- "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n",
- "I0318 00:58:32.852840 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
- "I0318 00:58:32.852852 2013098752 solver.cpp:204] Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n",
- "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
- "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
- "I0318 00:58:32.871438 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7816\n",
- "I0318 00:58:32.871461 2013098752 solver.cpp:315] Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n",
- "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n",
- "I0318 00:58:32.872131 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:32.872143 2013098752 solver.cpp:204] Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n",
- "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
- "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
- "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
- "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n",
- "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
- "I0318 00:58:32.893728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.782\n",
- "I0318 00:58:32.893757 2013098752 solver.cpp:315] Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n",
- "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n",
- "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n"
- ]
- }
- ],
- "source": [
- "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
- "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
- "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n",
- "\n",
- "The final accuracy of the new network should be higher than logistic regression!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from caffe import layers as L\n",
- "from caffe import params as P\n",
- "\n",
- "def nonlinear_net(hdf5, batch_size):\n",
- " # one small nonlinearity, one leap for model kind\n",
- " n = caffe.NetSpec()\n",
- " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
- " # define a hidden layer of dimension 40\n",
- " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
- " # transform the output through the ReLU (rectified linear) non-linearity\n",
- " n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
- " # score the (now non-linear) features\n",
- " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
- " # same accuracy and loss as before\n",
- " n.accuracy = L.Accuracy(n.ip2, n.label)\n",
- " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
- " return n.to_proto()\n",
- " \n",
- "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n",
- " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
- " \n",
- "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n",
- " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy: 0.832\n",
- "Accuracy: 0.832\n",
- "Accuracy: 0.832\n",
- "Accuracy: 0.831\n",
- "1 loops, best of 3: 386 ms per loop\n"
- ]
- }
- ],
- "source": [
- "%%timeit\n",
- "caffe.set_mode_cpu()\n",
- "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n",
- "solver.solve()\n",
- "\n",
- "accuracy = 0\n",
- "batch_size = solver.test_nets[0].blobs['data'].num\n",
- "test_iters = int(len(Xt) / batch_size)\n",
- "for i in range(test_iters):\n",
- " solver.test_nets[0].forward()\n",
- " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
- "accuracy /= test_iters\n",
- "\n",
- "print(\"Accuracy: {:.3f}\".format(accuracy))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Do the same through the command line interface for detailed output on the model and solving."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n",
- "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n",
- "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
- "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
- "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
- "test_iter: 250\n",
- "test_interval: 1000\n",
- "base_lr: 0.01\n",
- "display: 1000\n",
- "max_iter: 10000\n",
- "lr_policy: \"step\"\n",
- "gamma: 0.1\n",
- "momentum: 0.9\n",
- "weight_decay: 0.0005\n",
- "stepsize: 5000\n",
- "snapshot: 10000\n",
- "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
- "solver_mode: CPU\n",
- "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
- "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n",
- "state {\n",
- " phase: TRAIN\n",
- "}\n",
- "layer {\n",
- " name: \"data\"\n",
- " type: \"HDF5Data\"\n",
- " top: \"data\"\n",
- " top: \"label\"\n",
- " hdf5_data_param {\n",
- " source: \"examples/hdf5_classification/data/train.txt\"\n",
- " batch_size: 10\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"ip1\"\n",
- " type: \"InnerProduct\"\n",
- " bottom: \"data\"\n",
- " top: \"ip1\"\n",
- " inner_product_param {\n",
- " num_output: 40\n",
- " weight_filler {\n",
- " type: \"xavier\"\n",
- " }\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"relu1\"\n",
- " type: \"ReLU\"\n",
- " bottom: \"ip1\"\n",
- " top: \"ip1\"\n",
- "}\n",
- "layer {\n",
- " name: \"ip2\"\n",
- " type: \"InnerProduct\"\n",
- " bottom: \"ip1\"\n",
- " top: \"ip2\"\n",
- " inner_product_param {\n",
- " num_output: 2\n",
- " weight_filler {\n",
- " type: \"xavier\"\n",
- " }\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"accuracy\"\n",
- " type: \"Accuracy\"\n",
- " bottom: \"ip2\"\n",
- " bottom: \"label\"\n",
- " top: \"accuracy\"\n",
- "}\n",
- "layer {\n",
- " name: \"loss\"\n",
- " type: \"SoftmaxWithLoss\"\n",
- " bottom: \"ip2\"\n",
- " bottom: \"label\"\n",
- " top: \"loss\"\n",
- "}\n",
- "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n",
- "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n",
- "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n",
- "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n",
- "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n",
- "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
- "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
- "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
- "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
- "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
- "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n",
- "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
- "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
- "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n",
- "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
- "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n",
- "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n",
- "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n",
- "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n",
- "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
- "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
- "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n",
- "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n",
- "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
- "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n",
- "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
- "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
- "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n",
- "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n",
- "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n",
- "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n",
- "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
- "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
- "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
- "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
- "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
- "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
- "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
- "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n",
- "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
- "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
- "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n",
- "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n",
- "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n",
- "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
- "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
- "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n",
- "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n",
- "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:43.662909 2013098752 net.cpp:122] with loss weight 1\n",
- "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n",
- "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
- "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
- "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n",
- "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n",
- "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n",
- "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
- "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n",
- "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n",
- "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n",
- "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
- "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n",
- "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n",
- "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
- "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n",
- "state {\n",
- " phase: TEST\n",
- "}\n",
- "layer {\n",
- " name: \"data\"\n",
- " type: \"HDF5Data\"\n",
- " top: \"data\"\n",
- " top: \"label\"\n",
- " hdf5_data_param {\n",
- " source: \"examples/hdf5_classification/data/test.txt\"\n",
- " batch_size: 10\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"ip1\"\n",
- " type: \"InnerProduct\"\n",
- " bottom: \"data\"\n",
- " top: \"ip1\"\n",
- " inner_product_param {\n",
- " num_output: 40\n",
- " weight_filler {\n",
- " type: \"xavier\"\n",
- " }\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"relu1\"\n",
- " type: \"ReLU\"\n",
- " bottom: \"ip1\"\n",
- " top: \"ip1\"\n",
- "}\n",
- "layer {\n",
- " name: \"ip2\"\n",
- " type: \"InnerProduct\"\n",
- " bottom: \"ip1\"\n",
- " top: \"ip2\"\n",
- " inner_product_param {\n",
- " num_output: 2\n",
- " weight_filler {\n",
- " type: \"xavier\"\n",
- " }\n",
- " }\n",
- "}\n",
- "layer {\n",
- " name: \"accuracy\"\n",
- " type: \"Accuracy\"\n",
- " bottom: \"ip2\"\n",
- " bottom: \"label\"\n",
- " top: \"accuracy\"\n",
- "}\n",
- "layer {\n",
- " name: \"loss\"\n",
- " type: \"SoftmaxWithLoss\"\n",
- " bottom: \"ip2\"\n",
- " bottom: \"label\"\n",
- " top: \"loss\"\n",
- "}\n",
- "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n",
- "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n",
- "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n",
- "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n",
- "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n",
- "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
- "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
- "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
- "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
- "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
- "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n",
- "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
- "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
- "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n",
- "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n",
- "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
- "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n",
- "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n",
- "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n",
- "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n",
- "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
- "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
- "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n",
- "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n",
- "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
- "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n",
- "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
- "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
- "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n",
- "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n",
- "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n",
- "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n",
- "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
- "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
- "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
- "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
- "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
- "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
- "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
- "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
- "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n",
- "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
- "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
- "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n",
- "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n",
- "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n",
- "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
- "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
- "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n",
- "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n",
- "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n",
- "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n",
- "I0318 00:58:43.665282 2013098752 net.cpp:122] with loss weight 1\n",
- "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n",
- "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
- "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
- "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n",
- "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n",
- "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n",
- "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
- "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n",
- "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n",
- "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n",
- "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
- "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n",
- "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n",
- "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n",
- "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n",
- "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
- "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
- "I0318 00:58:43.683938 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.5184\n",
- "I0318 00:58:43.683981 2013098752 solver.cpp:315] Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n",
- "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n",
- "I0318 00:58:43.684267 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n",
- "I0318 00:58:43.684285 2013098752 solver.cpp:204] Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n",
- "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
- "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
- "I0318 00:58:43.721762 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8168\n",
- "I0318 00:58:43.721818 2013098752 solver.cpp:315] Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n",
- "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n",
- "I0318 00:58:43.721917 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:43.721932 2013098752 solver.cpp:204] Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n",
- "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
- "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
- "I0318 00:58:43.754590 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8224\n",
- "I0318 00:58:43.754621 2013098752 solver.cpp:315] Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n",
- "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n",
- "I0318 00:58:43.754672 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
- "I0318 00:58:43.754683 2013098752 solver.cpp:204] Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n",
- "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
- "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
- "I0318 00:58:43.789728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8176\n",
- "I0318 00:58:43.789777 2013098752 solver.cpp:315] Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n",
- "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n",
- "I0318 00:58:43.790510 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
- "I0318 00:58:43.790530 2013098752 solver.cpp:204] Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n",
- "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
- "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
- "I0318 00:58:43.821740 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8252\n",
- "I0318 00:58:43.821770 2013098752 solver.cpp:315] Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n",
- "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n",
- "I0318 00:58:43.821835 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:43.821846 2013098752 solver.cpp:204] Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n",
- "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
- "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
- "I0318 00:58:43.852102 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8256\n",
- "I0318 00:58:43.852145 2013098752 solver.cpp:315] Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n",
- "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n",
- "I0318 00:58:43.852200 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
- "I0318 00:58:43.852210 2013098752 solver.cpp:204] Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n",
- "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
- "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
- "I0318 00:58:43.880080 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8328\n",
- "I0318 00:58:43.880105 2013098752 solver.cpp:315] Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n",
- "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n",
- "I0318 00:58:43.880718 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:43.880729 2013098752 solver.cpp:204] Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n",
- "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
- "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
- "I0318 00:58:43.917851 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8316\n",
- "I0318 00:58:43.917876 2013098752 solver.cpp:315] Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n",
- "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n",
- "I0318 00:58:43.917971 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:43.917989 2013098752 solver.cpp:204] Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n",
- "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
- "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
- "I0318 00:58:43.947589 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8312\n",
- "I0318 00:58:43.947615 2013098752 solver.cpp:315] Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n",
- "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n",
- "I0318 00:58:43.947664 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n",
- "I0318 00:58:43.947674 2013098752 solver.cpp:204] Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n",
- "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
- "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
- "I0318 00:58:43.977033 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.834\n",
- "I0318 00:58:43.977056 2013098752 solver.cpp:315] Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n",
- "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n",
- "I0318 00:58:43.977735 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n",
- "I0318 00:58:43.977746 2013098752 solver.cpp:204] Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n",
- "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
- "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
- "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
- "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n",
- "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
- "I0318 00:58:44.008216 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8308\n",
- "I0318 00:58:44.008252 2013098752 solver.cpp:315] Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n",
- "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n",
- "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n"
- ]
- }
- ],
- "source": [
- "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
- "shutil.rmtree(dirname)"
- ]
- }
- ],
- "metadata": {
- "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
- "example_name": "Off-the-shelf SGD for classification",
- "include_in_docs": true,
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.9"
- },
- "priority": 3
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
new file mode 100644
index 0000000..07ca8df
--- /dev/null
+++ b/examples/02-fine-tuning.ipynb
@@ -0,0 +1,1175 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fine-tuning a Pretrained Network for Style Recognition\n",
+ "\n",
+ "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+ "\n",
+ "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we will need to prepare the data. This involves the following parts:\n",
+ "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+ "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+ "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+ "\n",
+ "import sys\n",
+ "sys.path.insert(0, caffe_root + 'python')\n",
+ "import caffe\n",
+ "\n",
+ "caffe.set_device(0)\n",
+ "caffe.set_mode_gpu()\n",
+ "\n",
+ "import numpy as np\n",
+ "from pylab import *\n",
+ "%matplotlib inline\n",
+ "import tempfile\n",
+ "\n",
+ "# Helper function for deprocessing preprocessed images, e.g., for display.\n",
+ "def deprocess_net_image(image):\n",
+ " image = image.copy() # don't modify destructively\n",
+ " image = image[::-1] # BGR -> RGB\n",
+ " image = image.transpose(1, 2, 0) # CHW -> HWC\n",
+ " image += [123, 117, 104] # (approximately) undo mean subtraction\n",
+ "\n",
+ " # clamp values in [0, 255]\n",
+ " image[image < 0], image[image > 255] = 0, 255\n",
+ "\n",
+ " # round and cast from float32 to uint8\n",
+ " image = np.round(image)\n",
+ " image = np.require(image, dtype=np.uint8)\n",
+ "\n",
+ " return image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. Setup and dataset download\n",
+ "\n",
+ "Download data required for this exercise.\n",
+ "\n",
+ "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n",
+ "- `download_model_binary.py` to download the pretrained reference model\n",
+ "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n",
+ "\n",
+ "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading...\n",
+ "--2016-02-24 00:28:36-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
+ "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
+ "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 17858008 (17M) [application/octet-stream]\n",
+ "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
+ "\n",
+ "100%[======================================>] 17,858,008 112MB/s in 0.2s \n",
+ "\n",
+ "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
+ "\n",
+ "Unzipping...\n",
+ "Done.\n",
+ "Model already exists.\n",
+ "Downloading 2000 images with 7 workers...\n",
+ "Writing train/val for 1996 successfully downloaded images.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download just a small subset of the data for this exercise.\n",
+ "# (2000 of 80K images, 5 of 20 labels.)\n",
+ "# To download the entire dataset, set `full_dataset = True`.\n",
+ "full_dataset = False\n",
+ "if full_dataset:\n",
+ " NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n",
+ "else:\n",
+ " NUM_STYLE_IMAGES = 2000\n",
+ " NUM_STYLE_LABELS = 5\n",
+ "\n",
+ "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+ "# and a subset of 2000 images for the style recognition task.\n",
+ "import os\n",
+ "os.chdir(caffe_root) # run scripts from caffe root\n",
+ "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+ "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+ "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+ " --workers=-1 --seed=1701 \\\n",
+ " --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS\n",
+ "# back to examples\n",
+ "os.chdir('examples')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+ "assert os.path.exists(weights)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded ImageNet labels:\n",
+ "n01440764 tench, Tinca tinca\n",
+ "n01443537 goldfish, Carassius auratus\n",
+ "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n",
+ "n01491361 tiger shark, Galeocerdo cuvieri\n",
+ "n01494475 hammerhead, hammerhead shark\n",
+ "n01496331 electric ray, crampfish, numbfish, torpedo\n",
+ "n01498041 stingray\n",
+ "n01514668 cock\n",
+ "n01514859 hen\n",
+ "n01518878 ostrich, Struthio camelus\n",
+ "...\n",
+ "\n",
+ "Loaded style labels:\n",
+ "Detailed, Pastel, Melancholy, Noir, HDR\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load ImageNet labels to imagenet_labels\n",
+ "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+ "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n",
+ "assert len(imagenet_labels) == 1000\n",
+ "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n",
+ "\n",
+ "# Load style labels to style_labels\n",
+ "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n",
+ "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n",
+ "if NUM_STYLE_LABELS > 0:\n",
+ " style_labels = style_labels[:NUM_STYLE_LABELS]\n",
+ "print '\\nLoaded style labels:\\n', ', '.join(style_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. Defining and running the nets\n",
+ "\n",
+ "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "from caffe import layers as L\n",
+ "from caffe import params as P\n",
+ "\n",
+ "weight_param = dict(lr_mult=1, decay_mult=1)\n",
+ "bias_param = dict(lr_mult=2, decay_mult=0)\n",
+ "learned_param = [weight_param, bias_param]\n",
+ "\n",
+ "frozen_param = [dict(lr_mult=0)] * 2\n",
+ "\n",
+ "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n",
+ " param=learned_param,\n",
+ " weight_filler=dict(type='gaussian', std=0.01),\n",
+ " bias_filler=dict(type='constant', value=0.1)):\n",
+ " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+ " num_output=nout, pad=pad, group=group,\n",
+ " param=param, weight_filler=weight_filler,\n",
+ " bias_filler=bias_filler)\n",
+ " return conv, L.ReLU(conv, in_place=True)\n",
+ "\n",
+ "def fc_relu(bottom, nout, param=learned_param,\n",
+ " weight_filler=dict(type='gaussian', std=0.005),\n",
+ " bias_filler=dict(type='constant', value=0.1)):\n",
+ " fc = L.InnerProduct(bottom, num_output=nout, param=param,\n",
+ " weight_filler=weight_filler,\n",
+ " bias_filler=bias_filler)\n",
+ " return fc, L.ReLU(fc, in_place=True)\n",
+ "\n",
+ "def max_pool(bottom, ks, stride=1):\n",
+ " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+ "\n",
+ "def caffenet(data, label=None, train=True, num_classes=1000,\n",
+ " classifier_name='fc8', learn_all=False):\n",
+ " \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n",
+ " specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n",
+ " n = caffe.NetSpec()\n",
+ " n.data = data\n",
+ " param = learned_param if learn_all else frozen_param\n",
+ " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n",
+ " n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+ " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+ " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n",
+ " n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+ " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+ " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n",
+ " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n",
+ " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n",
+ " n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+ " n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n",
+ " if train:\n",
+ " n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n",
+ " else:\n",
+ " fc7input = n.relu6\n",
+ " n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n",
+ " if train:\n",
+ " n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n",
+ " else:\n",
+ " fc8input = n.relu7\n",
+ " # always learn fc8 (param=learned_param)\n",
+ " fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n",
+ " # give fc8 the name specified by argument `classifier_name`\n",
+ " n.__setattr__(classifier_name, fc8)\n",
+ " if not train:\n",
+ " n.probs = L.Softmax(fc8)\n",
+ " if label is not None:\n",
+ " n.label = label\n",
+ " n.loss = L.SoftmaxWithLoss(fc8, n.label)\n",
+ " n.acc = L.Accuracy(fc8, n.label)\n",
+ " # write the net to a temporary file and return its filename\n",
+ " with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+ " f.write(str(n.to_proto()))\n",
+ " return f.name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n",
+ "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n",
+ "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n",
+ "\n",
+ "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n",
+ "\n",
+ "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n",
+ "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n",
+ "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def style_net(train=True, learn_all=False, subset=None):\n",
+ " if subset is None:\n",
+ " subset = 'train' if train else 'test'\n",
+ " source = caffe_root + 'data/flickr_style/%s.txt' % subset\n",
+ " transform_param = dict(mirror=train, crop_size=227,\n",
+ " mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n",
+ " style_data, style_label = L.ImageData(\n",
+ " transform_param=transform_param, source=source,\n",
+ " batch_size=50, new_height=256, new_width=256, ntop=2)\n",
+ " return caffenet(data=style_data, label=style_label, train=train,\n",
+ " num_classes=NUM_STYLE_LABELS,\n",
+ " classifier_name='fc8_flickr',\n",
+ " learn_all=learn_all)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n",
+ "\n",
+ "\n",
+ "Call `forward` on `untrained_style_net` to get a batch of style training data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n",
+ " weights, caffe.TEST)\n",
+ "untrained_style_net.forward()\n",
+ "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n",
+ "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here). Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n",
+ "\n",
+ "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories. For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modi [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n",
+ " input_blob = net.blobs['data']\n",
+ " net.blobs['data'].data[0, ...] = image\n",
+ " probs = net.forward(start='conv1')['probs'][0]\n",
+ " top_k = (-probs).argsort()[:k]\n",
+ " print 'top %d predicted %s labels =' % (k, name)\n",
+ " print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n",
+ " for i, p in enumerate(top_k))\n",
+ "\n",
+ "def disp_imagenet_preds(net, image):\n",
+ " disp_preds(net, image, imagenet_labels, name='ImageNet')\n",
+ "\n",
+ "def disp_style_preds(net, image):\n",
+ " disp_preds(net, image, style_labels, name='style')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "actual label = Melancholy\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvU2sLcuS3/WLyKy19j4f9+t19+tW08LQbmNhIZmJJ0iG\ngQcW4kOyEMiyxIQpc7eEGDCECWOEPGCA+JCQJ0g2bQaIKWZgZGRjEN3Cz253+71+r+895+y9V1VG\nMIjIrFy11zn34X5P5yKdvDp3r1WrKisrKyPiH58p7s6n9ql9ap+afuwBfGqf2qf23WifmMGn9ql9\nasAnZvCpfWqfWrZPzOBT+9Q+NeATM/jUPrVPLdsnZvCpfWqfGvBzYgYi8udF5O+KyP8pIn/553GP\nT+1T+9R+tk1+1nEGIlKA/wP4c8A/AP4X4C+6+9/5md7oU/vUPrWfaft5IIM/A/xf7v477r4C/zXw\nb/4c [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f75dc02e4d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "batch_index = 8\n",
+ "image = style_data_batch[batch_index]\n",
+ "plt.imshow(deprocess_net_image(image))\n",
+ "print 'actual label =', style_labels[style_label_batch[batch_index]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "top 5 predicted ImageNet labels =\n",
+ "\t(1) 69.89% n09421951 sandbar, sand bar\n",
+ "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n",
+ "\t(3) 3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n",
+ "\t(4) 1.89% n04592741 wing\n",
+ "\t(5) 1.23% n09332890 lakeside, lakeshore\n"
+ ]
+ }
+ ],
+ "source": [
+ "disp_imagenet_preds(imagenet_net, image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n",
+ "\n",
+ "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels). Since we set N = 5, we get a predicted probability of 20% for each class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "top 5 predicted style labels =\n",
+ "\t(1) 20.00% Detailed\n",
+ "\t(2) 20.00% Pastel\n",
+ "\t(3) 20.00% Melancholy\n",
+ "\t(4) 20.00% Noir\n",
+ "\t(5) 20.00% HDR\n"
+ ]
+ }
+ ],
+ "source": [
+ "disp_style_preds(untrained_style_net, image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n",
+ "error = (diff ** 2).sum()\n",
+ "assert error < 1e-8"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Delete `untrained_style_net` to save memory. (Hang on to `imagenet_net` as we'll use it again later.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "del untrained_style_net"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3. Training the style classifier\n",
+ "\n",
+ "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights). In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean. You may want to play with some of the learning parameters to see if you can improve on the results here!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from caffe.proto import caffe_pb2\n",
+ "\n",
+ "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n",
+ " s = caffe_pb2.SolverParameter()\n",
+ "\n",
+ " # Specify locations of the train and (maybe) test networks.\n",
+ " s.train_net = train_net_path\n",
+ " if test_net_path is not None:\n",
+ " s.test_net.append(test_net_path)\n",
+ " s.test_interval = 1000 # Test after every 1000 training iterations.\n",
+ " s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+ "\n",
+ " # The number of iterations over which to average the gradient.\n",
+ " # Effectively boosts the training batch size by the given factor, without\n",
+ " # affecting memory utilization.\n",
+ " s.iter_size = 1\n",
+ " \n",
+ " s.max_iter = 100000 # # of times to update the net (training iterations)\n",
+ " \n",
+ " # Solve using the stochastic gradient descent (SGD) algorithm.\n",
+ " # Other choices include 'Adam' and 'RMSProp'.\n",
+ " s.type = 'SGD'\n",
+ "\n",
+ " # Set the initial learning rate for SGD.\n",
+ " s.base_lr = base_lr\n",
+ "\n",
+ " # Set `lr_policy` to define how the learning rate changes during training.\n",
+ " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+ " # every `stepsize` iterations.\n",
+ " s.lr_policy = 'step'\n",
+ " s.gamma = 0.1\n",
+ " s.stepsize = 20000\n",
+ "\n",
+ " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n",
+ " # weighted average of the current gradient and previous gradients to make\n",
+ " # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+ " # the model from overfitting.\n",
+ " s.momentum = 0.9\n",
+ " s.weight_decay = 5e-4\n",
+ "\n",
+ " # Display the current training loss and accuracy every 1000 iterations.\n",
+ " s.display = 1000\n",
+ "\n",
+ " # Snapshots are files used to store networks we've trained. Here, we'll\n",
+ " # snapshot every 10K iterations -- ten times during training.\n",
+ " s.snapshot = 10000\n",
+ " s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n",
+ " \n",
+ " # Train on the GPU. Using the CPU to train large networks is very slow.\n",
+ " s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+ " \n",
+ " # Write the solver to a temporary file and return its filename.\n",
+ " with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+ " f.write(str(s))\n",
+ " return f.name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we'll invoke the solver to train the style net's classification layer.\n",
+ "\n",
+ "For the record, if you want to train the network using only the command line tool, this is the command:\n",
+ "\n",
+ "<code>\n",
+ "build/tools/caffe train \\\n",
+ " -solver models/finetune_flickr_style/solver.prototxt \\\n",
+ " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+ " -gpu 0\n",
+ "</code>\n",
+ "\n",
+ "However, we will train using Python in this example.\n",
+ "\n",
+ "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration. At the end, the learned weights are saved to a file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def run_solvers(niter, solvers, disp_interval=10):\n",
+ " \"\"\"Run solvers for niter iterations,\n",
+ " returning the loss and accuracy recorded each iteration.\n",
+ " `solvers` is a list of (name, solver) tuples.\"\"\"\n",
+ " blobs = ('loss', 'acc')\n",
+ " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n",
+ " for _ in blobs)\n",
+ " for it in range(niter):\n",
+ " for name, s in solvers:\n",
+ " s.step(1) # run a single SGD step in Caffe\n",
+ " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n",
+ " for b in blobs)\n",
+ " if it % disp_interval == 0 or it + 1 == niter:\n",
+ " loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n",
+ " (n, loss[n][it], np.round(100*acc[n][it]))\n",
+ " for n, _ in solvers)\n",
+ " print '%3d) %s' % (it, loss_disp) \n",
+ " # Save the learned weights from both nets.\n",
+ " weight_dir = tempfile.mkdtemp()\n",
+ " weights = {}\n",
+ " for name, s in solvers:\n",
+ " filename = 'weights.%s.caffemodel' % name\n",
+ " weights[name] = os.path.join(weight_dir, filename)\n",
+ " s.net.save(weights[name])\n",
+ " return loss, acc, weights"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's create and run solvers to train nets for the style recognition task. We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n",
+ "\n",
+ "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running solvers for 200 iterations...\n",
+ " 0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n",
+ " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n",
+ " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n",
+ " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n",
+ " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n",
+ " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n",
+ " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n",
+ " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n",
+ " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n",
+ " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n",
+ "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n",
+ "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n",
+ "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n",
+ "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n",
+ "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n",
+ "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n",
+ "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n",
+ "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n",
+ "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n",
+ "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n",
+ "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n",
+ "Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "niter = 200 # number of iterations to train\n",
+ "\n",
+ "# Reset style_solver as before.\n",
+ "style_solver_filename = solver(style_net(train=True))\n",
+ "style_solver = caffe.get_solver(style_solver_filename)\n",
+ "style_solver.net.copy_from(weights)\n",
+ "\n",
+ "# For reference, we also create a solver that isn't initialized from\n",
+ "# the pretrained ImageNet weights.\n",
+ "scratch_style_solver_filename = solver(style_net(train=True))\n",
+ "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+ "\n",
+ "print 'Running solvers for %d iterations...' % niter\n",
+ "solvers = [('pretrained', style_solver),\n",
+ " ('scratch', scratch_style_solver)]\n",
+ "loss, acc, weights = run_solvers(niter, solvers)\n",
+ "print 'Done.'\n",
+ "\n",
+ "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n",
+ "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n",
+ "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n",
+ "\n",
+ "# Delete solvers to save memory.\n",
+ "del style_solver, scratch_style_solver, solvers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the training loss and accuracy produced by the two training procedures. Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7f75d49e1090>"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VFX6/z8nJBAS0iAkQJCuoCC6IJbFEjvWtay6rq79\nu/ysW3R13XVXEAsdFZFFFFxXwYYVEQSlKChFWpBOSEgjpJOeSeb5/fF4cu/cuXfmzmRa4nm/XrxI\nJndumbn3fM7neZ5zjiAiKBQKhUKhJyrcJ6BQKBSKyEOJg0KhUCjcUOKgUCgUCjeUOCgUCoXCDSUO\nCoVCoXBDiYNCoVAo3AiqOAghFgghioUQWRZ/TxVCLBdCbBdC7BJC3BXM81EoFAqFPYLtHBYCGOfh\n7w8B2EZEpwPIBDBDCBEd5HNSKBQKhReCKg5E9C2ACg+bFAFI/PnnRABlRNQczHNSKBQKhXfC3Uuf\nD+Ab [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f75d496e890>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot(np.vstack([train_loss, scratch_train_loss]).T)\n",
+ "xlabel('Iteration #')\n",
+ "ylabel('Loss')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7f75d49e1a90>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXt8HNV5N/49uq+klWRLtizZxjYGCxMMgSQm0FzcEAiE\nWyEXAm3epkkb0r6Etklza/IW80vT/nJpkzaQvDRNoDQJl4YEsMEQbuJqwAaDDdiSjHyXfNm1td77\nTef949Gzc3Z2ZnZ2tKuV5PP9fPTR7uzs7JmZM8/3fJ/nOc8RUkpoaGhoaGioqKl2AzQ0NDQ0ph80\nOWhoaGhoFECTg4aGhoZGATQ5aGhoaGgUQJODhoaGhkYBNDloaGhoaBSgouQghLhYCLFDCDEkhPia\nxedzhBC/E0K8LoR4SQjxjkq2R0NDQ0PDHSpGDkKIWgC3ALgYwOkArhVCrDTt9vcAXpVSngXgfwH4\nt0q1 [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f75d47cad90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot(np.vstack([train_acc, scratch_train_acc]).T)\n",
+ "xlabel('Iteration #')\n",
+ "ylabel('Accuracy')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def eval_style_net(weights, test_iters=10):\n",
+ " test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n",
+ " accuracy = 0\n",
+ " for it in xrange(test_iters):\n",
+ " accuracy += test_net.forward()['acc']\n",
+ " accuracy /= test_iters\n",
+ " return test_net, accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy, trained from ImageNet initialization: 50.0%\n",
+ "Accuracy, trained from random initialization: 23.6%\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_net, accuracy = eval_style_net(style_weights)\n",
+ "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+ "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n",
+ "print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4. End-to-end finetuning for style\n",
+ "\n",
+ "Finally, we'll train both nets again, starting from the weights we just learned. The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image. We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters. [...]
+ "\n",
+ "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training. To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long. Feel free to try this yourself!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running solvers for 200 iterations...\n",
+ " 0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n",
+ " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n",
+ " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n",
+ " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n",
+ " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n",
+ " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n",
+ " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n",
+ " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n",
+ " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n",
+ " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n",
+ "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n",
+ "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n",
+ "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n",
+ "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n",
+ "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n",
+ "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n",
+ "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n",
+ "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n",
+ "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n",
+ "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n",
+ "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n",
+ "Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "end_to_end_net = style_net(train=True, learn_all=True)\n",
+ "\n",
+ "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n",
+ "# You may want to play around with different values of this or other\n",
+ "# optimization parameters when fine-tuning. For example, if learning diverges\n",
+ "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n",
+ "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n",
+ "# for which learning does not diverge).\n",
+ "base_lr = 0.001\n",
+ "\n",
+ "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+ "style_solver = caffe.get_solver(style_solver_filename)\n",
+ "style_solver.net.copy_from(style_weights)\n",
+ "\n",
+ "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+ "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+ "scratch_style_solver.net.copy_from(scratch_style_weights)\n",
+ "\n",
+ "print 'Running solvers for %d iterations...' % niter\n",
+ "solvers = [('pretrained, end-to-end', style_solver),\n",
+ " ('scratch, end-to-end', scratch_style_solver)]\n",
+ "_, _, finetuned_weights = run_solvers(niter, solvers)\n",
+ "print 'Done.'\n",
+ "\n",
+ "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n",
+ "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n",
+ "\n",
+ "# Delete solvers to save memory.\n",
+ "del style_solver, scratch_style_solver, solvers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's now test the end-to-end finetuned models. Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy, finetuned from ImageNet initialization: 53.6%\n",
+ "Accuracy, finetuned from random initialization: 39.2%\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_net, accuracy = eval_style_net(style_weights_ft)\n",
+ "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+ "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n",
+ "print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll first look back at the image we started with and check our end-to-end trained model's predictions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "top 5 predicted style labels =\n",
+ "\t(1) 55.67% Melancholy\n",
+ "\t(2) 27.21% HDR\n",
+ "\t(3) 16.46% Pastel\n",
+ "\t(4) 0.63% Detailed\n",
+ "\t(5) 0.03% Noir\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvU2sLcuS3/WLyKy19j4f9+t19+tW08LQbmNhIZmJJ0iG\ngQcW4kOyEMiyxIQpc7eEGDCECWOEPGCA+JCQJ0g2bQaIKWZgZGRjEN3Cz253+71+r+895+y9V1VG\nMIjIrFy11zn34X5P5yKdvDp3r1WrKisrKyPiH58p7s6n9ql9ap+afuwBfGqf2qf23WifmMGn9ql9\nasAnZvCpfWqfWrZPzOBT+9Q+NeATM/jUPrVPLdsnZvCpfWqfGvBzYgYi8udF5O+KyP8pIn/553GP\nT+1T+9R+tk1+1nEGIlKA/wP4c8A/AP4X4C+6+9/5md7oU/vUPrWfaft5IIM/A/xf7v477r4C/zXw\nb/4c [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f75d0683e10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(deprocess_net_image(image))\n",
+ "disp_style_preds(test_net, image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Whew, that looks a lot better than before! But note that this image was from the training set, so the net got to see its label at training time.\n",
+ "\n",
+ "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "actual label = Pastel\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvUmvLUmS3/cz9xjOOXd8Y86ZlTWg2N0cJZGE0N2QFlpo\nJe0k8DtoI0hQt8Q9CQr8ClpoJ30CQtBGgDZqiE1ShNhTVXdlVmZVTi/fcIdzTkS4u2lh5hFxX2ZV\nE1CVXgt4XvXyTufEiXA3N/vb3wYXVeX1eD1ej9cjvOobeD1ej9fjr8Z4rQxej9fj9QBeK4PX4/V4\nPXy8Vgavx+vxegCvlcHr8Xq8Hj5eK4PX4/V4PYBfkzIQkf9YRP5ERH4kIr/36/iM1+P1eD1+tUN+\n1XkGIhKBPwX+I+BnwP8J/ANV/eNf6Qe9Hq/H6/ErHb8OZPD3gB+r6keqOgH/E/Cf/ho+5/V4PV6P\nX+H4 [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f75d05ad810>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "batch_index = 1\n",
+ "image = test_net.blobs['data'].data[batch_index]\n",
+ "plt.imshow(deprocess_net_image(image))\n",
+ "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "top 5 predicted style labels =\n",
+ "\t(1) 99.76% Pastel\n",
+ "\t(2) 0.13% HDR\n",
+ "\t(3) 0.11% Detailed\n",
+ "\t(4) 0.00% Melancholy\n",
+ "\t(5) 0.00% Noir\n"
+ ]
+ }
+ ],
+ "source": [
+ "disp_style_preds(test_net, image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also look at the predictions of the network trained from scratch. We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "top 5 predicted style labels =\n",
+ "\t(1) 49.81% Pastel\n",
+ "\t(2) 19.76% Detailed\n",
+ "\t(3) 17.06% Melancholy\n",
+ "\t(4) 11.66% HDR\n",
+ "\t(5) 1.72% Noir\n"
+ ]
+ }
+ ],
+ "source": [
+ "disp_style_preds(scratch_test_net, image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Of course, we can again look at the ImageNet model's predictions for the above image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "top 5 predicted ImageNet labels =\n",
+ "\t(1) 34.90% n07579787 plate\n",
+ "\t(2) 21.63% n04263257 soup bowl\n",
+ "\t(3) 17.75% n07875152 potpie\n",
+ "\t(4) 5.72% n07711569 mashed potato\n",
+ "\t(5) 5.27% n07584110 consomme\n"
+ ]
+ }
+ ],
+ "source": [
+ "disp_imagenet_preds(imagenet_net, image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+ "\n",
+ "http://demo.vislab.berkeleyvision.org/"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+ "example_name": "Fine-tuning for Style Recognition",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ },
+ "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb
deleted file mode 100644
index cc90b16..0000000
--- a/examples/03-fine-tuning.ipynb
+++ /dev/null
@@ -1,947 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Fine-tuning a Pretrained Network for Style Recognition\n",
- "\n",
- "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
- "\n",
- "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, we will need to prepare the data. This involves the following parts:\n",
- "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
- "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
- "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import os\n",
- "os.chdir('..')\n",
- "import sys\n",
- "sys.path.insert(0, './python')\n",
- "\n",
- "import caffe\n",
- "import numpy as np\n",
- "from pylab import *\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
- "# and a subset of 2000 images for the style recognition task.\n",
- "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
- "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
- "!python examples/finetune_flickr_style/assemble_data.py \\\n",
- " --workers=-1 --images=2000 --seed=1701 --label=5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's show what is the difference between the fine-tuning network and the original caffe model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1c1\r\n",
- "< name: \"CaffeNet\"\r\n",
- "---\r\n",
- "> name: \"FlickrStyleCaffeNet\"\r\n",
- "4c4\r\n",
- "< type: \"Data\"\r\n",
- "---\r\n",
- "> type: \"ImageData\"\r\n",
- "15,26c15,19\r\n",
- "< # mean pixel / channel-wise mean instead of mean image\r\n",
- "< # transform_param {\r\n",
- "< # crop_size: 227\r\n",
- "< # mean_value: 104\r\n",
- "< # mean_value: 117\r\n",
- "< # mean_value: 123\r\n",
- "< # mirror: true\r\n",
- "< # }\r\n",
- "< data_param {\r\n",
- "< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
- "< batch_size: 256\r\n",
- "< backend: LMDB\r\n",
- "---\r\n",
- "> image_data_param {\r\n",
- "> source: \"data/flickr_style/train.txt\"\r\n",
- "> batch_size: 50\r\n",
- "> new_height: 256\r\n",
- "> new_width: 256\r\n",
- "31c24\r\n",
- "< type: \"Data\"\r\n",
- "---\r\n",
- "> type: \"ImageData\"\r\n",
- "42,51c35,36\r\n",
- "< # mean pixel / channel-wise mean instead of mean image\r\n",
- "< # transform_param {\r\n",
- "< # crop_size: 227\r\n",
- "< # mean_value: 104\r\n",
- "< # mean_value: 117\r\n",
- "< # mean_value: 123\r\n",
- "< # mirror: true\r\n",
- "< # }\r\n",
- "< data_param {\r\n",
- "< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
- "---\r\n",
- "> image_data_param {\r\n",
- "> source: \"data/flickr_style/test.txt\"\r\n",
- "53c38,39\r\n",
- "< backend: LMDB\r\n",
- "---\r\n",
- "> new_height: 256\r\n",
- "> new_width: 256\r\n",
- "323a310\r\n",
- "> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
- "360c347\r\n",
- "< name: \"fc8\"\r\n",
- "---\r\n",
- "> name: \"fc8_flickr\"\r\n",
- "363c350,351\r\n",
- "< top: \"fc8\"\r\n",
- "---\r\n",
- "> top: \"fc8_flickr\"\r\n",
- "> # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
- "365c353\r\n",
- "< lr_mult: 1\r\n",
- "---\r\n",
- "> lr_mult: 10\r\n",
- "369c357\r\n",
- "< lr_mult: 2\r\n",
- "---\r\n",
- "> lr_mult: 20\r\n",
- "373c361\r\n",
- "< num_output: 1000\r\n",
- "---\r\n",
- "> num_output: 20\r\n",
- "384a373,379\r\n",
- "> name: \"loss\"\r\n",
- "> type: \"SoftmaxWithLoss\"\r\n",
- "> bottom: \"fc8_flickr\"\r\n",
- "> bottom: \"label\"\r\n",
- "> top: \"loss\"\r\n",
- "> }\r\n",
- "> layer {\r\n",
- "387c382\r\n",
- "< bottom: \"fc8\"\r\n",
- "---\r\n",
- "> bottom: \"fc8_flickr\"\r\n",
- "393,399d387\r\n",
- "< }\r\n",
- "< layer {\r\n",
- "< name: \"loss\"\r\n",
- "< type: \"SoftmaxWithLoss\"\r\n",
- "< bottom: \"fc8\"\r\n",
- "< bottom: \"label\"\r\n",
- "< top: \"loss\"\r\n"
- ]
- }
- ],
- "source": [
- "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
- "\n",
- "<code>\n",
- "build/tools/caffe train \\\n",
- " -solver models/finetune_flickr_style/solver.prototxt \\\n",
- " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
- " -gpu 0\n",
- "</code>\n",
- "\n",
- "However, we will train using Python in this example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
- "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
- "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
- "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
- "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
- "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
- "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
- "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
- "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
- "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
- "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
- "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
- "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
- "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
- "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
- "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
- "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
- "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
- "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
- "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
- "done\n"
- ]
- }
- ],
- "source": [
- "niter = 200\n",
- "# losses will also be stored in the log\n",
- "train_loss = np.zeros(niter)\n",
- "scratch_train_loss = np.zeros(niter)\n",
- "\n",
- "caffe.set_device(0)\n",
- "caffe.set_mode_gpu()\n",
- "# We create a solver that fine-tunes from a previously trained network.\n",
- "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
- "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
- "# For reference, we also create a solver that does no finetuning.\n",
- "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
- "\n",
- "# We run the solver for niter times, and record the training loss.\n",
- "for it in range(niter):\n",
- " solver.step(1) # SGD by Caffe\n",
- " scratch_solver.step(1)\n",
- " # store the train loss\n",
- " train_loss[it] = solver.net.blobs['loss'].data\n",
- " scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
- " if it % 10 == 0:\n",
- " print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
- "print 'done'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's look at the training loss produced by the two training procedures respectively."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
- " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fbb37f20990>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot(np.vstack([train_loss, scratch_train_loss]).T)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
- " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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- ],
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy for fine-tuning: 0.570000001788\n",
- "Accuracy for training from scratch: 0.224000000954\n"
- ]
- }
- ],
- "source": [
- "test_iters = 10\n",
- "accuracy = 0\n",
- "scratch_accuracy = 0\n",
- "for it in arange(test_iters):\n",
- " solver.test_nets[0].forward()\n",
- " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
- " scratch_solver.test_nets[0].forward()\n",
- " scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
- "accuracy /= test_iters\n",
- "scratch_accuracy /= test_iters\n",
- "print 'Accuracy for fine-tuning:', accuracy\n",
- "print 'Accuracy for training from scratch:', scratch_accuracy"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
- "\n",
- "http://demo.vislab.berkeleyvision.org/"
- ]
- }
- ],
- "metadata": {
- "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
- "example_name": "Fine-tuning for Style Recognition",
- "include_in_docs": true,
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.9"
- },
- "priority": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
new file mode 100644
index 0000000..c053b73
--- /dev/null
+++ b/examples/brewing-logreg.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Brewing Logistic Regression then Going Deeper\n",
+ "\n",
+ "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import os\n",
+ "os.chdir('..')\n",
+ "\n",
+ "import sys\n",
+ "sys.path.insert(0, './python')\n",
+ "import caffe\n",
+ "\n",
+ "\n",
+ "import os\n",
+ "import h5py\n",
+ "import shutil\n",
+ "import tempfile\n",
+ "\n",
+ "import sklearn\n",
+ "import sklearn.datasets\n",
+ "import sklearn.linear_model\n",
+ "\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAImCAYAAACB54oCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXd0HOd5//uZ2d4XwKJXAiDYeyclShRFyYpNVTu2JRfZ\ncexEPx/7l+bEKec68c8n8XVy7ThO7o1jy6YsypapTolUoST2AoJgAdE7FlgssNjed6fcP0BBokhR\nhQRBUvs5BweLd+adeWbewewzz/s83xFUVSVHjhw5cuTIkWOmEGfagBw5cuTIkSPHx5ucM5IjR44c\nOXLkmFFyzkiOHDly5MiRY0bJOSM5cuTIkSNHjhkl54zkyJEjR44cOWaUnDOSI0eOHDly5JhRptUZ\nEQThx4Ig7BcE4SfvajcKgvBLQRBeFwTh36fThhw5cuTIkSPHtc20OSOCICwHLKqqbgT0giCsfMfi\nbwHb [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f8611575a50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X, y = sklearn.datasets.make_classification(\n",
+ " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+ " n_clusters_per_class=2, hypercube=False, random_state=0\n",
+ ")\n",
+ "\n",
+ "# Split into train and test\n",
+ "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+ "\n",
+ "# Visualize sample of the data\n",
+ "ind = np.random.permutation(X.shape[0])[:1000]\n",
+ "df = pd.DataFrame(X[ind])\n",
+ "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.781\n",
+ "Accuracy: 0.781\n",
+ "Accuracy: 0.781\n",
+ "Accuracy: 0.781\n",
+ "1 loop, best of 3: 372 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "# Train and test the scikit-learn SGD logistic regression.\n",
+ "clf = sklearn.linear_model.SGDClassifier(\n",
+ " loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n",
+ "\n",
+ "clf.fit(X, y)\n",
+ "yt_pred = clf.predict(Xt)\n",
+ "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Save the dataset to HDF5 for loading in Caffe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Write out the data to HDF5 files in a temp directory.\n",
+ "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+ "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+ "if not os.path.exists(dirname):\n",
+ " os.makedirs(dirname)\n",
+ "\n",
+ "train_filename = os.path.join(dirname, 'train.h5')\n",
+ "test_filename = os.path.join(dirname, 'test.h5')\n",
+ "\n",
+ "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+ "# To show this off, we'll list the same data file twice.\n",
+ "with h5py.File(train_filename, 'w') as f:\n",
+ " f['data'] = X\n",
+ " f['label'] = y.astype(np.float32)\n",
+ "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+ " f.write(train_filename + '\\n')\n",
+ " f.write(train_filename + '\\n')\n",
+ " \n",
+ "# HDF5 is pretty efficient, but can be further compressed.\n",
+ "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+ "with h5py.File(test_filename, 'w') as f:\n",
+ " f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+ " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+ "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+ " f.write(test_filename + '\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from caffe import layers as L\n",
+ "from caffe import params as P\n",
+ "\n",
+ "def logreg(hdf5, batch_size):\n",
+ " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+ " n = caffe.NetSpec()\n",
+ " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+ " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+ " n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+ " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+ " return n.to_proto()\n",
+ "\n",
+ "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n",
+ "with open(train_net_path, 'w') as f:\n",
+ " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+ "\n",
+ "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n",
+ "with open(test_net_path, 'w') as f:\n",
+ " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from caffe.proto import caffe_pb2\n",
+ "\n",
+ "def solver(train_net_path, test_net_path):\n",
+ " s = caffe_pb2.SolverParameter()\n",
+ "\n",
+ " # Specify locations of the train and test networks.\n",
+ " s.train_net = train_net_path\n",
+ " s.test_net.append(test_net_path)\n",
+ "\n",
+ " s.test_interval = 1000 # Test after every 1000 training iterations.\n",
+ " s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n",
+ "\n",
+ " s.max_iter = 10000 # # of times to update the net (training iterations)\n",
+ "\n",
+ " # Set the initial learning rate for stochastic gradient descent (SGD).\n",
+ " s.base_lr = 0.01 \n",
+ "\n",
+ " # Set `lr_policy` to define how the learning rate changes during training.\n",
+ " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+ " # every `stepsize` iterations.\n",
+ " s.lr_policy = 'step'\n",
+ " s.gamma = 0.1\n",
+ " s.stepsize = 5000\n",
+ "\n",
+ " # Set other optimization parameters. Setting a non-zero `momentum` takes a\n",
+ " # weighted average of the current gradient and previous gradients to make\n",
+ " # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+ " # the model from overfitting.\n",
+ " s.momentum = 0.9\n",
+ " s.weight_decay = 5e-4\n",
+ "\n",
+ " # Display the current training loss and accuracy every 1000 iterations.\n",
+ " s.display = 1000\n",
+ "\n",
+ " # Snapshots are files used to store networks we've trained. Here, we'll\n",
+ " # snapshot every 10K iterations -- just once at the end of training.\n",
+ " # For larger networks that take longer to train, you may want to set\n",
+ " # snapshot < max_iter to save the network and training state to disk during\n",
+ " # optimization, preventing disaster in case of machine crashes, etc.\n",
+ " s.snapshot = 10000\n",
+ " s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n",
+ "\n",
+ " # We'll train on the CPU for fair benchmarking against scikit-learn.\n",
+ " # Changing to GPU should result in much faster training!\n",
+ " s.solver_mode = caffe_pb2.SolverParameter.CPU\n",
+ " \n",
+ " return s\n",
+ "\n",
+ "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n",
+ "with open(solver_path, 'w') as f:\n",
+ " f.write(str(solver(train_net_path, test_net_path)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Time to learn and evaluate our Caffeinated logistic regression in Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.770\n",
+ "Accuracy: 0.770\n",
+ "Accuracy: 0.770\n",
+ "Accuracy: 0.770\n",
+ "1 loop, best of 3: 195 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "caffe.set_mode_cpu()\n",
+ "solver = caffe.get_solver(solver_path)\n",
+ "solver.solve()\n",
+ "\n",
+ "accuracy = 0\n",
+ "batch_size = solver.test_nets[0].blobs['data'].num\n",
+ "test_iters = int(len(Xt) / batch_size)\n",
+ "for i in range(test_iters):\n",
+ " solver.test_nets[0].forward()\n",
+ " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+ "accuracy /= test_iters\n",
+ "\n",
+ "print(\"Accuracy: {:.3f}\".format(accuracy))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do the same through the command line interface for detailed output on the model and solving."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "I0224 00:32:03.232779 655 caffe.cpp:178] Use CPU.\n",
+ "I0224 00:32:03.391911 655 solver.cpp:48] Initializing solver from parameters: \n",
+ "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+ "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+ "test_iter: 250\n",
+ "test_interval: 1000\n",
+ "base_lr: 0.01\n",
+ "display: 1000\n",
+ "max_iter: 10000\n",
+ "lr_policy: \"step\"\n",
+ "gamma: 0.1\n",
+ "momentum: 0.9\n",
+ "weight_decay: 0.0005\n",
+ "stepsize: 5000\n",
+ "snapshot: 10000\n",
+ "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+ "solver_mode: CPU\n",
+ "I0224 00:32:03.392065 655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+ "I0224 00:32:03.392215 655 net.cpp:49] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TRAIN\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/train.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0224 00:32:03.392365 655 layer_factory.hpp:77] Creating layer data\n",
+ "I0224 00:32:03.392382 655 net.cpp:106] Creating Layer data\n",
+ "I0224 00:32:03.392395 655 net.cpp:411] data -> data\n",
+ "I0224 00:32:03.392423 655 net.cpp:411] data -> label\n",
+ "I0224 00:32:03.392442 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+ "I0224 00:32:03.392473 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+ "I0224 00:32:03.393473 655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+ "I0224 00:32:03.393862 655 net.cpp:150] Setting up data\n",
+ "I0224 00:32:03.393884 655 net.cpp:157] Top shape: 10 4 (40)\n",
+ "I0224 00:32:03.393894 655 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:03.393901 655 net.cpp:165] Memory required for data: 200\n",
+ "I0224 00:32:03.393911 655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+ "I0224 00:32:03.393924 655 net.cpp:106] Creating Layer label_data_1_split\n",
+ "I0224 00:32:03.393934 655 net.cpp:454] label_data_1_split <- label\n",
+ "I0224 00:32:03.393945 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+ "I0224 00:32:03.393956 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+ "I0224 00:32:03.393970 655 net.cpp:150] Setting up label_data_1_split\n",
+ "I0224 00:32:03.393978 655 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:03.393986 655 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:03.393995 655 net.cpp:165] Memory required for data: 280\n",
+ "I0224 00:32:03.394001 655 layer_factory.hpp:77] Creating layer ip1\n",
+ "I0224 00:32:03.394012 655 net.cpp:106] Creating Layer ip1\n",
+ "I0224 00:32:03.394021 655 net.cpp:454] ip1 <- data\n",
+ "I0224 00:32:03.394029 655 net.cpp:411] ip1 -> ip1\n",
+ "I0224 00:32:03.394311 655 net.cpp:150] Setting up ip1\n",
+ "I0224 00:32:03.394323 655 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:03.394331 655 net.cpp:165] Memory required for data: 360\n",
+ "I0224 00:32:03.394348 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+ "I0224 00:32:03.394358 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+ "I0224 00:32:03.394366 655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+ "I0224 00:32:03.394374 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+ "I0224 00:32:03.394386 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+ "I0224 00:32:03.394395 655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+ "I0224 00:32:03.394404 655 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:03.394424 655 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:03.394443 655 net.cpp:165] Memory required for data: 520\n",
+ "I0224 00:32:03.394450 655 layer_factory.hpp:77] Creating layer accuracy\n",
+ "I0224 00:32:03.394462 655 net.cpp:106] Creating Layer accuracy\n",
+ "I0224 00:32:03.394479 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+ "I0224 00:32:03.394489 655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+ "I0224 00:32:03.394497 655 net.cpp:411] accuracy -> accuracy\n",
+ "I0224 00:32:03.394510 655 net.cpp:150] Setting up accuracy\n",
+ "I0224 00:32:03.394536 655 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:03.394543 655 net.cpp:165] Memory required for data: 524\n",
+ "I0224 00:32:03.394551 655 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:03.394562 655 net.cpp:106] Creating Layer loss\n",
+ "I0224 00:32:03.394569 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+ "I0224 00:32:03.394577 655 net.cpp:454] loss <- label_data_1_split_1\n",
+ "I0224 00:32:03.394587 655 net.cpp:411] loss -> loss\n",
+ "I0224 00:32:03.394603 655 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:03.394624 655 net.cpp:150] Setting up loss\n",
+ "I0224 00:32:03.394634 655 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:03.394641 655 net.cpp:160] with loss weight 1\n",
+ "I0224 00:32:03.394659 655 net.cpp:165] Memory required for data: 528\n",
+ "I0224 00:32:03.394665 655 net.cpp:226] loss needs backward computation.\n",
+ "I0224 00:32:03.394673 655 net.cpp:228] accuracy does not need backward computation.\n",
+ "I0224 00:32:03.394682 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+ "I0224 00:32:03.394690 655 net.cpp:226] ip1 needs backward computation.\n",
+ "I0224 00:32:03.394697 655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+ "I0224 00:32:03.394706 655 net.cpp:228] data does not need backward computation.\n",
+ "I0224 00:32:03.394712 655 net.cpp:270] This network produces output accuracy\n",
+ "I0224 00:32:03.394721 655 net.cpp:270] This network produces output loss\n",
+ "I0224 00:32:03.394731 655 net.cpp:283] Network initialization done.\n",
+ "I0224 00:32:03.394804 655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+ "I0224 00:32:03.394836 655 net.cpp:49] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TEST\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/test.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip1\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0224 00:32:03.394953 655 layer_factory.hpp:77] Creating layer data\n",
+ "I0224 00:32:03.394964 655 net.cpp:106] Creating Layer data\n",
+ "I0224 00:32:03.394973 655 net.cpp:411] data -> data\n",
+ "I0224 00:32:03.394984 655 net.cpp:411] data -> label\n",
+ "I0224 00:32:03.394994 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+ "I0224 00:32:03.395009 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+ "I0224 00:32:03.395937 655 net.cpp:150] Setting up data\n",
+ "I0224 00:32:03.395953 655 net.cpp:157] Top shape: 10 4 (40)\n",
+ "I0224 00:32:03.395963 655 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:03.395970 655 net.cpp:165] Memory required for data: 200\n",
+ "I0224 00:32:03.395978 655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+ "I0224 00:32:03.395989 655 net.cpp:106] Creating Layer label_data_1_split\n",
+ "I0224 00:32:03.395997 655 net.cpp:454] label_data_1_split <- label\n",
+ "I0224 00:32:03.396005 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+ "I0224 00:32:03.396016 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+ "I0224 00:32:03.396028 655 net.cpp:150] Setting up label_data_1_split\n",
+ "I0224 00:32:03.396036 655 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:03.396044 655 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:03.396051 655 net.cpp:165] Memory required for data: 280\n",
+ "I0224 00:32:03.396059 655 layer_factory.hpp:77] Creating layer ip1\n",
+ "I0224 00:32:03.396069 655 net.cpp:106] Creating Layer ip1\n",
+ "I0224 00:32:03.396075 655 net.cpp:454] ip1 <- data\n",
+ "I0224 00:32:03.396085 655 net.cpp:411] ip1 -> ip1\n",
+ "I0224 00:32:03.396100 655 net.cpp:150] Setting up ip1\n",
+ "I0224 00:32:03.396109 655 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:03.396116 655 net.cpp:165] Memory required for data: 360\n",
+ "I0224 00:32:03.396138 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+ "I0224 00:32:03.396148 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+ "I0224 00:32:03.396157 655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+ "I0224 00:32:03.396164 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+ "I0224 00:32:03.396174 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+ "I0224 00:32:03.396185 655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+ "I0224 00:32:03.396194 655 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:03.396203 655 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:03.396209 655 net.cpp:165] Memory required for data: 520\n",
+ "I0224 00:32:03.396216 655 layer_factory.hpp:77] Creating layer accuracy\n",
+ "I0224 00:32:03.396225 655 net.cpp:106] Creating Layer accuracy\n",
+ "I0224 00:32:03.396234 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+ "I0224 00:32:03.396241 655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+ "I0224 00:32:03.396250 655 net.cpp:411] accuracy -> accuracy\n",
+ "I0224 00:32:03.396260 655 net.cpp:150] Setting up accuracy\n",
+ "I0224 00:32:03.396270 655 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:03.396276 655 net.cpp:165] Memory required for data: 524\n",
+ "I0224 00:32:03.396283 655 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:03.396291 655 net.cpp:106] Creating Layer loss\n",
+ "I0224 00:32:03.396299 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+ "I0224 00:32:03.396307 655 net.cpp:454] loss <- label_data_1_split_1\n",
+ "I0224 00:32:03.396317 655 net.cpp:411] loss -> loss\n",
+ "I0224 00:32:03.396327 655 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:03.396339 655 net.cpp:150] Setting up loss\n",
+ "I0224 00:32:03.396349 655 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:03.396356 655 net.cpp:160] with loss weight 1\n",
+ "I0224 00:32:03.396365 655 net.cpp:165] Memory required for data: 528\n",
+ "I0224 00:32:03.396373 655 net.cpp:226] loss needs backward computation.\n",
+ "I0224 00:32:03.396381 655 net.cpp:228] accuracy does not need backward computation.\n",
+ "I0224 00:32:03.396389 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+ "I0224 00:32:03.396396 655 net.cpp:226] ip1 needs backward computation.\n",
+ "I0224 00:32:03.396404 655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+ "I0224 00:32:03.396412 655 net.cpp:228] data does not need backward computation.\n",
+ "I0224 00:32:03.396420 655 net.cpp:270] This network produces output accuracy\n",
+ "I0224 00:32:03.396427 655 net.cpp:270] This network produces output loss\n",
+ "I0224 00:32:03.396437 655 net.cpp:283] Network initialization done.\n",
+ "I0224 00:32:03.396455 655 solver.cpp:60] Solver scaffolding done.\n",
+ "I0224 00:32:03.396473 655 caffe.cpp:219] Starting Optimization\n",
+ "I0224 00:32:03.396482 655 solver.cpp:280] Solving \n",
+ "I0224 00:32:03.396489 655 solver.cpp:281] Learning Rate Policy: step\n",
+ "I0224 00:32:03.396499 655 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+ "I0224 00:32:03.932615 655 solver.cpp:406] Test net output #0: accuracy = 0.4268\n",
+ "I0224 00:32:03.932656 655 solver.cpp:406] Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n",
+ "I0224 00:32:03.932723 655 solver.cpp:229] Iteration 0, loss = 1.06081\n",
+ "I0224 00:32:03.932737 655 solver.cpp:245] Train net output #0: accuracy = 0.4\n",
+ "I0224 00:32:03.932749 655 solver.cpp:245] Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n",
+ "I0224 00:32:03.932765 655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+ "I0224 00:32:03.945551 655 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+ "I0224 00:32:03.948048 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n",
+ "I0224 00:32:03.948065 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+ "I0224 00:32:03.948091 655 solver.cpp:229] Iteration 1000, loss = 0.505853\n",
+ "I0224 00:32:03.948102 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:03.948113 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+ "I0224 00:32:03.948122 655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+ "I0224 00:32:03.960741 655 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+ "I0224 00:32:03.963214 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n",
+ "I0224 00:32:03.963249 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+ "I0224 00:32:03.963276 655 solver.cpp:229] Iteration 2000, loss = 0.549211\n",
+ "I0224 00:32:03.963289 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:03.963299 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+ "I0224 00:32:03.963309 655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+ "I0224 00:32:03.975945 655 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+ "I0224 00:32:03.978435 655 solver.cpp:406] Test net output #0: accuracy = 0.7732\n",
+ "I0224 00:32:03.978451 655 solver.cpp:406] Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n",
+ "I0224 00:32:03.978884 655 solver.cpp:229] Iteration 3000, loss = 0.66133\n",
+ "I0224 00:32:03.978911 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n",
+ "I0224 00:32:03.978932 655 solver.cpp:245] Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n",
+ "I0224 00:32:03.978950 655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+ "I0224 00:32:03.992017 655 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+ "I0224 00:32:03.994509 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n",
+ "I0224 00:32:03.994525 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+ "I0224 00:32:03.994551 655 solver.cpp:229] Iteration 4000, loss = 0.505853\n",
+ "I0224 00:32:03.994562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:03.994573 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+ "I0224 00:32:03.994583 655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+ "I0224 00:32:04.007200 655 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+ "I0224 00:32:04.009686 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n",
+ "I0224 00:32:04.009702 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+ "I0224 00:32:04.009727 655 solver.cpp:229] Iteration 5000, loss = 0.549211\n",
+ "I0224 00:32:04.009738 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:04.009749 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+ "I0224 00:32:04.009758 655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+ "I0224 00:32:04.022734 655 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+ "I0224 00:32:04.025177 655 solver.cpp:406] Test net output #0: accuracy = 0.7824\n",
+ "I0224 00:32:04.025193 655 solver.cpp:406] Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n",
+ "I0224 00:32:04.025545 655 solver.cpp:229] Iteration 6000, loss = 0.654873\n",
+ "I0224 00:32:04.025562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:04.025573 655 solver.cpp:245] Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n",
+ "I0224 00:32:04.025583 655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+ "I0224 00:32:04.038586 655 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+ "I0224 00:32:04.041016 655 solver.cpp:406] Test net output #0: accuracy = 0.7704\n",
+ "I0224 00:32:04.041033 655 solver.cpp:406] Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n",
+ "I0224 00:32:04.041059 655 solver.cpp:229] Iteration 7000, loss = 0.46611\n",
+ "I0224 00:32:04.041071 655 solver.cpp:245] Train net output #0: accuracy = 0.6\n",
+ "I0224 00:32:04.041082 655 solver.cpp:245] Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n",
+ "I0224 00:32:04.041091 655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+ "I0224 00:32:04.053722 655 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+ "I0224 00:32:04.056171 655 solver.cpp:406] Test net output #0: accuracy = 0.7788\n",
+ "I0224 00:32:04.056187 655 solver.cpp:406] Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n",
+ "I0224 00:32:04.056213 655 solver.cpp:229] Iteration 8000, loss = 0.615126\n",
+ "I0224 00:32:04.056224 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n",
+ "I0224 00:32:04.056236 655 solver.cpp:245] Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n",
+ "I0224 00:32:04.056244 655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+ "I0224 00:32:04.068853 655 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+ "I0224 00:32:04.071291 655 solver.cpp:406] Test net output #0: accuracy = 0.7808\n",
+ "I0224 00:32:04.071307 655 solver.cpp:406] Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n",
+ "I0224 00:32:04.071650 655 solver.cpp:229] Iteration 9000, loss = 0.654997\n",
+ "I0224 00:32:04.071666 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:04.071677 655 solver.cpp:245] Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n",
+ "I0224 00:32:04.071687 655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+ "I0224 00:32:04.084717 655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+ "I0224 00:32:04.084885 655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+ "I0224 00:32:04.084960 655 solver.cpp:318] Iteration 10000, loss = 0.466505\n",
+ "I0224 00:32:04.084977 655 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+ "I0224 00:32:04.087514 655 solver.cpp:406] Test net output #0: accuracy = 0.77\n",
+ "I0224 00:32:04.087532 655 solver.cpp:406] Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n",
+ "I0224 00:32:04.087541 655 solver.cpp:323] Optimization Done.\n",
+ "I0224 00:32:04.087548 655 caffe.cpp:222] Optimization Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+ "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+ "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n",
+ "\n",
+ "The final accuracy of the new network should be higher than logistic regression!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from caffe import layers as L\n",
+ "from caffe import params as P\n",
+ "\n",
+ "def nonlinear_net(hdf5, batch_size):\n",
+ " # one small nonlinearity, one leap for model kind\n",
+ " n = caffe.NetSpec()\n",
+ " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+ " # define a hidden layer of dimension 40\n",
+ " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+ " # transform the output through the ReLU (rectified linear) non-linearity\n",
+ " n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+ " # score the (now non-linear) features\n",
+ " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+ " # same accuracy and loss as before\n",
+ " n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+ " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+ " return n.to_proto()\n",
+ "\n",
+ "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n",
+ "with open(train_net_path, 'w') as f:\n",
+ " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+ "\n",
+ "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n",
+ "with open(test_net_path, 'w') as f:\n",
+ " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n",
+ "\n",
+ "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n",
+ "with open(solver_path, 'w') as f:\n",
+ " f.write(str(solver(train_net_path, test_net_path)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.838\n",
+ "Accuracy: 0.837\n",
+ "Accuracy: 0.838\n",
+ "Accuracy: 0.834\n",
+ "1 loop, best of 3: 277 ms per loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "caffe.set_mode_cpu()\n",
+ "solver = caffe.get_solver(solver_path)\n",
+ "solver.solve()\n",
+ "\n",
+ "accuracy = 0\n",
+ "batch_size = solver.test_nets[0].blobs['data'].num\n",
+ "test_iters = int(len(Xt) / batch_size)\n",
+ "for i in range(test_iters):\n",
+ " solver.test_nets[0].forward()\n",
+ " accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+ "accuracy /= test_iters\n",
+ "\n",
+ "print(\"Accuracy: {:.3f}\".format(accuracy))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do the same through the command line interface for detailed output on the model and solving."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "I0224 00:32:05.654265 658 caffe.cpp:178] Use CPU.\n",
+ "I0224 00:32:05.810444 658 solver.cpp:48] Initializing solver from parameters: \n",
+ "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+ "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+ "test_iter: 250\n",
+ "test_interval: 1000\n",
+ "base_lr: 0.01\n",
+ "display: 1000\n",
+ "max_iter: 10000\n",
+ "lr_policy: \"step\"\n",
+ "gamma: 0.1\n",
+ "momentum: 0.9\n",
+ "weight_decay: 0.0005\n",
+ "stepsize: 5000\n",
+ "snapshot: 10000\n",
+ "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+ "solver_mode: CPU\n",
+ "I0224 00:32:05.810634 658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+ "I0224 00:32:05.810835 658 net.cpp:49] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TRAIN\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/train.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 40\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu1\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip1\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip2\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip2\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0224 00:32:05.811061 658 layer_factory.hpp:77] Creating layer data\n",
+ "I0224 00:32:05.811079 658 net.cpp:106] Creating Layer data\n",
+ "I0224 00:32:05.811092 658 net.cpp:411] data -> data\n",
+ "I0224 00:32:05.811121 658 net.cpp:411] data -> label\n",
+ "I0224 00:32:05.811143 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+ "I0224 00:32:05.811189 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+ "I0224 00:32:05.812254 658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+ "I0224 00:32:05.812677 658 net.cpp:150] Setting up data\n",
+ "I0224 00:32:05.812705 658 net.cpp:157] Top shape: 10 4 (40)\n",
+ "I0224 00:32:05.812721 658 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:05.812729 658 net.cpp:165] Memory required for data: 200\n",
+ "I0224 00:32:05.812739 658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+ "I0224 00:32:05.812752 658 net.cpp:106] Creating Layer label_data_1_split\n",
+ "I0224 00:32:05.812762 658 net.cpp:454] label_data_1_split <- label\n",
+ "I0224 00:32:05.812774 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+ "I0224 00:32:05.812785 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+ "I0224 00:32:05.812798 658 net.cpp:150] Setting up label_data_1_split\n",
+ "I0224 00:32:05.812808 658 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:05.812816 658 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:05.812824 658 net.cpp:165] Memory required for data: 280\n",
+ "I0224 00:32:05.812831 658 layer_factory.hpp:77] Creating layer ip1\n",
+ "I0224 00:32:05.812841 658 net.cpp:106] Creating Layer ip1\n",
+ "I0224 00:32:05.812849 658 net.cpp:454] ip1 <- data\n",
+ "I0224 00:32:05.812860 658 net.cpp:411] ip1 -> ip1\n",
+ "I0224 00:32:05.813179 658 net.cpp:150] Setting up ip1\n",
+ "I0224 00:32:05.813196 658 net.cpp:157] Top shape: 10 40 (400)\n",
+ "I0224 00:32:05.813210 658 net.cpp:165] Memory required for data: 1880\n",
+ "I0224 00:32:05.813230 658 layer_factory.hpp:77] Creating layer relu1\n",
+ "I0224 00:32:05.813241 658 net.cpp:106] Creating Layer relu1\n",
+ "I0224 00:32:05.813251 658 net.cpp:454] relu1 <- ip1\n",
+ "I0224 00:32:05.813258 658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+ "I0224 00:32:05.813271 658 net.cpp:150] Setting up relu1\n",
+ "I0224 00:32:05.813279 658 net.cpp:157] Top shape: 10 40 (400)\n",
+ "I0224 00:32:05.813287 658 net.cpp:165] Memory required for data: 3480\n",
+ "I0224 00:32:05.813294 658 layer_factory.hpp:77] Creating layer ip2\n",
+ "I0224 00:32:05.813304 658 net.cpp:106] Creating Layer ip2\n",
+ "I0224 00:32:05.813313 658 net.cpp:454] ip2 <- ip1\n",
+ "I0224 00:32:05.813321 658 net.cpp:411] ip2 -> ip2\n",
+ "I0224 00:32:05.813336 658 net.cpp:150] Setting up ip2\n",
+ "I0224 00:32:05.813345 658 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:05.813379 658 net.cpp:165] Memory required for data: 3560\n",
+ "I0224 00:32:05.813401 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+ "I0224 00:32:05.813417 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+ "I0224 00:32:05.813426 658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+ "I0224 00:32:05.813434 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+ "I0224 00:32:05.813446 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+ "I0224 00:32:05.813457 658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+ "I0224 00:32:05.813465 658 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:05.813473 658 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:05.813480 658 net.cpp:165] Memory required for data: 3720\n",
+ "I0224 00:32:05.813488 658 layer_factory.hpp:77] Creating layer accuracy\n",
+ "I0224 00:32:05.813499 658 net.cpp:106] Creating Layer accuracy\n",
+ "I0224 00:32:05.813508 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+ "I0224 00:32:05.813515 658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+ "I0224 00:32:05.813524 658 net.cpp:411] accuracy -> accuracy\n",
+ "I0224 00:32:05.813539 658 net.cpp:150] Setting up accuracy\n",
+ "I0224 00:32:05.813547 658 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:05.813555 658 net.cpp:165] Memory required for data: 3724\n",
+ "I0224 00:32:05.813565 658 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:05.813585 658 net.cpp:106] Creating Layer loss\n",
+ "I0224 00:32:05.813599 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+ "I0224 00:32:05.813616 658 net.cpp:454] loss <- label_data_1_split_1\n",
+ "I0224 00:32:05.813627 658 net.cpp:411] loss -> loss\n",
+ "I0224 00:32:05.813642 658 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:05.813663 658 net.cpp:150] Setting up loss\n",
+ "I0224 00:32:05.813671 658 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:05.813679 658 net.cpp:160] with loss weight 1\n",
+ "I0224 00:32:05.813695 658 net.cpp:165] Memory required for data: 3728\n",
+ "I0224 00:32:05.813704 658 net.cpp:226] loss needs backward computation.\n",
+ "I0224 00:32:05.813712 658 net.cpp:228] accuracy does not need backward computation.\n",
+ "I0224 00:32:05.813720 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+ "I0224 00:32:05.813729 658 net.cpp:226] ip2 needs backward computation.\n",
+ "I0224 00:32:05.813735 658 net.cpp:226] relu1 needs backward computation.\n",
+ "I0224 00:32:05.813743 658 net.cpp:226] ip1 needs backward computation.\n",
+ "I0224 00:32:05.813751 658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+ "I0224 00:32:05.813760 658 net.cpp:228] data does not need backward computation.\n",
+ "I0224 00:32:05.813772 658 net.cpp:270] This network produces output accuracy\n",
+ "I0224 00:32:05.813787 658 net.cpp:270] This network produces output loss\n",
+ "I0224 00:32:05.813809 658 net.cpp:283] Network initialization done.\n",
+ "I0224 00:32:05.813905 658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+ "I0224 00:32:05.813944 658 net.cpp:49] Initializing net from parameters: \n",
+ "state {\n",
+ " phase: TEST\n",
+ "}\n",
+ "layer {\n",
+ " name: \"data\"\n",
+ " type: \"HDF5Data\"\n",
+ " top: \"data\"\n",
+ " top: \"label\"\n",
+ " hdf5_data_param {\n",
+ " source: \"examples/hdf5_classification/data/test.txt\"\n",
+ " batch_size: 10\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip1\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"data\"\n",
+ " top: \"ip1\"\n",
+ " inner_product_param {\n",
+ " num_output: 40\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"relu1\"\n",
+ " type: \"ReLU\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip1\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"ip2\"\n",
+ " type: \"InnerProduct\"\n",
+ " bottom: \"ip1\"\n",
+ " top: \"ip2\"\n",
+ " inner_product_param {\n",
+ " num_output: 2\n",
+ " weight_filler {\n",
+ " type: \"xavier\"\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "layer {\n",
+ " name: \"accuracy\"\n",
+ " type: \"Accuracy\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"accuracy\"\n",
+ "}\n",
+ "layer {\n",
+ " name: \"loss\"\n",
+ " type: \"SoftmaxWithLoss\"\n",
+ " bottom: \"ip2\"\n",
+ " bottom: \"label\"\n",
+ " top: \"loss\"\n",
+ "}\n",
+ "I0224 00:32:05.814131 658 layer_factory.hpp:77] Creating layer data\n",
+ "I0224 00:32:05.814142 658 net.cpp:106] Creating Layer data\n",
+ "I0224 00:32:05.814152 658 net.cpp:411] data -> data\n",
+ "I0224 00:32:05.814162 658 net.cpp:411] data -> label\n",
+ "I0224 00:32:05.814180 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+ "I0224 00:32:05.814220 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+ "I0224 00:32:05.815207 658 net.cpp:150] Setting up data\n",
+ "I0224 00:32:05.815227 658 net.cpp:157] Top shape: 10 4 (40)\n",
+ "I0224 00:32:05.815243 658 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:05.815253 658 net.cpp:165] Memory required for data: 200\n",
+ "I0224 00:32:05.815260 658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+ "I0224 00:32:05.815270 658 net.cpp:106] Creating Layer label_data_1_split\n",
+ "I0224 00:32:05.815279 658 net.cpp:454] label_data_1_split <- label\n",
+ "I0224 00:32:05.815287 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+ "I0224 00:32:05.815299 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+ "I0224 00:32:05.815310 658 net.cpp:150] Setting up label_data_1_split\n",
+ "I0224 00:32:05.815318 658 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:05.815326 658 net.cpp:157] Top shape: 10 (10)\n",
+ "I0224 00:32:05.815335 658 net.cpp:165] Memory required for data: 280\n",
+ "I0224 00:32:05.815341 658 layer_factory.hpp:77] Creating layer ip1\n",
+ "I0224 00:32:05.815351 658 net.cpp:106] Creating Layer ip1\n",
+ "I0224 00:32:05.815358 658 net.cpp:454] ip1 <- data\n",
+ "I0224 00:32:05.815367 658 net.cpp:411] ip1 -> ip1\n",
+ "I0224 00:32:05.815383 658 net.cpp:150] Setting up ip1\n",
+ "I0224 00:32:05.815398 658 net.cpp:157] Top shape: 10 40 (400)\n",
+ "I0224 00:32:05.815413 658 net.cpp:165] Memory required for data: 1880\n",
+ "I0224 00:32:05.815435 658 layer_factory.hpp:77] Creating layer relu1\n",
+ "I0224 00:32:05.815450 658 net.cpp:106] Creating Layer relu1\n",
+ "I0224 00:32:05.815459 658 net.cpp:454] relu1 <- ip1\n",
+ "I0224 00:32:05.815469 658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+ "I0224 00:32:05.815479 658 net.cpp:150] Setting up relu1\n",
+ "I0224 00:32:05.815486 658 net.cpp:157] Top shape: 10 40 (400)\n",
+ "I0224 00:32:05.815495 658 net.cpp:165] Memory required for data: 3480\n",
+ "I0224 00:32:05.815501 658 layer_factory.hpp:77] Creating layer ip2\n",
+ "I0224 00:32:05.815510 658 net.cpp:106] Creating Layer ip2\n",
+ "I0224 00:32:05.815518 658 net.cpp:454] ip2 <- ip1\n",
+ "I0224 00:32:05.815527 658 net.cpp:411] ip2 -> ip2\n",
+ "I0224 00:32:05.815542 658 net.cpp:150] Setting up ip2\n",
+ "I0224 00:32:05.815551 658 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:05.815559 658 net.cpp:165] Memory required for data: 3560\n",
+ "I0224 00:32:05.815570 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+ "I0224 00:32:05.815579 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+ "I0224 00:32:05.815587 658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+ "I0224 00:32:05.815600 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+ "I0224 00:32:05.815619 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+ "I0224 00:32:05.815640 658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+ "I0224 00:32:05.815654 658 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:05.815662 658 net.cpp:157] Top shape: 10 2 (20)\n",
+ "I0224 00:32:05.815670 658 net.cpp:165] Memory required for data: 3720\n",
+ "I0224 00:32:05.815677 658 layer_factory.hpp:77] Creating layer accuracy\n",
+ "I0224 00:32:05.815685 658 net.cpp:106] Creating Layer accuracy\n",
+ "I0224 00:32:05.815693 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+ "I0224 00:32:05.815702 658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+ "I0224 00:32:05.815711 658 net.cpp:411] accuracy -> accuracy\n",
+ "I0224 00:32:05.815722 658 net.cpp:150] Setting up accuracy\n",
+ "I0224 00:32:05.815732 658 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:05.815738 658 net.cpp:165] Memory required for data: 3724\n",
+ "I0224 00:32:05.815747 658 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:05.815754 658 net.cpp:106] Creating Layer loss\n",
+ "I0224 00:32:05.815762 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+ "I0224 00:32:05.815770 658 net.cpp:454] loss <- label_data_1_split_1\n",
+ "I0224 00:32:05.815779 658 net.cpp:411] loss -> loss\n",
+ "I0224 00:32:05.815790 658 layer_factory.hpp:77] Creating layer loss\n",
+ "I0224 00:32:05.815811 658 net.cpp:150] Setting up loss\n",
+ "I0224 00:32:05.815829 658 net.cpp:157] Top shape: (1)\n",
+ "I0224 00:32:05.815843 658 net.cpp:160] with loss weight 1\n",
+ "I0224 00:32:05.815867 658 net.cpp:165] Memory required for data: 3728\n",
+ "I0224 00:32:05.815876 658 net.cpp:226] loss needs backward computation.\n",
+ "I0224 00:32:05.815884 658 net.cpp:228] accuracy does not need backward computation.\n",
+ "I0224 00:32:05.815892 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+ "I0224 00:32:05.815901 658 net.cpp:226] ip2 needs backward computation.\n",
+ "I0224 00:32:05.815908 658 net.cpp:226] relu1 needs backward computation.\n",
+ "I0224 00:32:05.815915 658 net.cpp:226] ip1 needs backward computation.\n",
+ "I0224 00:32:05.815923 658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+ "I0224 00:32:05.815932 658 net.cpp:228] data does not need backward computation.\n",
+ "I0224 00:32:05.815938 658 net.cpp:270] This network produces output accuracy\n",
+ "I0224 00:32:05.815946 658 net.cpp:270] This network produces output loss\n",
+ "I0224 00:32:05.815958 658 net.cpp:283] Network initialization done.\n",
+ "I0224 00:32:05.815978 658 solver.cpp:60] Solver scaffolding done.\n",
+ "I0224 00:32:05.816000 658 caffe.cpp:219] Starting Optimization\n",
+ "I0224 00:32:05.816016 658 solver.cpp:280] Solving \n",
+ "I0224 00:32:05.816030 658 solver.cpp:281] Learning Rate Policy: step\n",
+ "I0224 00:32:05.816048 658 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+ "I0224 00:32:05.831967 658 solver.cpp:406] Test net output #0: accuracy = 0.4464\n",
+ "I0224 00:32:05.832033 658 solver.cpp:406] Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n",
+ "I0224 00:32:05.832186 658 solver.cpp:229] Iteration 0, loss = 0.798509\n",
+ "I0224 00:32:05.832218 658 solver.cpp:245] Train net output #0: accuracy = 0.6\n",
+ "I0224 00:32:05.832247 658 solver.cpp:245] Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n",
+ "I0224 00:32:05.832281 658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+ "I0224 00:32:05.859506 658 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+ "I0224 00:32:05.862799 658 solver.cpp:406] Test net output #0: accuracy = 0.8156\n",
+ "I0224 00:32:05.862818 658 solver.cpp:406] Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n",
+ "I0224 00:32:05.862853 658 solver.cpp:229] Iteration 1000, loss = 0.537015\n",
+ "I0224 00:32:05.862864 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:05.862875 658 solver.cpp:245] Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n",
+ "I0224 00:32:05.862885 658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+ "I0224 00:32:05.883155 658 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+ "I0224 00:32:05.886435 658 solver.cpp:406] Test net output #0: accuracy = 0.8116\n",
+ "I0224 00:32:05.886451 658 solver.cpp:406] Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n",
+ "I0224 00:32:05.886484 658 solver.cpp:229] Iteration 2000, loss = 0.43109\n",
+ "I0224 00:32:05.886497 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n",
+ "I0224 00:32:05.886508 658 solver.cpp:245] Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n",
+ "I0224 00:32:05.886518 658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+ "I0224 00:32:05.907243 658 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+ "I0224 00:32:05.910521 658 solver.cpp:406] Test net output #0: accuracy = 0.8168\n",
+ "I0224 00:32:05.910537 658 solver.cpp:406] Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n",
+ "I0224 00:32:05.910905 658 solver.cpp:229] Iteration 3000, loss = 0.430245\n",
+ "I0224 00:32:05.910922 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:05.910933 658 solver.cpp:245] Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n",
+ "I0224 00:32:05.910943 658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+ "I0224 00:32:05.931205 658 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+ "I0224 00:32:05.934479 658 solver.cpp:406] Test net output #0: accuracy = 0.8324\n",
+ "I0224 00:32:05.934496 658 solver.cpp:406] Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n",
+ "I0224 00:32:05.934530 658 solver.cpp:229] Iteration 4000, loss = 0.628955\n",
+ "I0224 00:32:05.934542 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:05.934553 658 solver.cpp:245] Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n",
+ "I0224 00:32:05.934583 658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+ "I0224 00:32:05.955108 658 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+ "I0224 00:32:05.958377 658 solver.cpp:406] Test net output #0: accuracy = 0.8364\n",
+ "I0224 00:32:05.958395 658 solver.cpp:406] Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n",
+ "I0224 00:32:05.958432 658 solver.cpp:229] Iteration 5000, loss = 0.394939\n",
+ "I0224 00:32:05.958444 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n",
+ "I0224 00:32:05.958456 658 solver.cpp:245] Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n",
+ "I0224 00:32:05.958466 658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+ "I0224 00:32:05.978703 658 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+ "I0224 00:32:05.981973 658 solver.cpp:406] Test net output #0: accuracy = 0.838\n",
+ "I0224 00:32:05.981991 658 solver.cpp:406] Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n",
+ "I0224 00:32:05.982347 658 solver.cpp:229] Iteration 6000, loss = 0.411537\n",
+ "I0224 00:32:05.982362 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n",
+ "I0224 00:32:05.982373 658 solver.cpp:245] Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n",
+ "I0224 00:32:05.982383 658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+ "I0224 00:32:06.003015 658 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+ "I0224 00:32:06.006283 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n",
+ "I0224 00:32:06.006301 658 solver.cpp:406] Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n",
+ "I0224 00:32:06.006335 658 solver.cpp:229] Iteration 7000, loss = 0.521072\n",
+ "I0224 00:32:06.006347 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n",
+ "I0224 00:32:06.006358 658 solver.cpp:245] Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n",
+ "I0224 00:32:06.006368 658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+ "I0224 00:32:06.026715 658 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+ "I0224 00:32:06.029965 658 solver.cpp:406] Test net output #0: accuracy = 0.8404\n",
+ "I0224 00:32:06.029983 658 solver.cpp:406] Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n",
+ "I0224 00:32:06.030015 658 solver.cpp:229] Iteration 8000, loss = 0.329477\n",
+ "I0224 00:32:06.030028 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n",
+ "I0224 00:32:06.030040 658 solver.cpp:245] Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n",
+ "I0224 00:32:06.030048 658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+ "I0224 00:32:06.050626 658 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+ "I0224 00:32:06.053889 658 solver.cpp:406] Test net output #0: accuracy = 0.8376\n",
+ "I0224 00:32:06.053906 658 solver.cpp:406] Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n",
+ "I0224 00:32:06.054271 658 solver.cpp:229] Iteration 9000, loss = 0.412227\n",
+ "I0224 00:32:06.054291 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n",
+ "I0224 00:32:06.054314 658 solver.cpp:245] Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n",
+ "I0224 00:32:06.054337 658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+ "I0224 00:32:06.074646 658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+ "I0224 00:32:06.074808 658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+ "I0224 00:32:06.074889 658 solver.cpp:318] Iteration 10000, loss = 0.532798\n",
+ "I0224 00:32:06.074906 658 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+ "I0224 00:32:06.078208 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n",
+ "I0224 00:32:06.078225 658 solver.cpp:406] Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n",
+ "I0224 00:32:06.078234 658 solver.cpp:323] Optimization Done.\n",
+ "I0224 00:32:06.078241 658 caffe.cpp:222] Optimization Done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+ "shutil.rmtree(dirname)"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+ "example_name": "Off-the-shelf SGD for classification",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ },
+ "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 446479d..83cf0d8 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -1,12 +1,11 @@
name: "CIFAR10_full_deploy"
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html
-input: "data"
-input_shape {
- dim: 1
- dim: 3
- dim: 32
- dim: 32
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}
layer {
name: "conv1"
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 9352fbf..cf3b2a3 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -1,10 +1,9 @@
name: "CIFAR10_quick_test"
-input: "data"
-input_shape {
- dim: 1
- dim: 3
- dim: 32
- dim: 32
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}
layer {
name: "conv1"
diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index e1b89f4..7385a74 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -91,6 +91,8 @@ void convert_dataset(const string& input_folder, const string& output_folder,
}
int main(int argc, char** argv) {
+ FLAGS_alsologtostderr = 1;
+
if (argc != 4) {
printf("This script converts the CIFAR dataset to the leveldb format used\n"
"by caffe to perform classification.\n"
diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index a42725c..7ee1d6a 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -1,5 +1,6 @@
#!/usr/bin/env sh
# This script converts the cifar data into leveldb format.
+set -e
EXAMPLE=examples/cifar10
DATA=data/cifar10
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index ef112e1..06ecc2d 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -1,16 +1,17 @@
#!/usr/bin/env sh
+set -e
TOOLS=./build/tools
$TOOLS/caffe train \
- --solver=examples/cifar10/cifar10_full_solver.prototxt
+ --solver=examples/cifar10/cifar10_full_solver.prototxt $@
# reduce learning rate by factor of 10
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
- --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5
+ --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 $@
# reduce learning rate by factor of 10
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
- --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5
+ --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 $@
diff --git a/examples/cifar10/train_full_sigmoid.sh b/examples/cifar10/train_full_sigmoid.sh
index 9cff06d..9b5d521 100755
--- a/examples/cifar10/train_full_sigmoid.sh
+++ b/examples/cifar10/train_full_sigmoid.sh
@@ -1,7 +1,8 @@
#!/usr/bin/env sh
+set -e
TOOLS=./build/tools
$TOOLS/caffe train \
- --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+ --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt $@
diff --git a/examples/cifar10/train_full_sigmoid_bn.sh b/examples/cifar10/train_full_sigmoid_bn.sh
index 011387c..05547f3 100755
--- a/examples/cifar10/train_full_sigmoid_bn.sh
+++ b/examples/cifar10/train_full_sigmoid_bn.sh
@@ -1,7 +1,8 @@
#!/usr/bin/env sh
+set -e
TOOLS=./build/tools
$TOOLS/caffe train \
- --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+ --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt $@
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 6b7d228..d2b8753 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -1,11 +1,12 @@
#!/usr/bin/env sh
+set -e
TOOLS=./build/tools
$TOOLS/caffe train \
- --solver=examples/cifar10/cifar10_quick_solver.prototxt
+ --solver=examples/cifar10/cifar10_quick_solver.prototxt $@
# reduce learning rate by factor of 10 after 8 epochs
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
- --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
+ --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
index 974662e..6b67c53 100644
--- a/examples/cpp_classification/classification.cpp
+++ b/examples/cpp_classification/classification.cpp
@@ -159,7 +159,7 @@ std::vector<float> Classifier::Predict(const cv::Mat& img) {
Preprocess(img, &input_channels);
- net_->ForwardPrefilled();
+ net_->Forward();
/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];
diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
index a086db1..0de2885 100644
--- a/examples/cpp_classification/readme.md
+++ b/examples/cpp_classification/readme.md
@@ -42,7 +42,7 @@ script:
The ImageNet labels file (also called the *synset file*) is also
required in order to map a prediction to the name of the class:
```
-./data/ilsvrc12/get_ilsvrc_aux.sh.
+./data/ilsvrc12/get_ilsvrc_aux.sh
```
Using the files that were downloaded, we can classify the provided cat
image (`examples/images/cat.jpg`) using this command:
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 4e9d41f..188dedf 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -14,9 +14,9 @@ Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Fli
## Explanation
The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained.
-Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
+Since that model works well for object category classification, we'd like to use this architecture for our style classifier.
We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
-If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
+If we provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
@@ -57,7 +57,11 @@ The prototxts in this example assume this, and also assume the presence of the I
We'll also need the ImageNet-trained model, which you can obtain by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.
-Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
+Now we can train! The key to fine-tuning is the `-weights` argument in the
+command below, which tells Caffe that we want to load weights from a pre-trained
+Caffe model.
+
+(You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
diff --git a/examples/hdf5_classification/nonlinear_solver.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt
deleted file mode 100644
index b4aacf6..0000000
--- a/examples/hdf5_classification/nonlinear_solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
-test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
deleted file mode 100644
index 8587b5a..0000000
--- a/examples/hdf5_classification/solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
-test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index e912ac4..1bf08b1 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -1,6 +1,7 @@
#!/usr/bin/env sh
# Create the imagenet lmdb inputs
# N.B. set the path to the imagenet train + val data dirs
+set -e
EXAMPLE=examples/imagenet
DATA=data/ilsvrc12
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index bf7945c..4aef204 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -1,5 +1,7 @@
#!/usr/bin/env sh
+set -e
./build/tools/caffe train \
--solver=models/bvlc_reference_caffenet/solver.prototxt \
- --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5
+ --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 \
+ $@
diff --git a/examples/imagenet/train_caffenet.sh b/examples/imagenet/train_caffenet.sh
index 94558ec..a5094d4 100755
--- a/examples/imagenet/train_caffenet.sh
+++ b/examples/imagenet/train_caffenet.sh
@@ -1,4 +1,5 @@
#!/usr/bin/env sh
+set -e
./build/tools/caffe train \
- --solver=models/bvlc_reference_caffenet/solver.prototxt
+ --solver=models/bvlc_reference_caffenet/solver.prototxt $@
diff --git a/examples/images/cat gray.jpg b/examples/images/cat gray.jpg
new file mode 100644
index 0000000..43c5ce3
Binary files /dev/null and b/examples/images/cat gray.jpg differ
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 16d2809..57ddef7 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -22,12 +22,15 @@
#include <fstream> // NOLINT(readability/streams)
#include <string>
+#include "boost/scoped_ptr.hpp"
#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
#include "caffe/util/format.hpp"
#if defined(USE_LEVELDB) && defined(USE_LMDB)
using namespace caffe; // NOLINT(build/namespaces)
+using boost::scoped_ptr;
using std::string;
DEFINE_string(backend, "lmdb", "The backend for storing the result");
@@ -67,43 +70,10 @@ void convert_dataset(const char* image_filename, const char* label_filename,
image_file.read(reinterpret_cast<char*>(&cols), 4);
cols = swap_endian(cols);
- // lmdb
- MDB_env *mdb_env;
- MDB_dbi mdb_dbi;
- MDB_val mdb_key, mdb_data;
- MDB_txn *mdb_txn;
- // leveldb
- leveldb::DB* db;
- leveldb::Options options;
- options.error_if_exists = true;
- options.create_if_missing = true;
- options.write_buffer_size = 268435456;
- leveldb::WriteBatch* batch = NULL;
-
- // Open db
- if (db_backend == "leveldb") { // leveldb
- LOG(INFO) << "Opening leveldb " << db_path;
- leveldb::Status status = leveldb::DB::Open(
- options, db_path, &db);
- CHECK(status.ok()) << "Failed to open leveldb " << db_path
- << ". Is it already existing?";
- batch = new leveldb::WriteBatch();
- } else if (db_backend == "lmdb") { // lmdb
- LOG(INFO) << "Opening lmdb " << db_path;
- CHECK_EQ(mkdir(db_path, 0744), 0)
- << "mkdir " << db_path << "failed";
- CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
- CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS) // 1TB
- << "mdb_env_set_mapsize failed";
- CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
- << "mdb_env_open failed";
- CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
- << "mdb_txn_begin failed";
- CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
- << "mdb_open failed. Does the lmdb already exist? ";
- } else {
- LOG(FATAL) << "Unknown db backend " << db_backend;
- }
+
+ scoped_ptr<db::DB> db(db::GetDB(db_backend));
+ db->Open(db_path, db::NEW);
+ scoped_ptr<db::Transaction> txn(db->NewTransaction());
// Storing to db
char label;
@@ -125,52 +95,19 @@ void convert_dataset(const char* image_filename, const char* label_filename,
string key_str = caffe::format_int(item_id, 8);
datum.SerializeToString(&value);
- // Put in db
- if (db_backend == "leveldb") { // leveldb
- batch->Put(key_str, value);
- } else if (db_backend == "lmdb") { // lmdb
- mdb_data.mv_size = value.size();
- mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
- mdb_key.mv_size = key_str.size();
- mdb_key.mv_data = reinterpret_cast<void*>(&key_str[0]);
- CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
- << "mdb_put failed";
- } else {
- LOG(FATAL) << "Unknown db backend " << db_backend;
- }
+ txn->Put(key_str, value);
if (++count % 1000 == 0) {
- // Commit txn
- if (db_backend == "leveldb") { // leveldb
- db->Write(leveldb::WriteOptions(), batch);
- delete batch;
- batch = new leveldb::WriteBatch();
- } else if (db_backend == "lmdb") { // lmdb
- CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
- << "mdb_txn_commit failed";
- CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
- << "mdb_txn_begin failed";
- } else {
- LOG(FATAL) << "Unknown db backend " << db_backend;
- }
+ txn->Commit();
}
}
// write the last batch
if (count % 1000 != 0) {
- if (db_backend == "leveldb") { // leveldb
- db->Write(leveldb::WriteOptions(), batch);
- delete batch;
- delete db;
- } else if (db_backend == "lmdb") { // lmdb
- CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
- mdb_close(mdb_env, mdb_dbi);
- mdb_env_close(mdb_env);
- } else {
- LOG(FATAL) << "Unknown db backend " << db_backend;
- }
- LOG(ERROR) << "Processed " << count << " files.";
+ txn->Commit();
}
+ LOG(INFO) << "Processed " << count << " files.";
delete[] pixels;
+ db->Close();
}
int main(int argc, char** argv) {
@@ -178,6 +115,8 @@ int main(int argc, char** argv) {
namespace gflags = google;
#endif
+ FLAGS_alsologtostderr = 1;
+
gflags::SetUsageMessage("This script converts the MNIST dataset to\n"
"the lmdb/leveldb format used by Caffe to load data.\n"
"Usage:\n"
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index 06ecc27..f5e2e79 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -1,6 +1,7 @@
#!/usr/bin/env sh
# This script converts the mnist data into lmdb/leveldb format,
# depending on the value assigned to $BACKEND.
+set -e
EXAMPLE=examples/mnist
DATA=data/mnist
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index dff7123..8cf78e6 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -1,10 +1,9 @@
name: "LeNet"
-input: "data"
-input_shape {
- dim: 64
- dim: 1
- dim: 28
- dim: 28
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
}
layer {
name: "conv1"
diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt
index fa4bbf0..481c844 100644
--- a/examples/mnist/lenet_auto_solver.prototxt
+++ b/examples/mnist/lenet_auto_solver.prototxt
@@ -1,6 +1,6 @@
# The train/test net protocol buffer definition
-train_net: "examples/mnist/lenet_auto_train.prototxt"
-test_net: "examples/mnist/lenet_auto_test.prototxt"
+train_net: "mnist/lenet_auto_train.prototxt"
+test_net: "mnist/lenet_auto_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
@@ -21,4 +21,4 @@ display: 100
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
-snapshot_prefix: "examples/mnist/lenet"
+snapshot_prefix: "mnist/lenet"
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index b87a0f5..3595215 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -248,7 +248,7 @@ These messages tell you the details about each layer, its connections and its ou
I1203 solver.cpp:36] Solver scaffolding done.
I1203 solver.cpp:44] Solving LeNet
-Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 1000 iterations. You will see messages like this:
+Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:
I1203 solver.cpp:204] Iteration 100, lr = 0.00992565
I1203 solver.cpp:66] Iteration 100, loss = 0.26044
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index 1b6bf7d..f7f9b86 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -1,3 +1,4 @@
#!/usr/bin/env sh
+set -e
-./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@
diff --git a/examples/mnist/train_lenet_adam.sh b/examples/mnist/train_lenet_adam.sh
index a32ecf2..7b4e905 100755
--- a/examples/mnist/train_lenet_adam.sh
+++ b/examples/mnist/train_lenet_adam.sh
@@ -1,3 +1,4 @@
#!/usr/bin/env sh
+set -e
-./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
index c855467..c5f0266 100755
--- a/examples/mnist/train_lenet_consolidated.sh
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -1,4 +1,5 @@
#!/usr/bin/env sh
+set -e
./build/tools/caffe train \
- --solver=examples/mnist/lenet_consolidated_solver.prototxt
+ --solver=examples/mnist/lenet_consolidated_solver.prototxt $@
diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh
new file mode 100755
index 0000000..32cf1c8
--- /dev/null
+++ b/examples/mnist/train_lenet_docker.sh
@@ -0,0 +1,119 @@
+#!/usr/bin/env sh
+set -e
+# The following example allows for the MNIST example (using LeNet) to be
+# trained using the caffe docker image instead of building from source.
+#
+# The GPU-enabled version of Caffe can be used, assuming that nvidia-docker
+# is installed, and the GPU-enabled Caffe image has been built.
+# Setting the GPU environment variable to 1 will enable the use of nvidia-docker.
+# e.g.
+# GPU=1 ./examples/mnist/train_lenet_docker.sh [ADDITIONAL_CAFFE_ARGS]
+#
+# With any arguments following the script being passed directly to caffe
+# when training the network.
+#
+# The steps that are performed by the script are as follows:
+# 1. The MNIST data set is downloaded
+# (see data/mnist/get_mnist.sh)
+# 2. An LMDB database is created from the downloaded data
+# (see examples/mnist/create_mnist.sh.
+# 3. A caffe network based on the LeNet solver is trained.
+# (see examples/mnist/lenet_solver.prototxt)
+#
+# For each of these, a step is executed to ensure that certain prerequisites
+# are available, after which a command that actually performs the work is
+# executed.
+#
+# In order to provide additional flexibility, the following shell (environment)
+# variables can be used to controll the execution of each of the phases:
+#
+# DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset
+# CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database
+# TRAIN: Enable (1) or disable (0) the training of the LeNet networkd.
+#
+# As an example, assuming that the data set has been downloaded, and an LMDB
+# database created, the following command can be used to train the LeNet
+# network with GPU computing enabled.
+#
+# DOWNLOAD_DATA=0 CREATE_LMDB=0 GPU=1 ./examples/mnist/train_lenet_docker.sh
+#
+
+
+if [ x"$(uname -s)" != x"Linux" ]
+then
+echo ""
+echo "This script is designed to run on Linux."
+echo "There may be problems with the way Docker mounts host volumes on other"
+echo "systems which will cause the docker commands to fail."
+echo ""
+read -p "Press [ENTER] to continue..." key
+echo ""
+fi
+
+
+# Check if GPU mode has been enabled and set the docker executable accordingly
+if [ ${GPU:-0} -eq 1 ]
+then
+DOCKER_CMD=nvidia-docker
+IMAGE=caffe:gpu
+else
+DOCKER_CMD=docker
+IMAGE=caffe:cpu
+fi
+echo "Using $DOCKER_CMD to launch $IMAGE"
+
+# On non-Linux systems, the Docker host is typically a virtual machine.
+# This means that the user and group id's may be different.
+# On OS X, for example, the user and group are 1000 and 50, respectively.
+if [ x"$(uname -s)" != x"Linux" ]
+then
+CUID=1000
+CGID=50
+else
+CUID=$(id -u)
+CGID=$(id -g)
+fi
+
+# Define some helper variables to make the running of the actual docker
+# commands less verbose.
+# Note:
+# -u $CUID:$CGID runs the docker image as the current user to ensure
+# that the file permissions are compatible with the
+# host system. The variables CUID and CGID have been
+# set above depending on the host operating system.
+# --volume $(pwd):/workspace mounts the current directory as the docker volume
+# /workspace
+# --workdir /workspace Ensures that the docker container starts in the right
+# working directory
+DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume=$(pwd):/workspace --workdir=/workspace"
+DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE"
+
+# Download the data
+if [ ${DOWNLOAD_DATA:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./data/mnist;
+ cp -ru \$CAFFE_ROOT/data/mnist/get_mnist.sh ./data/mnist/"
+$DOCKER_RUN ./data/mnist/get_mnist.sh
+fi
+
+# Create the LMDB database
+if [ ${CREATE_LMDB:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./examples/mnist;
+ cp -ru \$CAFFE_ROOT/examples/mnist/create_mnist.sh ./examples/mnist/;
+ sed -i s#BUILD=build#BUILD=\$CAFFE_ROOT/build## ./examples/mnist/create_mnist.sh"
+$DOCKER_RUN ./examples/mnist/create_mnist.sh
+fi
+
+# Train the network
+if [ ${TRAIN:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "cp \$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt ./examples/mnist/;
+ cp \$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt ./examples/mnist/"
+ # Ensure that the solver_mode is compatible with the desired GPU mode.
+ if [ ${GPU:-0} -eq 0 ]
+ then
+ $DOCKER_RUN sed -i 's#solver_mode: GPU#solver_mode: CPU##' ./examples/mnist/lenet_solver.prototxt
+ fi
+$DOCKER_RUN caffe train --solver=examples/mnist/lenet_solver.prototxt $*
+fi
diff --git a/examples/mnist/train_lenet_rmsprop.sh b/examples/mnist/train_lenet_rmsprop.sh
index 621cab2..adfa7ab 100755
--- a/examples/mnist/train_lenet_rmsprop.sh
+++ b/examples/mnist/train_lenet_rmsprop.sh
@@ -1,3 +1,5 @@
#!/usr/bin/env sh
+set -e
-./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt
+./build/tools/caffe train \
+ --solver=examples/mnist/lenet_solver_rmsprop.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index cfd67e8..724a0f1 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,4 +1,5 @@
#!/usr/bin/env sh
+set -e
./build/tools/caffe train \
- --solver=examples/mnist/mnist_autoencoder_solver.prototxt
+ --solver=examples/mnist/mnist_autoencoder_solver.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_adadelta.sh b/examples/mnist/train_mnist_autoencoder_adadelta.sh
index 4be0ebd..a660dbb 100755
--- a/examples/mnist/train_mnist_autoencoder_adadelta.sh
+++ b/examples/mnist/train_mnist_autoencoder_adadelta.sh
@@ -1,4 +1,5 @@
#!/bin/bash
+set -e
./build/tools/caffe train \
- --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+ --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_adagrad.sh b/examples/mnist/train_mnist_autoencoder_adagrad.sh
index 95fe1b1..4c11dfa 100755
--- a/examples/mnist/train_mnist_autoencoder_adagrad.sh
+++ b/examples/mnist/train_mnist_autoencoder_adagrad.sh
@@ -1,4 +1,5 @@
#!/bin/bash
+set -e
./build/tools/caffe train \
- --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+ --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_nesterov.sh b/examples/mnist/train_mnist_autoencoder_nesterov.sh
index cf19ea7..fd0559d 100755
--- a/examples/mnist/train_mnist_autoencoder_nesterov.sh
+++ b/examples/mnist/train_mnist_autoencoder_nesterov.sh
@@ -1,4 +1,5 @@
#!/bin/bash
+set -e
./build/tools/caffe train \
- --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+ --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt $@
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index ff780fb..d50d503 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -22,7 +22,6 @@
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
- "import Image\n",
"\n",
"# Make sure that caffe is on the python path:\n",
"caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
@@ -3511,7 +3510,7 @@
"print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
"\n",
"# load image and prepare as a single input batch for Caffe\n",
- "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+ "im = np.array(caffe.io.load_image('images/cat_gray.jpg', color=False)).squeeze()\n",
"plt.title(\"original image\")\n",
"plt.imshow(im)\n",
"plt.axis('off')\n",
@@ -4480,8 +4479,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "pre-surgery output mean -12.93\n",
- "post-surgery output mean -11.93\n"
+ "pre-surgery output mean -0.02\n",
+ "post-surgery output mean 0.98\n"
]
}
],
@@ -4489,7 +4488,7 @@
"# pick first filter output\n",
"conv0 = net.blobs['conv'].data[0, 0]\n",
"print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
- "# set first filter bias to 10\n",
+ "# set first filter bias to 1\n",
"net.params['conv'][1].data[0] = 1.\n",
"net.forward()\n",
"print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
@@ -5499,43 +5498,41 @@
"< name: \"CaffeNetConv\"\r\n",
"---\r\n",
"> name: \"CaffeNet\"\r\n",
- "4c3\r\n",
- "< input_dim: 1\r\n",
+ "7,11c6\r\n",
+ "< input_param {\r\n",
+ "< # initial shape for a fully convolutional network:\r\n",
+ "< # the shape can be set for each input by reshape.\r\n",
+ "< shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n",
+ "< }\r\n",
"---\r\n",
- "> input_dim: 10\r\n",
- "6,7c5,6\r\n",
- "< input_dim: 451\r\n",
- "< input_dim: 451\r\n",
- "---\r\n",
- "> input_dim: 227\r\n",
- "> input_dim: 227\r\n",
- "152,153c151,152\r\n",
+ "> input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n",
+ "157,158c152,153\r\n",
"< name: \"fc6-conv\"\r\n",
"< type: \"Convolution\"\r\n",
"---\r\n",
"> name: \"fc6\"\r\n",
"> type: \"InnerProduct\"\r\n",
- "155,156c154,155\r\n",
+ "160,161c155,156\r\n",
"< top: \"fc6-conv\"\r\n",
"< convolution_param {\r\n",
"---\r\n",
"> top: \"fc6\"\r\n",
"> inner_product_param {\r\n",
- "158d156\r\n",
+ "163d157\r\n",
"< kernel_size: 6\r\n",
- "164,165c162,163\r\n",
+ "169,170c163,164\r\n",
"< bottom: \"fc6-conv\"\r\n",
"< top: \"fc6-conv\"\r\n",
"---\r\n",
"> bottom: \"fc6\"\r\n",
"> top: \"fc6\"\r\n",
- "170,171c168,169\r\n",
+ "175,176c169,170\r\n",
"< bottom: \"fc6-conv\"\r\n",
"< top: \"fc6-conv\"\r\n",
"---\r\n",
"> bottom: \"fc6\"\r\n",
"> top: \"fc6\"\r\n",
- "177,181c175,179\r\n",
+ "182,186c176,180\r\n",
"< name: \"fc7-conv\"\r\n",
"< type: \"Convolution\"\r\n",
"< bottom: \"fc6-conv\"\r\n",
@@ -5547,21 +5544,21 @@
"> bottom: \"fc6\"\r\n",
"> top: \"fc7\"\r\n",
"> inner_product_param {\r\n",
- "183d180\r\n",
+ "188d181\r\n",
"< kernel_size: 1\r\n",
- "189,190c186,187\r\n",
+ "194,195c187,188\r\n",
"< bottom: \"fc7-conv\"\r\n",
"< top: \"fc7-conv\"\r\n",
"---\r\n",
"> bottom: \"fc7\"\r\n",
"> top: \"fc7\"\r\n",
- "195,196c192,193\r\n",
+ "200,201c193,194\r\n",
"< bottom: \"fc7-conv\"\r\n",
"< top: \"fc7-conv\"\r\n",
"---\r\n",
"> bottom: \"fc7\"\r\n",
"> top: \"fc7\"\r\n",
- "202,206c199,203\r\n",
+ "207,211c200,204\r\n",
"< name: \"fc8-conv\"\r\n",
"< type: \"Convolution\"\r\n",
"< bottom: \"fc7-conv\"\r\n",
@@ -5573,9 +5570,9 @@
"> bottom: \"fc7\"\r\n",
"> top: \"fc8\"\r\n",
"> inner_product_param {\r\n",
- "208d204\r\n",
+ "213d205\r\n",
"< kernel_size: 1\r\n",
- "214c210\r\n",
+ "219c211\r\n",
"< bottom: \"fc8-conv\"\r\n",
"---\r\n",
"> bottom: \"fc8\"\r\n"
@@ -5611,13 +5608,6 @@
}
],
"source": [
- "# Make sure that caffe is on the python path:\n",
- "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
- "import sys\n",
- "sys.path.insert(0, caffe_root + 'python')\n",
- "\n",
- "import caffe\n",
- "\n",
"# Load the original network and extract the fully connected layers' parameters.\n",
"net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
" '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 0cadde9..f8f5c3c 100644
--- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,11 +1,14 @@
# Fully convolutional network version of CaffeNet.
name: "CaffeNetConv"
-input: "data"
-input_shape {
- dim: 1
- dim: 3
- dim: 451
- dim: 451
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param {
+ # initial shape for a fully convolutional network:
+ # the shape can be set for each input by reshape.
+ shape: { dim: 1 dim: 3 dim: 451 dim: 451 }
+ }
}
layer {
name: "conv1"
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
index 6b3e5c7..8671bb5 100644
--- a/examples/net_surgery/conv.prototxt
+++ b/examples/net_surgery/conv.prototxt
@@ -1,11 +1,10 @@
# Simple single-layer network to showcase editing model parameters.
name: "convolution"
-input: "data"
-input_shape {
- dim: 1
- dim: 1
- dim: 100
- dim: 100
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } }
}
layer {
name: "conv"
diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb
new file mode 100644
index 0000000..94b9b4f
--- /dev/null
+++ b/examples/pascal-multilabel-with-datalayer.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Multilabel classification on PASCAL using python data-layers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this tutorial we will do multilabel classification on PASCAL VOC 2012.\n",
+ "\n",
+ "Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \"beach\" category and a \"vacation pictures\" category. In multiclass classification, on the other hand, each image belongs to a single class.\n",
+ "\n",
+ "Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. Preliminaries\n",
+ "\n",
+ "* First, make sure you compile caffe using\n",
+ "WITH_PYTHON_LAYER := 1\n",
+ "\n",
+ "* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
+ "\n",
+ "* Third, import modules:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import sys \n",
+ "import os\n",
+ "\n",
+ "import numpy as np\n",
+ "import os.path as osp\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from copy import copy\n",
+ "\n",
+ "% matplotlib inline\n",
+ "plt.rcParams['figure.figsize'] = (6, 6)\n",
+ "\n",
+ "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n",
+ "sys.path.append(caffe_root + 'python')\n",
+ "import caffe # If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path.\n",
+ "\n",
+ "from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\n",
+ "\n",
+ "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
+ "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
+ "\n",
+ "import tools #this contains some tools that we need"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Fourth, set data directories and initialize caffe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# set data root directory, e.g:\n",
+ "pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\n",
+ "\n",
+ "# these are the PASCAL classes, we'll need them later.\n",
+ "classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\n",
+ "\n",
+ "# make sure we have the caffenet weight downloaded.\n",
+ "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+ " print(\"Downloading pre-trained CaffeNet model...\")\n",
+ " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n",
+ "\n",
+ "# initialize caffe for gpu mode\n",
+ "caffe.set_mode_gpu()\n",
+ "caffe.set_device(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. Define network prototxts\n",
+ "\n",
+ "* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# helper function for common structures\n",
+ "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n",
+ " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+ " num_output=nout, pad=pad, group=group)\n",
+ " return conv, L.ReLU(conv, in_place=True)\n",
+ "\n",
+ "# another helper function\n",
+ "def fc_relu(bottom, nout):\n",
+ " fc = L.InnerProduct(bottom, num_output=nout)\n",
+ " return fc, L.ReLU(fc, in_place=True)\n",
+ "\n",
+ "# yet another helper function\n",
+ "def max_pool(bottom, ks, stride=1):\n",
+ " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+ "\n",
+ "# main netspec wrapper\n",
+ "def caffenet_multilabel(data_layer_params, datalayer):\n",
+ " # setup the python data layer \n",
+ " n = caffe.NetSpec()\n",
+ " n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n",
+ " ntop = 2, param_str=str(data_layer_params))\n",
+ "\n",
+ " # the net itself\n",
+ " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n",
+ " n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+ " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+ " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n",
+ " n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+ " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+ " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n",
+ " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n",
+ " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n",
+ " n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+ " n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n",
+ " n.drop6 = L.Dropout(n.relu6, in_place=True)\n",
+ " n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n",
+ " n.drop7 = L.Dropout(n.relu7, in_place=True)\n",
+ " n.score = L.InnerProduct(n.drop7, num_output=20)\n",
+ " n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n",
+ " \n",
+ " return str(n.to_proto())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3. Write nets and solver files\n",
+ "\n",
+ "* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "workdir = './pascal_multilabel_with_datalayer'\n",
+ "if not os.path.isdir(workdir):\n",
+ " os.makedirs(workdir)\n",
+ "\n",
+ "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
+ "solverprototxt.sp['display'] = \"1\"\n",
+ "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
+ "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n",
+ "\n",
+ "# write train net.\n",
+ "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n",
+ " # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
+ " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
+ " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n",
+ "\n",
+ "# write validation net.\n",
+ "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n",
+ " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
+ " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \n",
+ "\n",
+ "* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n",
+ "\n",
+ "* Now we can load the caffe solver as usual."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BatchLoader initialized with 5717 images\n",
+ "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n",
+ "BatchLoader initialized with 5823 images\n",
+ "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n"
+ ]
+ }
+ ],
+ "source": [
+ "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n",
+ "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+ "solver.test_nets[0].share_with(solver.net)\n",
+ "solver.step(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Let's check the data we have loaded."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAF6CAYAAAAEd9NkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsZUlyHvZFnnvv26vqvaree7p7Znp6SM5Mz9IUJYsQ\nQNMwLciEYMOAZAOEIQs2bP0wDC+gFxASSRiUCcKwDHiRAduSbdmWBHiBbBo2RVoWaYoSORQ5C2em\nt+qe7uqqrlf19u0u52T4R2ZkROY5575X1d3zqsc3C6/uvefkyYyMjPwiMjIyDzEzFmmRFmmRFunj\nm9xlE7BIi7RIi7RIHywtgHyRFmmRFuljnhZAvkiLtEiL9DFPCyBfpEVapEX6mKcFkC/SIi3SIn3M\n0wLIF2mRFmmRPuZpAeSLdCmJiH6MiDwRHRLRT3TcfyHeX8joR5SI6OeI6HjB549/WnTeIp2biOif\nJaJ/ [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fac306356d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
+ "image_index = 0 # First image in the batch.\n",
+ "plt.figure()\n",
+ "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n",
+ "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+ "plt.title('GT: {}'.format(classes[np.where(gtlist)]))\n",
+ "plt.axis('off');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4. Train a net.\n",
+ "\n",
+ "* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def hamming_distance(gt, est):\n",
+ " return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
+ "\n",
+ "def check_accuracy(net, num_batches, batch_size = 128):\n",
+ " acc = 0.0\n",
+ " for t in range(num_batches):\n",
+ " net.forward()\n",
+ " gts = net.blobs['label'].data\n",
+ " ests = net.blobs['score'].data > 0\n",
+ " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+ " acc += hamming_distance(gt, est)\n",
+ " return acc / (num_batches * batch_size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Alright, now let's train for a while"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "itt:100 accuracy:0.9526\n",
+ "itt:200 accuracy:0.9563\n",
+ "itt:300 accuracy:0.9582\n",
+ "itt:400 accuracy:0.9586\n",
+ "itt:500 accuracy:0.9597\n",
+ "itt:600 accuracy:0.9591\n"
+ ]
+ }
+ ],
+ "source": [
+ "for itt in range(6):\n",
+ " solver.step(100)\n",
+ " print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Baseline accuracy:0.9238\n"
+ ]
+ }
+ ],
+ "source": [
+ "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
+ " acc = 0.0\n",
+ " for t in range(num_batches):\n",
+ " net.forward()\n",
+ " gts = net.blobs['label'].data\n",
+ " ests = np.zeros((batch_size, len(gts)))\n",
+ " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+ " acc += hamming_distance(gt, est)\n",
+ " return acc / (num_batches * batch_size)\n",
+ "\n",
+ "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### 6. Look at some prediction results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAGJCAYAAACXcbjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXusfUty1/epXmvtfc7ved93Zjwvhgz4gQciRgnCQIhM\nLCvEECk45iWEnKBABETExDwE2CYJFkEhIUoiokRgGCNsHJwIIuQACiQIcBIr2AlMGOzYHo/v3LnP\n3/09zzl7r+7KH9WP6rXX+f3uzHh85o53X93f2XvtXt3V1d3fqq6urhZV5ZiO6ZiO6ZjeuSlcNQHH\ndEzHdEzH9PmlI5Af0zEd0zG9w9MRyI/pmI7pmN7h6Qjkx3RMx3RM7/B0BPJjOqZjOqZ3eDoC+TEd\n0zEd0zs8HYH8mK40icivFpEkIvdE5Os+y3e/S0T+w8+x3j8kIv/t5/LuOz2JyC8QkQciMovIv3XV\n9BzT [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fabdc077450>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAGJCAYAAACXcbjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvVuobeuW3/Vr3633Mcacc132WnufS9VJqiqpHMsQIimE\nMlEjQRGCRBC8gA/RoBjw8qB4AYXkQaNGE598CRqiiKgkIIKQpBSNxEQQSUGClKeqck6d2mfvtddt\nXsYYvffv1nxofcy1zjn7VJ267rVT47+Z7DnH6KOPr/f+fe1r7d/+rS1RVc4444wzzvj8wn3WAzjj\njDPOOONXh7MhP+OMM874nONsyM8444wzPuc4G/IzzjjjjM85zob8jDPOOONzjrMhP+OMM874nONs\nyM8444wzPuc4G/IzftNBRH6/iHQRuRWRf+RT3v+t6/ufuj5E5N8RkT/zy/i+73k+EfkjInK3vv/D\nv7wr [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fabdc193450>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAGJCAYAAACXcbjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmsbVt21vcbs1nN7k57+/devWptsDEQrESyheJAgpCw\nUKRIaSQUEZSI8EcUJUFAIgcMiiDISiBSGtJBGhI6QSISRYptRQEJiJFRbETjil3Na+6979577mn2\n2XuvZjYjf8x17rv1/KpeUeWqW8/enzR19l5rr7XmWnPNb475jTHHEVVljz322GOPjy/Mq67AHnvs\nscce3xz2RL7HHnvs8THHnsj32GOPPT7m2BP5HnvsscfHHHsi32OPPfb4mGNP5HvsscceH3PsiXyP\nVwIR+SERySKyFpHf8iH735z279/RbxFE5A+LyGb/nD/+2DfeHh8JEfkXReSnpk7/RET+HxH5PSLy\nxrTt [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fabd65950d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAGJCAYAAACXcbjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmsZVmW3vVbe+8z3OFNMWREZkZlZY9VPZW7TQtMW4DB\n0AKaFkhgYSHLNDQSWGIQGBnEIIQtZAFSA0JC/GMb5EZCbbCEQC08CBlD4wHsbrfdXVWdlVmVmTFl\nRLz5DuecPSz+2Ofc4cWLzKwhKzqr7hd6L969Z9pnn32+vfa31l5bVJUddthhhx0+vTAvuwA77LDD\nDjt8c9gR+Q477LDDpxw7It9hhx12+JRjR+Q77LDDDp9y7Ih8hx122OFTjh2R77DDDjt8yrEj8h2+\nIyEiv0dEkohciMhPf53H/iUR+flPqmwfsww/LyKX/T1878ssyw6//bEj8h1eOkTk94vIXxORmYh8\nICJ/ [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fabd63b3790>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAGJCAYAAACXcbjTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvV2obduW3/VrvY8x51prr332Oeeec++t8papGEMiaKlQ\nGDR+YeIHCYGAiCIBRRCNKIKR6IPBD5CggvokeTLkQTEaRAkEtEJEgvl4MRHJQylYpsqbqrpV997z\nsfdaa845em8+tNZ672POudbe555z7z7n1mybuddaY46PPvrHv/176621LqrKRS5ykYtc5Ksr6W0X\n4CIXuchFLvL55ALkF7nIRS7yFZcLkF/kIhe5yFdcLkB+kYtc5CJfcbkA+UUucpGLfMXlAuQXuchF\nLvIVlwuQX+QiF7nIV1wuQH6R37AiIv+wiFQR+URE/rEf8bP/nIjci8if/1E+9yI/nnIB8ot8JURE\n/hkR [...]
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fabdc913990>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "test_net = solver.test_nets[0]\n",
+ "for image_index in range(5):\n",
+ " plt.figure()\n",
+ " plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n",
+ " gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+ " estlist = test_net.blobs['score'].data[image_index, ...] > 0\n",
+ " plt.title('GT: {} \\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\n",
+ " plt.axis('off')"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Multilabel classification on PASCAL VOC using a Python data layer.",
+ "example_name": "Multilabel Classification with Python Data Layer",
+ "include_in_docs": true,
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.11"
+ },
+ "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
new file mode 100644
index 0000000..68e4fa7
--- /dev/null
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -0,0 +1,216 @@
+# imports
+import json
+import time
+import pickle
+import scipy.misc
+import skimage.io
+import caffe
+
+import numpy as np
+import os.path as osp
+
+from xml.dom import minidom
+from random import shuffle
+from threading import Thread
+from PIL import Image
+
+from tools import SimpleTransformer
+
+
+class PascalMultilabelDataLayerSync(caffe.Layer):
+
+ """
+ This is a simple syncronous datalayer for training a multilabel model on
+ PASCAL.
+ """
+
+ def setup(self, bottom, top):
+
+ self.top_names = ['data', 'label']
+
+ # === Read input parameters ===
+
+ # params is a python dictionary with layer parameters.
+ params = eval(self.param_str)
+
+ # Check the paramameters for validity.
+ check_params(params)
+
+ # store input as class variables
+ self.batch_size = params['batch_size']
+
+ # Create a batch loader to load the images.
+ self.batch_loader = BatchLoader(params, None)
+
+ # === reshape tops ===
+ # since we use a fixed input image size, we can shape the data layer
+ # once. Else, we'd have to do it in the reshape call.
+ top[0].reshape(
+ self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
+ # Note the 20 channels (because PASCAL has 20 classes.)
+ top[1].reshape(self.batch_size, 20)
+
+ print_info("PascalMultilabelDataLayerSync", params)
+
+ def forward(self, bottom, top):
+ """
+ Load data.
+ """
+ for itt in range(self.batch_size):
+ # Use the batch loader to load the next image.
+ im, multilabel = self.batch_loader.load_next_image()
+
+ # Add directly to the caffe data layer
+ top[0].data[itt, ...] = im
+ top[1].data[itt, ...] = multilabel
+
+ def reshape(self, bottom, top):
+ """
+ There is no need to reshape the data, since the input is of fixed size
+ (rows and columns)
+ """
+ pass
+
+ def backward(self, top, propagate_down, bottom):
+ """
+ These layers does not back propagate
+ """
+ pass
+
+
+class BatchLoader(object):
+
+ """
+ This class abstracts away the loading of images.
+ Images can either be loaded singly, or in a batch. The latter is used for
+ the asyncronous data layer to preload batches while other processing is
+ performed.
+ """
+
+ def __init__(self, params, result):
+ self.result = result
+ self.batch_size = params['batch_size']
+ self.pascal_root = params['pascal_root']
+ self.im_shape = params['im_shape']
+ # get list of image indexes.
+ list_file = params['split'] + '.txt'
+ self.indexlist = [line.rstrip('\n') for line in open(
+ osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
+ self._cur = 0 # current image
+ # this class does some simple data-manipulations
+ self.transformer = SimpleTransformer()
+
+ print "BatchLoader initialized with {} images".format(
+ len(self.indexlist))
+
+ def load_next_image(self):
+ """
+ Load the next image in a batch.
+ """
+ # Did we finish an epoch?
+ if self._cur == len(self.indexlist):
+ self._cur = 0
+ shuffle(self.indexlist)
+
+ # Load an image
+ index = self.indexlist[self._cur] # Get the image index
+ image_file_name = index + '.jpg'
+ im = np.asarray(Image.open(
+ osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
+ im = scipy.misc.imresize(im, self.im_shape) # resize
+
+ # do a simple horizontal flip as data augmentation
+ flip = np.random.choice(2)*2-1
+ im = im[:, ::flip, :]
+
+ # Load and prepare ground truth
+ multilabel = np.zeros(20).astype(np.float32)
+ anns = load_pascal_annotation(index, self.pascal_root)
+ for label in anns['gt_classes']:
+ # in the multilabel problem we don't care how MANY instances
+ # there are of each class. Only if they are present.
+ # The "-1" is b/c we are not interested in the background
+ # class.
+ multilabel[label - 1] = 1
+
+ self._cur += 1
+ return self.transformer.preprocess(im), multilabel
+
+
+def load_pascal_annotation(index, pascal_root):
+ """
+ This code is borrowed from Ross Girshick's FAST-RCNN code
+ (https://github.com/rbgirshick/fast-rcnn).
+ It parses the PASCAL .xml metadata files.
+ See publication for further details: (http://arxiv.org/abs/1504.08083).
+
+ Thanks Ross!
+
+ """
+ classes = ('__background__', # always index 0
+ 'aeroplane', 'bicycle', 'bird', 'boat',
+ 'bottle', 'bus', 'car', 'cat', 'chair',
+ 'cow', 'diningtable', 'dog', 'horse',
+ 'motorbike', 'person', 'pottedplant',
+ 'sheep', 'sofa', 'train', 'tvmonitor')
+ class_to_ind = dict(zip(classes, xrange(21)))
+
+ filename = osp.join(pascal_root, 'Annotations', index + '.xml')
+ # print 'Loading: {}'.format(filename)
+
+ def get_data_from_tag(node, tag):
+ return node.getElementsByTagName(tag)[0].childNodes[0].data
+
+ with open(filename) as f:
+ data = minidom.parseString(f.read())
+
+ objs = data.getElementsByTagName('object')
+ num_objs = len(objs)
+
+ boxes = np.zeros((num_objs, 4), dtype=np.uint16)
+ gt_classes = np.zeros((num_objs), dtype=np.int32)
+ overlaps = np.zeros((num_objs, 21), dtype=np.float32)
+
+ # Load object bounding boxes into a data frame.
+ for ix, obj in enumerate(objs):
+ # Make pixel indexes 0-based
+ x1 = float(get_data_from_tag(obj, 'xmin')) - 1
+ y1 = float(get_data_from_tag(obj, 'ymin')) - 1
+ x2 = float(get_data_from_tag(obj, 'xmax')) - 1
+ y2 = float(get_data_from_tag(obj, 'ymax')) - 1
+ cls = class_to_ind[
+ str(get_data_from_tag(obj, "name")).lower().strip()]
+ boxes[ix, :] = [x1, y1, x2, y2]
+ gt_classes[ix] = cls
+ overlaps[ix, cls] = 1.0
+
+ overlaps = scipy.sparse.csr_matrix(overlaps)
+
+ return {'boxes': boxes,
+ 'gt_classes': gt_classes,
+ 'gt_overlaps': overlaps,
+ 'flipped': False,
+ 'index': index}
+
+
+def check_params(params):
+ """
+ A utility function to check the parameters for the data layers.
+ """
+ assert 'split' in params.keys(
+ ), 'Params must include split (train, val, or test).'
+
+ required = ['batch_size', 'pascal_root', 'im_shape']
+ for r in required:
+ assert r in params.keys(), 'Params must include {}'.format(r)
+
+
+def print_info(name, params):
+ """
+ Ouput some info regarding the class
+ """
+ print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
+ name,
+ params['split'],
+ params['batch_size'],
+ params['im_shape'])
diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py
new file mode 100644
index 0000000..88b1834
--- /dev/null
+++ b/examples/pycaffe/tools.py
@@ -0,0 +1,121 @@
+import numpy as np
+
+
+class SimpleTransformer:
+
+ """
+ SimpleTransformer is a simple class for preprocessing and deprocessing
+ images for caffe.
+ """
+
+ def __init__(self, mean=[128, 128, 128]):
+ self.mean = np.array(mean, dtype=np.float32)
+ self.scale = 1.0
+
+ def set_mean(self, mean):
+ """
+ Set the mean to subtract for centering the data.
+ """
+ self.mean = mean
+
+ def set_scale(self, scale):
+ """
+ Set the data scaling.
+ """
+ self.scale = scale
+
+ def preprocess(self, im):
+ """
+ preprocess() emulate the pre-processing occuring in the vgg16 caffe
+ prototxt.
+ """
+
+ im = np.float32(im)
+ im = im[:, :, ::-1] # change to BGR
+ im -= self.mean
+ im *= self.scale
+ im = im.transpose((2, 0, 1))
+
+ return im
+
+ def deprocess(self, im):
+ """
+ inverse of preprocess()
+ """
+ im = im.transpose(1, 2, 0)
+ im /= self.scale
+ im += self.mean
+ im = im[:, :, ::-1] # change to RGB
+
+ return np.uint8(im)
+
+
+class CaffeSolver:
+
+ """
+ Caffesolver is a class for creating a solver.prototxt file. It sets default
+ values and can export a solver parameter file.
+ Note that all parameters are stored as strings. Strings variables are
+ stored as strings in strings.
+ """
+
+ def __init__(self, testnet_prototxt_path="testnet.prototxt",
+ trainnet_prototxt_path="trainnet.prototxt", debug=False):
+
+ self.sp = {}
+
+ # critical:
+ self.sp['base_lr'] = '0.001'
+ self.sp['momentum'] = '0.9'
+
+ # speed:
+ self.sp['test_iter'] = '100'
+ self.sp['test_interval'] = '250'
+
+ # looks:
+ self.sp['display'] = '25'
+ self.sp['snapshot'] = '2500'
+ self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string!
+
+ # learning rate policy
+ self.sp['lr_policy'] = '"fixed"'
+
+ # important, but rare:
+ self.sp['gamma'] = '0.1'
+ self.sp['weight_decay'] = '0.0005'
+ self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
+ self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
+
+ # pretty much never change these.
+ self.sp['max_iter'] = '100000'
+ self.sp['test_initialization'] = 'false'
+ self.sp['average_loss'] = '25' # this has to do with the display.
+ self.sp['iter_size'] = '1' # this is for accumulating gradients
+
+ if (debug):
+ self.sp['max_iter'] = '12'
+ self.sp['test_iter'] = '1'
+ self.sp['test_interval'] = '4'
+ self.sp['display'] = '1'
+
+ def add_from_file(self, filepath):
+ """
+ Reads a caffe solver prototxt file and updates the Caffesolver
+ instance parameters.
+ """
+ with open(filepath, 'r') as f:
+ for line in f:
+ if line[0] == '#':
+ continue
+ splitLine = line.split(':')
+ self.sp[splitLine[0].strip()] = splitLine[1].strip()
+
+ def write(self, filepath):
+ """
+ Export solver parameters to INPUT "filepath". Sorted alphabetically.
+ """
+ f = open(filepath, 'w')
+ for key, value in sorted(self.sp.items()):
+ if not(type(value) is str):
+ raise TypeError('All solver parameters must be strings')
+ f.write('%s: %s\n' % (key, value))
diff --git a/examples/siamese/create_mnist_siamese.sh b/examples/siamese/create_mnist_siamese.sh
index 43ad6b1..03adce5 100755
--- a/examples/siamese/create_mnist_siamese.sh
+++ b/examples/siamese/create_mnist_siamese.sh
@@ -1,5 +1,6 @@
#!/usr/bin/env sh
# This script converts the mnist data into leveldb format.
+set -e
EXAMPLES=./build/examples/siamese
DATA=./data/mnist
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 332731b..5d783ba 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -1,10 +1,11 @@
name: "mnist_siamese"
-input: "data"
-input_shape {
- dim: 10000
- dim: 1
- dim: 28
- dim: 28
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param {
+ shape: { dim: 10000 dim: 1 dim: 28 dim: 28 }
+ }
}
layer {
name: "conv1"
diff --git a/examples/siamese/train_mnist_siamese.sh b/examples/siamese/train_mnist_siamese.sh
index 84a30a8..e01ac2c 100755
--- a/examples/siamese/train_mnist_siamese.sh
+++ b/examples/siamese/train_mnist_siamese.sh
@@ -1,5 +1,6 @@
#!/usr/bin/env sh
+set -e
TOOLS=./build/tools
-$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt
+$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt $@
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 6b902a4..3c6a076 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -153,6 +153,11 @@ class Caffe {
static void SetDevice(const int device_id);
// Prints the current GPU status.
static void DeviceQuery();
+ // Check if specified device is available
+ static bool CheckDevice(const int device_id);
+ // Search from start_id to the highest possible device ordinal,
+ // return the ordinal of the first available device.
+ static int FindDevice(const int start_id = 0);
// Parallel training info
inline static int solver_count() { return Get().solver_count_; }
inline static void set_solver_count(int val) { Get().solver_count_ = val; }
diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
new file mode 100644
index 0000000..c4fda12
--- /dev/null
+++ b/include/caffe/layers/crop_layer.hpp
@@ -0,0 +1,76 @@
+#ifndef CAFFE_CROP_LAYER_HPP_
+#define CAFFE_CROP_LAYER_HPP_
+
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes a Blob and crop it, to the shape specified by the second input
+ * Blob, across all dimensions after the specified axis.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+
+template <typename Dtype>
+class CropLayer : public Layer<Dtype> {
+ public:
+ explicit CropLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "Crop"; }
+ virtual inline int ExactNumBottomBlobs() const { return 2; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ vector<int> offsets;
+
+ private:
+ // Recursive copy function.
+ void crop_copy(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top,
+ const vector<int>& offsets,
+ vector<int> indices,
+ int cur_dim,
+ const Dtype* src_data,
+ Dtype* dest_data,
+ bool is_forward);
+
+ // Recursive copy function: this is similar to crop_copy() but loops over all
+ // but the last two dimensions to allow for ND cropping while still relying on
+ // a CUDA kernel for the innermost two dimensions for performance reasons. An
+ // alterantive implementation could rely on the kernel more by passing
+ // offsets, but this is problematic because of its variable length.
+ // Since in the standard (N,C,W,H) case N,C are usually not cropped a speedup
+ // could be achieved by not looping the application of the copy_kernel around
+ // these dimensions.
+ void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top,
+ const vector<int>& offsets,
+ vector<int> indices,
+ int cur_dim,
+ const Dtype* src_data,
+ Dtype* dest_data,
+ bool is_forward);
+};
+} // namespace caffe
+
+#endif // CAFFE_CROP_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_relu_layer.hpp b/include/caffe/layers/cudnn_relu_layer.hpp
index e01f568..a1cb29e 100644
--- a/include/caffe/layers/cudnn_relu_layer.hpp
+++ b/include/caffe/layers/cudnn_relu_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
cudnnHandle_t handle_;
cudnnTensorDescriptor_t bottom_desc_;
cudnnTensorDescriptor_t top_desc_;
+ cudnnActivationDescriptor_t activ_desc_;
};
#endif
diff --git a/include/caffe/layers/cudnn_sigmoid_layer.hpp b/include/caffe/layers/cudnn_sigmoid_layer.hpp
index 9c59795..7b3486f 100644
--- a/include/caffe/layers/cudnn_sigmoid_layer.hpp
+++ b/include/caffe/layers/cudnn_sigmoid_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
cudnnHandle_t handle_;
cudnnTensorDescriptor_t bottom_desc_;
cudnnTensorDescriptor_t top_desc_;
+ cudnnActivationDescriptor_t activ_desc_;
};
#endif
diff --git a/include/caffe/layers/cudnn_tanh_layer.hpp b/include/caffe/layers/cudnn_tanh_layer.hpp
index c0f0053..59e758d 100644
--- a/include/caffe/layers/cudnn_tanh_layer.hpp
+++ b/include/caffe/layers/cudnn_tanh_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
cudnnHandle_t handle_;
cudnnTensorDescriptor_t bottom_desc_;
cudnnTensorDescriptor_t top_desc_;
+ cudnnActivationDescriptor_t activ_desc_;
};
#endif
diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp
index 250576a..18d0d61 100644
--- a/include/caffe/layers/inner_product_layer.hpp
+++ b/include/caffe/layers/inner_product_layer.hpp
@@ -44,6 +44,7 @@ class InnerProductLayer : public Layer<Dtype> {
int N_;
bool bias_term_;
Blob<Dtype> bias_multiplier_;
+ bool transpose_; ///< if true, assume transposed weights
};
} // namespace caffe
diff --git a/include/caffe/layers/input_layer.hpp b/include/caffe/layers/input_layer.hpp
new file mode 100644
index 0000000..f447267
--- /dev/null
+++ b/include/caffe/layers/input_layer.hpp
@@ -0,0 +1,44 @@
+#ifndef CAFFE_INPUT_LAYER_HPP_
+#define CAFFE_INPUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net by assigning tops directly.
+ *
+ * This data layer is a container that merely holds the data assigned to it;
+ * forward, backward, and reshape are all no-ops.
+ */
+template <typename Dtype>
+class InputLayer : public Layer<Dtype> {
+ public:
+ explicit InputLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ // Data layers should be shared by multiple solvers in parallel
+ virtual inline bool ShareInParallel() const { return true; }
+ // Data layers have no bottoms, so reshaping is trivial.
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+
+ virtual inline const char* type() const { return "Input"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {}
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+};
+
+} // namespace caffe
+
+#endif // CAFFE_INPUT_LAYER_HPP_
diff --git a/include/caffe/layers/lstm_layer.hpp b/include/caffe/layers/lstm_layer.hpp
new file mode 100644
index 0000000..a0e67c9
--- /dev/null
+++ b/include/caffe/layers/lstm_layer.hpp
@@ -0,0 +1,154 @@
+#ifndef CAFFE_LSTM_LAYER_HPP_
+#define CAFFE_LSTM_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief Processes sequential inputs using a "Long Short-Term Memory" (LSTM)
+ * [1] style recurrent neural network (RNN). Implemented by unrolling
+ * the LSTM computation through time.
+ *
+ * The specific architecture used in this implementation is as described in
+ * "Learning to Execute" [2], reproduced below:
+ * i_t := \sigmoid[ W_{hi} * h_{t-1} + W_{xi} * x_t + b_i ]
+ * f_t := \sigmoid[ W_{hf} * h_{t-1} + W_{xf} * x_t + b_f ]
+ * o_t := \sigmoid[ W_{ho} * h_{t-1} + W_{xo} * x_t + b_o ]
+ * g_t := \tanh[ W_{hg} * h_{t-1} + W_{xg} * x_t + b_g ]
+ * c_t := (f_t .* c_{t-1}) + (i_t .* g_t)
+ * h_t := o_t .* \tanh[c_t]
+ * In the implementation, the i, f, o, and g computations are performed as a
+ * single inner product.
+ *
+ * Notably, this implementation lacks the "diagonal" gates, as used in the
+ * LSTM architectures described by Alex Graves [3] and others.
+ *
+ * [1] Hochreiter, Sepp, and Schmidhuber, Jürgen. "Long short-term memory."
+ * Neural Computation 9, no. 8 (1997): 1735-1780.
+ *
+ * [2] Zaremba, Wojciech, and Sutskever, Ilya. "Learning to execute."
+ * arXiv preprint arXiv:1410.4615 (2014).
+ *
+ * [3] Graves, Alex. "Generating sequences with recurrent neural networks."
+ * arXiv preprint arXiv:1308.0850 (2013).
+ */
+template <typename Dtype>
+class LSTMLayer : public RecurrentLayer<Dtype> {
+ public:
+ explicit LSTMLayer(const LayerParameter& param)
+ : RecurrentLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "LSTM"; }
+
+ protected:
+ virtual void FillUnrolledNet(NetParameter* net_param) const;
+ virtual void RecurrentInputBlobNames(vector<string>* names) const;
+ virtual void RecurrentOutputBlobNames(vector<string>* names) const;
+ virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
+ virtual void OutputBlobNames(vector<string>* names) const;
+};
+
+/**
+ * @brief A helper for LSTMLayer: computes a single timestep of the
+ * non-linearity of the LSTM, producing the updated cell and hidden
+ * states.
+ */
+template <typename Dtype>
+class LSTMUnitLayer : public Layer<Dtype> {
+ public:
+ explicit LSTMUnitLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ virtual inline const char* type() const { return "LSTMUnit"; }
+ virtual inline int ExactNumBottomBlobs() const { return 3; }
+ virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ virtual inline bool AllowForceBackward(const int bottom_index) const {
+ // Can't propagate to sequence continuation indicators.
+ return bottom_index != 2;
+ }
+
+ protected:
+ /**
+ * @param bottom input Blob vector (length 3)
+ * -# @f$ (1 \times N \times D) @f$
+ * the previous timestep cell state @f$ c_{t-1} @f$
+ * -# @f$ (1 \times N \times 4D) @f$
+ * the "gate inputs" @f$ [i_t', f_t', o_t', g_t'] @f$
+ * -# @f$ (1 \times N) @f$
+ * the sequence continuation indicators @f$ \delta_t @f$
+ * @param top output Blob vector (length 2)
+ * -# @f$ (1 \times N \times D) @f$
+ * the updated cell state @f$ c_t @f$, computed as:
+ * i_t := \sigmoid[i_t']
+ * f_t := \sigmoid[f_t']
+ * o_t := \sigmoid[o_t']
+ * g_t := \tanh[g_t']
+ * c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
+ * -# @f$ (1 \times N \times D) @f$
+ * the updated hidden state @f$ h_t @f$, computed as:
+ * h_t := o_t .* \tanh[c_t]
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+
+ /**
+ * @brief Computes the error gradient w.r.t. the LSTMUnit inputs.
+ *
+ * @param top output Blob vector (length 2), providing the error gradient with
+ * respect to the outputs
+ * -# @f$ (1 \times N \times D) @f$:
+ * containing error gradients @f$ \frac{\partial E}{\partial c_t} @f$
+ * with respect to the updated cell state @f$ c_t @f$
+ * -# @f$ (1 \times N \times D) @f$:
+ * containing error gradients @f$ \frac{\partial E}{\partial h_t} @f$
+ * with respect to the updated cell state @f$ h_t @f$
+ * @param propagate_down see Layer::Backward.
+ * @param bottom input Blob vector (length 3), into which the error gradients
+ * with respect to the LSTMUnit inputs @f$ c_{t-1} @f$ and the gate
+ * inputs are computed. Computatation of the error gradients w.r.t.
+ * the sequence indicators is not implemented.
+ * -# @f$ (1 \times N \times D) @f$
+ * the error gradient w.r.t. the previous timestep cell state
+ * @f$ c_{t-1} @f$
+ * -# @f$ (1 \times N \times 4D) @f$
+ * the error gradient w.r.t. the "gate inputs"
+ * @f$ [
+ * \frac{\partial E}{\partial i_t}
+ * \frac{\partial E}{\partial f_t}
+ * \frac{\partial E}{\partial o_t}
+ * \frac{\partial E}{\partial g_t}
+ * ] @f$
+ * -# @f$ (1 \times 1 \times N) @f$
+ * the gradient w.r.t. the sequence continuation indicators
+ * @f$ \delta_t @f$ is currently not computed.
+ */
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+ virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// @brief The hidden and output dimension.
+ int hidden_dim_;
+ Blob<Dtype> X_acts_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_LSTM_LAYER_HPP_
diff --git a/include/caffe/layers/parameter_layer.hpp b/include/caffe/layers/parameter_layer.hpp
new file mode 100644
index 0000000..188b92a
--- /dev/null
+++ b/include/caffe/layers/parameter_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_PARAMETER_LAYER_HPP_
+#define CAFFE_PARAMETER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class ParameterLayer : public Layer<Dtype> {
+ public:
+ explicit ParameterLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ if (this->blobs_.size() > 0) {
+ LOG(INFO) << "Skipping parameter initialization";
+ } else {
+ this->blobs_.resize(1);
+ this->blobs_[0].reset(new Blob<Dtype>());
+ this->blobs_[0]->Reshape(this->layer_param_.parameter_param().shape());
+ }
+ top[0]->Reshape(this->layer_param_.parameter_param().shape());
+ }
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) { }
+ virtual inline const char* type() const { return "Parameter"; }
+ virtual inline int ExactNumBottomBlobs() const { return 0; }
+ virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ top[0]->ShareData(*(this->blobs_[0]));
+ top[0]->ShareDiff(*(this->blobs_[0]));
+ }
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom)
+ { }
+};
+
+} // namespace caffe
+
+#endif
diff --git a/include/caffe/layers/python_layer.hpp b/include/caffe/layers/python_layer.hpp
index b839d52..66dbbdf 100644
--- a/include/caffe/layers/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -26,6 +26,7 @@ class PythonLayer : public Layer<Dtype> {
}
self_.attr("param_str") = bp::str(
this->layer_param_.python_param().param_str());
+ self_.attr("phase") = static_cast<int>(this->phase_);
self_.attr("setup")(bottom, top);
}
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
diff --git a/include/caffe/layers/recurrent_layer.hpp b/include/caffe/layers/recurrent_layer.hpp
new file mode 100644
index 0000000..ca17371
--- /dev/null
+++ b/include/caffe/layers/recurrent_layer.hpp
@@ -0,0 +1,187 @@
+#ifndef CAFFE_RECURRENT_LAYER_HPP_
+#define CAFFE_RECURRENT_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief An abstract class for implementing recurrent behavior inside of an
+ * unrolled network. This Layer type cannot be instantiated -- instead,
+ * you should use one of its implementations which defines the recurrent
+ * architecture, such as RNNLayer or LSTMLayer.
+ */
+template <typename Dtype>
+class RecurrentLayer : public Layer<Dtype> {
+ public:
+ explicit RecurrentLayer(const LayerParameter& param)
+ : Layer<Dtype>(param) {}
+ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Reset();
+
+ virtual inline const char* type() const { return "Recurrent"; }
+ virtual inline int MinBottomBlobs() const {
+ int min_bottoms = 2;
+ if (this->layer_param_.recurrent_param().expose_hidden()) {
+ vector<string> inputs;
+ this->RecurrentInputBlobNames(&inputs);
+ min_bottoms += inputs.size();
+ }
+ return min_bottoms;
+ }
+ virtual inline int MaxBottomBlobs() const { return MinBottomBlobs() + 1; }
+ virtual inline int ExactNumTopBlobs() const {
+ int num_tops = 1;
+ if (this->layer_param_.recurrent_param().expose_hidden()) {
+ vector<string> outputs;
+ this->RecurrentOutputBlobNames(&outputs);
+ num_tops += outputs.size();
+ }
+ return num_tops;
+ }
+
+ virtual inline bool AllowForceBackward(const int bottom_index) const {
+ // Can't propagate to sequence continuation indicators.
+ return bottom_index != 1;
+ }
+
+ protected:
+ /**
+ * @brief Fills net_param with the recurrent network architecture. Subclasses
+ * should define this -- see RNNLayer and LSTMLayer for examples.
+ */
+ virtual void FillUnrolledNet(NetParameter* net_param) const = 0;
+
+ /**
+ * @brief Fills names with the names of the 0th timestep recurrent input
+ * Blob&s. Subclasses should define this -- see RNNLayer and LSTMLayer
+ * for examples.
+ */
+ virtual void RecurrentInputBlobNames(vector<string>* names) const = 0;
+
+ /**
+ * @brief Fills shapes with the shapes of the recurrent input Blob&s.
+ * Subclasses should define this -- see RNNLayer and LSTMLayer
+ * for examples.
+ */
+ virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const = 0;
+
+ /**
+ * @brief Fills names with the names of the Tth timestep recurrent output
+ * Blob&s. Subclasses should define this -- see RNNLayer and LSTMLayer
+ * for examples.
+ */
+ virtual void RecurrentOutputBlobNames(vector<string>* names) const = 0;
+
+ /**
+ * @brief Fills names with the names of the output blobs, concatenated across
+ * all timesteps. Should return a name for each top Blob.
+ * Subclasses should define this -- see RNNLayer and LSTMLayer for
+ * examples.
+ */
+ virtual void OutputBlobNames(vector<string>* names) const = 0;
+
+ /**
+ * @param bottom input Blob vector (length 2-3)
+ *
+ * -# @f$ (T \times N \times ...) @f$
+ * the time-varying input @f$ x @f$. After the first two axes, whose
+ * dimensions must correspond to the number of timesteps @f$ T @f$ and
+ * the number of independent streams @f$ N @f$, respectively, its
+ * dimensions may be arbitrary. Note that the ordering of dimensions --
+ * @f$ (T \times N \times ...) @f$, rather than
+ * @f$ (N \times T \times ...) @f$ -- means that the @f$ N @f$
+ * independent input streams must be "interleaved".
+ *
+ * -# @f$ (T \times N) @f$
+ * the sequence continuation indicators @f$ \delta @f$.
+ * These inputs should be binary (0 or 1) indicators, where
+ * @f$ \delta_{t,n} = 0 @f$ means that timestep @f$ t @f$ of stream
+ * @f$ n @f$ is the beginning of a new sequence, and hence the previous
+ * hidden state @f$ h_{t-1} @f$ is multiplied by @f$ \delta_t = 0 @f$
+ * and has no effect on the cell's output at timestep @f$ t @f$, and
+ * a value of @f$ \delta_{t,n} = 1 @f$ means that timestep @f$ t @f$ of
+ * stream @f$ n @f$ is a continuation from the previous timestep
+ * @f$ t-1 @f$, and the previous hidden state @f$ h_{t-1} @f$ affects the
+ * updated hidden state and output.
+ *
+ * -# @f$ (N \times ...) @f$ (optional)
+ * the static (non-time-varying) input @f$ x_{static} @f$.
+ * After the first axis, whose dimension must be the number of
+ * independent streams, its dimensions may be arbitrary.
+ * This is mathematically equivalent to using a time-varying input of
+ * @f$ x'_t = [x_t; x_{static}] @f$ -- i.e., tiling the static input
+ * across the @f$ T @f$ timesteps and concatenating with the time-varying
+ * input. Note that if this input is used, all timesteps in a single
+ * batch within a particular one of the @f$ N @f$ streams must share the
+ * same static input, even if the sequence continuation indicators
+ * suggest that difference sequences are ending and beginning within a
+ * single batch. This may require padding and/or truncation for uniform
+ * length.
+ *
+ * @param top output Blob vector (length 1)
+ * -# @f$ (T \times N \times D) @f$
+ * the time-varying output @f$ y @f$, where @f$ D @f$ is
+ * <code>recurrent_param.num_output()</code>.
+ * Refer to documentation for particular RecurrentLayer implementations
+ * (such as RNNLayer and LSTMLayer) for the definition of @f$ y @f$.
+ */
+ virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top);
+ virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ /// @brief A Net to implement the Recurrent functionality.
+ shared_ptr<Net<Dtype> > unrolled_net_;
+
+ /// @brief The number of independent streams to process simultaneously.
+ int N_;
+
+ /**
+ * @brief The number of timesteps in the layer's input, and the number of
+ * timesteps over which to backpropagate through time.
+ */
+ int T_;
+
+ /// @brief Whether the layer has a "static" input copied across all timesteps.
+ bool static_input_;
+
+ /**
+ * @brief The last layer to run in the network. (Any later layers are losses
+ * added to force the recurrent net to do backprop.)
+ */
+ int last_layer_index_;
+
+ /**
+ * @brief Whether the layer's hidden state at the first and last timesteps
+ * are layer inputs and outputs, respectively.
+ */
+ bool expose_hidden_;
+
+ vector<Blob<Dtype>* > recur_input_blobs_;
+ vector<Blob<Dtype>* > recur_output_blobs_;
+ vector<Blob<Dtype>* > output_blobs_;
+ Blob<Dtype>* x_input_blob_;
+ Blob<Dtype>* x_static_input_blob_;
+ Blob<Dtype>* cont_input_blob_;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_RECURRENT_LAYER_HPP_
diff --git a/include/caffe/layers/rnn_layer.hpp b/include/caffe/layers/rnn_layer.hpp
new file mode 100644
index 0000000..6dce238
--- /dev/null
+++ b/include/caffe/layers/rnn_layer.hpp
@@ -0,0 +1,47 @@
+#ifndef CAFFE_RNN_LAYER_HPP_
+#define CAFFE_RNN_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief Processes time-varying inputs using a simple recurrent neural network
+ * (RNN). Implemented as a network unrolling the RNN computation in time.
+ *
+ * Given time-varying inputs @f$ x_t @f$, computes hidden state @f$
+ * h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ]
+ * @f$, and outputs @f$
+ * o_t := \tanh[ W_{ho} h_t + b_o ]
+ * @f$.
+ */
+template <typename Dtype>
+class RNNLayer : public RecurrentLayer<Dtype> {
+ public:
+ explicit RNNLayer(const LayerParameter& param)
+ : RecurrentLayer<Dtype>(param) {}
+
+ virtual inline const char* type() const { return "RNN"; }
+
+ protected:
+ virtual void FillUnrolledNet(NetParameter* net_param) const;
+ virtual void RecurrentInputBlobNames(vector<string>* names) const;
+ virtual void RecurrentOutputBlobNames(vector<string>* names) const;
+ virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
+ virtual void OutputBlobNames(vector<string>* names) const;
+};
+
+} // namespace caffe
+
+#endif // CAFFE_RNN_LAYER_HPP_
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 543133e..493bdf2 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -25,6 +25,7 @@ class Net {
public:
explicit Net(const NetParameter& param, const Net* root_net = NULL);
explicit Net(const string& param_file, Phase phase,
+ const int level = 0, const vector<string>* stages = NULL,
const Net* root_net = NULL);
virtual ~Net() {}
@@ -32,11 +33,16 @@ class Net {
void Init(const NetParameter& param);
/**
- * @brief Run Forward with the input Blob%s already fed separately.
+ * @brief Run Forward and return the result.
*
- * You can get the input blobs using input_blobs().
*/
- const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+ const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
+ /// @brief DEPRECATED; use Forward() instead.
+ const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
+ LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
+ << "will be removed in a future version. Use Forward().";
+ return Forward(loss);
+ }
/**
* The From and To variants of Forward and Backward operate on the
@@ -49,14 +55,9 @@ class Net {
Dtype ForwardFromTo(int start, int end);
Dtype ForwardFrom(int start);
Dtype ForwardTo(int end);
- /// @brief Run forward using a set of bottom blobs, and return the result.
+ /// @brief DEPRECATED; set input blobs then use Forward() instead.
const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
Dtype* loss = NULL);
- /**
- * @brief Run forward using a serialized BlobProtoVector and return the
- * result as a serialized BlobProtoVector
- */
- string Forward(const string& input_blob_protos, Dtype* loss = NULL);
/**
* @brief Zeroes out the diffs of all net parameters.
@@ -82,9 +83,9 @@ class Net {
*/
void Reshape();
- Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
+ Dtype ForwardBackward() {
Dtype loss;
- Forward(bottom, &loss);
+ Forward(&loss);
Backward();
return loss;
}
@@ -229,7 +230,7 @@ class Net {
protected:
// Helpers for Init.
- /// @brief Append a new input or top blob to the net.
+ /// @brief Append a new top blob to the net.
void AppendTop(const NetParameter& param, const int layer_id,
const int top_id, set<string>* available_blobs,
map<string, int>* blob_name_to_idx);
@@ -241,8 +242,6 @@ class Net {
void AppendParam(const NetParameter& param, const int layer_id,
const int param_id);
- /// @brief Helper for displaying debug info in Forward about input Blobs.
- void InputDebugInfo(const int layer_id);
/// @brief Helper for displaying debug info in Forward.
void ForwardDebugInfo(const int layer_id);
/// @brief Helper for displaying debug info in Backward.
diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp
index 85fc2b5..6c496c8 100644
--- a/include/caffe/parallel.hpp
+++ b/include/caffe/parallel.hpp
@@ -93,7 +93,10 @@ class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
return solver_;
}
- void run(const vector<int>& gpus);
+ void Run(const vector<int>& gpus);
+ void Prepare(const vector<int>& gpus,
+ vector<shared_ptr<P2PSync<Dtype> > >* syncs);
+ inline const int initial_iter() const { return initial_iter_; }
protected:
void on_start();
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index 8a7e17c..a7d8dbb 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -91,8 +91,13 @@ template <typename Dtype>
inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
int n, int c, int h, int w) {
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
- n, c, h, w));
+ CUDNN_TENSOR_NCHW, n, c, h, w));
+#else
+ CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
+ CUDNN_TENSOR_NCHW, n, c, h, w));
+#endif
}
template <typename Dtype>
@@ -123,8 +128,21 @@ inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
LOG(FATAL) << "Unknown pooling method.";
}
CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
- CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode, h, w,
- pad_h, pad_w, stride_h, stride_w));
+#if CUDNN_VERSION_MIN(5, 0, 0)
+ CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
+ CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
+#else
+ CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
+ CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
+#endif
+}
+
+template <typename Dtype>
+inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
+ cudnnActivationMode_t mode) {
+ CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
+ CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
+ CUDNN_PROPAGATE_NAN, Dtype(0)));
}
} // namespace cudnn
diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
index 4e1568a..ee37032 100644
--- a/include/caffe/util/db_lmdb.hpp
+++ b/include/caffe/util/db_lmdb.hpp
@@ -3,6 +3,7 @@
#define CAFFE_UTIL_DB_LMDB_HPP
#include <string>
+#include <vector>
#include "lmdb.h"
@@ -54,14 +55,16 @@ class LMDBCursor : public Cursor {
class LMDBTransaction : public Transaction {
public:
- explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
- : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+ explicit LMDBTransaction(MDB_env* mdb_env)
+ : mdb_env_(mdb_env) { }
virtual void Put(const string& key, const string& value);
- virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+ virtual void Commit();
private:
- MDB_dbi* mdb_dbi_;
- MDB_txn* mdb_txn_;
+ MDB_env* mdb_env_;
+ vector<string> keys, values;
+
+ void DoubleMapSize();
DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
};
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index c94bb3c..14e1936 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -59,6 +59,12 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
+// Return true iff the Net contains input fields.
+bool NetNeedsInputUpgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade input fields into layers.
+void UpgradeNetInput(NetParameter* net_param);
+
// Return true iff the solver contains any old solver_type specified as enums
bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index ff10daa..45b2b0e 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -1,10 +1,9 @@
name: "AlexNet"
-input: "data"
-input_shape {
- dim: 10
- dim: 3
- dim: 227
- dim: 227
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
name: "conv1"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index 1f90ee2..50b54a9 100644
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -1,10 +1,9 @@
name: "GoogleNet"
-input: "data"
-input_shape {
- dim: 10
- dim: 3
- dim: 224
- dim: 224
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
}
layer {
name: "conv1/7x7_s2"
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 127f1e2..907116e 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -1,10 +1,9 @@
name: "CaffeNet"
-input: "data"
-input_shape {
- dim: 10
- dim: 3
- dim: 227
- dim: 227
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
name: "conv1"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ae1df96..e330a77 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -1,10 +1,9 @@
name: "R-CNN-ilsvrc13"
-input: "data"
-input_shape {
- dim: 10
- dim: 3
- dim: 227
- dim: 227
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
name: "conv1"
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index 0f07e47..b8f99c7 100644
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -1,10 +1,9 @@
name: "FlickrStyleCaffeNet"
-input: "data"
-input_shape {
- dim: 10
- dim: 3
- dim: 227
- dim: 227
+layer {
+ name: "data"
+ type: "Input"
+ top: "data"
+ input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
name: "conv1"
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index a226414..bf492a2 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -22,13 +22,19 @@ if(UNIX OR APPLE)
endif()
# ---[ Install
-file(GLOB files1 *.py requirements.txt)
-install(FILES ${files1} DESTINATION python)
-
-file(GLOB files2 caffe/*.py)
-install(FILES ${files2} DESTINATION python/caffe)
+# scripts
+file(GLOB python_files *.py requirements.txt)
+install(FILES ${python_files} DESTINATION python)
+
+# module
+install(DIRECTORY caffe
+ DESTINATION python
+ FILES_MATCHING
+ PATTERN "*.py"
+ PATTERN "ilsvrc_2012_mean.npy"
+ PATTERN "test" EXCLUDE
+ )
+
+# _caffe.so
install(TARGETS pycaffe DESTINATION python/caffe)
-install(DIRECTORY caffe/imagenet caffe/proto caffe/test DESTINATION python/caffe)
-
-
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index e2881b8..35868a4 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
from ._caffe import __version__
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 12a5745..bdee75a 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -26,6 +26,19 @@
#define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
#endif
+/* Fix to avoid registration warnings in pycaffe (#3960) */
+#define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \
+ const boost::python::type_info info = \
+ boost::python::type_id<shared_ptr<PTR > >(); \
+ const boost::python::converter::registration* reg = \
+ boost::python::converter::registry::query(info); \
+ if (reg == NULL) { \
+ bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+ } else if ((*reg).m_to_python == NULL) { \
+ bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+ } \
+} while (0)
+
namespace bp = boost::python;
namespace caffe {
@@ -38,6 +51,8 @@ const int NPY_DTYPE = NPY_FLOAT32;
void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
+void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }
+
// For convenience, check that input files can be opened, and raise an
// exception that boost will send to Python if not (caffe could still crash
// later if the input files are disturbed before they are actually used, but
@@ -73,19 +88,42 @@ void CheckContiguousArray(PyArrayObject* arr, string name,
}
}
-// Net constructor for passing phase as int
-shared_ptr<Net<Dtype> > Net_Init(
- string param_file, int phase) {
- CheckFile(param_file);
+// Net constructor
+shared_ptr<Net<Dtype> > Net_Init(string network_file, int phase,
+ const int level, const bp::object& stages,
+ const bp::object& weights) {
+ CheckFile(network_file);
+
+ // Convert stages from list to vector
+ vector<string> stages_vector;
+ if (!stages.is_none()) {
+ for (int i = 0; i < len(stages); i++) {
+ stages_vector.push_back(bp::extract<string>(stages[i]));
+ }
+ }
+
+ // Initialize net
+ shared_ptr<Net<Dtype> > net(new Net<Dtype>(network_file,
+ static_cast<Phase>(phase), level, &stages_vector));
+
+ // Load weights
+ if (!weights.is_none()) {
+ std::string weights_file_str = bp::extract<std::string>(weights);
+ CheckFile(weights_file_str);
+ net->CopyTrainedLayersFrom(weights_file_str);
+ }
- shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
- static_cast<Phase>(phase)));
return net;
}
-// Net construct-and-load convenience constructor
+// Legacy Net construct-and-load convenience constructor
shared_ptr<Net<Dtype> > Net_Init_Load(
string param_file, string pretrained_param_file, int phase) {
+ LOG(WARNING) << "DEPRECATION WARNING - deprecated use of Python interface";
+ LOG(WARNING) << "Use this instead (with the named \"weights\""
+ << " parameter):";
+ LOG(WARNING) << "Net('" << param_file << "', " << phase
+ << ", weights='" << pretrained_param_file << "')";
CheckFile(param_file);
CheckFile(pretrained_param_file);
@@ -101,6 +139,14 @@ void Net_Save(const Net<Dtype>& net, string filename) {
WriteProtoToBinaryFile(net_param, filename.c_str());
}
+void Net_SaveHDF5(const Net<Dtype>& net, string filename) {
+ net.ToHDF5(filename);
+}
+
+void Net_LoadHDF5(Net<Dtype>* net, string filename) {
+ net->CopyTrainedLayersFromHDF5(filename.c_str());
+}
+
void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
bp::object labels_obj) {
// check that this network has an input MemoryDataLayer
@@ -207,6 +253,27 @@ bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
return bp::object();
}
+template<typename Dtype>
+class PythonCallback: public Solver<Dtype>::Callback {
+ protected:
+ bp::object on_start_, on_gradients_ready_;
+
+ public:
+ PythonCallback(bp::object on_start, bp::object on_gradients_ready)
+ : on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }
+ virtual void on_gradients_ready() {
+ on_gradients_ready_();
+ }
+ virtual void on_start() {
+ on_start_();
+ }
+};
+template<typename Dtype>
+void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
+ bp::object on_gradients_ready) {
+ solver->add_callback(new PythonCallback<Dtype>(on_start, on_gradients_ready));
+}
+
BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
BOOST_PYTHON_MODULE(_caffe) {
@@ -218,17 +285,24 @@ BOOST_PYTHON_MODULE(_caffe) {
// Caffe utility functions
bp::def("set_mode_cpu", &set_mode_cpu);
bp::def("set_mode_gpu", &set_mode_gpu);
+ bp::def("set_random_seed", &set_random_seed);
bp::def("set_device", &Caffe::SetDevice);
bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
bp::no_init)
- .def("__init__", bp::make_constructor(&Net_Init))
+ // Constructor
+ .def("__init__", bp::make_constructor(&Net_Init,
+ bp::default_call_policies(), (bp::arg("network_file"), "phase",
+ bp::arg("level")=0, bp::arg("stages")=bp::object(),
+ bp::arg("weights")=bp::object())))
+ // Legacy constructor
.def("__init__", bp::make_constructor(&Net_Init_Load))
.def("_forward", &Net<Dtype>::ForwardFromTo)
.def("_backward", &Net<Dtype>::BackwardFromTo)
.def("reshape", &Net<Dtype>::Reshape)
+ .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
// The cast is to select a particular overload.
.def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
&Net<Dtype>::CopyTrainedLayersFrom))
@@ -254,7 +328,10 @@ BOOST_PYTHON_MODULE(_caffe) {
bp::return_value_policy<bp::copy_const_reference>()))
.def("_set_input_arrays", &Net_SetInputArrays,
bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
- .def("save", &Net_Save);
+ .def("save", &Net_Save)
+ .def("save_hdf5", &Net_SaveHDF5)
+ .def("load_hdf5", &Net_LoadHDF5);
+ BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
"Blob", bp::no_init)
@@ -274,6 +351,7 @@ BOOST_PYTHON_MODULE(_caffe) {
NdarrayCallPolicies()))
.add_property("diff", bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
NdarrayCallPolicies()));
+ BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);
bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
@@ -282,7 +360,7 @@ BOOST_PYTHON_MODULE(_caffe) {
.def("setup", &Layer<Dtype>::LayerSetUp)
.def("reshape", &Layer<Dtype>::Reshape)
.add_property("type", bp::make_function(&Layer<Dtype>::type));
- bp::register_ptr_to_python<shared_ptr<Layer<Dtype> > >();
+ BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);
bp::class_<LayerParameter>("LayerParameter", bp::no_init);
@@ -292,11 +370,13 @@ BOOST_PYTHON_MODULE(_caffe) {
.add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
bp::return_internal_reference<>()))
.add_property("iter", &Solver<Dtype>::iter)
+ .def("add_callback", &Solver_add_callback<Dtype>)
.def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
&Solver<Dtype>::Solve), SolveOverloads())
.def("step", &Solver<Dtype>::Step)
.def("restore", &Solver<Dtype>::Restore)
.def("snapshot", &Solver<Dtype>::Snapshot);
+ BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 537193d..ea29fed 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -79,6 +79,7 @@ class Classifier(caffe.Net):
-self.crop_dims / 2.0,
self.crop_dims / 2.0
])
+ crop = crop.astype(int)
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py
new file mode 100644
index 0000000..a3413cf
--- /dev/null
+++ b/python/caffe/coord_map.py
@@ -0,0 +1,185 @@
+"""
+Determine spatial relationships between layers to relate their coordinates.
+Coordinates are mapped from input-to-output (forward), but can
+be mapped output-to-input (backward) by the inverse mapping too.
+This helps crop and align feature maps among other uses.
+"""
+
+from __future__ import division
+import numpy as np
+from caffe import layers as L
+
+PASS_THROUGH_LAYERS = ['AbsVal', 'BatchNorm', 'Bias', 'BNLL', 'Dropout',
+ 'Eltwise', 'ELU', 'Log', 'LRN', 'Exp', 'MVN', 'Power',
+ 'ReLU', 'PReLU', 'Scale', 'Sigmoid', 'Split', 'TanH',
+ 'Threshold']
+
+
+def conv_params(fn):
+ """
+ Extract the spatial parameters that determine the coordinate mapping:
+ kernel size, stride, padding, and dilation.
+
+ Implementation detail: Convolution, Deconvolution, and Im2col layers
+ define these in the convolution_param message, while Pooling has its
+ own fields in pooling_param. This method deals with these details to
+ extract canonical parameters.
+ """
+ params = fn.params.get('convolution_param', fn.params)
+ axis = params.get('axis', 1)
+ ks = np.array(params['kernel_size'], ndmin=1)
+ dilation = np.array(params.get('dilation', 1), ndmin=1)
+ assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
+ 'stride_w'} & set(fn.params)) == 0, \
+ 'cropping does not support legacy _h/_w params'
+ return (axis, np.array(params.get('stride', 1), ndmin=1),
+ (ks - 1) * dilation + 1,
+ np.array(params.get('pad', 0), ndmin=1))
+
+
+def crop_params(fn):
+ """
+ Extract the crop layer parameters with defaults.
+ """
+ params = fn.params.get('crop_param', fn.params)
+ axis = params.get('axis', 2) # default to spatial crop for N, C, H, W
+ offset = np.array(params.get('offset', 0), ndmin=1)
+ return (axis, offset)
+
+
+class UndefinedMapException(Exception):
+ """
+ Exception raised for layers that do not have a defined coordinate mapping.
+ """
+ pass
+
+
+def coord_map(fn):
+ """
+ Define the coordinate mapping by its
+ - axis
+ - scale: output coord[i * scale] <- input_coord[i]
+ - shift: output coord[i] <- output_coord[i + shift]
+ s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
+ (None, 1, 0) since it is independent of axis and does not transform coords.
+ """
+ if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
+ axis, stride, ks, pad = conv_params(fn)
+ return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
+ elif fn.type_name == 'Deconvolution':
+ axis, stride, ks, pad = conv_params(fn)
+ return axis, stride, (ks - 1) / 2 - pad
+ elif fn.type_name in PASS_THROUGH_LAYERS:
+ return None, 1, 0
+ elif fn.type_name == 'Crop':
+ axis, offset = crop_params(fn)
+ axis -= 1 # -1 for last non-coordinate dim.
+ return axis, 1, - offset
+ else:
+ raise UndefinedMapException
+
+
+class AxisMismatchException(Exception):
+ """
+ Exception raised for mappings with incompatible axes.
+ """
+ pass
+
+
+def compose(base_map, next_map):
+ """
+ Compose a base coord map with scale a1, shift b1 with a further coord map
+ with scale a2, shift b2. The scales multiply and the further shift, b2,
+ is scaled by base coord scale a1.
+ """
+ ax1, a1, b1 = base_map
+ ax2, a2, b2 = next_map
+ if ax1 is None:
+ ax = ax2
+ elif ax2 is None or ax1 == ax2:
+ ax = ax1
+ else:
+ raise AxisMismatchException
+ return ax, a1 * a2, a1 * b2 + b1
+
+
+def inverse(coord_map):
+ """
+ Invert a coord map by de-scaling and un-shifting;
+ this gives the backward mapping for the gradient.
+ """
+ ax, a, b = coord_map
+ return ax, 1 / a, -b / a
+
+
+def coord_map_from_to(top_from, top_to):
+ """
+ Determine the coordinate mapping betweeen a top (from) and a top (to).
+ Walk the graph to find a common ancestor while composing the coord maps for
+ from and to until they meet. As a last step the from map is inverted.
+ """
+ # We need to find a common ancestor of top_from and top_to.
+ # We'll assume that all ancestors are equivalent here (otherwise the graph
+ # is an inconsistent state (which we could improve this to check for)).
+ # For now use a brute-force algorithm.
+
+ def collect_bottoms(top):
+ """
+ Collect the bottoms to walk for the coordinate mapping.
+ The general rule is that all the bottoms of a layer can be mapped, as
+ most layers have the same coordinate mapping for each bottom.
+ Crop layer is a notable exception. Only the first/cropped bottom is
+ mappable; the second/dimensions bottom is excluded from the walk.
+ """
+ bottoms = top.fn.inputs
+ if top.fn.type_name == 'Crop':
+ bottoms = bottoms[:1]
+ return bottoms
+
+ # walk back from top_from, keeping the coord map as we go
+ from_maps = {top_from: (None, 1, 0)}
+ frontier = {top_from}
+ while frontier:
+ top = frontier.pop()
+ try:
+ bottoms = collect_bottoms(top)
+ for bottom in bottoms:
+ from_maps[bottom] = compose(from_maps[top], coord_map(top.fn))
+ frontier.add(bottom)
+ except UndefinedMapException:
+ pass
+
+ # now walk back from top_to until we hit a common blob
+ to_maps = {top_to: (None, 1, 0)}
+ frontier = {top_to}
+ while frontier:
+ top = frontier.pop()
+ if top in from_maps:
+ return compose(to_maps[top], inverse(from_maps[top]))
+ try:
+ bottoms = collect_bottoms(top)
+ for bottom in bottoms:
+ to_maps[bottom] = compose(to_maps[top], coord_map(top.fn))
+ frontier.add(bottom)
+ except UndefinedMapException:
+ continue
+
+ # if we got here, we did not find a blob in common
+ raise RuntimeError('Could not compute map between tops; are they '
+ 'connected by spatial layers?')
+
+
+def crop(top_from, top_to):
+ """
+ Define a Crop layer to crop a top (from) to another top (to) by
+ determining the coordinate mapping between the two and net spec'ing
+ the axis and shift parameters of the crop.
+ """
+ ax, a, b = coord_map_from_to(top_from, top_to)
+ assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
+ assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b)
+ assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \
+ '(b = {})'.format(b)
+ return L.Crop(top_from, top_to,
+ crop_param=dict(axis=ax + 1, # +1 for first cropping dim.
+ offset=list(-np.round(b).astype(int))))
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 75cd3b1..ef1f917 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -83,7 +83,7 @@ class Detector(caffe.Net):
for ix, window_in in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
- predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
+ predictions = out[self.outputs[0]]
# Package predictions with images and windows.
detections = []
diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index cfa3fc5..9eecf6d 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -127,7 +127,7 @@ def choose_color_by_layertype(layertype):
return color
-def get_pydot_graph(caffe_net, rankdir, label_edges=True):
+def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
"""Create a data structure which represents the `caffe_net`.
Parameters
@@ -137,17 +137,33 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
Direction of graph layout.
label_edges : boolean, optional
Label the edges (default is True).
+ phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+ Include layers from this network phase. If None, include all layers.
+ (the default is None)
Returns
-------
pydot graph object
"""
- pydot_graph = pydot.Dot(caffe_net.name,
+ pydot_graph = pydot.Dot(caffe_net.name if caffe_net.name else 'Net',
graph_type='digraph',
rankdir=rankdir)
pydot_nodes = {}
pydot_edges = []
for layer in caffe_net.layer:
+ if phase is not None:
+ included = False
+ if len(layer.include) == 0:
+ included = True
+ if len(layer.include) > 0 and len(layer.exclude) > 0:
+ raise ValueError('layer ' + layer.name + ' has both include '
+ 'and exclude specified.')
+ for layer_phase in layer.include:
+ included = included or layer_phase.phase == phase
+ for layer_phase in layer.exclude:
+ included = included and not layer_phase.phase == phase
+ if not included:
+ continue
node_label = get_layer_label(layer, rankdir)
node_name = "%s_%s" % (layer.name, layer.type)
if (len(layer.bottom) == 1 and len(layer.top) == 1 and
@@ -186,7 +202,7 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
return pydot_graph
-def draw_net(caffe_net, rankdir, ext='png'):
+def draw_net(caffe_net, rankdir, ext='png', phase=None):
"""Draws a caffe net and returns the image string encoded using the given
extension.
@@ -195,16 +211,19 @@ def draw_net(caffe_net, rankdir, ext='png'):
caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
ext : string, optional
The image extension (the default is 'png').
+ phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+ Include layers from this network phase. If None, include all layers.
+ (the default is None)
Returns
-------
string :
Postscript representation of the graph.
"""
- return get_pydot_graph(caffe_net, rankdir).create(format=ext)
+ return get_pydot_graph(caffe_net, rankdir, phase=phase).create(format=ext)
-def draw_net_to_file(caffe_net, filename, rankdir='LR'):
+def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):
"""Draws a caffe net, and saves it to file using the format given as the
file extension. Use '.raw' to output raw text that you can manually feed
to graphviz to draw graphs.
@@ -216,7 +235,10 @@ def draw_net_to_file(caffe_net, filename, rankdir='LR'):
The path to a file where the networks visualization will be stored.
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
+ phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+ Include layers from this network phase. If None, include all layers.
+ (the default is None)
"""
ext = filename[filename.rfind('.')+1:]
with open(filename, 'wb') as fid:
- fid.write(draw_net(caffe_net, rankdir, ext))
+ fid.write(draw_net(caffe_net, rankdir, ext, phase))
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 7531058..e1759be 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -46,7 +46,7 @@ def array_to_blobproto(arr, diff=None):
return blob
-def arraylist_to_blobprotovecor_str(arraylist):
+def arraylist_to_blobprotovector_str(arraylist):
"""Converts a list of arrays to a serialized blobprotovec, which could be
then passed to a network for processing.
"""
@@ -63,7 +63,7 @@ def blobprotovector_str_to_arraylist(str):
return [blobproto_to_array(blob) for blob in vec.blobs]
-def array_to_datum(arr, label=0):
+def array_to_datum(arr, label=None):
"""Converts a 3-dimensional array to datum. If the array has dtype uint8,
the output data will be encoded as a string. Otherwise, the output data
will be stored in float format.
@@ -76,7 +76,8 @@ def array_to_datum(arr, label=0):
datum.data = arr.tostring()
else:
datum.float_data.extend(arr.flat)
- datum.label = label
+ if label is not None:
+ datum.label = label
return datum
diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 93fc019..5fb1f0b 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -32,7 +32,7 @@ def param_name_dict():
# get all parameter names (typically underscore case) and corresponding
# type names (typically camel case), which contain the layer names
# (note that not all parameters correspond to layers, but we'll ignore that)
- param_names = [s for s in dir(layer) if s.endswith('_param')]
+ param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]
param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
# strip the final '_param' or 'Parameter'
param_names = [s[:-len('_param')] for s in param_names]
@@ -175,6 +175,12 @@ class NetSpec(object):
def __getattr__(self, name):
return self.tops[name]
+ def __setitem__(self, key, value):
+ self.__setattr__(key, value)
+
+ def __getitem__(self, item):
+ return self.__getattr__(item)
+
def to_proto(self):
names = {v: k for k, v in six.iteritems(self.tops)}
autonames = Counter()
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 3054110..5bae18d 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -14,6 +14,8 @@ from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
RMSPropSolver, AdaDeltaSolver, AdamSolver
import caffe.io
+import six
+
# We directly update methods from Net here (rather than using composition or
# inheritance) so that nets created by caffe (e.g., by SGDSolver) will
# automatically have the improved interface.
@@ -25,7 +27,9 @@ def _Net_blobs(self):
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
"""
- return OrderedDict(zip(self._blob_names, self._blobs))
+ if not hasattr(self, '_blobs_dict'):
+ self._blobs_dict = OrderedDict(zip(self._blob_names, self._blobs))
+ return self._blobs_dict
@property
@@ -34,7 +38,10 @@ def _Net_blob_loss_weights(self):
An OrderedDict (bottom to top, i.e., input to output) of network
blob loss weights indexed by name
"""
- return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
+ if not hasattr(self, '_blobs_loss_weights_dict'):
+ self._blob_loss_weights_dict = OrderedDict(zip(self._blob_names,
+ self._blob_loss_weights))
+ return self._blob_loss_weights_dict
@property
@@ -44,19 +51,28 @@ def _Net_params(self):
parameters indexed by name; each is a list of multiple blobs (e.g.,
weights and biases)
"""
- return OrderedDict([(name, lr.blobs)
- for name, lr in zip(self._layer_names, self.layers)
- if len(lr.blobs) > 0])
+ if not hasattr(self, '_params_dict'):
+ self._params_dict = OrderedDict([(name, lr.blobs)
+ for name, lr in zip(
+ self._layer_names, self.layers)
+ if len(lr.blobs) > 0])
+ return self._params_dict
@property
def _Net_inputs(self):
- return [list(self.blobs.keys())[i] for i in self._inputs]
+ if not hasattr(self, '_input_list'):
+ keys = list(self.blobs.keys())
+ self._input_list = [keys[i] for i in self._inputs]
+ return self._input_list
@property
def _Net_outputs(self):
- return [list(self.blobs.keys())[i] for i in self._outputs]
+ if not hasattr(self, '_output_list'):
+ keys = list(self.blobs.keys())
+ self._output_list = [keys[i] for i in self._outputs]
+ return self._output_list
def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
@@ -97,8 +113,8 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
raise Exception('Input blob arguments do not match net inputs.')
# Set input according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
- for in_, blob in kwargs.iteritems():
- if blob.shape[0] != self.blobs[in_].num:
+ for in_, blob in six.iteritems(kwargs):
+ if blob.shape[0] != self.blobs[in_].shape[0]:
raise Exception('Input is not batch sized')
self.blobs[in_].data[...] = blob
@@ -145,8 +161,8 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
raise Exception('Top diff arguments do not match net outputs.')
# Set top diffs according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
- for top, diff in kwargs.iteritems():
- if diff.shape[0] != self.blobs[top].num:
+ for top, diff in six.iteritems(kwargs):
+ if diff.shape[0] != self.blobs[top].shape[0]:
raise Exception('Diff is not batch sized')
self.blobs[top].diff[...] = diff
@@ -174,13 +190,13 @@ def _Net_forward_all(self, blobs=None, **kwargs):
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
for batch in self._batch(kwargs):
outs = self.forward(blobs=blobs, **batch)
- for out, out_blob in outs.iteritems():
+ for out, out_blob in six.iteritems(outs):
all_outs[out].extend(out_blob.copy())
# Package in ndarray.
for out in all_outs:
all_outs[out] = np.asarray(all_outs[out])
# Discard padding.
- pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+ pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out in all_outs:
all_outs[out] = all_outs[out][:-pad]
@@ -215,16 +231,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
batch_blobs = self.forward(blobs=blobs, **fb)
batch_diffs = self.backward(diffs=diffs, **bb)
- for out, out_blobs in batch_blobs.iteritems():
+ for out, out_blobs in six.iteritems(batch_blobs):
all_outs[out].extend(out_blobs.copy())
- for diff, out_diffs in batch_diffs.iteritems():
+ for diff, out_diffs in six.iteritems(batch_diffs):
all_diffs[diff].extend(out_diffs.copy())
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
all_diffs[diff] = np.asarray(all_diffs[diff])
# Discard padding at the end and package in ndarray.
- pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+ pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = all_outs[out][:-pad]
@@ -256,10 +272,10 @@ def _Net_batch(self, blobs):
------
batch: {blob name: list of blobs} dict for a single batch.
"""
- num = len(blobs.itervalues().next())
- batch_size = self.blobs.itervalues().next().num
+ num = len(six.next(six.itervalues(blobs)))
+ batch_size = six.next(six.itervalues(self.blobs)).shape[0]
remainder = num % batch_size
- num_batches = num / batch_size
+ num_batches = num // batch_size
# Yield full batches.
for b in range(num_batches):
@@ -276,21 +292,31 @@ def _Net_batch(self, blobs):
padding])
yield padded_batch
-
-class _Net_IdNameWrapper:
- """
- A simple wrapper that allows the ids propery to be accessed as a dict
- indexed by names. Used for top and bottom names
+def _Net_get_id_name(func, field):
"""
- def __init__(self, net, func):
- self.net, self.func = net, func
+ Generic property that maps func to the layer names into an OrderedDict.
+
+ Used for top_names and bottom_names.
+
+ Parameters
+ ----------
+ func: function id -> [id]
+ field: implementation field name (cache)
- def __getitem__(self, name):
- # Map the layer name to id
- ids = self.func(self.net, list(self.net._layer_names).index(name))
- # Map the blob id to name
- id_to_name = list(self.net.blobs)
- return [id_to_name[i] for i in ids]
+ Returns
+ ------
+ A one-parameter function that can be set as a property.
+ """
+ @property
+ def get_id_name(self):
+ if not hasattr(self, field):
+ id_to_name = list(self.blobs)
+ res = OrderedDict([(self._layer_names[i],
+ [id_to_name[j] for j in func(self, i)])
+ for i in range(len(self.layers))])
+ setattr(self, field, res)
+ return getattr(self, field)
+ return get_id_name
# Attach methods to Net.
Net.blobs = _Net_blobs
@@ -304,5 +330,5 @@ Net.set_input_arrays = _Net_set_input_arrays
Net._batch = _Net_batch
Net.inputs = _Net_inputs
Net.outputs = _Net_outputs
-Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids))
-Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids))
+Net.top_names = _Net_get_id_name(Net._top_ids, "_top_names")
+Net.bottom_names = _Net_get_id_name(Net._bottom_ids, "_bottom_names")
diff --git a/python/caffe/test/test_coord_map.py b/python/caffe/test/test_coord_map.py
new file mode 100644
index 0000000..613260e
--- /dev/null
+++ b/python/caffe/test/test_coord_map.py
@@ -0,0 +1,192 @@
+import unittest
+
+import numpy as np
+import random
+
+import caffe
+from caffe import layers as L
+from caffe import params as P
+from caffe.coord_map import coord_map_from_to, crop
+
+
+def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
+ """
+ Define net spec for simple conv-pool-deconv pattern common to all
+ coordinate mapping tests.
+ """
+ n = caffe.NetSpec()
+ n.data = L.Input(shape=dict(dim=[2, 1, 100, 100]))
+ n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20]))
+ n.conv = L.Convolution(
+ n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad)
+ n.pool = L.Pooling(
+ n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0)
+ # for upsampling kernel size is 2x stride
+ try:
+ deconv_ks = [s*2 for s in dstride]
+ except:
+ deconv_ks = dstride*2
+ n.deconv = L.Deconvolution(
+ n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad)
+ return n
+
+
+class TestCoordMap(unittest.TestCase):
+ def setUp(self):
+ pass
+
+ def test_conv_pool_deconv(self):
+ """
+ Map through conv, pool, and deconv.
+ """
+ n = coord_net_spec()
+ # identity for 2x pool, 2x deconv
+ ax, a, b = coord_map_from_to(n.deconv, n.data)
+ self.assertEquals(ax, 1)
+ self.assertEquals(a, 1)
+ self.assertEquals(b, 0)
+ # shift-by-one for 4x pool, 4x deconv
+ n = coord_net_spec(pool=4, dstride=4)
+ ax, a, b = coord_map_from_to(n.deconv, n.data)
+ self.assertEquals(ax, 1)
+ self.assertEquals(a, 1)
+ self.assertEquals(b, -1)
+
+ def test_pass(self):
+ """
+ A pass-through layer (ReLU) and conv (1x1, stride 1, pad 0)
+ both do identity mapping.
+ """
+ n = coord_net_spec()
+ ax, a, b = coord_map_from_to(n.deconv, n.data)
+ n.relu = L.ReLU(n.deconv)
+ n.conv1x1 = L.Convolution(
+ n.relu, num_output=10, kernel_size=1, stride=1, pad=0)
+ for top in [n.relu, n.conv1x1]:
+ ax_pass, a_pass, b_pass = coord_map_from_to(top, n.data)
+ self.assertEquals(ax, ax_pass)
+ self.assertEquals(a, a_pass)
+ self.assertEquals(b, b_pass)
+
+ def test_padding(self):
+ """
+ Padding conv adds offset while padding deconv subtracts offset.
+ """
+ n = coord_net_spec()
+ ax, a, b = coord_map_from_to(n.deconv, n.data)
+ pad = random.randint(0, 10)
+ # conv padding
+ n = coord_net_spec(pad=pad)
+ _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+ self.assertEquals(a, a_pad)
+ self.assertEquals(b - pad, b_pad)
+ # deconv padding
+ n = coord_net_spec(dpad=pad)
+ _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+ self.assertEquals(a, a_pad)
+ self.assertEquals(b + pad, b_pad)
+ # pad both to cancel out
+ n = coord_net_spec(pad=pad, dpad=pad)
+ _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+ self.assertEquals(a, a_pad)
+ self.assertEquals(b, b_pad)
+
+ def test_multi_conv(self):
+ """
+ Multiple bottoms/tops of a layer are identically mapped.
+ """
+ n = coord_net_spec()
+ # multi bottom/top
+ n.conv_data, n.conv_aux = L.Convolution(
+ n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2,
+ pad=0)
+ ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data)
+ ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux)
+ self.assertEquals(ax1, ax2)
+ self.assertEquals(a1, a2)
+ self.assertEquals(b1, b2)
+
+ def test_rect(self):
+ """
+ Anisotropic mapping is equivalent to its isotropic parts.
+ """
+ n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
+ n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
+ n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
+ ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
+ ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
+ ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
+ self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
+ self.assertEquals(a_3x3, a_3x5[0])
+ self.assertEquals(b_3x3, b_3x5[0])
+ self.assertEquals(a_5x5, a_3x5[1])
+ self.assertEquals(b_5x5, b_3x5[1])
+
+ def test_nd_conv(self):
+ """
+ ND conv maps the same way in more dimensions.
+ """
+ n = caffe.NetSpec()
+ # define data with 3 spatial dimensions, otherwise the same net
+ n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
+ n.conv = L.Convolution(
+ n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
+ pad=[0, 1, 2])
+ n.pool = L.Pooling(
+ n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
+ n.deconv = L.Deconvolution(
+ n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
+ ax, a, b = coord_map_from_to(n.deconv, n.data)
+ self.assertEquals(ax, 1)
+ self.assertTrue(len(a) == len(b))
+ self.assertTrue(np.all(a == 1))
+ self.assertEquals(b[0] - 1, b[1])
+ self.assertEquals(b[1] - 1, b[2])
+
+ def test_crop_of_crop(self):
+ """
+ Map coordinates through Crop layer:
+ crop an already-cropped output to the input and check change in offset.
+ """
+ n = coord_net_spec()
+ offset = random.randint(0, 10)
+ ax, a, b = coord_map_from_to(n.deconv, n.data)
+ n.crop = L.Crop(n.deconv, n.data, axis=2, offset=offset)
+ ax_crop, a_crop, b_crop = coord_map_from_to(n.crop, n.data)
+ self.assertEquals(ax, ax_crop)
+ self.assertEquals(a, a_crop)
+ self.assertEquals(b + offset, b_crop)
+
+ def test_crop_helper(self):
+ """
+ Define Crop layer by crop().
+ """
+ n = coord_net_spec()
+ crop(n.deconv, n.data)
+
+ def test_catch_unconnected(self):
+ """
+ Catch mapping spatially unconnected tops.
+ """
+ n = coord_net_spec()
+ n.ip = L.InnerProduct(n.deconv, num_output=10)
+ with self.assertRaises(RuntimeError):
+ coord_map_from_to(n.ip, n.data)
+
+ def test_catch_scale_mismatch(self):
+ """
+ Catch incompatible scales, such as when the top to be cropped
+ is mapped to a differently strided reference top.
+ """
+ n = coord_net_spec(pool=3, dstride=2) # pool 3x but deconv 2x
+ with self.assertRaises(AssertionError):
+ crop(n.deconv, n.data)
+
+ def test_catch_negative_crop(self):
+ """
+ Catch impossible offsets, such as when the top to be cropped
+ is mapped to a larger reference top.
+ """
+ n = coord_net_spec(dpad=10) # make output smaller than input
+ with self.assertRaises(AssertionError):
+ crop(n.deconv, n.data)
diff --git a/python/caffe/test/test_io.py b/python/caffe/test/test_io.py
index 8c86ef7..4a16b5b 100644
--- a/python/caffe/test/test_io.py
+++ b/python/caffe/test/test_io.py
@@ -39,3 +39,18 @@ class TestBlobProtoToArray(unittest.TestCase):
arr = caffe.io.blobproto_to_array(blob)
self.assertEqual(arr, 123)
+
+
+class TestArrayToDatum(unittest.TestCase):
+
+ def test_label_none_size(self):
+ # Set label
+ d1 = caffe.io.array_to_datum(
+ np.ones((10,10,3)), label=1)
+ # Don't set label
+ d2 = caffe.io.array_to_datum(
+ np.ones((10,10,3)))
+ # Not setting the label should result in a smaller object
+ self.assertGreater(
+ len(d1.SerializeToString()),
+ len(d2.SerializeToString()))
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index aad828a..e109093 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -3,6 +3,7 @@ import tempfile
import os
import numpy as np
import six
+from collections import OrderedDict
import caffe
@@ -63,19 +64,282 @@ class TestNet(unittest.TestCase):
self.net.forward()
self.net.backward()
+ def test_clear_param_diffs(self):
+ # Run a forward/backward step to have non-zero diffs
+ self.net.forward()
+ self.net.backward()
+ diff = self.net.params["conv"][0].diff
+ # Check that we have non-zero diffs
+ self.assertTrue(diff.max() > 0)
+ self.net.clear_param_diffs()
+ # Check that the diffs are now 0
+ self.assertTrue((diff == 0).all())
+
def test_inputs_outputs(self):
self.assertEqual(self.net.inputs, [])
self.assertEqual(self.net.outputs, ['loss'])
+ def test_top_bottom_names(self):
+ self.assertEqual(self.net.top_names,
+ OrderedDict([('data', ['data', 'label']),
+ ('conv', ['conv']),
+ ('ip', ['ip']),
+ ('loss', ['loss'])]))
+ self.assertEqual(self.net.bottom_names,
+ OrderedDict([('data', []),
+ ('conv', ['data']),
+ ('ip', ['conv']),
+ ('loss', ['ip', 'label'])]))
+
def test_save_and_read(self):
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.close()
self.net.save(f.name)
net_file = simple_net_file(self.num_output)
- net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
+ # Test legacy constructor
+ # should print deprecation warning
+ caffe.Net(net_file, f.name, caffe.TRAIN)
+ # Test named constructor
+ net2 = caffe.Net(net_file, caffe.TRAIN, weights=f.name)
+ os.remove(net_file)
+ os.remove(f.name)
+ for name in self.net.params:
+ for i in range(len(self.net.params[name])):
+ self.assertEqual(abs(self.net.params[name][i].data
+ - net2.params[name][i].data).sum(), 0)
+
+ def test_save_hdf5(self):
+ f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+ f.close()
+ self.net.save_hdf5(f.name)
+ net_file = simple_net_file(self.num_output)
+ net2 = caffe.Net(net_file, caffe.TRAIN)
+ net2.load_hdf5(f.name)
os.remove(net_file)
os.remove(f.name)
for name in self.net.params:
for i in range(len(self.net.params[name])):
self.assertEqual(abs(self.net.params[name][i].data
- net2.params[name][i].data).sum(), 0)
+
+class TestLevels(unittest.TestCase):
+
+ TEST_NET = """
+layer {
+ name: "data"
+ type: "DummyData"
+ top: "data"
+ dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+}
+layer {
+ name: "NoLevel"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "NoLevel"
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "Level0Only"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "Level0Only"
+ include { min_level: 0 max_level: 0 }
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "Level1Only"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "Level1Only"
+ include { min_level: 1 max_level: 1 }
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "Level>=0"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "Level>=0"
+ include { min_level: 0 }
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "Level>=1"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "Level>=1"
+ include { min_level: 1 }
+ inner_product_param { num_output: 1 }
+}
+"""
+
+ def setUp(self):
+ self.f = tempfile.NamedTemporaryFile(mode='w+')
+ self.f.write(self.TEST_NET)
+ self.f.flush()
+
+ def tearDown(self):
+ self.f.close()
+
+ def check_net(self, net, blobs):
+ net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
+ self.assertEqual(net_blobs, blobs)
+
+ def test_0(self):
+ net = caffe.Net(self.f.name, caffe.TEST)
+ self.check_net(net, ['NoLevel', 'Level0Only', 'Level>=0'])
+
+ def test_1(self):
+ net = caffe.Net(self.f.name, caffe.TEST, level=1)
+ self.check_net(net, ['NoLevel', 'Level1Only', 'Level>=0', 'Level>=1'])
+
+
+class TestStages(unittest.TestCase):
+
+ TEST_NET = """
+layer {
+ name: "data"
+ type: "DummyData"
+ top: "data"
+ dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+}
+layer {
+ name: "A"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "A"
+ include { stage: "A" }
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "B"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "B"
+ include { stage: "B" }
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "AorB"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "AorB"
+ include { stage: "A" }
+ include { stage: "B" }
+ inner_product_param { num_output: 1 }
+}
+layer {
+ name: "AandB"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "AandB"
+ include { stage: "A" stage: "B" }
+ inner_product_param { num_output: 1 }
+}
+"""
+
+ def setUp(self):
+ self.f = tempfile.NamedTemporaryFile(mode='w+')
+ self.f.write(self.TEST_NET)
+ self.f.flush()
+
+ def tearDown(self):
+ self.f.close()
+
+ def check_net(self, net, blobs):
+ net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
+ self.assertEqual(net_blobs, blobs)
+
+ def test_A(self):
+ net = caffe.Net(self.f.name, caffe.TEST, stages=['A'])
+ self.check_net(net, ['A', 'AorB'])
+
+ def test_B(self):
+ net = caffe.Net(self.f.name, caffe.TEST, stages=['B'])
+ self.check_net(net, ['B', 'AorB'])
+
+ def test_AandB(self):
+ net = caffe.Net(self.f.name, caffe.TEST, stages=['A', 'B'])
+ self.check_net(net, ['A', 'B', 'AorB', 'AandB'])
+
+
+class TestAllInOne(unittest.TestCase):
+
+ TEST_NET = """
+layer {
+ name: "train_data"
+ type: "DummyData"
+ top: "data"
+ top: "label"
+ dummy_data_param {
+ shape { dim: 1 dim: 1 dim: 10 dim: 10 }
+ shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+ }
+ include { phase: TRAIN stage: "train" }
+}
+layer {
+ name: "val_data"
+ type: "DummyData"
+ top: "data"
+ top: "label"
+ dummy_data_param {
+ shape { dim: 1 dim: 1 dim: 10 dim: 10 }
+ shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+ }
+ include { phase: TEST stage: "val" }
+}
+layer {
+ name: "deploy_data"
+ type: "Input"
+ top: "data"
+ input_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+ include { phase: TEST stage: "deploy" }
+}
+layer {
+ name: "ip"
+ type: "InnerProduct"
+ bottom: "data"
+ top: "ip"
+ inner_product_param { num_output: 2 }
+}
+layer {
+ name: "loss"
+ type: "SoftmaxWithLoss"
+ bottom: "ip"
+ bottom: "label"
+ top: "loss"
+ include: { phase: TRAIN stage: "train" }
+ include: { phase: TEST stage: "val" }
+}
+layer {
+ name: "pred"
+ type: "Softmax"
+ bottom: "ip"
+ top: "pred"
+ include: { phase: TEST stage: "deploy" }
+}
+"""
+
+ def setUp(self):
+ self.f = tempfile.NamedTemporaryFile(mode='w+')
+ self.f.write(self.TEST_NET)
+ self.f.flush()
+
+ def tearDown(self):
+ self.f.close()
+
+ def check_net(self, net, outputs):
+ self.assertEqual(list(net.blobs['data'].shape), [1,1,10,10])
+ self.assertEqual(net.outputs, outputs)
+
+ def test_train(self):
+ net = caffe.Net(self.f.name, caffe.TRAIN, stages=['train'])
+ self.check_net(net, ['loss'])
+
+ def test_val(self):
+ net = caffe.Net(self.f.name, caffe.TEST, stages=['val'])
+ self.check_net(net, ['loss'])
+
+ def test_deploy(self):
+ net = caffe.Net(self.f.name, caffe.TEST, stages=['deploy'])
+ self.check_net(net, ['pred'])
+
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index e46b711..899514e 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -44,6 +44,18 @@ class ParameterLayer(caffe.Layer):
def backward(self, top, propagate_down, bottom):
self.blobs[0].diff[0] = 1
+class PhaseLayer(caffe.Layer):
+ """A layer for checking attribute `phase`"""
+
+ def setup(self, bottom, top):
+ pass
+
+ def reshape(self, bootom, top):
+ top[0].reshape()
+
+ def forward(self, bottom, top):
+ top[0].data[()] = self.phase
+
def python_net_file():
with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
f.write("""name: 'pythonnet' force_backward: true
@@ -76,6 +88,14 @@ def parameter_net_file():
""")
return f.name
+def phase_net_file():
+ with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+ f.write("""name: 'pythonnet' force_backward: true
+ layer { type: 'Python' name: 'layer' top: 'phase'
+ python_param { module: 'test_python_layer' layer: 'PhaseLayer' } }
+ """)
+ return f.name
+
@unittest.skipIf('Python' not in caffe.layer_type_list(),
'Caffe built without Python layer support')
@@ -140,3 +160,9 @@ class TestPythonLayer(unittest.TestCase):
self.assertEqual(layer.blobs[0].data[0], 1)
os.remove(net_file)
+
+ def test_phase(self):
+ net_file = phase_net_file()
+ for phase in caffe.TRAIN, caffe.TEST:
+ net = caffe.Net(net_file, phase)
+ self.assertEqual(net.forward()['phase'], phase)
diff --git a/python/draw_net.py b/python/draw_net.py
index ec76a74..dfe70d2 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -28,6 +28,11 @@ def parse_args():
'http://www.graphviz.org/doc/info/'
'attrs.html#k:rankdir'),
default='LR')
+ parser.add_argument('--phase',
+ help=('Which network phase to draw: can be TRAIN, '
+ 'TEST, or ALL. If ALL, then all layers are drawn '
+ 'regardless of phase.'),
+ default="ALL")
args = parser.parse_args()
return args
@@ -38,7 +43,15 @@ def main():
net = caffe_pb2.NetParameter()
text_format.Merge(open(args.input_net_proto_file).read(), net)
print('Drawing net to %s' % args.output_image_file)
- caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
+ phase=None;
+ if args.phase == "TRAIN":
+ phase = caffe.TRAIN
+ elif args.phase == "TEST":
+ phase = caffe.TEST
+ elif args.phase != "ALL":
+ raise ValueError("Unknown phase: " + args.phase)
+ caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir,
+ phase)
if __name__ == '__main__':
diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index f750489..14c76ec 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1564,7 +1564,7 @@ def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
caffe_alt_function_list = (
('memset', ['caffe_set', 'caffe_memset']),
('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
- ('memcpy', ['caffe_copy', 'caffe_memcpy']),
+ ('memcpy', ['caffe_copy']),
('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
)
diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index 66f72f2..fcdbb5a 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -60,7 +60,7 @@ if __name__ == '__main__':
# Closure-d function for checking SHA1.
def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
- with open(filename, 'r') as f:
+ with open(filename, 'rb') as f:
return hashlib.sha1(f.read()).hexdigest() == sha1
# Check if model exists.
diff --git a/scripts/travis/build.sh b/scripts/travis/build.sh
new file mode 100755
index 0000000..bb9406f
--- /dev/null
+++ b/scripts/travis/build.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+# build the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_CMAKE ; then
+ make --jobs $NUM_THREADS all test pycaffe warn
+else
+ cd build
+ make --jobs $NUM_THREADS all test.testbin
+fi
+make lint
diff --git a/scripts/travis/configure-cmake.sh b/scripts/travis/configure-cmake.sh
new file mode 100644
index 0000000..772f1e2
--- /dev/null
+++ b/scripts/travis/configure-cmake.sh
@@ -0,0 +1,32 @@
+# CMake configuration
+
+mkdir -p build
+cd build
+
+ARGS="-DCMAKE_BUILD_TYPE=Release -DBLAS=Open"
+
+if $WITH_PYTHON3 ; then
+ ARGS="$ARGS -Dpython_version=3"
+fi
+
+if $WITH_IO ; then
+ ARGS="$ARGS -DUSE_OPENCV=On -DUSE_LMDB=On -DUSE_LEVELDB=On"
+else
+ ARGS="$ARGS -DUSE_OPENCV=Off -DUSE_LMDB=Off -DUSE_LEVELDB=Off"
+fi
+
+if $WITH_CUDA ; then
+ # Only build SM50
+ ARGS="$ARGS -DCPU_ONLY=Off -DCUDA_ARCH_NAME=Manual -DCUDA_ARCH_BIN=\"50\" -DCUDA_ARCH_PTX=\"\""
+else
+ ARGS="$ARGS -DCPU_ONLY=On"
+fi
+
+if $WITH_CUDNN ; then
+ ARGS="$ARGS -DUSE_CUDNN=On"
+else
+ ARGS="$ARGS -DUSE_CUDNN=Off"
+fi
+
+cmake .. $ARGS
+
diff --git a/scripts/travis/configure-make.sh b/scripts/travis/configure-make.sh
new file mode 100644
index 0000000..ddc40ff
--- /dev/null
+++ b/scripts/travis/configure-make.sh
@@ -0,0 +1,36 @@
+# raw Makefile configuration
+
+LINE () {
+ echo "$@" >> Makefile.config
+}
+
+cp Makefile.config.example Makefile.config
+
+LINE "BLAS := open"
+LINE "WITH_PYTHON_LAYER := 1"
+
+if $WITH_PYTHON3 ; then
+ # TODO(lukeyeager) this path is currently disabled because of test errors like:
+ # ImportError: dynamic module does not define init function (PyInit__caffe)
+ LINE "PYTHON_LIBRARIES := python3.4m boost_python-py34"
+ LINE "PYTHON_INCLUDE := /usr/include/python3.4 /usr/lib/python3/dist-packages/numpy/core/include"
+ LINE "INCLUDE_DIRS := \$(INCLUDE_DIRS) \$(PYTHON_INCLUDE)"
+fi
+
+if ! $WITH_IO ; then
+ LINE "USE_OPENCV := 0"
+ LINE "USE_LEVELDB := 0"
+ LINE "USE_LMDB := 0"
+fi
+
+if $WITH_CUDA ; then
+ # Only build SM50
+ LINE "CUDA_ARCH := -gencode arch=compute_50,code=sm_50"
+else
+ LINE "CPU_ONLY := 1"
+fi
+
+if $WITH_CUDNN ; then
+ LINE "USE_CUDNN := 1"
+fi
+
diff --git a/scripts/travis/configure.sh b/scripts/travis/configure.sh
new file mode 100755
index 0000000..ef740c8
--- /dev/null
+++ b/scripts/travis/configure.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+# configure the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_CMAKE ; then
+ source $BASEDIR/configure-make.sh
+else
+ source $BASEDIR/configure-cmake.sh
+fi
diff --git a/scripts/travis/defaults.sh b/scripts/travis/defaults.sh
new file mode 100755
index 0000000..d69c0a7
--- /dev/null
+++ b/scripts/travis/defaults.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# set default environment variables
+
+set -e
+
+WITH_CMAKE=${WITH_CMAKE:-false}
+WITH_PYTHON3=${WITH_PYTHON3:-false}
+WITH_IO=${WITH_IO:-true}
+WITH_CUDA=${WITH_CUDA:-false}
+WITH_CUDNN=${WITH_CUDNN:-false}
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
new file mode 100755
index 0000000..ee16d36
--- /dev/null
+++ b/scripts/travis/install-deps.sh
@@ -0,0 +1,110 @@
+#!/bin/bash
+# install dependencies
+# (this script must be run as root)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+apt-get -y update
+apt-get install -y --no-install-recommends \
+ build-essential \
+ libboost-filesystem-dev \
+ libboost-python-dev \
+ libboost-system-dev \
+ libboost-thread-dev \
+ libgflags-dev \
+ libgoogle-glog-dev \
+ libhdf5-serial-dev \
+ libopenblas-dev \
+ python-virtualenv \
+ wget
+
+if $WITH_CMAKE ; then
+ apt-get install -y --no-install-recommends cmake
+fi
+
+if ! $WITH_PYTHON3 ; then
+ # Python2
+ apt-get install -y --no-install-recommends \
+ libprotobuf-dev \
+ protobuf-compiler \
+ python-dev \
+ python-numpy \
+ python-protobuf \
+ python-skimage
+else
+ # Python3
+ apt-get install -y --no-install-recommends \
+ python3-dev \
+ python3-numpy \
+ python3-skimage
+
+ # build Protobuf3 since it's needed for Python3
+ PROTOBUF3_DIR=~/protobuf3
+ pushd .
+ if [ -d "$PROTOBUF3_DIR" ] && [ -e "$PROTOBUF3_DIR/src/protoc" ]; then
+ echo "Using cached protobuf3 build ..."
+ cd $PROTOBUF3_DIR
+ else
+ echo "Building protobuf3 from source ..."
+ rm -rf $PROTOBUF3_DIR
+ mkdir $PROTOBUF3_DIR
+
+ # install some more dependencies required to build protobuf3
+ apt-get install -y --no-install-recommends \
+ curl \
+ dh-autoreconf \
+ unzip
+
+ wget https://github.com/google/protobuf/archive/v3.0.0-beta-3.tar.gz -O protobuf3.tar.gz
+ tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
+ rm protobuf3.tar.gz
+ cd $PROTOBUF3_DIR
+ ./autogen.sh
+ ./configure --prefix=/usr
+ make --jobs=$NUM_THREADS
+ fi
+ make install
+ popd
+fi
+
+if $WITH_IO ; then
+ apt-get install -y --no-install-recommends \
+ libleveldb-dev \
+ liblmdb-dev \
+ libopencv-dev \
+ libsnappy-dev
+fi
+
+if $WITH_CUDA ; then
+ # install repo packages
+ CUDA_REPO_PKG=cuda-repo-ubuntu1404_7.5-18_amd64.deb
+ wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/$CUDA_REPO_PKG
+ dpkg -i $CUDA_REPO_PKG
+ rm $CUDA_REPO_PKG
+
+ if $WITH_CUDNN ; then
+ ML_REPO_PKG=nvidia-machine-learning-repo_4.0-2_amd64.deb
+ wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/$ML_REPO_PKG
+ dpkg -i $ML_REPO_PKG
+ fi
+
+ # update package lists
+ apt-get -y update
+
+ # install packages
+ CUDA_PKG_VERSION="7-5"
+ CUDA_VERSION="7.5"
+ apt-get install -y --no-install-recommends \
+ cuda-core-$CUDA_PKG_VERSION \
+ cuda-cudart-dev-$CUDA_PKG_VERSION \
+ cuda-cublas-dev-$CUDA_PKG_VERSION \
+ cuda-curand-dev-$CUDA_PKG_VERSION
+ # manually create CUDA symlink
+ ln -s /usr/local/cuda-$CUDA_VERSION /usr/local/cuda
+
+ if $WITH_CUDNN ; then
+ apt-get install -y --no-install-recommends libcudnn5-dev
+ fi
+fi
+
diff --git a/scripts/travis/install-python-deps.sh b/scripts/travis/install-python-deps.sh
new file mode 100755
index 0000000..eeec302
--- /dev/null
+++ b/scripts/travis/install-python-deps.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+# install extra Python dependencies
+# (must come after setup-venv)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_PYTHON3 ; then
+ # Python2
+ :
+else
+ # Python3
+ pip install --pre protobuf==3.0.0b3
+fi
diff --git a/scripts/travis/setup-venv.sh b/scripts/travis/setup-venv.sh
new file mode 100755
index 0000000..81245f1
--- /dev/null
+++ b/scripts/travis/setup-venv.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# setup a Python virtualenv
+# (must come after install-deps)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+VENV_DIR=${1:-~/venv}
+
+# setup our own virtualenv
+if $WITH_PYTHON3; then
+ PYTHON_EXE='/usr/bin/python3'
+else
+ PYTHON_EXE='/usr/bin/python2'
+fi
+
+# use --system-site-packages so that Python will use deb packages
+virtualenv $VENV_DIR -p $PYTHON_EXE --system-site-packages
diff --git a/scripts/travis/test.sh b/scripts/travis/test.sh
new file mode 100755
index 0000000..fedd7e6
--- /dev/null
+++ b/scripts/travis/test.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# test the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if $WITH_CUDA ; then
+ echo "Skipping tests for CUDA build"
+ exit 0
+fi
+
+if ! $WITH_CMAKE ; then
+ make runtest
+ make pytest
+else
+ cd build
+ make runtest
+ make pytest
+fi
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
deleted file mode 100755
index 174f1ee..0000000
--- a/scripts/travis/travis_build_and_test.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-# Script called by Travis to build and test Caffe.
-# Travis CI tests are CPU-only for lack of compatible hardware.
-
-set -e
-MAKE="make --jobs=$NUM_THREADS --keep-going"
-
-if $WITH_CMAKE; then
- mkdir build
- cd build
- CPU_ONLY=" -DCPU_ONLY=ON"
- if ! $WITH_CUDA; then
- CPU_ONLY=" -DCPU_ONLY=OFF"
- fi
- PYTHON_ARGS=""
- if [ "$PYTHON_VERSION" = "3" ]; then
- PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
- fi
- if $WITH_IO; then
- IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON"
- else
- IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF"
- fi
- cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS ..
- $MAKE
- $MAKE pytest
- if ! $WITH_CUDA; then
- $MAKE runtest
- $MAKE lint
- fi
- $MAKE clean
- cd -
-else
- if ! $WITH_CUDA; then
- export CPU_ONLY=1
- fi
- if $WITH_IO; then
- export USE_LMDB=1
- export USE_LEVELDB=1
- export USE_OPENCV=1
- fi
- $MAKE all test pycaffe warn lint || true
- if ! $WITH_CUDA; then
- $MAKE runtest
- fi
- $MAKE all
- $MAKE test
- $MAKE pycaffe
- $MAKE pytest
- $MAKE warn
- if ! $WITH_CUDA; then
- $MAKE lint
- fi
-fi
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
deleted file mode 100755
index d18dc22..0000000
--- a/scripts/travis/travis_install.sh
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/bin/bash
-# This script must be run with sudo.
-
-set -e
-
-MAKE="make --jobs=$NUM_THREADS"
-# Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
-
-# This ppa is for gflags and glog
-add-apt-repository -y ppa:tuleu/precise-backports
-apt-get -y update
-apt-get install \
- wget git curl \
- python-dev python-numpy python3-dev\
- libleveldb-dev libsnappy-dev libopencv-dev \
- libprotobuf-dev protobuf-compiler \
- libatlas-dev libatlas-base-dev \
- libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
- bc
-
-# Add a special apt-repository to install CMake 2.8.9 for CMake Caffe build,
-# if needed. By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
-# Caffe requires a minimum CMake version of 2.8.8.
-if $WITH_CMAKE; then
- # cmake 3 will make sure that the python interpreter and libraries match
- wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
- chmod +x cmake3.sh
- ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
-fi
-
-# Install CUDA, if needed
-if $WITH_CUDA; then
- CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.5-14_amd64.deb
- CUDA_FILE=/tmp/cuda_install.deb
- curl $CUDA_URL -o $CUDA_FILE
- dpkg -i $CUDA_FILE
- rm -f $CUDA_FILE
- apt-get -y update
- # Install the minimal CUDA subpackages required to test Caffe build.
- # For a full CUDA installation, add 'cuda' to the list of packages.
- apt-get -y install cuda-core-6-5 cuda-cublas-6-5 cuda-cublas-dev-6-5 cuda-cudart-6-5 cuda-cudart-dev-6-5 cuda-curand-6-5 cuda-curand-dev-6-5
- # Create CUDA symlink at /usr/local/cuda
- # (This would normally be created by the CUDA installer, but we create it
- # manually since we did a partial installation.)
- ln -s /usr/local/cuda-6.5 /usr/local/cuda
-fi
-
-# Install LMDB
-LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
-LMDB_FILE=/tmp/lmdb.tar.gz
-pushd .
-wget $LMDB_URL -O $LMDB_FILE
-tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/lmdb*/libraries/liblmdb/
-$MAKE
-$MAKE install
-popd
-rm -f $LMDB_FILE
-
-# Install the Python runtime dependencies via miniconda (this is much faster
-# than using pip for everything).
-export PATH=$CONDA_DIR/bin:$PATH
-if [ ! -d $CONDA_DIR ]; then
- if [ "$PYTHON_VERSION" -eq "3" ]; then
- wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
- else
- wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
- fi
- chmod +x miniconda.sh
- ./miniconda.sh -b -p $CONDA_DIR
-
- conda update --yes conda
- # The version of boost we're using for Python 3 depends on 3.4 for now.
- if [ "$PYTHON_VERSION" -eq "3" ]; then
- conda install --yes python=3.4
- fi
- conda install --yes numpy scipy matplotlib scikit-image pip
- # Let conda install boost (so that boost_python matches)
- conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
-fi
-
-# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
-if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
- pushd .
- wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
- tar -C /tmp -xzvf protobuf-3.tar.gz
- cd /tmp/protobuf-3*/
- ./autogen.sh
- ./configure --prefix=$CONDA_DIR
- $MAKE
- $MAKE install
- popd
-fi
-
-if [ "$PYTHON_VERSION" -eq "3" ]; then
- pip install --pre protobuf
-else
- pip install protobuf
-fi
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
deleted file mode 100755
index 83aacf1..0000000
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -e
-
-mv Makefile.config.example Makefile.config
-
-if $WITH_CUDA; then
- # Only generate compute_50.
- GENCODE="-gencode arch=compute_50,code=sm_50"
- GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
- echo "CUDA_ARCH := $GENCODE" >> Makefile.config
-fi
-
-# Remove IO library settings from Makefile.config
-# to avoid conflicts with CI configuration
-sed -i -e '/USE_LMDB/d' Makefile.config
-sed -i -e '/USE_LEVELDB/d' Makefile.config
-sed -i -e '/USE_OPENCV/d' Makefile.config
-
-cat << 'EOF' >> Makefile.config
-# Travis' nvcc doesn't like newer boost versions
-NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
-ANACONDA_HOME := $(CONDA_DIR)
-PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
- $(ANACONDA_HOME)/include/python2.7 \
- $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
-PYTHON_LIB := $(ANACONDA_HOME)/lib
-INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
-LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
-WITH_PYTHON_LAYER := 1
-EOF
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index c86fd5d..4a34e4c 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -30,7 +30,9 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
for (int i = 0; i < shape.size(); ++i) {
CHECK_GE(shape[i], 0);
- CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+ if (count_ != 0) {
+ CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+ }
count_ *= shape[i];
shape_[i] = shape[i];
shape_data[i] = shape[i];
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 299d67d..dee6816 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -70,6 +70,15 @@ void Caffe::DeviceQuery() {
NO_GPU;
}
+bool Caffe::CheckDevice(const int device_id) {
+ NO_GPU;
+ return false;
+}
+
+int Caffe::FindDevice(const int start_id) {
+ NO_GPU;
+ return -1;
+}
class Caffe::RNG::Generator {
public:
@@ -192,6 +201,39 @@ void Caffe::DeviceQuery() {
return;
}
+bool Caffe::CheckDevice(const int device_id) {
+ // This function checks the availability of GPU #device_id.
+ // It attempts to create a context on the device by calling cudaFree(0).
+ // cudaSetDevice() alone is not sufficient to check the availability.
+ // It lazily records device_id, however, does not initialize a
+ // context. So it does not know if the host thread has the permission to use
+ // the device or not.
+ //
+ // In a shared environment where the devices are set to EXCLUSIVE_PROCESS
+ // or EXCLUSIVE_THREAD mode, cudaSetDevice() returns cudaSuccess
+ // even if the device is exclusively occupied by another process or thread.
+ // Cuda operations that initialize the context are needed to check
+ // the permission. cudaFree(0) is one of those with no side effect,
+ // except the context initialization.
+ bool r = ((cudaSuccess == cudaSetDevice(device_id)) &&
+ (cudaSuccess == cudaFree(0)));
+ // reset any error that may have occurred.
+ cudaGetLastError();
+ return r;
+}
+
+int Caffe::FindDevice(const int start_id) {
+ // This function finds the first available device by checking devices with
+ // ordinal from start_id to the highest available value. In the
+ // EXCLUSIVE_PROCESS or EXCLUSIVE_THREAD mode, if it succeeds, it also
+ // claims the device due to the initialization of the context.
+ int count = 0;
+ CUDA_CHECK(cudaGetDeviceCount(&count));
+ for (int i = start_id; i < count; i++) {
+ if (CheckDevice(i)) return i;
+ }
+ return -1;
+}
class Caffe::RNG::Generator {
public:
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
new file mode 100644
index 0000000..aecdcd6
--- /dev/null
+++ b/src/caffe/layers/crop_layer.cpp
@@ -0,0 +1,143 @@
+#include <algorithm>
+#include <functional>
+#include <map>
+#include <set>
+#include <vector>
+
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/crop_layer.hpp"
+#include "caffe/net.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ // LayerSetup() handles the number of dimensions; Reshape() handles the sizes.
+ // bottom[0] supplies the data
+ // bottom[1] supplies the size
+ const CropParameter& param = this->layer_param_.crop_param();
+ CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
+ int input_dim = bottom[0]->num_axes();
+ const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+ CHECK_LT(start_axis, input_dim) << "crop axis bigger than input dim";
+ if (param.offset_size() > 1) {
+ // the number of crop values specified must be equal to the number
+ // of dimensions following axis
+ CHECK_EQ(start_axis + param.offset_size(), input_dim)
+ << "number of offset values specified must be equal to the number of "
+ << "dimensions following axis.";
+ }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const CropParameter& param = this->layer_param_.crop_param();
+ int input_dim = bottom[0]->num_axes();
+ const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+
+ // Initialize offsets to 0 and the new shape to the current shape of the data.
+ offsets = vector<int>(input_dim, 0);
+ vector<int> new_shape(bottom[0]->shape());
+
+ // Determine crop offsets and the new shape post-crop.
+ for (int i = 0; i < input_dim; ++i) {
+ int crop_offset = 0;
+ int new_size = bottom[0]->shape(i);
+ if (i >= start_axis) {
+ new_size = bottom[1]->shape(i);
+ if (param.offset_size() == 1) {
+ // If only one offset is given, all crops have the same offset.
+ crop_offset = param.offset(0);
+ } else if (param.offset_size() > 1) {
+ // For several offsets, the number of offsets must be equal to the
+ // number of dimensions to crop, that is dimensions after the axis.
+ crop_offset = param.offset(i - start_axis);
+ }
+ // Check that the crop and offset are within the dimension's bounds.
+ CHECK_GE(bottom[0]->shape(i) - crop_offset, bottom[1]->shape(i))
+ << "the crop for dimension " << i << " is out-of-bounds with "
+ << "size " << bottom[1]->shape(i) << " and offset " << crop_offset;
+ }
+ new_shape[i] = new_size;
+ offsets[i] = crop_offset;
+ }
+ top[0]->Reshape(new_shape);
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top,
+ const vector<int>& offsets,
+ vector<int> indices,
+ int cur_dim,
+ const Dtype* src_data,
+ Dtype* dest_data,
+ bool is_forward) {
+ if (cur_dim + 1 < top[0]->num_axes()) {
+ // We are not yet at the final dimension, call copy recursively
+ for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+ indices[cur_dim] = i;
+ crop_copy(bottom, top, offsets, indices, cur_dim+1,
+ src_data, dest_data, is_forward);
+ }
+ } else {
+ // We are at the last dimensions, which is stored continously in memory
+ for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+ // prepare index vector reduced(red) and with offsets(off)
+ std::vector<int> ind_red(cur_dim, 0);
+ std::vector<int> ind_off(cur_dim+1, 0);
+ for (int j = 0; j < cur_dim; ++j) {
+ ind_red[j] = indices[j];
+ ind_off[j] = indices[j] + offsets[j];
+ }
+ ind_off[cur_dim] = offsets[cur_dim];
+ // do the copy
+ if (is_forward) {
+ caffe_copy(top[0]->shape(cur_dim),
+ src_data + bottom[0]->offset(ind_off),
+ dest_data + top[0]->offset(ind_red));
+ } else {
+ // in the backwards pass the src_data is top_diff
+ // and the dest_data is bottom_diff
+ caffe_copy(top[0]->shape(cur_dim),
+ src_data + top[0]->offset(ind_red),
+ dest_data + bottom[0]->offset(ind_off));
+ }
+ }
+ }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ std::vector<int> indices(top[0]->num_axes(), 0);
+ const Dtype* bottom_data = bottom[0]->cpu_data();
+ Dtype* top_data = top[0]->mutable_cpu_data();
+ crop_copy(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ const Dtype* top_diff = top[0]->cpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+
+ if (propagate_down[0]) {
+ caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+ std::vector<int> indices(top[0]->num_axes(), 0);
+ crop_copy(bottom, top, offsets, indices, 0, top_diff, bottom_diff, false);
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(CropLayer);
+#endif
+
+INSTANTIATE_CLASS(CropLayer);
+REGISTER_LAYER_CLASS(Crop);
+
+} // namespace caffe
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
new file mode 100644
index 0000000..f78cecb
--- /dev/null
+++ b/src/caffe/layers/crop_layer.cu
@@ -0,0 +1,115 @@
+#include <vector>
+
+#include "caffe/layers/crop_layer.hpp"
+
+namespace caffe {
+
+// Copy (one line per thread) from one array to another, with arbitrary
+// strides in the last two dimensions.
+template <typename Dtype>
+__global__ void copy_kernel(const int n, const int height, const int width,
+ const int src_outer_stride, const int src_inner_stride,
+ const int dest_outer_stride, const int dest_inner_stride,
+ const Dtype* src, Dtype* dest) {
+ CUDA_KERNEL_LOOP(index, n) {
+ int src_start = index / height * src_outer_stride
+ + index % height * src_inner_stride;
+ int dest_start = index / height * dest_outer_stride
+ + index % height * dest_inner_stride;
+ for (int i = 0; i < width; ++i) {
+ dest[dest_start + i] = src[src_start + i];
+ }
+ }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top,
+ const vector<int>& offsets,
+ vector<int> indices,
+ int cur_dim,
+ const Dtype* src_data,
+ Dtype* dest_data,
+ bool is_forward) {
+ if (cur_dim + 2 < top[0]->num_axes()) {
+ // We are not yet at the final dimension, call copy recursivley
+ for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+ indices[cur_dim] = i;
+ crop_copy_gpu(bottom, top, offsets, indices, cur_dim+1,
+ src_data, dest_data, is_forward);
+ }
+ } else {
+ // We are at the last two dimensions, which are stored continously in memory
+ // With (N,C,H,W)
+ // (0,1,2,3) cur_dim -> H
+ // cur_dim+1 -> W
+ const int lines = top[0]->shape(cur_dim);
+ const int height = top[0]->shape(cur_dim);
+ const int width = top[0]->shape(cur_dim+1);
+ std::vector<int> ind_off(cur_dim+2, 0);
+ for (int j = 0; j < cur_dim; ++j) {
+ ind_off[j] = indices[j] + offsets[j];
+ }
+ ind_off[cur_dim] = offsets[cur_dim];
+ ind_off[cur_dim+1] = offsets[cur_dim+1];
+ // Compute copy strides
+ const int src_outer_stride =
+ bottom[0]->shape(cur_dim)*bottom[0]->shape(cur_dim+1);
+ const int src_inner_stride = bottom[0]->shape(cur_dim+1);
+ const int dest_outer_stride =
+ top[0]->shape(cur_dim)*top[0]->shape(cur_dim+1);
+ const int dest_inner_stride = top[0]->shape(cur_dim+1);
+
+ if (is_forward) {
+ const Dtype* bottom_data = bottom[0]->gpu_data() +
+ bottom[0]->offset(ind_off);
+ Dtype* top_data = top[0]->mutable_gpu_data() +
+ top[0]->offset(indices);
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+ lines, height, width,
+ src_outer_stride, src_inner_stride,
+ dest_outer_stride, dest_inner_stride,
+ bottom_data, top_data);
+
+ } else {
+ const Dtype* top_diff = top[0]->gpu_diff() +
+ top[0]->offset(indices);
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff() +
+ bottom[0]->offset(ind_off);
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+ lines, height, width,
+ dest_outer_stride, dest_inner_stride,
+ src_outer_stride, src_inner_stride,
+ top_diff, bottom_diff);
+ }
+ }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ std::vector<int> indices(top[0]->num_axes(), 0);
+ const Dtype* bottom_data = bottom[0]->gpu_data();
+ Dtype* top_data = top[0]->mutable_gpu_data();
+ crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ const Dtype* top_diff = top[0]->gpu_diff();
+ Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+ if (propagate_down[0]) {
+ caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+ std::vector<int> indices(top[0]->num_axes(), 0);
+ crop_copy_gpu(bottom, top, offsets, indices, 0, top_diff, bottom_diff,
+ false);
+ }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CropLayer);
+
+} // namespace caffe
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 42c4fd0..8bc5346 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -30,19 +30,11 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
// Bias.
if (this->bias_term_) {
const Dtype* bias_data = this->blobs_[1]->gpu_data();
-#if CUDNN_VERSION_MIN(4, 0, 0)
CUDNN_CHECK(cudnnAddTensor(handle_[g],
cudnn::dataType<Dtype>::one,
bias_desc_, bias_data + bias_offset_ * g,
cudnn::dataType<Dtype>::one,
top_descs_[i], top_data + top_offset_ * g));
-#else
- CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
- cudnn::dataType<Dtype>::one,
- bias_desc_, bias_data + bias_offset_ * g,
- cudnn::dataType<Dtype>::one,
- top_descs_[i], top_data + top_offset_ * g));
-#endif
}
}
@@ -82,7 +74,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
// Gradient w.r.t. weights.
if (this->param_propagate_down_[0]) {
const Dtype* bottom_data = bottom[i]->gpu_data();
- CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3(
+ CUDNN_CHECK(cudnnConvolutionBackwardFilter(
handle_[1*this->group_ + g],
cudnn::dataType<Dtype>::one,
bottom_descs_[i], bottom_data + bottom_offset_ * g,
@@ -100,7 +92,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
weight = this->blobs_[0]->gpu_data();
}
Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
- CUDNN_CHECK(cudnnConvolutionBackwardData_v3(
+ CUDNN_CHECK(cudnnConvolutionBackwardData(
handle_[2*this->group_ + g],
cudnn::dataType<Dtype>::one,
filter_desc_, weight + this->weight_offset_ * g,
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index c86c690..795e0a9 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -13,6 +13,7 @@ void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
CUDNN_CHECK(cudnnCreate(&handle_));
cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+ cudnn::createActivationDescriptor<Dtype>(&activ_desc_, CUDNN_ACTIVATION_RELU);
handles_setup_ = true;
}
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 9f61718..e7928bb 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -15,12 +15,21 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnActivationForward(this->handle_,
- CUDNN_ACTIVATION_RELU,
+ activ_desc_,
cudnn::dataType<Dtype>::one,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->top_desc_, top_data));
+#else
+ CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+ activ_desc_,
+ cudnn::dataType<Dtype>::one,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->top_desc_, top_data));
+#endif
}
template <typename Dtype>
@@ -40,13 +49,23 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const Dtype* top_diff = top[0]->gpu_diff();
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
- CUDNN_ACTIVATION_RELU,
+ activ_desc_,
cudnn::dataType<Dtype>::one,
this->top_desc_, top_data, this->top_desc_, top_diff,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->bottom_desc_, bottom_diff));
+#else
+ CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+ activ_desc_,
+ cudnn::dataType<Dtype>::one,
+ this->top_desc_, top_data, this->top_desc_, top_diff,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->bottom_desc_, bottom_diff));
+#endif
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index ccb955c..3ce6aef 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -13,6 +13,8 @@ void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
CUDNN_CHECK(cudnnCreate(&handle_));
cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+ cudnn::createActivationDescriptor<Dtype>(&activ_desc_,
+ CUDNN_ACTIVATION_SIGMOID);
handles_setup_ = true;
}
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index e2a4b46..48d6cba 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -10,12 +10,21 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnActivationForward(this->handle_,
- CUDNN_ACTIVATION_SIGMOID,
+ activ_desc_,
cudnn::dataType<Dtype>::one,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->top_desc_, top_data));
+#else
+ CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+ activ_desc_,
+ cudnn::dataType<Dtype>::one,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->top_desc_, top_data));
+#endif
}
template <typename Dtype>
@@ -30,13 +39,23 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const Dtype* top_diff = top[0]->gpu_diff();
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
- CUDNN_ACTIVATION_SIGMOID,
+ activ_desc_,
cudnn::dataType<Dtype>::one,
this->top_desc_, top_data, this->top_desc_, top_diff,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->bottom_desc_, bottom_diff));
+#else
+ CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+ activ_desc_,
+ cudnn::dataType<Dtype>::one,
+ this->top_desc_, top_data, this->top_desc_, top_diff,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->bottom_desc_, bottom_diff));
+#endif
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 1a56418..e87dd9d 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -13,6 +13,7 @@ void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
CUDNN_CHECK(cudnnCreate(&handle_));
cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+ cudnn::createActivationDescriptor<Dtype>(&activ_desc_, CUDNN_ACTIVATION_TANH);
handles_setup_ = true;
}
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index 89df28a..6b5d7ae 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -10,12 +10,21 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnActivationForward(this->handle_,
- CUDNN_ACTIVATION_TANH,
+ activ_desc_,
cudnn::dataType<Dtype>::one,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->top_desc_, top_data));
+#else
+ CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+ activ_desc_,
+ cudnn::dataType<Dtype>::one,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->top_desc_, top_data));
+#endif
}
template <typename Dtype>
@@ -31,13 +40,23 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnActivationBackward(this->handle_,
- CUDNN_ACTIVATION_TANH,
+ activ_desc_,
cudnn::dataType<Dtype>::one,
this->top_desc_, top_data, this->top_desc_, top_diff,
this->bottom_desc_, bottom_data,
cudnn::dataType<Dtype>::zero,
this->bottom_desc_, bottom_diff));
+#else
+ CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+ activ_desc_,
+ cudnn::dataType<Dtype>::one,
+ this->top_desc_, top_data, this->top_desc_, top_diff,
+ this->bottom_desc_, bottom_data,
+ cudnn::dataType<Dtype>::zero,
+ this->bottom_desc_, bottom_diff));
+#endif
}
INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 9cb64d9..533ab26 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -23,8 +23,8 @@ void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
NeuronLayer<Dtype>::Reshape(bottom, top);
// Set up the cache for random number generation
- rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
- bottom[0]->height(), bottom[0]->width());
+ // ReshapeLike does not work because rand_vec_ is of Dtype uint
+ rand_vec_.Reshape(bottom[0]->shape());
}
template <typename Dtype>
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index 1f4a309..0c1b463 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -23,7 +23,8 @@ void ExpLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const Dtype input_scale = this->layer_param_.exp_param().scale();
const Dtype input_shift = this->layer_param_.exp_param().shift();
inner_scale_ = log_base * input_scale;
- outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) : pow(base, input_shift);
+ outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) :
+ ( (base != Dtype(-1)) ? pow(base, input_shift) : exp(input_shift) );
}
template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 62fda4a..7ee7dc4 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -37,12 +37,17 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const string& source = this->layer_param_.image_data_param().source();
LOG(INFO) << "Opening file " << source;
std::ifstream infile(source.c_str());
- string filename;
+ string line;
+ size_t pos;
int label;
- while (infile >> filename >> label) {
- lines_.push_back(std::make_pair(filename, label));
+ while (std::getline(infile, line)) {
+ pos = line.find_last_of(' ');
+ label = atoi(line.substr(pos + 1).c_str());
+ lines_.push_back(std::make_pair(line.substr(0, pos), label));
}
+ CHECK(!lines_.empty()) << "File is empty";
+
if (this->layer_param_.image_data_param().shuffle()) {
// randomly shuffle data
LOG(INFO) << "Shuffling data";
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index d908880..e65349f 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -11,6 +11,7 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const int num_output = this->layer_param_.inner_product_param().num_output();
bias_term_ = this->layer_param_.inner_product_param().bias_term();
+ transpose_ = this->layer_param_.inner_product_param().transpose();
N_ = num_output;
const int axis = bottom[0]->CanonicalAxisIndex(
this->layer_param_.inner_product_param().axis());
@@ -27,10 +28,15 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
} else {
this->blobs_.resize(1);
}
- // Intialize the weight
+ // Initialize the weights
vector<int> weight_shape(2);
- weight_shape[0] = N_;
- weight_shape[1] = K_;
+ if (transpose_) {
+ weight_shape[0] = K_;
+ weight_shape[1] = N_;
+ } else {
+ weight_shape[0] = N_;
+ weight_shape[1] = K_;
+ }
this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
// fill the weights
shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
@@ -80,7 +86,8 @@ void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = top[0]->mutable_cpu_data();
const Dtype* weight = this->blobs_[0]->cpu_data();
- caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans,
+ M_, N_, K_, (Dtype)1.,
bottom_data, weight, (Dtype)0., top_data);
if (bias_term_) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -97,8 +104,17 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const Dtype* top_diff = top[0]->cpu_diff();
const Dtype* bottom_data = bottom[0]->cpu_data();
// Gradient with respect to weight
- caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
- top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+ if (transpose_) {
+ caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+ K_, N_, M_,
+ (Dtype)1., bottom_data, top_diff,
+ (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+ } else {
+ caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+ N_, K_, M_,
+ (Dtype)1., top_diff, bottom_data,
+ (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+ }
}
if (bias_term_ && this->param_propagate_down_[1]) {
const Dtype* top_diff = top[0]->cpu_diff();
@@ -110,9 +126,17 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
if (propagate_down[0]) {
const Dtype* top_diff = top[0]->cpu_diff();
// Gradient with respect to bottom data
- caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
- top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
- bottom[0]->mutable_cpu_diff());
+ if (transpose_) {
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+ M_, K_, N_,
+ (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+ (Dtype)0., bottom[0]->mutable_cpu_diff());
+ } else {
+ caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+ M_, K_, N_,
+ (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+ (Dtype)0., bottom[0]->mutable_cpu_diff());
+ }
}
}
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index dc25aa3..a58b56e 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -19,7 +19,9 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
caffe_gpu_axpy<Dtype>(N_, bias_multiplier_.cpu_data()[0],
this->blobs_[1]->gpu_data(), top_data);
} else {
- caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+ caffe_gpu_gemm<Dtype>(CblasNoTrans,
+ transpose_ ? CblasNoTrans : CblasTrans,
+ M_, N_, K_, (Dtype)1.,
bottom_data, weight, (Dtype)0., top_data);
if (bias_term_)
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -36,8 +38,17 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const Dtype* top_diff = top[0]->gpu_diff();
const Dtype* bottom_data = bottom[0]->gpu_data();
// Gradient with respect to weight
- caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
- top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+ if (transpose_) {
+ caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+ K_, N_, M_,
+ (Dtype)1., bottom_data, top_diff,
+ (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+ } else {
+ caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+ N_, K_, M_,
+ (Dtype)1., top_diff, bottom_data,
+ (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+ }
}
if (bias_term_ && this->param_propagate_down_[1]) {
const Dtype* top_diff = top[0]->gpu_diff();
@@ -49,9 +60,17 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
if (propagate_down[0]) {
const Dtype* top_diff = top[0]->gpu_diff();
// Gradient with respect to bottom data
- caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
- top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
- bottom[0]->mutable_gpu_diff());
+ if (transpose_) {
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+ M_, K_, N_,
+ (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+ (Dtype)0., bottom[0]->mutable_gpu_diff());
+ } else {
+ caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+ M_, K_, N_,
+ (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+ (Dtype)0., bottom[0]->mutable_gpu_diff());
+ }
}
}
diff --git a/src/caffe/layers/input_layer.cpp b/src/caffe/layers/input_layer.cpp
new file mode 100644
index 0000000..667d8ad
--- /dev/null
+++ b/src/caffe/layers/input_layer.cpp
@@ -0,0 +1,27 @@
+#include <vector>
+
+#include "caffe/layers/input_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void InputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int num_top = top.size();
+ const InputParameter& param = this->layer_param_.input_param();
+ const int num_shape = param.shape_size();
+ CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top)
+ << "Must specify 'shape' once, once per top blob, or not at all: "
+ << num_top << " tops vs. " << num_shape << " shapes.";
+ if (num_shape > 0) {
+ for (int i = 0; i < num_top; ++i) {
+ const int shape_index = (param.shape_size() == 1) ? 0 : i;
+ top[i]->Reshape(param.shape(shape_index));
+ }
+ }
+}
+
+INSTANTIATE_CLASS(InputLayer);
+REGISTER_LAYER_CLASS(Input);
+
+} // namespace caffe
diff --git a/src/caffe/layers/lstm_layer.cpp b/src/caffe/layers/lstm_layer.cpp
new file mode 100644
index 0000000..da48dba
--- /dev/null
+++ b/src/caffe/layers/lstm_layer.cpp
@@ -0,0 +1,244 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
+ names->resize(2);
+ (*names)[0] = "h_0";
+ (*names)[1] = "c_0";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
+ names->resize(2);
+ (*names)[0] = "h_" + format_int(this->T_);
+ (*names)[1] = "c_T";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
+ const int num_output = this->layer_param_.recurrent_param().num_output();
+ const int num_blobs = 2;
+ shapes->resize(num_blobs);
+ for (int i = 0; i < num_blobs; ++i) {
+ (*shapes)[i].Clear();
+ (*shapes)[i].add_dim(1); // a single timestep
+ (*shapes)[i].add_dim(this->N_);
+ (*shapes)[i].add_dim(num_output);
+ }
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
+ names->resize(1);
+ (*names)[0] = "h";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
+ const int num_output = this->layer_param_.recurrent_param().num_output();
+ CHECK_GT(num_output, 0) << "num_output must be positive";
+ const FillerParameter& weight_filler =
+ this->layer_param_.recurrent_param().weight_filler();
+ const FillerParameter& bias_filler =
+ this->layer_param_.recurrent_param().bias_filler();
+
+ // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
+ // use to save redundant code.
+ LayerParameter hidden_param;
+ hidden_param.set_type("InnerProduct");
+ hidden_param.mutable_inner_product_param()->set_num_output(num_output * 4);
+ hidden_param.mutable_inner_product_param()->set_bias_term(false);
+ hidden_param.mutable_inner_product_param()->set_axis(2);
+ hidden_param.mutable_inner_product_param()->
+ mutable_weight_filler()->CopyFrom(weight_filler);
+
+ LayerParameter biased_hidden_param(hidden_param);
+ biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
+ biased_hidden_param.mutable_inner_product_param()->
+ mutable_bias_filler()->CopyFrom(bias_filler);
+
+ LayerParameter sum_param;
+ sum_param.set_type("Eltwise");
+ sum_param.mutable_eltwise_param()->set_operation(
+ EltwiseParameter_EltwiseOp_SUM);
+
+ LayerParameter scale_param;
+ scale_param.set_type("Scale");
+ scale_param.mutable_scale_param()->set_axis(0);
+
+ LayerParameter slice_param;
+ slice_param.set_type("Slice");
+ slice_param.mutable_slice_param()->set_axis(0);
+
+ LayerParameter split_param;
+ split_param.set_type("Split");
+
+ vector<BlobShape> input_shapes;
+ RecurrentInputShapes(&input_shapes);
+ CHECK_EQ(2, input_shapes.size());
+
+ LayerParameter* input_layer_param = net_param->add_layer();
+ input_layer_param->set_type("Input");
+ InputParameter* input_param = input_layer_param->mutable_input_param();
+
+ input_layer_param->add_top("c_0");
+ input_param->add_shape()->CopyFrom(input_shapes[0]);
+
+ input_layer_param->add_top("h_0");
+ input_param->add_shape()->CopyFrom(input_shapes[1]);
+
+ LayerParameter* cont_slice_param = net_param->add_layer();
+ cont_slice_param->CopyFrom(slice_param);
+ cont_slice_param->set_name("cont_slice");
+ cont_slice_param->add_bottom("cont");
+ cont_slice_param->mutable_slice_param()->set_axis(0);
+
+ // Add layer to transform all timesteps of x to the hidden state dimension.
+ // W_xc_x = W_xc * x + b_c
+ {
+ LayerParameter* x_transform_param = net_param->add_layer();
+ x_transform_param->CopyFrom(biased_hidden_param);
+ x_transform_param->set_name("x_transform");
+ x_transform_param->add_param()->set_name("W_xc");
+ x_transform_param->add_param()->set_name("b_c");
+ x_transform_param->add_bottom("x");
+ x_transform_param->add_top("W_xc_x");
+ x_transform_param->add_propagate_down(true);
+ }
+
+ if (this->static_input_) {
+ // Add layer to transform x_static to the gate dimension.
+ // W_xc_x_static = W_xc_static * x_static
+ LayerParameter* x_static_transform_param = net_param->add_layer();
+ x_static_transform_param->CopyFrom(hidden_param);
+ x_static_transform_param->mutable_inner_product_param()->set_axis(1);
+ x_static_transform_param->set_name("W_xc_x_static");
+ x_static_transform_param->add_param()->set_name("W_xc_static");
+ x_static_transform_param->add_bottom("x_static");
+ x_static_transform_param->add_top("W_xc_x_static_preshape");
+ x_static_transform_param->add_propagate_down(true);
+
+ LayerParameter* reshape_param = net_param->add_layer();
+ reshape_param->set_type("Reshape");
+ BlobShape* new_shape =
+ reshape_param->mutable_reshape_param()->mutable_shape();
+ new_shape->add_dim(1); // One timestep.
+ // Should infer this->N as the dimension so we can reshape on batch size.
+ new_shape->add_dim(-1);
+ new_shape->add_dim(
+ x_static_transform_param->inner_product_param().num_output());
+ reshape_param->set_name("W_xc_x_static_reshape");
+ reshape_param->add_bottom("W_xc_x_static_preshape");
+ reshape_param->add_top("W_xc_x_static");
+ }
+
+ LayerParameter* x_slice_param = net_param->add_layer();
+ x_slice_param->CopyFrom(slice_param);
+ x_slice_param->add_bottom("W_xc_x");
+ x_slice_param->set_name("W_xc_x_slice");
+
+ LayerParameter output_concat_layer;
+ output_concat_layer.set_name("h_concat");
+ output_concat_layer.set_type("Concat");
+ output_concat_layer.add_top("h");
+ output_concat_layer.mutable_concat_param()->set_axis(0);
+
+ for (int t = 1; t <= this->T_; ++t) {
+ string tm1s = format_int(t - 1);
+ string ts = format_int(t);
+
+ cont_slice_param->add_top("cont_" + ts);
+ x_slice_param->add_top("W_xc_x_" + ts);
+
+ // Add layers to flush the hidden state when beginning a new
+ // sequence, as indicated by cont_t.
+ // h_conted_{t-1} := cont_t * h_{t-1}
+ //
+ // Normally, cont_t is binary (i.e., 0 or 1), so:
+ // h_conted_{t-1} := h_{t-1} if cont_t == 1
+ // 0 otherwise
+ {
+ LayerParameter* cont_h_param = net_param->add_layer();
+ cont_h_param->CopyFrom(scale_param);
+ cont_h_param->set_name("h_conted_" + tm1s);
+ cont_h_param->add_bottom("h_" + tm1s);
+ cont_h_param->add_bottom("cont_" + ts);
+ cont_h_param->add_top("h_conted_" + tm1s);
+ }
+
+ // Add layer to compute
+ // W_hc_h_{t-1} := W_hc * h_conted_{t-1}
+ {
+ LayerParameter* w_param = net_param->add_layer();
+ w_param->CopyFrom(hidden_param);
+ w_param->set_name("transform_" + ts);
+ w_param->add_param()->set_name("W_hc");
+ w_param->add_bottom("h_conted_" + tm1s);
+ w_param->add_top("W_hc_h_" + tm1s);
+ w_param->mutable_inner_product_param()->set_axis(2);
+ }
+
+ // Add the outputs of the linear transformations to compute the gate input.
+ // gate_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c
+ // = W_hc_h_{t-1} + W_xc_x_t + b_c
+ {
+ LayerParameter* input_sum_layer = net_param->add_layer();
+ input_sum_layer->CopyFrom(sum_param);
+ input_sum_layer->set_name("gate_input_" + ts);
+ input_sum_layer->add_bottom("W_hc_h_" + tm1s);
+ input_sum_layer->add_bottom("W_xc_x_" + ts);
+ if (this->static_input_) {
+ input_sum_layer->add_bottom("W_xc_x_static");
+ }
+ input_sum_layer->add_top("gate_input_" + ts);
+ }
+
+ // Add LSTMUnit layer to compute the cell & hidden vectors c_t and h_t.
+ // Inputs: c_{t-1}, gate_input_t = (i_t, f_t, o_t, g_t), cont_t
+ // Outputs: c_t, h_t
+ // [ i_t' ]
+ // [ f_t' ] := gate_input_t
+ // [ o_t' ]
+ // [ g_t' ]
+ // i_t := \sigmoid[i_t']
+ // f_t := \sigmoid[f_t']
+ // o_t := \sigmoid[o_t']
+ // g_t := \tanh[g_t']
+ // c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
+ // h_t := o_t .* \tanh[c_t]
+ {
+ LayerParameter* lstm_unit_param = net_param->add_layer();
+ lstm_unit_param->set_type("LSTMUnit");
+ lstm_unit_param->add_bottom("c_" + tm1s);
+ lstm_unit_param->add_bottom("gate_input_" + ts);
+ lstm_unit_param->add_bottom("cont_" + ts);
+ lstm_unit_param->add_top("c_" + ts);
+ lstm_unit_param->add_top("h_" + ts);
+ lstm_unit_param->set_name("unit_" + ts);
+ }
+ output_concat_layer.add_bottom("h_" + ts);
+ } // for (int t = 1; t <= this->T_; ++t)
+
+ {
+ LayerParameter* c_T_copy_param = net_param->add_layer();
+ c_T_copy_param->CopyFrom(split_param);
+ c_T_copy_param->add_bottom("c_" + format_int(this->T_));
+ c_T_copy_param->add_top("c_T");
+ }
+ net_param->add_layer()->CopyFrom(output_concat_layer);
+}
+
+INSTANTIATE_CLASS(LSTMLayer);
+REGISTER_LAYER_CLASS(LSTM);
+
+} // namespace caffe
diff --git a/src/caffe/layers/lstm_unit_layer.cpp b/src/caffe/layers/lstm_unit_layer.cpp
new file mode 100644
index 0000000..277c031
--- /dev/null
+++ b/src/caffe/layers/lstm_unit_layer.cpp
@@ -0,0 +1,131 @@
+#include <algorithm>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+inline Dtype sigmoid(Dtype x) {
+ return 1. / (1. + exp(-x));
+}
+
+template <typename Dtype>
+inline Dtype tanh(Dtype x) {
+ return 2. * sigmoid(2. * x) - 1.;
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int num_instances = bottom[0]->shape(1);
+ for (int i = 0; i < bottom.size(); ++i) {
+ if (i == 2) {
+ CHECK_EQ(2, bottom[i]->num_axes());
+ } else {
+ CHECK_EQ(3, bottom[i]->num_axes());
+ }
+ CHECK_EQ(1, bottom[i]->shape(0));
+ CHECK_EQ(num_instances, bottom[i]->shape(1));
+ }
+ hidden_dim_ = bottom[0]->shape(2);
+ CHECK_EQ(num_instances, bottom[1]->shape(1));
+ CHECK_EQ(4 * hidden_dim_, bottom[1]->shape(2));
+ top[0]->ReshapeLike(*bottom[0]);
+ top[1]->ReshapeLike(*bottom[0]);
+ X_acts_.ReshapeLike(*bottom[1]);
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int num = bottom[0]->shape(1);
+ const int x_dim = hidden_dim_ * 4;
+ const Dtype* C_prev = bottom[0]->cpu_data();
+ const Dtype* X = bottom[1]->cpu_data();
+ const Dtype* cont = bottom[2]->cpu_data();
+ Dtype* C = top[0]->mutable_cpu_data();
+ Dtype* H = top[1]->mutable_cpu_data();
+ for (int n = 0; n < num; ++n) {
+ for (int d = 0; d < hidden_dim_; ++d) {
+ const Dtype i = sigmoid(X[d]);
+ const Dtype f = (*cont == 0) ? 0 :
+ (*cont * sigmoid(X[1 * hidden_dim_ + d]));
+ const Dtype o = sigmoid(X[2 * hidden_dim_ + d]);
+ const Dtype g = tanh(X[3 * hidden_dim_ + d]);
+ const Dtype c_prev = C_prev[d];
+ const Dtype c = f * c_prev + i * g;
+ C[d] = c;
+ const Dtype tanh_c = tanh(c);
+ H[d] = o * tanh_c;
+ }
+ C_prev += hidden_dim_;
+ X += x_dim;
+ C += hidden_dim_;
+ H += hidden_dim_;
+ ++cont;
+ }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[2]) << "Cannot backpropagate to sequence indicators.";
+ if (!propagate_down[0] && !propagate_down[1]) { return; }
+
+ const int num = bottom[0]->shape(1);
+ const int x_dim = hidden_dim_ * 4;
+ const Dtype* C_prev = bottom[0]->cpu_data();
+ const Dtype* X = bottom[1]->cpu_data();
+ const Dtype* cont = bottom[2]->cpu_data();
+ const Dtype* C = top[0]->cpu_data();
+ const Dtype* H = top[1]->cpu_data();
+ const Dtype* C_diff = top[0]->cpu_diff();
+ const Dtype* H_diff = top[1]->cpu_diff();
+ Dtype* C_prev_diff = bottom[0]->mutable_cpu_diff();
+ Dtype* X_diff = bottom[1]->mutable_cpu_diff();
+ for (int n = 0; n < num; ++n) {
+ for (int d = 0; d < hidden_dim_; ++d) {
+ const Dtype i = sigmoid(X[d]);
+ const Dtype f = (*cont == 0) ? 0 :
+ (*cont * sigmoid(X[1 * hidden_dim_ + d]));
+ const Dtype o = sigmoid(X[2 * hidden_dim_ + d]);
+ const Dtype g = tanh(X[3 * hidden_dim_ + d]);
+ const Dtype c_prev = C_prev[d];
+ const Dtype c = C[d];
+ const Dtype tanh_c = tanh(c);
+ Dtype* c_prev_diff = C_prev_diff + d;
+ Dtype* i_diff = X_diff + d;
+ Dtype* f_diff = X_diff + 1 * hidden_dim_ + d;
+ Dtype* o_diff = X_diff + 2 * hidden_dim_ + d;
+ Dtype* g_diff = X_diff + 3 * hidden_dim_ + d;
+ const Dtype c_term_diff =
+ C_diff[d] + H_diff[d] * o * (1 - tanh_c * tanh_c);
+ *c_prev_diff = c_term_diff * f;
+ *i_diff = c_term_diff * g * i * (1 - i);
+ *f_diff = c_term_diff * c_prev * f * (1 - f);
+ *o_diff = H_diff[d] * tanh_c * o * (1 - o);
+ *g_diff = c_term_diff * i * (1 - g * g);
+ }
+ C_prev += hidden_dim_;
+ X += x_dim;
+ C += hidden_dim_;
+ H += hidden_dim_;
+ C_diff += hidden_dim_;
+ H_diff += hidden_dim_;
+ X_diff += x_dim;
+ C_prev_diff += hidden_dim_;
+ ++cont;
+ }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LSTMUnitLayer);
+#endif
+
+INSTANTIATE_CLASS(LSTMUnitLayer);
+REGISTER_LAYER_CLASS(LSTMUnit);
+
+} // namespace caffe
diff --git a/src/caffe/layers/lstm_unit_layer.cu b/src/caffe/layers/lstm_unit_layer.cu
new file mode 100644
index 0000000..15bb451
--- /dev/null
+++ b/src/caffe/layers/lstm_unit_layer.cu
@@ -0,0 +1,154 @@
+#include <algorithm>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__device__ Dtype sigmoid(const Dtype x) {
+ return Dtype(1) / (Dtype(1) + exp(-x));
+}
+
+template <typename Dtype>
+__device__ Dtype tanh(const Dtype x) {
+ return Dtype(2) * sigmoid(Dtype(2) * x) - Dtype(1);
+}
+
+template <typename Dtype>
+__global__ void LSTMActsForward(const int nthreads, const int dim,
+ const Dtype* X, Dtype* X_acts) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int x_dim = 4 * dim;
+ const int d = index % x_dim;
+ if (d < 3 * dim) {
+ X_acts[index] = sigmoid(X[index]);
+ } else {
+ X_acts[index] = tanh(X[index]);
+ }
+ }
+}
+
+template <typename Dtype>
+__global__ void LSTMUnitForward(const int nthreads, const int dim,
+ const Dtype* C_prev, const Dtype* X, const Dtype* cont,
+ Dtype* C, Dtype* H) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int n = index / dim;
+ const int d = index % dim;
+ const Dtype* X_offset = X + 4 * dim * n;
+ const Dtype i = X_offset[d];
+ const Dtype f = X_offset[1 * dim + d];
+ const Dtype o = X_offset[2 * dim + d];
+ const Dtype g = X_offset[3 * dim + d];
+ const Dtype c_prev = C_prev[index];
+ const Dtype c = cont[n] * f * c_prev + i * g;
+ C[index] = c;
+ const Dtype tanh_c = tanh(c);
+ H[index] = o * tanh_c;
+ }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ const int count = top[1]->count();
+ const Dtype* C_prev = bottom[0]->gpu_data();
+ const Dtype* X = bottom[1]->gpu_data();
+ const Dtype* cont = bottom[2]->gpu_data();
+ Dtype* X_acts = X_acts_.mutable_gpu_data();
+ Dtype* C = top[0]->mutable_gpu_data();
+ Dtype* H = top[1]->mutable_gpu_data();
+ const int X_count = bottom[1]->count();
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ LSTMActsForward<Dtype><<<CAFFE_GET_BLOCKS(X_count), CAFFE_CUDA_NUM_THREADS>>>(
+ X_count, hidden_dim_, X, X_acts);
+ CUDA_POST_KERNEL_CHECK;
+ // NOLINT_NEXT_LINE(whitespace/operators)
+ LSTMUnitForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+ count, hidden_dim_, C_prev, X_acts, cont, C, H);
+ CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void LSTMUnitBackward(const int nthreads, const int dim,
+ const Dtype* C_prev, const Dtype* X, const Dtype* C, const Dtype* H,
+ const Dtype* cont, const Dtype* C_diff, const Dtype* H_diff,
+ Dtype* C_prev_diff, Dtype* X_diff) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int n = index / dim;
+ const int d = index % dim;
+ const Dtype* X_offset = X + 4 * dim * n;
+ const Dtype i = X_offset[d];
+ const Dtype f = X_offset[1 * dim + d];
+ const Dtype o = X_offset[2 * dim + d];
+ const Dtype g = X_offset[3 * dim + d];
+ const Dtype c_prev = C_prev[index];
+ const Dtype c = C[index];
+ const Dtype tanh_c = tanh(c);
+ Dtype* c_prev_diff = C_prev_diff + index;
+ Dtype* X_diff_offset = X_diff + 4 * dim * n;
+ Dtype* i_diff = X_diff_offset + d;
+ Dtype* f_diff = X_diff_offset + 1 * dim + d;
+ Dtype* o_diff = X_diff_offset + 2 * dim + d;
+ Dtype* g_diff = X_diff_offset + 3 * dim + d;
+ const Dtype c_term_diff =
+ C_diff[index] + H_diff[index] * o * (1 - tanh_c * tanh_c);
+ const Dtype cont_n = cont[n];
+ *c_prev_diff = cont_n * c_term_diff * f;
+ *i_diff = c_term_diff * g;
+ *f_diff = cont_n * c_term_diff * c_prev;
+ *o_diff = H_diff[index] * tanh_c;
+ *g_diff = c_term_diff * i;
+ }
+}
+
+template <typename Dtype>
+__global__ void LSTMActsBackward(const int nthreads, const int dim,
+ const Dtype* X_acts, const Dtype* X_acts_diff, Dtype* X_diff) {
+ CUDA_KERNEL_LOOP(index, nthreads) {
+ const int x_dim = 4 * dim;
+ const int d = index % x_dim;
+ const Dtype X_act = X_acts[index];
+ if (d < 3 * dim) {
+ X_diff[index] = X_acts_diff[index] * X_act * (Dtype(1) - X_act);
+ } else {
+ X_diff[index] = X_acts_diff[index] * (Dtype(1) - X_act * X_act);
+ }
+ }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down,
+ const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[2]) << "Cannot backpropagate to sequence indicators.";
+ if (!propagate_down[0] && !propagate_down[1]) { return; }
+
+ const int count = top[1]->count();
+ const Dtype* C_prev = bottom[0]->gpu_data();
+ const Dtype* X_acts = X_acts_.gpu_data();
+ const Dtype* cont = bottom[2]->gpu_data();
+ const Dtype* C = top[0]->gpu_data();
+ const Dtype* H = top[1]->gpu_data();
+ const Dtype* C_diff = top[0]->gpu_diff();
+ const Dtype* H_diff = top[1]->gpu_diff();
+ Dtype* C_prev_diff = bottom[0]->mutable_gpu_diff();
+ Dtype* X_acts_diff = X_acts_.mutable_gpu_diff();
+ LSTMUnitBackward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(count, hidden_dim_,
+ C_prev, X_acts, C, H, cont, C_diff, H_diff, C_prev_diff, X_acts_diff);
+ CUDA_POST_KERNEL_CHECK;
+ const int X_count = bottom[1]->count();
+ Dtype* X_diff = bottom[1]->mutable_gpu_diff();
+ LSTMActsBackward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
+ <<<CAFFE_GET_BLOCKS(X_count), CAFFE_CUDA_NUM_THREADS>>>(
+ X_count, hidden_dim_, X_acts, X_acts_diff, X_diff);
+ CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LSTMUnitLayer);
+
+} // namespace caffe
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 8290987..975f484 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -107,7 +107,7 @@ void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
template <typename Dtype>
void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
- CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
+ CHECK(data_) << "MemoryDataLayer needs to be initialized by calling Reset";
top[0]->Reshape(batch_size_, channels_, height_, width_);
top[1]->Reshape(batch_size_, 1, 1, 1);
top[0]->set_cpu_data(data_ + pos_ * size_);
diff --git a/src/caffe/layers/parameter_layer.cpp b/src/caffe/layers/parameter_layer.cpp
new file mode 100644
index 0000000..fbd326f
--- /dev/null
+++ b/src/caffe/layers/parameter_layer.cpp
@@ -0,0 +1,8 @@
+#include "caffe/layers/parameter_layer.hpp"
+
+namespace caffe {
+
+INSTANTIATE_CLASS(ParameterLayer);
+REGISTER_LAYER_CLASS(Parameter);
+
+} // namespace caffe
diff --git a/src/caffe/layers/recurrent_layer.cpp b/src/caffe/layers/recurrent_layer.cpp
new file mode 100644
index 0000000..e0c8277
--- /dev/null
+++ b/src/caffe/layers/recurrent_layer.cpp
@@ -0,0 +1,295 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ CHECK_GE(bottom[0]->num_axes(), 2)
+ << "bottom[0] must have at least 2 axes -- (#timesteps, #streams, ...)";
+ T_ = bottom[0]->shape(0);
+ N_ = bottom[0]->shape(1);
+ LOG(INFO) << "Initializing recurrent layer: assuming input batch contains "
+ << T_ << " timesteps of " << N_ << " independent streams.";
+
+ CHECK_EQ(bottom[1]->num_axes(), 2)
+ << "bottom[1] must have exactly 2 axes -- (#timesteps, #streams)";
+ CHECK_EQ(T_, bottom[1]->shape(0));
+ CHECK_EQ(N_, bottom[1]->shape(1));
+
+ // If expose_hidden is set, we take as input and produce as output
+ // the hidden state blobs at the first and last timesteps.
+ expose_hidden_ = this->layer_param_.recurrent_param().expose_hidden();
+
+ // Get (recurrent) input/output names.
+ vector<string> output_names;
+ OutputBlobNames(&output_names);
+ vector<string> recur_input_names;
+ RecurrentInputBlobNames(&recur_input_names);
+ vector<string> recur_output_names;
+ RecurrentOutputBlobNames(&recur_output_names);
+ const int num_recur_blobs = recur_input_names.size();
+ CHECK_EQ(num_recur_blobs, recur_output_names.size());
+
+ // If provided, bottom[2] is a static input to the recurrent net.
+ const int num_hidden_exposed = expose_hidden_ * num_recur_blobs;
+ static_input_ = (bottom.size() > 2 + num_hidden_exposed);
+ if (static_input_) {
+ CHECK_GE(bottom[2]->num_axes(), 1);
+ CHECK_EQ(N_, bottom[2]->shape(0));
+ }
+
+ // Create a NetParameter; setup the inputs that aren't unique to particular
+ // recurrent architectures.
+ NetParameter net_param;
+
+ LayerParameter* input_layer_param = net_param.add_layer();
+ input_layer_param->set_type("Input");
+ InputParameter* input_param = input_layer_param->mutable_input_param();
+ input_layer_param->add_top("x");
+ BlobShape input_shape;
+ for (int i = 0; i < bottom[0]->num_axes(); ++i) {
+ input_shape.add_dim(bottom[0]->shape(i));
+ }
+ input_param->add_shape()->CopyFrom(input_shape);
+
+ input_shape.Clear();
+ for (int i = 0; i < bottom[1]->num_axes(); ++i) {
+ input_shape.add_dim(bottom[1]->shape(i));
+ }
+ input_layer_param->add_top("cont");
+ input_param->add_shape()->CopyFrom(input_shape);
+
+ if (static_input_) {
+ input_shape.Clear();
+ for (int i = 0; i < bottom[2]->num_axes(); ++i) {
+ input_shape.add_dim(bottom[2]->shape(i));
+ }
+ input_layer_param->add_top("x_static");
+ input_param->add_shape()->CopyFrom(input_shape);
+ }
+
+ // Call the child's FillUnrolledNet implementation to specify the unrolled
+ // recurrent architecture.
+ this->FillUnrolledNet(&net_param);
+
+ // Prepend this layer's name to the names of each layer in the unrolled net.
+ const string& layer_name = this->layer_param_.name();
+ if (layer_name.size()) {
+ for (int i = 0; i < net_param.layer_size(); ++i) {
+ LayerParameter* layer = net_param.mutable_layer(i);
+ layer->set_name(layer_name + "_" + layer->name());
+ }
+ }
+
+ // Add "pseudo-losses" to all outputs to force backpropagation.
+ // (Setting force_backward is too aggressive as we may not need to backprop to
+ // all inputs, e.g., the sequence continuation indicators.)
+ vector<string> pseudo_losses(output_names.size());
+ for (int i = 0; i < output_names.size(); ++i) {
+ LayerParameter* layer = net_param.add_layer();
+ pseudo_losses[i] = output_names[i] + "_pseudoloss";
+ layer->set_name(pseudo_losses[i]);
+ layer->set_type("Reduction");
+ layer->add_bottom(output_names[i]);
+ layer->add_top(pseudo_losses[i]);
+ layer->add_loss_weight(1);
+ }
+
+ // Create the unrolled net.
+ unrolled_net_.reset(new Net<Dtype>(net_param));
+ unrolled_net_->set_debug_info(
+ this->layer_param_.recurrent_param().debug_info());
+
+ // Setup pointers to the inputs.
+ x_input_blob_ = CHECK_NOTNULL(unrolled_net_->blob_by_name("x").get());
+ cont_input_blob_ = CHECK_NOTNULL(unrolled_net_->blob_by_name("cont").get());
+ if (static_input_) {
+ x_static_input_blob_ =
+ CHECK_NOTNULL(unrolled_net_->blob_by_name("x_static").get());
+ }
+
+ // Setup pointers to paired recurrent inputs/outputs.
+ recur_input_blobs_.resize(num_recur_blobs);
+ recur_output_blobs_.resize(num_recur_blobs);
+ for (int i = 0; i < recur_input_names.size(); ++i) {
+ recur_input_blobs_[i] =
+ CHECK_NOTNULL(unrolled_net_->blob_by_name(recur_input_names[i]).get());
+ recur_output_blobs_[i] =
+ CHECK_NOTNULL(unrolled_net_->blob_by_name(recur_output_names[i]).get());
+ }
+
+ // Setup pointers to outputs.
+ CHECK_EQ(top.size() - num_hidden_exposed, output_names.size())
+ << "OutputBlobNames must provide an output blob name for each top.";
+ output_blobs_.resize(output_names.size());
+ for (int i = 0; i < output_names.size(); ++i) {
+ output_blobs_[i] =
+ CHECK_NOTNULL(unrolled_net_->blob_by_name(output_names[i]).get());
+ }
+
+ // We should have 2 inputs (x and cont), plus a number of recurrent inputs,
+ // plus maybe a static input.
+ CHECK_EQ(2 + num_recur_blobs + static_input_,
+ unrolled_net_->input_blobs().size());
+
+ // This layer's parameters are any parameters in the layers of the unrolled
+ // net. We only want one copy of each parameter, so check that the parameter
+ // is "owned" by the layer, rather than shared with another.
+ this->blobs_.clear();
+ for (int i = 0; i < unrolled_net_->params().size(); ++i) {
+ if (unrolled_net_->param_owners()[i] == -1) {
+ LOG(INFO) << "Adding parameter " << i << ": "
+ << unrolled_net_->param_display_names()[i];
+ this->blobs_.push_back(unrolled_net_->params()[i]);
+ }
+ }
+ // Check that param_propagate_down is set for all of the parameters in the
+ // unrolled net; set param_propagate_down to true in this layer.
+ for (int i = 0; i < unrolled_net_->layers().size(); ++i) {
+ for (int j = 0; j < unrolled_net_->layers()[i]->blobs().size(); ++j) {
+ CHECK(unrolled_net_->layers()[i]->param_propagate_down(j))
+ << "param_propagate_down not set for layer " << i << ", param " << j;
+ }
+ }
+ this->param_propagate_down_.clear();
+ this->param_propagate_down_.resize(this->blobs_.size(), true);
+
+ // Set the diffs of recurrent outputs to 0 -- we can't backpropagate across
+ // batches.
+ for (int i = 0; i < recur_output_blobs_.size(); ++i) {
+ caffe_set(recur_output_blobs_[i]->count(), Dtype(0),
+ recur_output_blobs_[i]->mutable_cpu_diff());
+ }
+
+ // Check that the last output_names.size() layers are the pseudo-losses;
+ // set last_layer_index so that we don't actually run these layers.
+ const vector<string>& layer_names = unrolled_net_->layer_names();
+ last_layer_index_ = layer_names.size() - 1 - pseudo_losses.size();
+ for (int i = last_layer_index_ + 1, j = 0; i < layer_names.size(); ++i, ++j) {
+ CHECK_EQ(layer_names[i], pseudo_losses[j]);
+ }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ CHECK_GE(bottom[0]->num_axes(), 2)
+ << "bottom[0] must have at least 2 axes -- (#timesteps, #streams, ...)";
+ CHECK_EQ(T_, bottom[0]->shape(0)) << "input number of timesteps changed";
+ N_ = bottom[0]->shape(1);
+ CHECK_EQ(bottom[1]->num_axes(), 2)
+ << "bottom[1] must have exactly 2 axes -- (#timesteps, #streams)";
+ CHECK_EQ(T_, bottom[1]->shape(0));
+ CHECK_EQ(N_, bottom[1]->shape(1));
+ x_input_blob_->ReshapeLike(*bottom[0]);
+ vector<int> cont_shape = bottom[1]->shape();
+ cont_input_blob_->Reshape(cont_shape);
+ if (static_input_) {
+ x_static_input_blob_->ReshapeLike(*bottom[2]);
+ }
+ vector<BlobShape> recur_input_shapes;
+ RecurrentInputShapes(&recur_input_shapes);
+ CHECK_EQ(recur_input_shapes.size(), recur_input_blobs_.size());
+ for (int i = 0; i < recur_input_shapes.size(); ++i) {
+ recur_input_blobs_[i]->Reshape(recur_input_shapes[i]);
+ }
+ unrolled_net_->Reshape();
+ x_input_blob_->ShareData(*bottom[0]);
+ x_input_blob_->ShareDiff(*bottom[0]);
+ cont_input_blob_->ShareData(*bottom[1]);
+ if (static_input_) {
+ x_static_input_blob_->ShareData(*bottom[2]);
+ x_static_input_blob_->ShareDiff(*bottom[2]);
+ }
+ if (expose_hidden_) {
+ const int bottom_offset = 2 + static_input_;
+ for (int i = bottom_offset, j = 0; i < bottom.size(); ++i, ++j) {
+ CHECK(recur_input_blobs_[j]->shape() == bottom[i]->shape())
+ << "bottom[" << i << "] shape must match hidden state input shape: "
+ << recur_input_blobs_[j]->shape_string();
+ recur_input_blobs_[j]->ShareData(*bottom[i]);
+ }
+ }
+ for (int i = 0; i < output_blobs_.size(); ++i) {
+ top[i]->ReshapeLike(*output_blobs_[i]);
+ top[i]->ShareData(*output_blobs_[i]);
+ top[i]->ShareDiff(*output_blobs_[i]);
+ }
+ if (expose_hidden_) {
+ const int top_offset = output_blobs_.size();
+ for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+ top[i]->ReshapeLike(*recur_output_blobs_[j]);
+ }
+ }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Reset() {
+ // "Reset" the hidden state of the net by zeroing out all recurrent outputs.
+ for (int i = 0; i < recur_output_blobs_.size(); ++i) {
+ caffe_set(recur_output_blobs_[i]->count(), Dtype(0),
+ recur_output_blobs_[i]->mutable_cpu_data());
+ }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ // Hacky fix for test time: reshare all the internal shared blobs, which may
+ // currently point to a stale owner blob that was dropped when Solver::Test
+ // called test_net->ShareTrainedLayersWith(net_.get()).
+ // TODO: somehow make this work non-hackily.
+ if (this->phase_ == TEST) {
+ unrolled_net_->ShareWeights();
+ }
+
+ DCHECK_EQ(recur_input_blobs_.size(), recur_output_blobs_.size());
+ if (!expose_hidden_) {
+ for (int i = 0; i < recur_input_blobs_.size(); ++i) {
+ const int count = recur_input_blobs_[i]->count();
+ DCHECK_EQ(count, recur_output_blobs_[i]->count());
+ const Dtype* timestep_T_data = recur_output_blobs_[i]->cpu_data();
+ Dtype* timestep_0_data = recur_input_blobs_[i]->mutable_cpu_data();
+ caffe_copy(count, timestep_T_data, timestep_0_data);
+ }
+ }
+
+ unrolled_net_->ForwardTo(last_layer_index_);
+
+ if (expose_hidden_) {
+ const int top_offset = output_blobs_.size();
+ for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+ top[i]->ShareData(*recur_output_blobs_[j]);
+ }
+ }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+ const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+ CHECK(!propagate_down[1]) << "Cannot backpropagate to sequence indicators.";
+
+ // TODO: skip backpropagation to inputs and parameters inside the unrolled
+ // net according to propagate_down[0] and propagate_down[2]. For now just
+ // backprop to inputs and parameters unconditionally, as either the inputs or
+ // the parameters do need backward (or Net would have set
+ // layer_needs_backward_[i] == false for this layer).
+ unrolled_net_->BackwardFrom(last_layer_index_);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(RecurrentLayer, Forward);
+#endif
+
+INSTANTIATE_CLASS(RecurrentLayer);
+
+} // namespace caffe
diff --git a/src/caffe/layers/recurrent_layer.cu b/src/caffe/layers/recurrent_layer.cu
new file mode 100644
index 0000000..4dd2b0e
--- /dev/null
+++ b/src/caffe/layers/recurrent_layer.cu
@@ -0,0 +1,44 @@
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+ const vector<Blob<Dtype>*>& top) {
+ // Hacky fix for test time... reshare all the shared blobs.
+ // TODO: somehow make this work non-hackily.
+ if (this->phase_ == TEST) {
+ unrolled_net_->ShareWeights();
+ }
+
+ DCHECK_EQ(recur_input_blobs_.size(), recur_output_blobs_.size());
+ if (!expose_hidden_) {
+ for (int i = 0; i < recur_input_blobs_.size(); ++i) {
+ const int count = recur_input_blobs_[i]->count();
+ DCHECK_EQ(count, recur_output_blobs_[i]->count());
+ const Dtype* timestep_T_data = recur_output_blobs_[i]->gpu_data();
+ Dtype* timestep_0_data = recur_input_blobs_[i]->mutable_gpu_data();
+ caffe_copy(count, timestep_T_data, timestep_0_data);
+ }
+ }
+
+ unrolled_net_->ForwardTo(last_layer_index_);
+
+ if (expose_hidden_) {
+ const int top_offset = output_blobs_.size();
+ for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+ top[i]->ShareData(*recur_output_blobs_[j]);
+ }
+ }
+}
+
+INSTANTIATE_LAYER_GPU_FORWARD(RecurrentLayer);
+
+} // namespace caffe
diff --git a/src/caffe/layers/rnn_layer.cpp b/src/caffe/layers/rnn_layer.cpp
new file mode 100644
index 0000000..f62ae8c
--- /dev/null
+++ b/src/caffe/layers/rnn_layer.cpp
@@ -0,0 +1,236 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/rnn_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
+ names->resize(1);
+ (*names)[0] = "h_0";
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
+ names->resize(1);
+ (*names)[0] = "h_" + format_int(this->T_);
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
+ const int num_output = this->layer_param_.recurrent_param().num_output();
+ shapes->resize(1);
+ (*shapes)[0].Clear();
+ (*shapes)[0].add_dim(1); // a single timestep
+ (*shapes)[0].add_dim(this->N_);
+ (*shapes)[0].add_dim(num_output);
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
+ names->resize(1);
+ (*names)[0] = "o";
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
+ const int num_output = this->layer_param_.recurrent_param().num_output();
+ CHECK_GT(num_output, 0) << "num_output must be positive";
+ const FillerParameter& weight_filler =
+ this->layer_param_.recurrent_param().weight_filler();
+ const FillerParameter& bias_filler =
+ this->layer_param_.recurrent_param().bias_filler();
+
+ // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
+ // use to save redundant code.
+ LayerParameter hidden_param;
+ hidden_param.set_type("InnerProduct");
+ hidden_param.mutable_inner_product_param()->set_num_output(num_output);
+ hidden_param.mutable_inner_product_param()->set_bias_term(false);
+ hidden_param.mutable_inner_product_param()->set_axis(2);
+ hidden_param.mutable_inner_product_param()->
+ mutable_weight_filler()->CopyFrom(weight_filler);
+
+ LayerParameter biased_hidden_param(hidden_param);
+ biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
+ biased_hidden_param.mutable_inner_product_param()->
+ mutable_bias_filler()->CopyFrom(bias_filler);
+
+ LayerParameter sum_param;
+ sum_param.set_type("Eltwise");
+ sum_param.mutable_eltwise_param()->set_operation(
+ EltwiseParameter_EltwiseOp_SUM);
+
+ LayerParameter tanh_param;
+ tanh_param.set_type("TanH");
+
+ LayerParameter scale_param;
+ scale_param.set_type("Scale");
+ scale_param.mutable_scale_param()->set_axis(0);
+
+ LayerParameter slice_param;
+ slice_param.set_type("Slice");
+ slice_param.mutable_slice_param()->set_axis(0);
+
+ vector<BlobShape> input_shapes;
+ RecurrentInputShapes(&input_shapes);
+ CHECK_EQ(1, input_shapes.size());
+
+ LayerParameter* input_layer_param = net_param->add_layer();
+ input_layer_param->set_type("Input");
+ InputParameter* input_param = input_layer_param->mutable_input_param();
+ input_layer_param->add_top("h_0");
+ input_param->add_shape()->CopyFrom(input_shapes[0]);
+
+ LayerParameter* cont_slice_param = net_param->add_layer();
+ cont_slice_param->CopyFrom(slice_param);
+ cont_slice_param->set_name("cont_slice");
+ cont_slice_param->add_bottom("cont");
+ cont_slice_param->mutable_slice_param()->set_axis(0);
+
+ // Add layer to transform all timesteps of x to the hidden state dimension.
+ // W_xh_x = W_xh * x + b_h
+ {
+ LayerParameter* x_transform_param = net_param->add_layer();
+ x_transform_param->CopyFrom(biased_hidden_param);
+ x_transform_param->set_name("x_transform");
+ x_transform_param->add_param()->set_name("W_xh");
+ x_transform_param->add_param()->set_name("b_h");
+ x_transform_param->add_bottom("x");
+ x_transform_param->add_top("W_xh_x");
+ x_transform_param->add_propagate_down(true);
+ }
+
+ if (this->static_input_) {
+ // Add layer to transform x_static to the hidden state dimension.
+ // W_xh_x_static = W_xh_static * x_static
+ LayerParameter* x_static_transform_param = net_param->add_layer();
+ x_static_transform_param->CopyFrom(hidden_param);
+ x_static_transform_param->mutable_inner_product_param()->set_axis(1);
+ x_static_transform_param->set_name("W_xh_x_static");
+ x_static_transform_param->add_param()->set_name("W_xh_static");
+ x_static_transform_param->add_bottom("x_static");
+ x_static_transform_param->add_top("W_xh_x_static_preshape");
+ x_static_transform_param->add_propagate_down(true);
+
+ LayerParameter* reshape_param = net_param->add_layer();
+ reshape_param->set_type("Reshape");
+ BlobShape* new_shape =
+ reshape_param->mutable_reshape_param()->mutable_shape();
+ new_shape->add_dim(1); // One timestep.
+ // Should infer this->N as the dimension so we can reshape on batch size.
+ new_shape->add_dim(-1);
+ new_shape->add_dim(
+ x_static_transform_param->inner_product_param().num_output());
+ reshape_param->set_name("W_xh_x_static_reshape");
+ reshape_param->add_bottom("W_xh_x_static_preshape");
+ reshape_param->add_top("W_xh_x_static");
+ }
+
+ LayerParameter* x_slice_param = net_param->add_layer();
+ x_slice_param->CopyFrom(slice_param);
+ x_slice_param->set_name("W_xh_x_slice");
+ x_slice_param->add_bottom("W_xh_x");
+
+ LayerParameter output_concat_layer;
+ output_concat_layer.set_name("o_concat");
+ output_concat_layer.set_type("Concat");
+ output_concat_layer.add_top("o");
+ output_concat_layer.mutable_concat_param()->set_axis(0);
+
+ for (int t = 1; t <= this->T_; ++t) {
+ string tm1s = format_int(t - 1);
+ string ts = format_int(t);
+
+ cont_slice_param->add_top("cont_" + ts);
+ x_slice_param->add_top("W_xh_x_" + ts);
+
+ // Add layer to flush the hidden state when beginning a new sequence,
+ // as indicated by cont_t.
+ // h_conted_{t-1} := cont_t * h_{t-1}
+ //
+ // Normally, cont_t is binary (i.e., 0 or 1), so:
+ // h_conted_{t-1} := h_{t-1} if cont_t == 1
+ // 0 otherwise
+ {
+ LayerParameter* cont_h_param = net_param->add_layer();
+ cont_h_param->CopyFrom(scale_param);
+ cont_h_param->set_name("h_conted_" + tm1s);
+ cont_h_param->add_bottom("h_" + tm1s);
+ cont_h_param->add_bottom("cont_" + ts);
+ cont_h_param->add_top("h_conted_" + tm1s);
+ }
+
+ // Add layer to compute
+ // W_hh_h_{t-1} := W_hh * h_conted_{t-1}
+ {
+ LayerParameter* w_param = net_param->add_layer();
+ w_param->CopyFrom(hidden_param);
+ w_param->set_name("W_hh_h_" + tm1s);
+ w_param->add_param()->set_name("W_hh");
+ w_param->add_bottom("h_conted_" + tm1s);
+ w_param->add_top("W_hh_h_" + tm1s);
+ w_param->mutable_inner_product_param()->set_axis(2);
+ }
+
+ // Add layers to compute
+ // h_t := \tanh( W_hh * h_conted_{t-1} + W_xh * x_t + b_h )
+ // = \tanh( W_hh_h_{t-1} + W_xh_t )
+ {
+ LayerParameter* h_input_sum_param = net_param->add_layer();
+ h_input_sum_param->CopyFrom(sum_param);
+ h_input_sum_param->set_name("h_input_sum_" + ts);
+ h_input_sum_param->add_bottom("W_hh_h_" + tm1s);
+ h_input_sum_param->add_bottom("W_xh_x_" + ts);
+ if (this->static_input_) {
+ h_input_sum_param->add_bottom("W_xh_x_static");
+ }
+ h_input_sum_param->add_top("h_neuron_input_" + ts);
+ }
+ {
+ LayerParameter* h_neuron_param = net_param->add_layer();
+ h_neuron_param->CopyFrom(tanh_param);
+ h_neuron_param->set_name("h_neuron_" + ts);
+ h_neuron_param->add_bottom("h_neuron_input_" + ts);
+ h_neuron_param->add_top("h_" + ts);
+ }
+
+ // Add layer to compute
+ // W_ho_h_t := W_ho * h_t + b_o
+ {
+ LayerParameter* w_param = net_param->add_layer();
+ w_param->CopyFrom(biased_hidden_param);
+ w_param->set_name("W_ho_h_" + ts);
+ w_param->add_param()->set_name("W_ho");
+ w_param->add_param()->set_name("b_o");
+ w_param->add_bottom("h_" + ts);
+ w_param->add_top("W_ho_h_" + ts);
+ w_param->mutable_inner_product_param()->set_axis(2);
+ }
+
+ // Add layers to compute
+ // o_t := \tanh( W_ho h_t + b_o)
+ // = \tanh( W_ho_h_t )
+ {
+ LayerParameter* o_neuron_param = net_param->add_layer();
+ o_neuron_param->CopyFrom(tanh_param);
+ o_neuron_param->set_name("o_neuron_" + ts);
+ o_neuron_param->add_bottom("W_ho_h_" + ts);
+ o_neuron_param->add_top("o_" + ts);
+ }
+ output_concat_layer.add_bottom("o_" + ts);
+ } // for (int t = 1; t <= this->T_; ++t)
+
+ net_param->add_layer()->CopyFrom(output_concat_layer);
+}
+
+INSTANTIATE_CLASS(RNNLayer);
+REGISTER_LAYER_CLASS(RNN);
+
+} // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 4ca8315..103dd4b 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -265,6 +265,9 @@ void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
const int num_samples[2] = { batch_size - num_fg, num_fg };
int item_id = 0;
+ CHECK_GT(fg_windows_.size(), 0);
+ CHECK_GT(bg_windows_.size(), 0);
+
// sample from bg set then fg set
for (int is_fg = 0; is_fg < 2; ++is_fg) {
for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 05bee79..644cb7e 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -28,11 +28,20 @@ Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
}
template <typename Dtype>
-Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)
+Net<Dtype>::Net(const string& param_file, Phase phase,
+ const int level, const vector<string>* stages,
+ const Net* root_net)
: root_net_(root_net) {
NetParameter param;
ReadNetParamsFromTextFileOrDie(param_file, ¶m);
+ // Set phase, stages and level
param.mutable_state()->set_phase(phase);
+ if (stages != NULL) {
+ for (int i = 0; i < stages->size(); i++) {
+ param.mutable_state()->add_stage((*stages)[i]);
+ }
+ }
+ param.mutable_state()->set_level(level);
Init(param);
}
@@ -56,22 +65,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
name_ = param.name();
map<string, int> blob_name_to_idx;
set<string> available_blobs;
- CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)
- << "Must specify either input_shape OR deprecated input_dim, not both.";
- if (param.input_dim_size() > 0) {
- // Deprecated 4D dimensions.
- CHECK_EQ(param.input_size() * 4, param.input_dim_size())
- << "Incorrect input blob dimension specifications.";
- } else {
- CHECK_EQ(param.input_size(), param.input_shape_size())
- << "Exactly one input_shape must be specified per input.";
- }
memory_used_ = 0;
- // set the input blobs
- for (int input_id = 0; input_id < param.input_size(); ++input_id) {
- const int layer_id = -1; // inputs have fake layer ID -1
- AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
- }
// For each layer, set up its input and output
bottom_vecs_.resize(param.layer_size());
top_vecs_.resize(param.layer_size());
@@ -118,6 +112,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
int num_top = layer_param.top_size();
for (int top_id = 0; top_id < num_top; ++top_id) {
AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
+ // Collect Input layer tops as Net inputs.
+ if (layer_param.type() == "Input") {
+ const int blob_id = blobs_.size() - 1;
+ net_input_blob_indices_.push_back(blob_id);
+ net_input_blobs_.push_back(blobs_[blob_id].get());
+ }
}
// If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
// specified fewer than the required number (as specified by
@@ -379,19 +379,17 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
return true;
}
-// Helper for Net::Init: add a new input or top blob to the net. (Inputs have
-// layer_id == -1, tops have layer_id >= 0.)
+// Helper for Net::Init: add a new top blob to the net.
template <typename Dtype>
void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
const int top_id, set<string>* available_blobs,
map<string, int>* blob_name_to_idx) {
- shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
- (new LayerParameter(param.layer(layer_id))) : NULL);
- const string& blob_name = layer_param ?
- (layer_param->top_size() > top_id ?
- layer_param->top(top_id) : "(automatic)") : param.input(top_id);
+ shared_ptr<LayerParameter> layer_param(
+ new LayerParameter(param.layer(layer_id)));
+ const string& blob_name = (layer_param->top_size() > top_id) ?
+ layer_param->top(top_id) : "(automatic)";
// Check if we are doing in-place computation
- if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
+ if (blob_name_to_idx && layer_param->bottom_size() > top_id &&
blob_name == layer_param->bottom(top_id)) {
// In-place computation
LOG_IF(INFO, Caffe::root_solver())
@@ -407,11 +405,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
} else {
// Normal output.
if (Caffe::root_solver()) {
- if (layer_param) {
- LOG(INFO) << layer_param->name() << " -> " << blob_name;
- } else {
- LOG(INFO) << "Input " << top_id << " -> " << blob_name;
- }
+ LOG(INFO) << layer_param->name() << " -> " << blob_name;
}
shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
const int blob_id = blobs_.size();
@@ -419,22 +413,8 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
blob_names_.push_back(blob_name);
blob_need_backward_.push_back(false);
if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
- if (layer_id == -1) {
- // Set the (explicitly specified) dimensions of the input blob.
- if (param.input_dim_size() > 0) {
- blob_pointer->Reshape(param.input_dim(top_id * 4),
- param.input_dim(top_id * 4 + 1),
- param.input_dim(top_id * 4 + 2),
- param.input_dim(top_id * 4 + 3));
- } else {
- blob_pointer->Reshape(param.input_shape(top_id));
- }
- net_input_blob_indices_.push_back(blob_id);
- net_input_blobs_.push_back(blob_pointer.get());
- } else {
- top_id_vecs_[layer_id].push_back(blob_id);
- top_vecs_[layer_id].push_back(blob_pointer.get());
- }
+ top_id_vecs_[layer_id].push_back(blob_id);
+ top_vecs_[layer_id].push_back(blob_pointer.get());
}
if (available_blobs) { available_blobs->insert(blob_name); }
}
@@ -456,12 +436,11 @@ int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
bottom_id_vecs_[layer_id].push_back(blob_id);
available_blobs->erase(blob_name);
- bool propagate_down = true;
+ bool need_backward = blob_need_backward_[blob_id];
// Check if the backpropagation on bottom_id should be skipped
- if (layer_param.propagate_down_size() > 0)
- propagate_down = layer_param.propagate_down(bottom_id);
- const bool need_backward = blob_need_backward_[blob_id] &&
- propagate_down;
+ if (layer_param.propagate_down_size() > 0) {
+ need_backward = layer_param.propagate_down(bottom_id);
+ }
bottom_need_backward_[layer_id].push_back(need_backward);
return blob_id;
}
@@ -566,11 +545,6 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
CHECK_GE(start, 0);
CHECK_LT(end, layers_.size());
Dtype loss = 0;
- if (debug_info_) {
- for (int i = 0; i < net_input_blobs_.size(); ++i) {
- InputDebugInfo(i);
- }
- }
for (int i = start; i <= end; ++i) {
// LOG(ERROR) << "Forwarding " << layer_names_[i];
Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
@@ -591,7 +565,7 @@ Dtype Net<Dtype>::ForwardTo(int end) {
}
template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
+const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {
if (loss != NULL) {
*loss = ForwardFromTo(0, layers_.size() - 1);
} else {
@@ -603,32 +577,13 @@ const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
template <typename Dtype>
const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
- // Copy bottom to internal bottom
+ LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "
+ << "will be removed in a future version. Use Forward(loss).";
+ // Copy bottom to net bottoms
for (int i = 0; i < bottom.size(); ++i) {
net_input_blobs_[i]->CopyFrom(*bottom[i]);
}
- return ForwardPrefilled(loss);
-}
-
-template <typename Dtype>
-string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
- BlobProtoVector blob_proto_vec;
- if (net_input_blobs_.size()) {
- blob_proto_vec.ParseFromString(input_blob_protos);
- CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())
- << "Incorrect input size.";
- for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {
- net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));
- }
- }
- ForwardPrefilled(loss);
- blob_proto_vec.Clear();
- for (int i = 0; i < net_output_blobs_.size(); ++i) {
- net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());
- }
- string output;
- blob_proto_vec.SerializeToString(&output);
- return output;
+ return Forward(loss);
}
template <typename Dtype>
@@ -645,16 +600,6 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
}
template <typename Dtype>
-void Net<Dtype>::InputDebugInfo(const int input_id) {
- const Blob<Dtype>& blob = *net_input_blobs_[input_id];
- const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
- const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
- LOG_IF(INFO, Caffe::root_solver())
- << " [Forward] "
- << "Input " << blob_name << " data: " << data_abs_val_mean;
-}
-
-template <typename Dtype>
void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
@@ -912,9 +857,6 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
param->Clear();
param->set_name(name_);
// Add bottom and top
- for (int i = 0; i < net_input_blob_indices_.size(); ++i) {
- param->add_input(blob_names_[net_input_blob_indices_[i]]);
- }
DLOG(INFO) << "Serializing " << layers_.size() << " layers";
for (int i = 0; i < layers_.size(); ++i) {
LayerParameter* layer_param = param->add_layer();
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 62f5d73..5bc41c6 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -380,7 +380,8 @@ void P2PSync<Dtype>::on_gradients_ready() {
}
template<typename Dtype>
-void P2PSync<Dtype>::run(const vector<int>& gpus) {
+void P2PSync<Dtype>::Prepare(const vector<int>& gpus,
+ vector<shared_ptr<P2PSync<Dtype> > >* syncs) {
// Pair devices for map-reduce synchronization
vector<DevicePair> pairs;
DevicePair::compute(gpus, &pairs);
@@ -391,15 +392,14 @@ void P2PSync<Dtype>::run(const vector<int>& gpus) {
LOG(INFO)<< "GPUs pairs " << s.str();
SolverParameter param(solver_->param());
- vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
// Build the GPU tree by finding the parent for each solver
for (int attempts = 0; attempts < pairs.size(); ++attempts) {
for (int i = 1; i < pairs.size(); ++i) {
- if (!syncs[i].get()) {
+ if (!syncs->at(i).get()) {
P2PSync<Dtype>* parent = NULL;
- for (int j = 0; j < syncs.size(); ++j) {
- P2PSync<Dtype>* sync = j == 0 ? this : syncs[j].get();
+ for (int j = 0; j < syncs->size(); ++j) {
+ P2PSync<Dtype>* sync = j == 0 ? this : syncs->at(j).get();
if (sync) {
const SolverParameter& p = sync->solver()->param();
if (p.device_id() == pairs[i].parent()) {
@@ -409,12 +409,18 @@ void P2PSync<Dtype>::run(const vector<int>& gpus) {
}
if (parent) {
param.set_device_id(pairs[i].device());
- syncs[i].reset(new P2PSync<Dtype>(solver_, parent, param));
- parent->children_.push_back((P2PSync<Dtype>*) syncs[i].get());
+ syncs->at(i).reset(new P2PSync<Dtype>(solver_, parent, param));
+ parent->children_.push_back((P2PSync<Dtype>*) syncs->at(i).get());
}
}
}
}
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::Run(const vector<int>& gpus) {
+ vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
+ Prepare(gpus, &syncs);
LOG(INFO)<< "Starting Optimization";
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6493a72..1556781 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,12 +63,12 @@ message FillerParameter {
message NetParameter {
optional string name = 1; // consider giving the network a name
- // The input blobs to the network.
+ // DEPRECATED. See InputParameter. The input blobs to the network.
repeated string input = 3;
- // The shape of the input blobs.
+ // DEPRECATED. See InputParameter. The shape of the input blobs.
repeated BlobShape input_shape = 8;
- // 4D input dimensions -- deprecated. Use "shape" instead.
+ // 4D input dimensions -- deprecated. Use "input_shape" instead.
// If specified, for each input blob there should be four
// values specifying the num, channels, height and width of the input blob.
// Thus, there should be a total of (4 * #input) numbers.
@@ -306,7 +306,7 @@ message ParamSpec {
// NOTE
// Update the next available ID when you add a new LayerParameter field.
//
-// LayerParameter next available layer-specific ID: 143 (last added: scale_param)
+// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the layer type
@@ -328,7 +328,12 @@ message LayerParameter {
// The blobs containing the numeric parameters of the layer.
repeated BlobProto blobs = 7;
- // Specifies on which bottoms the backpropagation should be skipped.
+ // Specifies whether to backpropagate to each bottom. If unspecified,
+ // Caffe will automatically infer whether each input needs backpropagation
+ // to compute parameter gradients. If set to true for some inputs,
+ // backpropagation to those inputs is forced; if set false for some inputs,
+ // backpropagation to those inputs is skipped.
+ //
// The size must be either 0 or equal to the number of bottoms.
repeated bool propagate_down = 11;
@@ -360,6 +365,7 @@ message LayerParameter {
optional ConcatParameter concat_param = 104;
optional ContrastiveLossParameter contrastive_loss_param = 105;
optional ConvolutionParameter convolution_param = 106;
+ optional CropParameter crop_param = 144;
optional DataParameter data_param = 107;
optional DropoutParameter dropout_param = 108;
optional DummyDataParameter dummy_data_param = 109;
@@ -374,14 +380,17 @@ message LayerParameter {
optional ImageDataParameter image_data_param = 115;
optional InfogainLossParameter infogain_loss_param = 116;
optional InnerProductParameter inner_product_param = 117;
+ optional InputParameter input_param = 143;
optional LogParameter log_param = 134;
optional LRNParameter lrn_param = 118;
optional MemoryDataParameter memory_data_param = 119;
optional MVNParameter mvn_param = 120;
+ optional ParameterParameter parameter_param = 145;
optional PoolingParameter pooling_param = 121;
optional PowerParameter power_param = 122;
optional PReLUParameter prelu_param = 131;
optional PythonParameter python_param = 130;
+ optional RecurrentParameter recurrent_param = 146;
optional ReductionParameter reduction_param = 136;
optional ReLUParameter relu_param = 123;
optional ReshapeParameter reshape_param = 133;
@@ -431,7 +440,7 @@ message LossParameter {
// Outputs that receive the ignore label will NOT be ignored in computing
// the normalization factor.
FULL = 0;
- // Divide by the total number of output locations that do not take the
+ // Divide by the total number of output locations that do not take the
// ignore_label. If ignore_label is not set, this behaves like FULL.
VALID = 1;
// Divide by the batch size.
@@ -597,6 +606,24 @@ message ConvolutionParameter {
optional bool force_nd_im2col = 17 [default = false];
}
+message CropParameter {
+ // To crop, elements of the first bottom are selected to fit the dimensions
+ // of the second, reference bottom. The crop is configured by
+ // - the crop `axis` to pick the dimensions for cropping
+ // - the crop `offset` to set the shift for all/each dimension
+ // to align the cropped bottom with the reference bottom.
+ // All dimensions up to but excluding `axis` are preserved, while
+ // the dimensions including and trailing `axis` are cropped.
+ // If only one `offset` is set, then all dimensions are offset by this amount.
+ // Otherwise, the number of offsets must equal the number of cropped axes to
+ // shift the crop in each dimension accordingly.
+ // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+ // and `axis` may be negative to index from the end (e.g., -1 for the last
+ // axis).
+ optional int32 axis = 1 [default = 2];
+ repeated uint32 offset = 2;
+}
+
message DataParameter {
enum DB {
LEVELDB = 0;
@@ -671,7 +698,7 @@ message EltwiseParameter {
// Message that stores parameters used by ELULayer
message ELUParameter {
// Described in:
- // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+ // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
// Deep Network Learning by Exponential Linear Units (ELUs). arXiv
optional float alpha = 1 [default = 1];
}
@@ -786,6 +813,19 @@ message InnerProductParameter {
// all preceding axes are retained in the output.
// May be negative to index from the end (e.g., -1 for the last axis).
optional int32 axis = 5 [default = 1];
+ // Specify whether to transpose the weight matrix or not.
+ // If transpose == true, any operations will be performed on the transpose
+ // of the weight matrix. The weight matrix itself is not going to be transposed
+ // but rather the transfer flag of operations will be toggled accordingly.
+ optional bool transpose = 6 [default = false];
+}
+
+message InputParameter {
+ // This layer produces N >= 1 top blob(s) to be assigned manually.
+ // Define N shapes to set a shape for each top.
+ // Define 1 shape to set the same shape for every top.
+ // Define no shape to defer to reshaping manually.
+ repeated BlobShape shape = 1;
}
// Message that stores parameters used by LogLayer
@@ -835,6 +875,10 @@ message MVNParameter {
optional float eps = 3 [default = 1e-9];
}
+message ParameterParameter {
+ optional BlobShape shape = 1;
+}
+
message PoolingParameter {
enum PoolMethod {
MAX = 0;
@@ -885,6 +929,25 @@ message PythonParameter {
optional bool share_in_parallel = 4 [default = false];
}
+// Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+ // The dimension of the output (and usually hidden state) representation --
+ // must be explicitly set to non-zero.
+ optional uint32 num_output = 1 [default = 0];
+
+ optional FillerParameter weight_filler = 2; // The filler for the weight
+ optional FillerParameter bias_filler = 3; // The filler for the bias
+
+ // Whether to enable displaying debug_info in the unrolled recurrent net.
+ optional bool debug_info = 4 [default = false];
+
+ // Whether to add as additional inputs (bottoms) the initial hidden state
+ // blobs, and add as additional outputs (tops) the final timestep hidden state
+ // blobs. The number of additional bottom/top blobs required depends on the
+ // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+ optional bool expose_hidden = 5 [default = false];
+}
+
// Message that stores parameters used by ReductionLayer
message ReductionParameter {
enum ReductionOp {
@@ -949,7 +1012,7 @@ message ReshapeParameter {
// reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
- // reshape_param { shape { dim: -1 dim: 0 dim: 2 } }
+ // reshape_param { shape { dim: 0 dim:-1 dim: 4 } }
//
optional BlobShape shape = 1;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a5ccf9c..ece3913 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -192,7 +192,6 @@ void Solver<Dtype>::InitTestNets() {
template <typename Dtype>
void Solver<Dtype>::Step(int iters) {
- vector<Blob<Dtype>*> bottom_vec;
const int start_iter = iter_;
const int stop_iter = iter_ + iters;
int average_loss = this->param_.average_loss();
@@ -220,7 +219,7 @@ void Solver<Dtype>::Step(int iters) {
// accumulate the loss and gradient
Dtype loss = 0;
for (int i = 0; i < param_.iter_size(); ++i) {
- loss += net_->ForwardBackward(bottom_vec);
+ loss += net_->ForwardBackward();
}
loss /= param_.iter_size();
// average the loss across iterations for smoothed reporting
@@ -311,7 +310,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
if (param_.display() && iter_ % param_.display() == 0) {
int average_loss = this->param_.average_loss();
Dtype loss;
- net_->ForwardPrefilled(&loss);
+ net_->Forward(&loss);
UpdateSmoothedLoss(loss, start_iter, average_loss);
@@ -341,7 +340,6 @@ void Solver<Dtype>::Test(const int test_net_id) {
ShareTrainedLayersWith(net_.get());
vector<Dtype> test_score;
vector<int> test_score_output_id;
- vector<Blob<Dtype>*> bottom_vec;
const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
Dtype loss = 0;
for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
@@ -362,7 +360,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
Dtype iter_loss;
const vector<Blob<Dtype>*>& result =
- test_net->Forward(bottom_vec, &iter_loss);
+ test_net->Forward(&iter_loss);
if (param_.test_compute_loss()) {
loss += iter_loss;
}
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index a9d7d51..b885622 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -51,6 +51,14 @@ TYPED_TEST(BlobSimpleTest, TestReshape) {
EXPECT_EQ(this->blob_->count(), 120);
}
+TYPED_TEST(BlobSimpleTest, TestReshapeZero) {
+ vector<int> shape(2);
+ shape[0] = 0;
+ shape[1] = 5;
+ this->blob_->Reshape(shape);
+ EXPECT_EQ(this->blob_->count(), 0);
+}
+
TYPED_TEST(BlobSimpleTest, TestLegacyBlobProtoShapeEquals) {
BlobProto blob_proto;
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index c8caf5a..6473b74 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,6 +1,3 @@
-// The main caffe test code. Your test cpp code should include this hpp
-// to allow a main function to be compiled into the binary.
-
#include "caffe/caffe.hpp"
#include "caffe/test/test_caffe_main.hpp"
@@ -34,6 +31,7 @@ int main(int argc, char** argv) {
cudaGetDevice(&device);
cout << "Current device id: " << device << endl;
cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device);
+ cout << "Current device name: " << CAFFE_TEST_CUDA_PROP.name << endl;
#endif
// invoke the test.
return RUN_ALL_TESTS();
diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp
new file mode 100644
index 0000000..ce2c736
--- /dev/null
+++ b/src/caffe/test/test_crop_layer.cpp
@@ -0,0 +1,283 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/crop_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class CropLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ CropLayerTest()
+ : blob_bottom_0_(new Blob<Dtype>(2, 4, 5, 4)),
+ blob_bottom_1_(new Blob<Dtype>(2, 3, 4, 2)),
+ blob_top_(new Blob<Dtype>()) {}
+ virtual void SetUp() {
+ // fill the values
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_0_);
+ filler.Fill(this->blob_bottom_1_);
+
+ blob_bottom_vec_.push_back(blob_bottom_0_);
+ blob_bottom_vec_.push_back(blob_bottom_1_);
+ blob_top_vec_.push_back(blob_top_);
+ }
+
+ virtual ~CropLayerTest() {
+ delete blob_bottom_0_; delete blob_bottom_1_;
+ delete blob_top_;
+ }
+
+ Blob<Dtype>* const blob_bottom_0_;
+ Blob<Dtype>* const blob_bottom_1_;
+ Blob<Dtype>* const blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+
+TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(CropLayerTest, TestSetupShapeAll) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ // Crop all dimensions
+ layer_param.mutable_crop_param()->set_axis(0);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+ EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeDefault) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ // Crop last two dimensions, axis is 2 by default
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+ if (i < 2) {
+ EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+ } else {
+ EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ // Crop last dimension by negative indexing
+ layer_param.mutable_crop_param()->set_axis(-1);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+ if (i < 3) {
+ EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+ } else {
+ EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestDimensionsCheck) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ // Reshape size blob to have incompatible sizes for uncropped dimensions:
+ // the size blob has more channels than the data blob, but this is fine
+ // since the channels dimension is not cropped in this configuration.
+ this->blob_bottom_1_->Reshape(2, 5, 4, 2);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+ if (i < 2) {
+ EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+ } else {
+ EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAll) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(0);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+ for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+ for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+ for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+ if ( n < this->blob_top_->shape(0) &&
+ c < this->blob_top_->shape(1) &&
+ h < this->blob_top_->shape(2) &&
+ w < this->blob_top_->shape(3) ) {
+ EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+ this->blob_bottom_0_->data_at(n, c, h, w));
+ }
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAllOffset) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(0);
+ layer_param.mutable_crop_param()->add_offset(0);
+ layer_param.mutable_crop_param()->add_offset(1);
+ layer_param.mutable_crop_param()->add_offset(1);
+ layer_param.mutable_crop_param()->add_offset(2);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+ for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+ for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+ for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+ if ( n < this->blob_top_->shape(0) &&
+ c < this->blob_top_->shape(1) &&
+ h < this->blob_top_->shape(2) &&
+ w < this->blob_top_->shape(3) ) {
+ EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+ this->blob_bottom_0_->data_at(n, c+1, h+1, w+2));
+ }
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestCropHW) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(2);
+ layer_param.mutable_crop_param()->add_offset(1);
+ layer_param.mutable_crop_param()->add_offset(2);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+ for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+ for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+ for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+ if (n < this->blob_top_->shape(0) &&
+ c < this->blob_top_->shape(1) &&
+ h < this->blob_top_->shape(2) &&
+ w < this->blob_top_->shape(3)) {
+ EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+ this->blob_bottom_0_->data_at(n, c, h+1, w+2));
+ }
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5D) {
+ typedef typename TypeParam::Dtype Dtype;
+ // Add dimension to each bottom for >4D check
+ vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+ vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+ bottom_0_shape.push_back(2);
+ bottom_1_shape.push_back(1);
+ this->blob_bottom_0_->Reshape(bottom_0_shape);
+ this->blob_bottom_1_->Reshape(bottom_1_shape);
+ FillerParameter filler_param;
+ GaussianFiller<Dtype> filler(filler_param);
+ filler.Fill(this->blob_bottom_0_);
+ filler.Fill(this->blob_bottom_1_);
+ // Make layer
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(2);
+ layer_param.mutable_crop_param()->add_offset(1);
+ layer_param.mutable_crop_param()->add_offset(2);
+ layer_param.mutable_crop_param()->add_offset(0);
+ CropLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ vector<int> bottom_idx = vector<int>(5, 0);
+ vector<int> top_idx = vector<int>(5, 0);
+ for (int n = 0; n < this->blob_bottom_0_->shape(0); ++n) {
+ for (int c = 0; c < this->blob_bottom_0_->shape(1); ++c) {
+ for (int z = 0; z < this->blob_bottom_0_->shape(2); ++z) {
+ for (int h = 0; h < this->blob_bottom_0_->shape(3); ++h) {
+ for (int w = 0; w < this->blob_bottom_0_->shape(4); ++w) {
+ if (n < this->blob_top_->shape(0) &&
+ c < this->blob_top_->shape(1) &&
+ z < this->blob_top_->shape(2) &&
+ h < this->blob_top_->shape(3) &&
+ w < this->blob_top_->shape(4)) {
+ bottom_idx[0] = top_idx[0] = n;
+ bottom_idx[1] = top_idx[1] = c;
+ bottom_idx[2] = z;
+ bottom_idx[3] = h;
+ bottom_idx[4] = top_idx[4] = w;
+ top_idx[2] = z+1;
+ top_idx[3] = h+2;
+ EXPECT_EQ(this->blob_top_->data_at(bottom_idx),
+ this->blob_bottom_0_->data_at(top_idx));
+ }
+ }
+ }
+ }
+ }
+ }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAllGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(0);
+ CropLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCropHWGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(2);
+ layer_param.mutable_crop_param()->add_offset(1);
+ layer_param.mutable_crop_param()->add_offset(2);
+ CropLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5DGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ layer_param.mutable_crop_param()->set_axis(2);
+ layer_param.mutable_crop_param()->add_offset(1);
+ layer_param.mutable_crop_param()->add_offset(2);
+ layer_param.mutable_crop_param()->add_offset(0);
+ CropLayer<Dtype> layer(layer_param);
+ // Add dimension to each bottom for >4D check
+ vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+ vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+ bottom_0_shape.push_back(2);
+ bottom_1_shape.push_back(1);
+ this->blob_bottom_0_->Reshape(bottom_0_shape);
+ this->blob_bottom_1_->Reshape(bottom_1_shape);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+}
+
+} // namespace caffe
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 6103918..31bf1c1 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -3,7 +3,6 @@
#include <vector>
#include "gtest/gtest.h"
-#include "leveldb/db.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
index dc7f5c4..13f13a8 100644
--- a/src/caffe/test/test_embed_layer.cpp
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -124,7 +124,7 @@ TYPED_TEST(EmbedLayerTest, TestForwardWithBias) {
top_offset[4] = 0;
bias_offset[0] = 0;
for (int j = 0; j < kNumOutput; ++j) {
- EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) +
+ EXPECT_FLOAT_EQ(layer->blobs()[0]->data_at(weight_offset) +
layer->blobs()[1]->data_at(bias_offset),
this->blob_top_->data_at(top_offset));
++top_offset[4];
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 84c6747..975a8f0 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -185,9 +185,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
this->InitSolverFromProtoString(proto.str());
if (from_snapshot != NULL) {
this->solver_->Restore(from_snapshot);
- vector<Blob<Dtype>*> empty_bottom_vec;
for (int i = 0; i < this->solver_->iter(); ++i) {
- this->solver_->net()->Forward(empty_bottom_vec);
+ this->solver_->net()->Forward();
}
}
if (devices == 1) {
@@ -205,7 +204,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
Caffe::set_solver_count(gpus.size());
this->sync_.reset(new P2PSync<Dtype>(
this->solver_, NULL, this->solver_->param()));
- this->sync_->run(gpus);
+ this->sync_->Run(gpus);
Caffe::set_solver_count(1);
}
if (snapshot) {
@@ -231,8 +230,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
// Run a forward pass, and manually compute the update values from the
// result.
Net<Dtype>& net = *this->solver_->net();
- vector<Blob<Dtype>*> empty_bottom_vec;
- net.Forward(empty_bottom_vec);
+ net.Forward();
ASSERT_TRUE(net.has_blob("data"));
const Blob<Dtype>& data = *net.blob_by_name("data");
ASSERT_TRUE(net.has_blob("targets"));
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index a4080cc..ce5e0bc 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -34,16 +34,24 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
std::ofstream outfile(filename_.c_str(), std::ofstream::out);
LOG(INFO) << "Using temporary file " << filename_;
for (int i = 0; i < 5; ++i) {
- outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
+ outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i << std::endl;
}
outfile.close();
// Create test input file for images of distinct sizes.
MakeTempFilename(&filename_reshape_);
std::ofstream reshapefile(filename_reshape_.c_str(), std::ofstream::out);
LOG(INFO) << "Using temporary file " << filename_reshape_;
- reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0;
- reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1;
+ reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0 << std::endl;
+ reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1
+ << std::endl;
reshapefile.close();
+ // Create test input file for images with space in names
+ MakeTempFilename(&filename_space_);
+ std::ofstream spacefile(filename_space_.c_str(), std::ofstream::out);
+ LOG(INFO) << "Using temporary file " << filename_space_;
+ spacefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0 << std::endl;
+ spacefile << EXAMPLES_SOURCE_DIR "images/cat gray.jpg " << 1 << std::endl;
+ spacefile.close();
}
virtual ~ImageDataLayerTest() {
@@ -54,6 +62,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
int seed_;
string filename_;
string filename_reshape_;
+ string filename_space_;
Blob<Dtype>* const blob_top_data_;
Blob<Dtype>* const blob_top_label_;
vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -177,5 +186,34 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
}
}
+TYPED_TEST(ImageDataLayerTest, TestSpace) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter param;
+ ImageDataParameter* image_data_param = param.mutable_image_data_param();
+ image_data_param->set_batch_size(1);
+ image_data_param->set_source(this->filename_space_.c_str());
+ image_data_param->set_shuffle(false);
+ ImageDataLayer<Dtype> layer(param);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_label_->num(), 1);
+ EXPECT_EQ(this->blob_top_label_->channels(), 1);
+ EXPECT_EQ(this->blob_top_label_->height(), 1);
+ EXPECT_EQ(this->blob_top_label_->width(), 1);
+ // cat.jpg
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_data_->num(), 1);
+ EXPECT_EQ(this->blob_top_data_->channels(), 3);
+ EXPECT_EQ(this->blob_top_data_->height(), 360);
+ EXPECT_EQ(this->blob_top_data_->width(), 480);
+ EXPECT_EQ(this->blob_top_label_->cpu_data()[0], 0);
+ // cat gray.jpg
+ layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(this->blob_top_data_->num(), 1);
+ EXPECT_EQ(this->blob_top_data_->channels(), 3);
+ EXPECT_EQ(this->blob_top_data_->height(), 360);
+ EXPECT_EQ(this->blob_top_data_->width(), 480);
+ EXPECT_EQ(this->blob_top_label_->cpu_data()[0], 1);
+}
+
} // namespace caffe
#endif // USE_OPENCV
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index b888b51..f1ec233 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -60,6 +60,50 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
EXPECT_EQ(this->blob_top_->channels(), 10);
}
+/** @brief TestSetUp while toggling tranpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeFalse) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
+ LayerParameter layer_param;
+ InnerProductParameter* inner_product_param =
+ layer_param.mutable_inner_product_param();
+ inner_product_param->set_num_output(10);
+ inner_product_param->set_transpose(false);
+ shared_ptr<InnerProductLayer<Dtype> > layer(
+ new InnerProductLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(2, this->blob_top_->num());
+ EXPECT_EQ(1, this->blob_top_->height());
+ EXPECT_EQ(1, this->blob_top_->width());
+ EXPECT_EQ(10, this->blob_top_->channels());
+ EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+ EXPECT_EQ(10, layer->blobs()[0]->shape(0));
+ EXPECT_EQ(60, layer->blobs()[0]->shape(1));
+}
+
+/** @brief TestSetUp while toggling tranpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeTrue) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
+ LayerParameter layer_param;
+ InnerProductParameter* inner_product_param =
+ layer_param.mutable_inner_product_param();
+ inner_product_param->set_num_output(10);
+ inner_product_param->set_transpose(true);
+ shared_ptr<InnerProductLayer<Dtype> > layer(
+ new InnerProductLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(2, this->blob_top_->num());
+ EXPECT_EQ(1, this->blob_top_->height());
+ EXPECT_EQ(1, this->blob_top_->width());
+ EXPECT_EQ(10, this->blob_top_->channels());
+ EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+ EXPECT_EQ(60, layer->blobs()[0]->shape(0));
+ EXPECT_EQ(10, layer->blobs()[0]->shape(1));
+}
+
TYPED_TEST(InnerProductLayerTest, TestForward) {
typedef typename TypeParam::Dtype Dtype;
this->blob_bottom_vec_.push_back(this->blob_bottom_);
@@ -91,6 +135,79 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
}
}
+/**
+ * @brief Init. an IP layer without transpose + random weights,
+ * run Forward, save the result.
+ * Init. another IP layer with transpose.
+ * manually copy and transpose the weights from the first IP layer,
+ * then run Forward on the same input and check that the result is the same
+ */
+TYPED_TEST(InnerProductLayerTest, TestForwardTranspose) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
+ bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+ IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+ if (Caffe::mode() == Caffe::CPU ||
+ sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+ LayerParameter layer_param;
+ InnerProductParameter* inner_product_param =
+ layer_param.mutable_inner_product_param();
+ inner_product_param->set_num_output(10);
+ inner_product_param->mutable_weight_filler()->set_type("uniform");
+ inner_product_param->mutable_bias_filler()->set_type("uniform");
+ inner_product_param->mutable_bias_filler()->set_min(1);
+ inner_product_param->mutable_bias_filler()->set_max(2);
+ inner_product_param->set_transpose(false);
+ shared_ptr<InnerProductLayer<Dtype> > layer(
+ new InnerProductLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ const int count = this->blob_top_->count();
+ Blob<Dtype>* const top = new Blob<Dtype>();
+ top->ReshapeLike(*this->blob_top_);
+ caffe_copy(count, this->blob_top_->cpu_data(), top->mutable_cpu_data());
+ this->blob_top_vec_.clear();
+ this->blob_top_vec_.push_back(new Blob<Dtype>());
+ inner_product_param->set_transpose(true);
+ shared_ptr<InnerProductLayer<Dtype> > ip_t(
+ new InnerProductLayer<Dtype>(layer_param));
+ ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ const int count_w = layer->blobs()[0]->count();
+ EXPECT_EQ(count_w, ip_t->blobs()[0]->count());
+ // manually copy and transpose the weights from 1st IP layer into 2nd
+ const Dtype* w = layer->blobs()[0]->cpu_data();
+ Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+ const int width = layer->blobs()[0]->shape(1);
+ const int width_t = ip_t->blobs()[0]->shape(1);
+ for (int i = 0; i < count_w; ++i) {
+ int r = i / width;
+ int c = i % width;
+ w_t[c*width_t+r] = w[r*width+c]; // copy while transposing
+ }
+ // copy bias from 1st IP layer to 2nd IP layer
+ ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+ caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+ ip_t->blobs()[1]->mutable_cpu_data());
+ ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ EXPECT_EQ(count, this->blob_top_->count())
+ << "Invalid count for top blob for IP with transpose.";
+ Blob<Dtype>* const top_t = new Blob<Dtype>();\
+ top_t->ReshapeLike(*this->blob_top_vec_[0]);
+ caffe_copy(count,
+ this->blob_top_vec_[0]->cpu_data(),
+ top_t->mutable_cpu_data());
+ const Dtype* data = top->cpu_data();
+ const Dtype* data_t = top_t->cpu_data();
+ for (int i = 0; i < count; ++i) {
+ EXPECT_FLOAT_EQ(data[i], data_t[i]);
+ }
+ } else {
+ LOG(ERROR) << "Skipping test due to old architecture.";
+ }
+}
+
TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {
typedef typename TypeParam::Dtype Dtype;
this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);
@@ -148,4 +265,127 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) {
}
}
+TYPED_TEST(InnerProductLayerTest, TestGradientTranspose) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
+ bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+ IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+ if (Caffe::mode() == Caffe::CPU ||
+ sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+ LayerParameter layer_param;
+ InnerProductParameter* inner_product_param =
+ layer_param.mutable_inner_product_param();
+ inner_product_param->set_num_output(11);
+ inner_product_param->mutable_weight_filler()->set_type("gaussian");
+ inner_product_param->mutable_bias_filler()->set_type("gaussian");
+ inner_product_param->mutable_bias_filler()->set_min(1);
+ inner_product_param->mutable_bias_filler()->set_max(2);
+ inner_product_param->set_transpose(true);
+ InnerProductLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_);
+ } else {
+ LOG(ERROR) << "Skipping test due to old architecture.";
+ }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestBackwardTranspose) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->blob_bottom_vec_.push_back(this->blob_bottom_);
+ bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+ IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+ if (Caffe::mode() == Caffe::CPU ||
+ sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+ LayerParameter layer_param;
+ InnerProductParameter* inner_product_param =
+ layer_param.mutable_inner_product_param();
+ inner_product_param->set_num_output(10);
+ inner_product_param->mutable_weight_filler()->set_type("uniform");
+ inner_product_param->mutable_bias_filler()->set_type("uniform");
+ inner_product_param->mutable_bias_filler()->set_min(1);
+ inner_product_param->mutable_bias_filler()->set_max(2);
+ inner_product_param->set_transpose(false);
+ shared_ptr<InnerProductLayer<Dtype> > layer(
+ new InnerProductLayer<Dtype>(layer_param));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ // copy top blob
+ Blob<Dtype>* const top = new Blob<Dtype>();
+ top->CopyFrom(*this->blob_top_, false, true);
+ // fake top diff
+ Blob<Dtype>* const diff = new Blob<Dtype>();
+ diff->ReshapeLike(*this->blob_top_);
+ {
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(diff);
+ }
+ caffe_copy(this->blob_top_vec_[0]->count(),
+ diff->cpu_data(),
+ this->blob_top_vec_[0]->mutable_cpu_diff());
+ vector<bool> propagate_down(1, true);
+ layer->Backward(this->blob_top_vec_,
+ propagate_down,
+ this->blob_bottom_vec_);
+ // copy first ip's weights and their diffs
+ Blob<Dtype>* const w = new Blob<Dtype>();
+ w->CopyFrom(*layer->blobs()[0], false, true);
+ w->CopyFrom(*layer->blobs()[0], true, true);
+ // copy bottom diffs
+ Blob<Dtype>* const bottom_diff = new Blob<Dtype>();
+ bottom_diff->CopyFrom(*this->blob_bottom_vec_[0], true, true);
+ // repeat original top with tranposed ip
+ this->blob_top_vec_.clear();
+ this->blob_top_vec_.push_back(new Blob<Dtype>());
+ inner_product_param->set_transpose(true);
+ shared_ptr<InnerProductLayer<Dtype> > ip_t(
+ new InnerProductLayer<Dtype>(layer_param));
+ ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ // manually copy and transpose the weights from 1st IP layer into 2nd
+ {
+ const Dtype* w_src = w->cpu_data();
+ Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+ const int width = layer->blobs()[0]->shape(1);
+ const int width_t = ip_t->blobs()[0]->shape(1);
+ for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+ int r = i / width;
+ int c = i % width;
+ w_t[c*width_t+r] = w_src[r*width+c]; // copy while transposing
+ }
+ // copy bias from 1st IP layer to 2nd IP layer
+ ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+ caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+ ip_t->blobs()[1]->mutable_cpu_data());
+ }
+ ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ caffe_copy(this->blob_top_vec_[0]->count(),
+ diff->cpu_data(),
+ this->blob_top_vec_[0]->mutable_cpu_diff());
+ ip_t->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+ const Dtype* data = w->cpu_diff();
+ const Dtype* data_t = ip_t->blobs()[0]->cpu_diff();
+ const int WIDTH = layer->blobs()[0]->shape(1);
+ const int WIDTH_T = ip_t->blobs()[0]->shape(1);
+ for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+ int r = i / WIDTH;
+ int c = i % WIDTH;
+ EXPECT_NE(Dtype(0.), data[r*WIDTH+c]);
+ EXPECT_FLOAT_EQ(data[r*WIDTH+c], data_t[c*WIDTH_T+r]);
+ }
+ data = bottom_diff->cpu_diff();
+ data_t = this->blob_bottom_vec_[0]->cpu_diff();
+ for (int i = 0; i < this->blob_bottom_vec_[0]->count(); ++i) {
+ EXPECT_NE(Dtype(0.), data[i]);
+ EXPECT_FLOAT_EQ(data[i], data_t[i]);
+ }
+ } else {
+ LOG(ERROR) << "Skipping test due to old architecture.";
+ }
+}
+
} // namespace caffe
diff --git a/src/caffe/test/test_lstm_layer.cpp b/src/caffe/test/test_lstm_layer.cpp
new file mode 100644
index 0000000..51905ba
--- /dev/null
+++ b/src/caffe/test/test_lstm_layer.cpp
@@ -0,0 +1,288 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class LSTMLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ LSTMLayerTest() : num_output_(7) {
+ blob_bottom_vec_.push_back(&blob_bottom_);
+ blob_bottom_vec_.push_back(&blob_bottom_cont_);
+ blob_top_vec_.push_back(&blob_top_);
+ unit_blob_bottom_vec_.push_back(&unit_blob_bottom_c_prev_);
+ unit_blob_bottom_vec_.push_back(&unit_blob_bottom_x_);
+ unit_blob_bottom_vec_.push_back(&unit_blob_bottom_cont_);
+ unit_blob_top_vec_.push_back(&unit_blob_top_c_);
+ unit_blob_top_vec_.push_back(&unit_blob_top_h_);
+
+ ReshapeBlobs(1, 3);
+
+ layer_param_.mutable_recurrent_param()->set_num_output(num_output_);
+ FillerParameter* weight_filler =
+ layer_param_.mutable_recurrent_param()->mutable_weight_filler();
+ weight_filler->set_type("gaussian");
+ weight_filler->set_std(0.2);
+ FillerParameter* bias_filler =
+ layer_param_.mutable_recurrent_param()->mutable_bias_filler();
+ bias_filler->set_type("gaussian");
+ bias_filler->set_std(0.1);
+
+ layer_param_.set_phase(TEST);
+ }
+
+ void ReshapeBlobs(int num_timesteps, int num_instances) {
+ blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);
+ blob_bottom_static_.Reshape(num_instances, 2, 3, 4);
+ vector<int> shape(2);
+ shape[0] = num_timesteps;
+ shape[1] = num_instances;
+ blob_bottom_cont_.Reshape(shape);
+ shape.push_back(num_output_);
+
+ shape[0] = 1; shape[1] = num_instances; shape[2] = 4 * num_output_;
+ unit_blob_bottom_x_.Reshape(shape);
+ shape[0] = 1; shape[1] = num_instances; shape[2] = num_output_;
+ unit_blob_bottom_c_prev_.Reshape(shape);
+ shape.resize(2);
+ shape[0] = 1; shape[1] = num_instances;
+ unit_blob_bottom_cont_.Reshape(shape);
+
+ FillerParameter filler_param;
+ filler_param.set_min(-1);
+ filler_param.set_max(1);
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(&blob_bottom_);
+ filler.Fill(&unit_blob_bottom_c_prev_);
+ filler.Fill(&unit_blob_bottom_x_);
+ }
+
+ int num_output_;
+ LayerParameter layer_param_;
+ Blob<Dtype> blob_bottom_;
+ Blob<Dtype> blob_bottom_cont_;
+ Blob<Dtype> blob_bottom_static_;
+ Blob<Dtype> blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+
+ Blob<Dtype> unit_blob_bottom_cont_;
+ Blob<Dtype> unit_blob_bottom_c_prev_;
+ Blob<Dtype> unit_blob_bottom_x_;
+ Blob<Dtype> unit_blob_top_c_;
+ Blob<Dtype> unit_blob_top_h_;
+ vector<Blob<Dtype>*> unit_blob_bottom_vec_;
+ vector<Blob<Dtype>*> unit_blob_top_vec_;
+};
+
+TYPED_TEST_CASE(LSTMLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(LSTMLayerTest, TestSetUp) {
+ typedef typename TypeParam::Dtype Dtype;
+ LSTMLayer<Dtype> layer(this->layer_param_);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ vector<int> expected_top_shape = this->blob_bottom_.shape();
+ expected_top_shape.resize(3);
+ expected_top_shape[2] = this->num_output_;
+ EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);
+}
+
+TYPED_TEST(LSTMLayerTest, TestForward) {
+ typedef typename TypeParam::Dtype Dtype;
+ const int kNumTimesteps = 3;
+ const int num = this->blob_bottom_.shape(1);
+ this->ReshapeBlobs(kNumTimesteps, num);
+
+ // Fill the cont blob with <0, 1, 1, ..., 1>,
+ // indicating a sequence that begins at the first timestep
+ // then continues for the rest of the sequence.
+ for (int t = 0; t < kNumTimesteps; ++t) {
+ for (int n = 0; n < num; ++n) {
+ this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;
+ }
+ }
+
+ // Process the full sequence in a single batch.
+ FillerParameter filler_param;
+ filler_param.set_mean(0);
+ filler_param.set_std(1);
+ GaussianFiller<Dtype> sequence_filler(filler_param);
+ Caffe::set_random_seed(1);
+ sequence_filler.Fill(&this->blob_bottom_);
+ shared_ptr<LSTMLayer<Dtype> > layer(new LSTMLayer<Dtype>(this->layer_param_));
+ Caffe::set_random_seed(1701);
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ LOG(INFO) << "Calling forward for full sequence LSTM";
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ // Copy the inputs and outputs to reuse/check them later.
+ Blob<Dtype> bottom_copy(this->blob_bottom_.shape());
+ bottom_copy.CopyFrom(this->blob_bottom_);
+ Blob<Dtype> top_copy(this->blob_top_.shape());
+ top_copy.CopyFrom(this->blob_top_);
+
+ // Process the batch one timestep at a time;
+ // check that we get the same result.
+ this->ReshapeBlobs(1, num);
+ layer.reset(new LSTMLayer<Dtype>(this->layer_param_));
+ Caffe::set_random_seed(1701);
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ const int bottom_count = this->blob_bottom_.count();
+ const int top_count = this->blob_top_.count();
+ const Dtype kEpsilon = 1e-5;
+ for (int t = 0; t < kNumTimesteps; ++t) {
+ caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+ this->blob_bottom_.mutable_cpu_data());
+ for (int n = 0; n < num; ++n) {
+ this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;
+ }
+ LOG(INFO) << "Calling forward for LSTM timestep " << t;
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < top_count; ++i) {
+ ASSERT_LT(t * top_count + i, top_copy.count());
+ EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+ top_copy.cpu_data()[t * top_count + i], kEpsilon)
+ << "t = " << t << "; i = " << i;
+ }
+ }
+
+ // Process the batch one timestep at a time with all cont blobs set to 0.
+ // Check that we get a different result, except in the first timestep.
+ Caffe::set_random_seed(1701);
+ layer.reset(new LSTMLayer<Dtype>(this->layer_param_));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int t = 0; t < kNumTimesteps; ++t) {
+ caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+ this->blob_bottom_.mutable_cpu_data());
+ for (int n = 0; n < num; ++n) {
+ this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;
+ }
+ LOG(INFO) << "Calling forward for LSTM timestep " << t;
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < top_count; ++i) {
+ if (t == 0) {
+ EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+ top_copy.cpu_data()[t * top_count + i], kEpsilon)
+ << "t = " << t << "; i = " << i;
+ } else {
+ EXPECT_NE(this->blob_top_.cpu_data()[i],
+ top_copy.cpu_data()[t * top_count + i])
+ << "t = " << t << "; i = " << i;
+ }
+ }
+ }
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitSetUp) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ LSTMUnitLayer<Dtype> layer(layer_param);
+ layer.SetUp(this->unit_blob_bottom_vec_, this->unit_blob_top_vec_);
+ const int num_axes = this->unit_blob_bottom_c_prev_.num_axes();
+ ASSERT_EQ(num_axes, this->unit_blob_top_c_.num_axes());
+ ASSERT_EQ(num_axes, this->unit_blob_top_h_.num_axes());
+ for (int i = 0; i < num_axes; ++i) {
+ EXPECT_EQ(this->unit_blob_bottom_c_prev_.shape(i),
+ this->unit_blob_top_c_.shape(i));
+ EXPECT_EQ(this->unit_blob_bottom_c_prev_.shape(i),
+ this->unit_blob_top_h_.shape(i));
+ }
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ LSTMUnitLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ Dtype* cont_data = this->blob_bottom_cont_.mutable_cpu_data();
+ cont_data[0] = 0;
+ cont_data[1] = 0;
+ cont_data[2] = 0;
+ checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+ this->unit_blob_top_vec_, 0);
+ checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+ this->unit_blob_top_vec_, 1);
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitGradientNonZeroCont) {
+ typedef typename TypeParam::Dtype Dtype;
+ LayerParameter layer_param;
+ LSTMUnitLayer<Dtype> layer(layer_param);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ Dtype* cont_data = this->blob_bottom_cont_.mutable_cpu_data();
+ cont_data[0] = 1;
+ cont_data[1] = 0;
+ cont_data[2] = 1;
+ checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+ this->unit_blob_top_vec_, 0);
+ checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+ this->unit_blob_top_vec_, 1);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ LSTMLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroCont) {
+ typedef typename TypeParam::Dtype Dtype;
+ LSTMLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+ this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroContBufferSize2) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->ReshapeBlobs(2, 2);
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(&this->blob_bottom_);
+ LSTMLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+ this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->ReshapeBlobs(2, 2);
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(&this->blob_bottom_);
+ filler.Fill(&this->blob_bottom_static_);
+ this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);
+ LSTMLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+ this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 2);
+}
+
+
+} // namespace caffe
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index ab4afba..24b957f 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -9,6 +9,7 @@
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/net.hpp"
+#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/test/test_caffe_main.hpp"
@@ -29,6 +30,17 @@ class NetTest : public MultiDeviceTest<TypeParam> {
net_.reset(new Net<Dtype>(param));
}
+ virtual void InitNetFromProtoFileWithState(const string& proto,
+ Phase phase = caffe::TRAIN, const int level = 0,
+ const vector<string>* stages = NULL) {
+ NetParameter param;
+ CHECK(google::protobuf::TextFormat::ParseFromString(proto, ¶m));
+ string param_file;
+ MakeTempFilename(¶m_file);
+ WriteProtoToTextFile(param, param_file);
+ net_.reset(new Net<Dtype>(param_file, phase, level, stages));
+ }
+
virtual void CopyNetBlobs(const bool copy_diff,
vector<shared_ptr<Blob<Dtype> > >* blobs_copy) {
CHECK(net_);
@@ -555,11 +567,14 @@ class NetTest : public MultiDeviceTest<TypeParam> {
virtual void InitReshapableNet() {
const string& proto =
"name: 'ReshapableNetwork' "
- "input: 'data' "
- "input_dim: 1 "
- "input_dim: 3 "
- "input_dim: 100 "
- "input_dim: 100 "
+ "layer { "
+ " name: 'data' "
+ " type: 'Input' "
+ " top: 'data' "
+ " input_param { "
+ " shape: { dim: 1 dim: 3 dim: 100 dim: 100 } "
+ " } "
+ "} "
"layer { "
" name: 'conv1' "
" type: 'Convolution' "
@@ -713,6 +728,117 @@ class NetTest : public MultiDeviceTest<TypeParam> {
InitNetFromProtoString(proto);
}
+ virtual void InitForcePropNet(bool test_force_true) {
+ string proto =
+ "name: 'ForcePropTestNetwork' "
+ "layer { "
+ " name: 'data' "
+ " type: 'DummyData' "
+ " dummy_data_param { "
+ " shape { "
+ " dim: 5 "
+ " dim: 2 "
+ " dim: 3 "
+ " dim: 4 "
+ " } "
+ " data_filler { "
+ " type: 'gaussian' "
+ " std: 0.01 "
+ " } "
+ " shape { "
+ " dim: 5 "
+ " } "
+ " data_filler { "
+ " type: 'constant' "
+ " value: 0 "
+ " } "
+ " } "
+ " top: 'data' "
+ " top: 'label' "
+ "} "
+ "layer { "
+ " name: 'innerproduct' "
+ " type: 'InnerProduct' "
+ " inner_product_param { "
+ " num_output: 1 "
+ " weight_filler { "
+ " type: 'gaussian' "
+ " std: 0.01 "
+ " } "
+ " } "
+ " bottom: 'data' "
+ " top: 'innerproduct' ";
+ if (test_force_true) {
+ proto += " propagate_down: true ";
+ }
+ proto +=
+ "} "
+ "layer { "
+ " name: 'loss' "
+ " bottom: 'innerproduct' "
+ " bottom: 'label' "
+ " top: 'cross_entropy_loss' "
+ " type: 'SigmoidCrossEntropyLoss' "
+ "} ";
+ InitNetFromProtoString(proto);
+ }
+
+ virtual void InitAllInOneNet(Phase phase = caffe::TRAIN,
+ const int level = 0, const vector<string>* stages = NULL) {
+ string proto =
+ "name: 'All-in-one Network'"
+ "layer { "
+ " name: 'train-data' "
+ " type: 'DummyData' "
+ " top: 'data' "
+ " top: 'label' "
+ " dummy_data_param { "
+ " shape { dim: 1 dim: 10 } "
+ " shape { dim: 1 dim: 1 } "
+ " } "
+ " include { phase: TRAIN stage: 'train' } "
+ "} "
+ "layer { "
+ " name: 'val-data' "
+ " type: 'DummyData' "
+ " top: 'data' "
+ " top: 'label' "
+ " dummy_data_param { "
+ " shape { dim: 1 dim: 10 } "
+ " shape { dim: 1 dim: 1 } "
+ " } "
+ " include { phase: TEST stage: 'val' } "
+ "} "
+ "layer { "
+ " name: 'deploy-data' "
+ " type: 'Input' "
+ " top: 'data' "
+ " input_param { "
+ " shape { dim: 1 dim: 10 } "
+ " } "
+ " include { phase: TEST stage: 'deploy' } "
+ "} "
+ "layer { "
+ " name: 'ip' "
+ " type: 'InnerProduct' "
+ " bottom: 'data' "
+ " top: 'ip' "
+ " inner_product_param { "
+ " num_output: 2 "
+ " } "
+ "} "
+ "layer { "
+ " name: 'loss' "
+ " type: 'SoftmaxWithLoss' "
+ " bottom: 'ip' "
+ " bottom: 'label' "
+ " top: 'loss' "
+ " include { phase: TRAIN stage: 'train' } "
+ " include { phase: TEST stage: 'val' } "
+ "} ";
+ InitNetFromProtoFileWithState(proto, phase, level, stages);
+ }
+
int seed_;
shared_ptr<Net<Dtype> > net_;
};
@@ -821,7 +947,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
Caffe::set_random_seed(this->seed_);
const bool kForceBackward = true;
this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
- const Dtype loss = this->net_->ForwardBackward(bottom);
+ const Dtype loss = this->net_->ForwardBackward();
const bool kCopyDiff = true;
vector<shared_ptr<Blob<Dtype> > > blob_grads;
this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -836,7 +962,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
for (int i = 0; i < kNumLossWeights; ++i) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward);
- const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+ const Dtype weighted_loss = this->net_->ForwardBackward();
const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
<< "loss weight = " << kLossWeights[i];
@@ -865,14 +991,13 @@ TYPED_TEST(NetTest, TestLossWeight) {
TYPED_TEST(NetTest, TestLossWeightMidNet) {
typedef typename TypeParam::Dtype Dtype;
- vector<Blob<Dtype>*> bottom;
Caffe::set_random_seed(this->seed_);
const bool kForceBackward = true;
Dtype loss_weight = 0;
Dtype midnet_loss_weight = 1;
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
- const Dtype loss = this->net_->ForwardBackward(bottom);
+ const Dtype loss = this->net_->ForwardBackward();
const bool kCopyDiff = true;
const bool kReshape = true;
Blob<Dtype> data_grad;
@@ -887,7 +1012,7 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i],
kForceBackward);
- const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+ const Dtype weighted_loss = this->net_->ForwardBackward();
const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
<< "loss weight = " << kLossWeights[i];
@@ -903,7 +1028,6 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
TYPED_TEST(NetTest, TestComboLossWeight) {
typedef typename TypeParam::Dtype Dtype;
- vector<Blob<Dtype>*> bottom;
Dtype loss_weight;
Dtype midnet_loss_weight;
const bool kForceBackward = true;
@@ -916,7 +1040,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
- const Dtype loss = this->net_->ForwardBackward(bottom);
+ const Dtype loss = this->net_->ForwardBackward();
const bool kCopyDiff = true;
vector<shared_ptr<Blob<Dtype> > > blob_grads;
this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -928,7 +1052,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
- const Dtype loss_main_2 = this->net_->ForwardBackward(bottom);
+ const Dtype loss_main_2 = this->net_->ForwardBackward();
vector<shared_ptr<Blob<Dtype> > > blob_grads_loss_2;
this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
vector<shared_ptr<Blob<Dtype> > > param_grads_loss_2;
@@ -939,7 +1063,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
- const Dtype loss_main_3 = this->net_->ForwardBackward(bottom);
+ const Dtype loss_main_3 = this->net_->ForwardBackward();
const vector<shared_ptr<Blob<Dtype> > >& blob_grads_loss_3 =
this->net_->blobs();
ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size());
@@ -974,7 +1098,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
- const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom);
+ const Dtype loss_midnet_2 = this->net_->ForwardBackward();
this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
this->CopyNetParams(kCopyDiff, ¶m_grads_loss_2);
@@ -983,7 +1107,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
Caffe::set_random_seed(this->seed_);
this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
kForceBackward);
- const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom);
+ const Dtype loss_midnet_3 = this->net_->ForwardBackward();
const vector<shared_ptr<Blob<Dtype> > >& blob_grads_midnet_loss_3 =
this->net_->blobs();
ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size());
@@ -1032,40 +1156,35 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
}
TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
- typedef typename TypeParam::Dtype Dtype;
const bool kForceBackward = false;
const bool kAccuracyLayer = true;
this->InitTinyNet(kForceBackward, kAccuracyLayer);
EXPECT_TRUE(this->net_->has_blob("accuracy"));
- vector<Blob<Dtype>*> bottom;
// Test that we can do Backward even though we have an 'Accuracy' layer.
- this->net_->ForwardBackward(bottom);
+ this->net_->ForwardBackward();
}
TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
typedef typename TypeParam::Dtype Dtype;
this->InitUnsharedWeightsNet();
- vector<Blob<Dtype>*> bottom;
Dtype loss;
- this->net_->Forward(bottom, &loss);
+ this->net_->Forward(&loss);
EXPECT_GT(loss, 0);
}
TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
typedef typename TypeParam::Dtype Dtype;
this->InitSharedWeightsNet();
- vector<Blob<Dtype>*> bottom;
Dtype loss;
- this->net_->Forward(bottom, &loss);
+ this->net_->Forward(&loss);
EXPECT_FLOAT_EQ(loss, 0);
}
TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
typedef typename TypeParam::Dtype Dtype;
this->InitUnsharedWeightsNet();
- vector<Blob<Dtype>*> bottom;
Net<Dtype>* net = this->net_.get();
- net->Forward(bottom);
+ net->Forward();
net->Backward();
Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
@@ -1081,10 +1200,9 @@ TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
typedef typename TypeParam::Dtype Dtype;
this->InitSharedWeightsNet();
- vector<Blob<Dtype>*> bottom;
Net<Dtype>* net = this->net_.get();
Dtype loss;
- net->Forward(bottom, &loss);
+ net->Forward(&loss);
net->Backward();
EXPECT_FLOAT_EQ(loss, 0);
Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
@@ -1102,7 +1220,6 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
typedef typename TypeParam::Dtype Dtype;
Caffe::set_random_seed(this->seed_);
this->InitDiffDataSharedWeightsNet();
- vector<Blob<Dtype>*> bottom;
EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
@@ -1111,7 +1228,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
// locations.
EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
- this->net_->Forward(bottom);
+ this->net_->Forward();
this->net_->Backward();
// Compute the expected update as the data minus the two diffs.
Blob<Dtype> shared_params;
@@ -1146,7 +1263,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
// locations in memory.
EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
- this->net_->Forward(bottom);
+ this->net_->Forward();
this->net_->Backward();
// Compute the expected update.
Blob<Dtype> unshared_params1;
@@ -1186,7 +1303,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
// Create a net with weight sharing; Update it once.
Caffe::set_random_seed(this->seed_);
this->InitDiffDataSharedWeightsNet();
- vector<Blob<Dtype>*> bottom;
EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
@@ -1195,7 +1311,7 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
// locations.
EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
- this->net_->ForwardBackward(bottom);
+ this->net_->ForwardBackward();
this->net_->Update();
Blob<Dtype> shared_params;
const bool kReshape = true;
@@ -1228,7 +1344,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
TYPED_TEST(NetTest, TestParamPropagateDown) {
typedef typename TypeParam::Dtype Dtype;
- vector<Blob<Dtype>*> bottom;
const bool kBiasTerm = true, kForceBackward = false;
const Dtype* kLossWeight1 = NULL;
const Dtype* kLossWeight2 = NULL;
@@ -1238,7 +1353,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
- this->net_->Forward(bottom);
+ this->net_->Forward();
this->net_->Backward();
const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
const int num_params = params.size();
@@ -1258,7 +1373,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
- this->net_->Forward(bottom);
+ this->net_->Forward();
this->net_->Backward();
const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
ASSERT_EQ(num_params, params2.size());
@@ -1274,7 +1389,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
- this->net_->Forward(bottom);
+ this->net_->Forward();
this->net_->Backward();
const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
ASSERT_EQ(num_params, params3.size());
@@ -1293,7 +1408,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
- this->net_->Forward(bottom);
+ this->net_->Forward();
this->net_->Backward();
const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
ASSERT_EQ(num_params, params4.size());
@@ -1315,7 +1430,7 @@ TYPED_TEST(NetTest, TestFromTo) {
// Run Forward and Backward, recording the data diff and loss.
Blob<Dtype> data;
data.ReshapeLike(*this->net_->blob_by_name("data"));
- this->net_->ForwardPrefilled();
+ this->net_->Forward();
this->net_->Backward();
data.CopyFrom(*this->net_->blob_by_name("data"), true, true);
const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data();
@@ -2277,12 +2392,12 @@ TYPED_TEST(NetTest, TestReshape) {
filler.Fill(&blob2);
this->InitReshapableNet();
- Blob<Dtype>* input_blob = this->net_->input_blobs()[0];
+ shared_ptr<Blob<Dtype> > input_blob = this->net_->blob_by_name("data");
Blob<Dtype>* output_blob = this->net_->output_blobs()[0];
input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
blob1.width());
caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
- this->net_->ForwardPrefilled();
+ this->net_->Forward();
// call backward just to make sure it runs
this->net_->Backward();
Blob<Dtype> output1(output_blob->num(), output_blob->channels(),
@@ -2293,7 +2408,7 @@ TYPED_TEST(NetTest, TestReshape) {
input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
blob2.width());
caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
- this->net_->ForwardPrefilled();
+ this->net_->Forward();
this->net_->Backward();
Blob<Dtype> output2(output_blob->num(), output_blob->channels(),
output_blob->height(), output_blob->width());
@@ -2303,7 +2418,7 @@ TYPED_TEST(NetTest, TestReshape) {
input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
blob1.width());
caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
- this->net_->ForwardPrefilled();
+ this->net_->Forward();
this->net_->Backward();
for (int i = 0; i < output1.count(); ++i) {
EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
@@ -2312,7 +2427,7 @@ TYPED_TEST(NetTest, TestReshape) {
input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
blob2.width());
caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
- this->net_->ForwardPrefilled();
+ this->net_->Forward();
this->net_->Backward();
for (int i = 0; i < output2.count(); ++i) {
EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
@@ -2379,4 +2494,111 @@ TYPED_TEST(NetTest, TestSkipPropagateDown) {
}
}
+TYPED_TEST(NetTest, TestForcePropagateDown) {
+ this->InitForcePropNet(false);
+ vector<bool> layer_need_backward = this->net_->layer_need_backward();
+ for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+ const string& layer_name = this->net_->layer_names()[layer_id];
+ const vector<bool> need_backward =
+ this->net_->bottom_need_backward()[layer_id];
+ if (layer_name == "data") {
+ ASSERT_EQ(need_backward.size(), 0);
+ EXPECT_FALSE(layer_need_backward[layer_id]);
+ } else if (layer_name == "innerproduct") {
+ ASSERT_EQ(need_backward.size(), 1);
+ EXPECT_FALSE(need_backward[0]); // data
+ EXPECT_TRUE(layer_need_backward[layer_id]);
+ } else if (layer_name == "loss") {
+ ASSERT_EQ(need_backward.size(), 2);
+ EXPECT_TRUE(need_backward[0]); // innerproduct
+ EXPECT_FALSE(need_backward[1]); // label
+ EXPECT_TRUE(layer_need_backward[layer_id]);
+ } else {
+ LOG(FATAL) << "Unknown layer: " << layer_name;
+ }
+ }
+ this->InitForcePropNet(true);
+ layer_need_backward = this->net_->layer_need_backward();
+ for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+ const string& layer_name = this->net_->layer_names()[layer_id];
+ const vector<bool> need_backward =
+ this->net_->bottom_need_backward()[layer_id];
+ if (layer_name == "data") {
+ ASSERT_EQ(need_backward.size(), 0);
+ EXPECT_FALSE(layer_need_backward[layer_id]);
+ } else if (layer_name == "innerproduct") {
+ ASSERT_EQ(need_backward.size(), 1);
+ EXPECT_TRUE(need_backward[0]); // data
+ EXPECT_TRUE(layer_need_backward[layer_id]);
+ } else if (layer_name == "loss") {
+ ASSERT_EQ(need_backward.size(), 2);
+ EXPECT_TRUE(need_backward[0]); // innerproduct
+ EXPECT_FALSE(need_backward[1]); // label
+ EXPECT_TRUE(layer_need_backward[layer_id]);
+ } else {
+ LOG(FATAL) << "Unknown layer: " << layer_name;
+ }
+ }
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetTrain) {
+ vector<string> stages;
+ stages.push_back("train");
+ this->InitAllInOneNet(caffe::TRAIN, 0, &stages);
+ bool found_data = false;
+ bool found_loss = false;
+ for (int i = 0; i < this->net_->layers().size(); ++i) {
+ const string& layer_name = this->net_->layer_names()[i];
+ if (layer_name == "train-data") {
+ found_data = true;
+ } else if (layer_name == "loss") {
+ found_loss = true;
+ } else {
+ ASSERT_NE(layer_name, "val-data");
+ ASSERT_NE(layer_name, "deploy-data");
+ }
+ }
+ ASSERT_TRUE(found_data);
+ ASSERT_TRUE(found_loss);
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetVal) {
+ vector<string> stages;
+ stages.push_back("val");
+ this->InitAllInOneNet(caffe::TEST, 0, &stages);
+ bool found_data = false;
+ bool found_loss = false;
+ for (int i = 0; i < this->net_->layers().size(); ++i) {
+ const string& layer_name = this->net_->layer_names()[i];
+ if (layer_name == "val-data") {
+ found_data = true;
+ } else if (layer_name == "loss") {
+ found_loss = true;
+ } else {
+ ASSERT_NE(layer_name, "train-data");
+ ASSERT_NE(layer_name, "deploy-data");
+ }
+ }
+ ASSERT_TRUE(found_data);
+ ASSERT_TRUE(found_loss);
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetDeploy) {
+ vector<string> stages;
+ stages.push_back("deploy");
+ this->InitAllInOneNet(caffe::TEST, 0, &stages);
+ bool found_data = false;
+ for (int i = 0; i < this->net_->layers().size(); ++i) {
+ const string& layer_name = this->net_->layer_names()[i];
+ if (layer_name == "deploy-data") {
+ found_data = true;
+ } else {
+ ASSERT_NE(layer_name, "train-data");
+ ASSERT_NE(layer_name, "val-data");
+ ASSERT_NE(layer_name, "loss");
+ }
+ }
+ ASSERT_TRUE(found_data);
+}
+
} // namespace caffe
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index dd591f7..342f825 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -394,6 +394,26 @@ TYPED_TEST(NeuronLayerTest, TestExpGradient) {
this->TestExpGradient(kBase, kScale, kShift);
}
+TYPED_TEST(NeuronLayerTest, TestExpLayerWithShift) {
+ typedef typename TypeParam::Dtype Dtype;
+ // Test default base of "-1" -- should actually set base := e,
+ // with a non-zero shift
+ const Dtype kBase = -1;
+ const Dtype kScale = 1;
+ const Dtype kShift = 1;
+ this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientWithShift) {
+ typedef typename TypeParam::Dtype Dtype;
+ // Test default base of "-1" -- should actually set base := e,
+ // with a non-zero shift
+ const Dtype kBase = -1;
+ const Dtype kScale = 1;
+ const Dtype kShift = 1;
+ this->TestExpGradient(kBase, kScale, kShift);
+}
+
TYPED_TEST(NeuronLayerTest, TestExpLayerBase2) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
diff --git a/src/caffe/test/test_rnn_layer.cpp b/src/caffe/test/test_rnn_layer.cpp
new file mode 100644
index 0000000..dd8952d
--- /dev/null
+++ b/src/caffe/test/test_rnn_layer.cpp
@@ -0,0 +1,217 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/rnn_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class RNNLayerTest : public MultiDeviceTest<TypeParam> {
+ typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+ RNNLayerTest() : num_output_(7) {
+ blob_bottom_vec_.push_back(&blob_bottom_);
+ blob_bottom_vec_.push_back(&blob_bottom_cont_);
+ blob_top_vec_.push_back(&blob_top_);
+
+ ReshapeBlobs(1, 3);
+
+ layer_param_.mutable_recurrent_param()->set_num_output(num_output_);
+ FillerParameter* weight_filler =
+ layer_param_.mutable_recurrent_param()->mutable_weight_filler();
+ weight_filler->set_type("gaussian");
+ weight_filler->set_std(0.2);
+ FillerParameter* bias_filler =
+ layer_param_.mutable_recurrent_param()->mutable_bias_filler();
+ bias_filler->set_type("gaussian");
+ bias_filler->set_std(0.1);
+
+ layer_param_.set_phase(TEST);
+ }
+
+ void ReshapeBlobs(int num_timesteps, int num_instances) {
+ blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);
+ blob_bottom_static_.Reshape(num_instances, 2, 3, 4);
+ vector<int> shape(2);
+ shape[0] = num_timesteps;
+ shape[1] = num_instances;
+ blob_bottom_cont_.Reshape(shape);
+
+ FillerParameter filler_param;
+ filler_param.set_min(-1);
+ filler_param.set_max(1);
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(&blob_bottom_);
+ }
+
+ int num_output_;
+ LayerParameter layer_param_;
+ Blob<Dtype> blob_bottom_;
+ Blob<Dtype> blob_bottom_cont_;
+ Blob<Dtype> blob_bottom_static_;
+ Blob<Dtype> blob_top_;
+ vector<Blob<Dtype>*> blob_bottom_vec_;
+ vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(RNNLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(RNNLayerTest, TestSetUp) {
+ typedef typename TypeParam::Dtype Dtype;
+ RNNLayer<Dtype> layer(this->layer_param_);
+ layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ vector<int> expected_top_shape = this->blob_bottom_.shape();
+ expected_top_shape.resize(3);
+ expected_top_shape[2] = this->num_output_;
+ EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);
+}
+
+TYPED_TEST(RNNLayerTest, TestForward) {
+ typedef typename TypeParam::Dtype Dtype;
+ const int kNumTimesteps = 3;
+ const int num = this->blob_bottom_.shape(1);
+ this->ReshapeBlobs(kNumTimesteps, num);
+
+ // Fill the cont blob with <0, 1, 1, ..., 1>,
+ // indicating a sequence that begins at the first timestep
+ // then continues for the rest of the sequence.
+ for (int t = 0; t < kNumTimesteps; ++t) {
+ for (int n = 0; n < num; ++n) {
+ this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;
+ }
+ }
+
+ // Process the full sequence in a single batch.
+ FillerParameter filler_param;
+ filler_param.set_mean(0);
+ filler_param.set_std(1);
+ GaussianFiller<Dtype> sequence_filler(filler_param);
+ sequence_filler.Fill(&this->blob_bottom_);
+ shared_ptr<RNNLayer<Dtype> > layer(new RNNLayer<Dtype>(this->layer_param_));
+ Caffe::set_random_seed(1701);
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ LOG(INFO) << "Calling forward for full sequence RNN";
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+ // Copy the inputs and outputs to reuse/check them later.
+ Blob<Dtype> bottom_copy(this->blob_bottom_.shape());
+ bottom_copy.CopyFrom(this->blob_bottom_);
+ Blob<Dtype> top_copy(this->blob_top_.shape());
+ top_copy.CopyFrom(this->blob_top_);
+
+ // Process the batch one timestep at a time;
+ // check that we get the same result.
+ this->ReshapeBlobs(1, num);
+ layer.reset(new RNNLayer<Dtype>(this->layer_param_));
+ Caffe::set_random_seed(1701);
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ const int bottom_count = this->blob_bottom_.count();
+ const int top_count = this->blob_top_.count();
+ const Dtype kEpsilon = 1e-5;
+ for (int t = 0; t < kNumTimesteps; ++t) {
+ caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+ this->blob_bottom_.mutable_cpu_data());
+ for (int n = 0; n < num; ++n) {
+ this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;
+ }
+ LOG(INFO) << "Calling forward for RNN timestep " << t;
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < top_count; ++i) {
+ ASSERT_LT(t * top_count + i, top_copy.count());
+ EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+ top_copy.cpu_data()[t * top_count + i], kEpsilon)
+ << "t = " << t << "; i = " << i;
+ }
+ }
+
+ // Process the batch one timestep at a time with all cont blobs set to 0.
+ // Check that we get a different result, except in the first timestep.
+ Caffe::set_random_seed(1701);
+ layer.reset(new RNNLayer<Dtype>(this->layer_param_));
+ layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int t = 0; t < kNumTimesteps; ++t) {
+ caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+ this->blob_bottom_.mutable_cpu_data());
+ for (int n = 0; n < num; ++n) {
+ this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;
+ }
+ LOG(INFO) << "Calling forward for RNN timestep " << t;
+ layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+ for (int i = 0; i < top_count; ++i) {
+ if (t == 0) {
+ EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+ top_copy.cpu_data()[t * top_count + i], kEpsilon)
+ << "t = " << t << "; i = " << i;
+ } else {
+ EXPECT_NE(this->blob_top_.cpu_data()[i],
+ top_copy.cpu_data()[t * top_count + i])
+ << "t = " << t << "; i = " << i;
+ }
+ }
+ }
+}
+
+TYPED_TEST(RNNLayerTest, TestGradient) {
+ typedef typename TypeParam::Dtype Dtype;
+ RNNLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroCont) {
+ typedef typename TypeParam::Dtype Dtype;
+ RNNLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+ this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->ReshapeBlobs(2, 2);
+ // fill the values
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(&this->blob_bottom_);
+ RNNLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+ this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {
+ typedef typename TypeParam::Dtype Dtype;
+ this->ReshapeBlobs(2, 2);
+ FillerParameter filler_param;
+ UniformFiller<Dtype> filler(filler_param);
+ filler.Fill(&this->blob_bottom_);
+ filler.Fill(&this->blob_bottom_static_);
+ this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);
+ RNNLayer<Dtype> layer(this->layer_param_);
+ GradientChecker<Dtype> checker(1e-2, 1e-3);
+ for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+ this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+ }
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 0);
+ checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+ this->blob_top_vec_, 2);
+}
+
+} // namespace caffe
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index ba2ccbb..0071421 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -886,67 +886,6 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
this->RunInsertionTest(input_proto, expected_output_proto);
}
-TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
- const string& input_proto =
- "name: 'TestNetwork' "
- "input: 'data' "
- "input_dim: 10 "
- "input_dim: 3 "
- "input_dim: 227 "
- "input_dim: 227 "
- "layer { "
- " name: 'innerprod1' "
- " type: 'InnerProduct' "
- " bottom: 'data' "
- " top: 'innerprod1' "
- "} "
- "layer { "
- " name: 'innerprod2' "
- " type: 'InnerProduct' "
- " bottom: 'data' "
- " top: 'innerprod2' "
- "} "
- "layer { "
- " name: 'loss' "
- " type: 'EuclideanLoss' "
- " bottom: 'innerprod1' "
- " bottom: 'innerprod2' "
- "} ";
- const string& expected_output_proto =
- "name: 'TestNetwork' "
- "input: 'data' "
- "input_dim: 10 "
- "input_dim: 3 "
- "input_dim: 227 "
- "input_dim: 227 "
- "layer { "
- " name: 'data_input_0_split' "
- " type: 'Split' "
- " bottom: 'data' "
- " top: 'data_input_0_split_0' "
- " top: 'data_input_0_split_1' "
- "} "
- "layer { "
- " name: 'innerprod1' "
- " type: 'InnerProduct' "
- " bottom: 'data_input_0_split_0' "
- " top: 'innerprod1' "
- "} "
- "layer { "
- " name: 'innerprod2' "
- " type: 'InnerProduct' "
- " bottom: 'data_input_0_split_1' "
- " top: 'innerprod2' "
- "} "
- "layer { "
- " name: 'loss' "
- " type: 'EuclideanLoss' "
- " bottom: 'innerprod1' "
- " bottom: 'innerprod2' "
- "} ";
- this->RunInsertionTest(input_proto, expected_output_proto);
-}
-
TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
const string& input_proto =
"name: 'TestNetwork' "
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index 0bc82b5..fb1d495 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -7,13 +7,10 @@
namespace caffe { namespace db {
-const size_t LMDB_MAP_SIZE = 1099511627776; // 1 TB
-
void LMDB::Open(const string& source, Mode mode) {
MDB_CHECK(mdb_env_create(&mdb_env_));
- MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
if (mode == NEW) {
- CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+ CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << " failed";
}
int flags = 0;
if (mode == READ) {
@@ -48,19 +45,67 @@ LMDBCursor* LMDB::NewCursor() {
}
LMDBTransaction* LMDB::NewTransaction() {
- MDB_txn* mdb_txn;
- MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
- MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
- return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+ return new LMDBTransaction(mdb_env_);
}
void LMDBTransaction::Put(const string& key, const string& value) {
- MDB_val mdb_key, mdb_value;
- mdb_key.mv_data = const_cast<char*>(key.data());
- mdb_key.mv_size = key.size();
- mdb_value.mv_data = const_cast<char*>(value.data());
- mdb_value.mv_size = value.size();
- MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+ keys.push_back(key);
+ values.push_back(value);
+}
+
+void LMDBTransaction::Commit() {
+ MDB_dbi mdb_dbi;
+ MDB_val mdb_key, mdb_data;
+ MDB_txn *mdb_txn;
+
+ // Initialize MDB variables
+ MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+ MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi));
+
+ for (int i = 0; i < keys.size(); i++) {
+ mdb_key.mv_size = keys[i].size();
+ mdb_key.mv_data = const_cast<char*>(keys[i].data());
+ mdb_data.mv_size = values[i].size();
+ mdb_data.mv_data = const_cast<char*>(values[i].data());
+
+ // Add data to the transaction
+ int put_rc = mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0);
+ if (put_rc == MDB_MAP_FULL) {
+ // Out of memory - double the map size and retry
+ mdb_txn_abort(mdb_txn);
+ mdb_dbi_close(mdb_env_, mdb_dbi);
+ DoubleMapSize();
+ Commit();
+ return;
+ }
+ // May have failed for some other reason
+ MDB_CHECK(put_rc);
+ }
+
+ // Commit the transaction
+ int commit_rc = mdb_txn_commit(mdb_txn);
+ if (commit_rc == MDB_MAP_FULL) {
+ // Out of memory - double the map size and retry
+ mdb_dbi_close(mdb_env_, mdb_dbi);
+ DoubleMapSize();
+ Commit();
+ return;
+ }
+ // May have failed for some other reason
+ MDB_CHECK(commit_rc);
+
+ // Cleanup after successful commit
+ mdb_dbi_close(mdb_env_, mdb_dbi);
+ keys.clear();
+ values.clear();
+}
+
+void LMDBTransaction::DoubleMapSize() {
+ struct MDB_envinfo current_info;
+ MDB_CHECK(mdb_env_info(mdb_env_, ¤t_info));
+ size_t new_size = current_info.me_mapsize * 2;
+ DLOG(INFO) << "Doubling LMDB map size to " << (new_size>>20) << "MB ...";
+ MDB_CHECK(mdb_env_set_mapsize(mdb_env_, new_size));
}
} // namespace db
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 475a2a9..7a899c6 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -19,12 +19,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
map<pair<int, int>, float> top_idx_to_loss_weight;
map<pair<int, int>, int> top_idx_to_bottom_split_idx;
map<int, string> layer_idx_to_layer_name;
- layer_idx_to_layer_name[-1] = "input";
- // Determine the number of times each blob is used as an input (bottom) blob.
- for (int i = 0; i < param.input_size(); ++i) {
- const string& blob_name = param.input(i);
- blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
- }
for (int i = 0; i < param.layer_size(); ++i) {
const LayerParameter& layer_param = param.layer(i);
layer_idx_to_layer_name[i] = layer_param.name();
@@ -45,7 +39,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
}
// A use of a top blob as a loss should be handled similarly to the use of
- // a top blob as an input (bottom) blob to another layer.
+ // a top blob as a bottom blob to another layer.
const int last_loss =
std::min(layer_param.loss_weight_size(), layer_param.top_size());
for (int j = 0; j < last_loss; ++j) {
@@ -57,19 +51,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
}
}
}
- // Create split layer for any input blobs used by other layer as bottom
- // blobs more than once.
- for (int i = 0; i < param.input_size(); ++i) {
- const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
- if (split_count > 1) {
- const string& layer_name = layer_idx_to_layer_name[-1];
- const string& blob_name = param.input(i);
- LayerParameter* split_layer_param = param_split->add_layer();
- const float kZeroLossWeight = 0;
- ConfigureSplitLayer(layer_name, blob_name, i, split_count,
- kZeroLossWeight, split_layer_param);
- }
- }
for (int i = 0; i < param.layer_size(); ++i) {
LayerParameter* layer_param = param_split->add_layer();
layer_param->CopyFrom(param.layer(i));
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index ff3f8ff..9e18691 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,7 +13,8 @@
namespace caffe {
bool NetNeedsUpgrade(const NetParameter& net_param) {
- return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
+ return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param)
+ || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param);
}
bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
@@ -60,6 +61,16 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
<< "V1LayerParameter";
}
}
+ // NetParameter uses old style input fields; try to upgrade it.
+ if (NetNeedsInputUpgrade(*param)) {
+ LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+ << "input fields: " << param_file;
+ UpgradeNetInput(param);
+ LOG(INFO) << "Successfully upgraded file specified using deprecated "
+ << "input fields.";
+ LOG(WARNING) << "Note that future Caffe releases will only support "
+ << "input layers and not input fields.";
+ }
return success;
}
@@ -645,12 +656,14 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
}
bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
- bool is_fully_compatible = true;
if (v1_net_param.layer_size() > 0) {
- LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
- << "fields; these will be ignored for the upgrade.";
- is_fully_compatible = false;
+ LOG(FATAL) << "Refusing to upgrade inconsistent NetParameter input; "
+ << "the definition includes both 'layer' and 'layers' fields. "
+ << "The current format defines 'layer' fields with string type like "
+ << "layer { type: 'Layer' ... } and not layers { type: LAYER ... }. "
+ << "Manually switch the definition to 'layer' format to continue.";
}
+ bool is_fully_compatible = true;
net_param->CopyFrom(v1_net_param);
net_param->clear_layers();
net_param->clear_layer();
@@ -937,6 +950,47 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
}
}
+bool NetNeedsInputUpgrade(const NetParameter& net_param) {
+ return net_param.input_size() > 0;
+}
+
+void UpgradeNetInput(NetParameter* net_param) {
+ // Collect inputs and convert to Input layer definitions.
+ // If the NetParameter holds an input alone, without shape/dim, then
+ // it's a legacy caffemodel and simply stripping the input field is enough.
+ bool has_shape = net_param->input_shape_size() > 0;
+ bool has_dim = net_param->input_dim_size() > 0;
+ if (has_shape || has_dim) {
+ LayerParameter* layer_param = net_param->add_layer();
+ layer_param->set_name("input");
+ layer_param->set_type("Input");
+ InputParameter* input_param = layer_param->mutable_input_param();
+ // Convert input fields into a layer.
+ for (int i = 0; i < net_param->input_size(); ++i) {
+ layer_param->add_top(net_param->input(i));
+ if (has_shape) {
+ input_param->add_shape()->CopyFrom(net_param->input_shape(i));
+ } else {
+ // Turn legacy input dimensions into shape.
+ BlobShape* shape = input_param->add_shape();
+ int first_dim = i*4;
+ int last_dim = first_dim + 4;
+ for (int j = first_dim; j < last_dim; j++) {
+ shape->add_dim(net_param->input_dim(j));
+ }
+ }
+ }
+ // Swap input layer to beginning of net to satisfy layer dependencies.
+ for (int i = net_param->layer_size() - 1; i > 0; --i) {
+ net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i));
+ }
+ }
+ // Clear inputs.
+ net_param->clear_input();
+ net_param->clear_input_shape();
+ net_param->clear_input_dim();
+}
+
// Return true iff the solver contains any old solver_type specified as enums
bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
if (solver_param.has_solver_type()) {
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 470165a..9bf4214 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -33,7 +33,14 @@ DEFINE_string(gpu, "",
DEFINE_string(solver, "",
"The solver definition protocol buffer text file.");
DEFINE_string(model, "",
- "The model definition protocol buffer text file..");
+ "The model definition protocol buffer text file.");
+DEFINE_string(phase, "",
+ "Optional; network phase (TRAIN or TEST). Only used for 'time'.");
+DEFINE_int32(level, 0,
+ "Optional; network level.");
+DEFINE_string(stage, "",
+ "Optional; network stages (not to be confused with phase), "
+ "separated by ','.");
DEFINE_string(snapshot, "",
"Optional; the snapshot solver state to resume training.");
DEFINE_string(weights, "",
@@ -101,6 +108,25 @@ static void get_gpus(vector<int>* gpus) {
}
}
+// Parse phase from flags
+caffe::Phase get_phase_from_flags(caffe::Phase default_value) {
+ if (FLAGS_phase == "")
+ return default_value;
+ if (FLAGS_phase == "TRAIN")
+ return caffe::TRAIN;
+ if (FLAGS_phase == "TEST")
+ return caffe::TEST;
+ LOG(FATAL) << "phase must be \"TRAIN\" or \"TEST\"";
+ return caffe::TRAIN; // Avoid warning
+}
+
+// Parse stages from flags
+vector<string> get_stages_from_flags() {
+ vector<string> stages;
+ boost::split(stages, FLAGS_stage, boost::is_any_of(","));
+ return stages;
+}
+
// caffe commands to call by
// caffe <command> <args>
//
@@ -156,10 +182,16 @@ int train() {
CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
<< "Give a snapshot to resume training or weights to finetune "
"but not both.";
+ vector<string> stages = get_stages_from_flags();
caffe::SolverParameter solver_param;
caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
+ solver_param.mutable_train_state()->set_level(FLAGS_level);
+ for (int i = 0; i < stages.size(); i++) {
+ solver_param.mutable_train_state()->add_stage(stages[i]);
+ }
+
// If the gpus flag is not provided, allow the mode and device to be set
// in the solver prototxt.
if (FLAGS_gpu.size() == 0
@@ -183,7 +215,13 @@ int train() {
s << (i ? ", " : "") << gpus[i];
}
LOG(INFO) << "Using GPUs " << s.str();
-
+#ifndef CPU_ONLY
+ cudaDeviceProp device_prop;
+ for (int i = 0; i < gpus.size(); ++i) {
+ cudaGetDeviceProperties(&device_prop, gpus[i]);
+ LOG(INFO) << "GPU " << gpus[i] << ": " << device_prop.name;
+ }
+#endif
solver_param.set_device_id(gpus[0]);
Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
@@ -208,7 +246,7 @@ int train() {
if (gpus.size() > 1) {
caffe::P2PSync<float> sync(solver, NULL, solver->param());
- sync.run(gpus);
+ sync.Run(gpus);
} else {
LOG(INFO) << "Starting Optimization";
solver->Solve();
@@ -223,12 +261,18 @@ RegisterBrewFunction(train);
int test() {
CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
+ vector<string> stages = get_stages_from_flags();
// Set device id and mode
vector<int> gpus;
get_gpus(&gpus);
if (gpus.size() != 0) {
LOG(INFO) << "Use GPU with device ID " << gpus[0];
+#ifndef CPU_ONLY
+ cudaDeviceProp device_prop;
+ cudaGetDeviceProperties(&device_prop, gpus[0]);
+ LOG(INFO) << "GPU device name: " << device_prop.name;
+#endif
Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
} else {
@@ -236,18 +280,17 @@ int test() {
Caffe::set_mode(Caffe::CPU);
}
// Instantiate the caffe net.
- Net<float> caffe_net(FLAGS_model, caffe::TEST);
+ Net<float> caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages);
caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
- vector<Blob<float>* > bottom_vec;
vector<int> test_score_output_id;
vector<float> test_score;
float loss = 0;
for (int i = 0; i < FLAGS_iterations; ++i) {
float iter_loss;
const vector<Blob<float>*>& result =
- caffe_net.Forward(bottom_vec, &iter_loss);
+ caffe_net.Forward(&iter_loss);
loss += iter_loss;
int idx = 0;
for (int j = 0; j < result.size(); ++j) {
@@ -290,6 +333,8 @@ RegisterBrewFunction(test);
// Time: benchmark the execution time of a model.
int time() {
CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
+ caffe::Phase phase = get_phase_from_flags(caffe::TRAIN);
+ vector<string> stages = get_stages_from_flags();
// Set device id and mode
vector<int> gpus;
@@ -303,7 +348,7 @@ int time() {
Caffe::set_mode(Caffe::CPU);
}
// Instantiate the caffe net.
- Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
+ Net<float> caffe_net(FLAGS_model, phase, FLAGS_level, &stages);
// Do a clean forward and backward pass, so that memory allocation are done
// and future iterations will be more stable.
@@ -311,7 +356,7 @@ int time() {
// Note that for the speed benchmark, we will assume that the network does
// not take any input blobs.
float initial_loss;
- caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+ caffe_net.Forward(&initial_loss);
LOG(INFO) << "Initial loss: " << initial_loss;
LOG(INFO) << "Performing Backward";
caffe_net.Backward();
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 9c52bfa..90cdb15 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -73,10 +73,13 @@ int main(int argc, char** argv) {
std::ifstream infile(argv[2]);
std::vector<std::pair<std::string, int> > lines;
- std::string filename;
+ std::string line;
+ size_t pos;
int label;
- while (infile >> filename >> label) {
- lines.push_back(std::make_pair(filename, label));
+ while (std::getline(infile, line)) {
+ pos = line.find_last_of(' ');
+ label = atoi(line.substr(pos + 1).c_str());
+ lines.push_back(std::make_pair(line.substr(0, pos), label));
}
if (FLAGS_shuffle) {
// randomly shuffle data
diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 48f9bee..375b0db 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -16,13 +16,10 @@ from collections import OrderedDict
def parse_log(path_to_log):
"""Parse log file
- Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
+ Returns (train_dict_list, test_dict_list)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
-
- train_dict_names and test_dict_names are ordered tuples of the column names
- for the two dict_lists
"""
regex_iteration = re.compile('Iteration (\d+)')
@@ -149,6 +146,11 @@ def write_csv(output_filename, dict_list, delimiter, verbose=False):
"""Write a CSV file
"""
+ if not dict_list:
+ if verbose:
+ print('Not writing %s; no lines to write' % output_filename)
+ return
+
dialect = csv.excel
dialect.delimiter = delimiter
diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index 4d3ed0d..79924ae 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -10,7 +10,8 @@ import matplotlib.legend as lgd
import matplotlib.markers as mks
def get_log_parsing_script():
- dirname = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+ dirname = os.path.dirname(os.path.abspath(inspect.getfile(
+ inspect.currentframe())))
return dirname + '/parse_log.sh'
def get_log_file_suffix():
@@ -61,16 +62,17 @@ def get_data_file_type(chart_type):
return data_file_type
def get_data_file(chart_type, path_to_log):
- return os.path.basename(path_to_log) + '.' + get_data_file_type(chart_type).lower()
+ return (os.path.basename(path_to_log) + '.' +
+ get_data_file_type(chart_type).lower())
def get_field_descriptions(chart_type):
description = get_chart_type_description(chart_type).split(
get_chart_type_description_separator())
y_axis_field = description[0]
x_axis_field = description[1]
- return x_axis_field, y_axis_field
+ return x_axis_field, y_axis_field
-def get_field_indecies(x_axis_field, y_axis_field):
+def get_field_indices(x_axis_field, y_axis_field):
data_file_type = get_data_file_type(chart_type)
fields = create_field_index()[0][data_file_type]
return fields[x_axis_field], fields[y_axis_field]
@@ -111,7 +113,7 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
os.system('%s %s' % (get_log_parsing_script(), path_to_log))
data_file = get_data_file(chart_type, path_to_log)
x_axis_field, y_axis_field = get_field_descriptions(chart_type)
- x, y = get_field_indecies(x_axis_field, y_axis_field)
+ x, y = get_field_indices(x_axis_field, y_axis_field)
data = load_data(data_file, x, y)
## TODO: more systematic color cycle for lines
color = [random.random(), random.random(), random.random()]
@@ -138,8 +140,8 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
plt.legend(loc = legend_loc, ncol = 1) # ajust ncol to fit the space
plt.title(get_chart_type_description(chart_type))
plt.xlabel(x_axis_field)
- plt.ylabel(y_axis_field)
- plt.savefig(path_to_png)
+ plt.ylabel(y_axis_field)
+ plt.savefig(path_to_png)
plt.show()
def print_help():
@@ -160,28 +162,30 @@ Supported chart types:""" % (len(get_supported_chart_types()) - 1,
num = len(supported_chart_types)
for i in xrange(num):
print ' %d: %s' % (i, supported_chart_types[i])
- exit
+ sys.exit()
def is_valid_chart_type(chart_type):
return chart_type >= 0 and chart_type < len(get_supported_chart_types())
-
+
if __name__ == '__main__':
if len(sys.argv) < 4:
print_help()
else:
chart_type = int(sys.argv[1])
if not is_valid_chart_type(chart_type):
+ print '%s is not a valid chart type.' % chart_type
print_help()
path_to_png = sys.argv[2]
if not path_to_png.endswith('.png'):
print 'Path must ends with png' % path_to_png
- exit
+ sys.exit()
path_to_logs = sys.argv[3:]
for path_to_log in path_to_logs:
if not os.path.exists(path_to_log):
print 'Path does not exist: %s' % path_to_log
- exit
+ sys.exit()
if not path_to_log.endswith(get_log_file_suffix()):
+ print 'Log file must end in %s.' % get_log_file_suffix()
print_help()
## plot_chart accpets multiple path_to_logs
plot_chart(chart_type, path_to_png, path_to_logs)
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index d6562f9..51c791e 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -130,13 +130,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
txns.push_back(txn);
}
- LOG(ERROR)<< "Extacting Features";
+ LOG(ERROR)<< "Extracting Features";
Datum datum;
- std::vector<Blob<float>*> input_vec;
std::vector<int> image_indices(num_features, 0);
for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
- feature_extraction_net->Forward(input_vec);
+ feature_extraction_net->Forward();
for (int i = 0; i < num_features; ++i) {
const boost::shared_ptr<Blob<Dtype> > feature_blob =
feature_extraction_net->blob_by_name(blob_names[i]);
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index 8a0dd7a..ede07ec 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
using namespace caffe; // NOLINT(build/namespaces)
int main(int argc, char** argv) {
+ FLAGS_alsologtostderr = 1; // Print output to stderr (while still logging)
::google::InitGoogleLogging(argv[0]);
if (argc != 3) {
LOG(ERROR) << "Usage: "
@@ -39,11 +40,11 @@ int main(int argc, char** argv) {
<< "see details above.";
}
} else {
- LOG(ERROR) << "File already in V1 proto format: " << argv[1];
+ LOG(ERROR) << "File already in latest proto format: " << input_filename;
}
WriteProtoToBinaryFile(net_param, argv[2]);
- LOG(ERROR) << "Wrote upgraded NetParameter binary proto to " << argv[2];
+ LOG(INFO) << "Wrote upgraded NetParameter binary proto to " << argv[2];
return !success;
}
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 9200431..617b48d 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
using namespace caffe; // NOLINT(build/namespaces)
int main(int argc, char** argv) {
+ FLAGS_alsologtostderr = 1; // Print output to stderr (while still logging)
::google::InitGoogleLogging(argv[0]);
if (argc != 3) {
LOG(ERROR) << "Usage: "
@@ -31,7 +32,6 @@ int main(int argc, char** argv) {
return 2;
}
bool need_upgrade = NetNeedsUpgrade(net_param);
- bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
bool success = true;
if (need_upgrade) {
success = UpgradeNetAsNeeded(input_filename, &net_param);
@@ -43,13 +43,9 @@ int main(int argc, char** argv) {
LOG(ERROR) << "File already in latest proto format: " << input_filename;
}
- if (need_data_upgrade) {
- UpgradeNetDataTransformation(&net_param);
- }
-
// Save new format prototxt.
WriteProtoToTextFile(net_param, argv[2]);
- LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2];
+ LOG(INFO) << "Wrote upgraded NetParameter text proto to " << argv[2];
return !success;
}
diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp
index 7130232..ddff1ce 100644
--- a/tools/upgrade_solver_proto_text.cpp
+++ b/tools/upgrade_solver_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
using namespace caffe; // NOLINT(build/namespaces)
int main(int argc, char** argv) {
+ FLAGS_alsologtostderr = 1; // Print output to stderr (while still logging)
::google::InitGoogleLogging(argv[0]);
if (argc != 3) {
LOG(ERROR) << "Usage: upgrade_solver_proto_text "
@@ -45,6 +46,6 @@ int main(int argc, char** argv) {
// Save new format prototxt.
WriteProtoToTextFile(solver_param, argv[2]);
- LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2];
+ LOG(INFO) << "Wrote upgraded SolverParameter text proto to " << argv[2];
return !success;
}
--
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