CMakeLists.txt

@@ -20,7 +20,6 @@ caffe_option(BUILD_python "Build Python wrapper" ON)
 set(python_version "2" CACHE STRING "Specify which python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
-caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)

Makefile

@@ -1,19 +1,11 @@
 PROJECT := caffe
 
 CONFIG_FILE := Makefile.config
-# Explicitly check for the config file, otherwise make -k will proceed anyway.
-ifeq ($(wildcard $(CONFIG_FILE)),)
-$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
-endif
 include $(CONFIG_FILE)
 
 BUILD_DIR_LINK := $(BUILD_DIR)
-ifeq ($(RELEASE_BUILD_DIR),)
-	RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
-endif
-ifeq ($(DEBUG_BUILD_DIR),)
-	DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
-endif
+RELEASE_BUILD_DIR ?= .$(BUILD_DIR)_release
+DEBUG_BUILD_DIR ?= .$(BUILD_DIR)_debug
 
 DEBUG ?= 0
 ifeq ($(DEBUG), 1)
@@ -179,7 +171,6 @@ WARNINGS := -Wall -Wno-sign-compare
 # Set build directories
 ##############################
 
-DISTRIBUTE_DIR ?= distribute
 DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
 DIST_ALIASES := dist
 ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
@@ -241,15 +232,13 @@ endif
 # libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
-	ifneq ($(CPU_ONLY), 1)
-		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
-		ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
-			CXXFLAGS += -stdlib=libstdc++
-			LINKFLAGS += -stdlib=libstdc++
-		endif
-		# clang throws this warning for cuda headers
-		WARNINGS += -Wno-unneeded-internal-declaration
+	CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+	ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
+		CXXFLAGS += -stdlib=libstdc++
+		LINKFLAGS += -stdlib=libstdc++
 	endif
+	# clang throws this warning for cuda headers
+	WARNINGS += -Wno-unneeded-internal-declaration
 	# 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

cmake/Dependencies.cmake

@@ -25,7 +25,7 @@ include(cmake/ProtoBuf.cmake)
 
 # ---[ HDF5
 find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
+include_directories(SYSTEM ${HDF5_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
 
 # ---[ LMDB
@@ -35,7 +35,7 @@ list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
 
 # ---[ LevelDB
 find_package(LevelDB REQUIRED)
-include_directories(SYSTEM ${LevelDB_INCLUDE})
+include_directories(SYSTEM ${LEVELDB_INCLUDE})
 list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
 
 # ---[ Snappy
@@ -127,11 +127,6 @@ if(BUILD_python)
   endif()
   if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
     set(HAVE_PYTHON TRUE)
-    if(BUILD_python_layer)
-      add_definitions(-DWITH_PYTHON_LAYER)
-      include_directories(SYSTEM ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
-      list(APPEND Caffe_LINKER_LIBS ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
-    endif()
   endif()
 endif()
 

cmake/Modules/FindGFlags.cmake

@@ -38,7 +38,7 @@ else()
     find_library(GFLAGS_LIBRARY gflags)
 endif()
 
-find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
 
 
 if(GFLAGS_FOUND)

cmake/Modules/FindGlog.cmake

@@ -37,7 +37,7 @@ else()
         PATH_SUFFIXES lib lib64)
 endif()
 
-find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
 
 if(GLOG_FOUND)
   set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})

docs/development.md

@@ -30,7 +30,7 @@ Similarly for IPython notebooks: simply include `"include_in_docs": true` in the
 
 Other docs, such as installation guides, are written in the `docs` directory and manually linked to from the `index.md` page.
 
-We strive to provide lots of usage examples, and to document all code in docstrings.
+We strive to provide provide lots of usage examples, and to document all code in docstrings.
 We absolutely appreciate any contribution to this effort!
 
 ### Versioning

docs/install_apt.md

@@ -8,24 +8,12 @@ title: Installation: Ubuntu
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
 
-**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
-Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
-This can be skipped for CPU-only installation.
-
-**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
-
-**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
-
 **Remaining dependencies, 14.04**
 
-Everything is packaged in 14.04.
-
     sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
 
 **Remaining dependencies, 12.04**
 
-These dependencies need manual installation in 12.04.
-
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
@@ -40,10 +28,17 @@ These dependencies need manual installation in 12.04.
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
     # lmdb
-    git clone https://gitorious.org/mdb/mdb.git
+    git clone git://gitorious.org/mdb/mdb.git
     cd mdb/libraries/liblmdb
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
 
+**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+
+**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
+
 Continue with [compilation](installation.html#compilation).

docs/install_osx.md

@@ -10,15 +10,15 @@ In the following, we assume that you're using Anaconda Python and Homebrew.
 
 **CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
 
-**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
 In other `ENV` settings, things may not work as expected.
 
 **General dependencies**
 
     brew install --fresh -vd snappy leveldb gflags glog szip lmdb
     # need the homebrew science source for OpenCV and hdf5
     brew tap homebrew/science
-    brew install hdf5 opencv
+    hdf5 opencv
 
 If using Anaconda Python, a modification to the OpenCV formula might be needed
 Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
@@ -32,7 +32,7 @@ If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
 
     # with Python pycaffe needs dependencies built from source
     brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
+    brew install --build-from-source --fresh -vd boost
     # without Python the usual installation suffices
     brew install protobuf boost
 
@@ -115,7 +115,7 @@ Then, whenever you want to update homebrew, switch back to the master branches,
     # Update homebrew; hopefully this works without errors!
     brew update
 
-    # Switch back to the caffe branches with the formulae that you modified earlier
+    # Switch back to the caffe branches with the forumlae that you modified earlier
     cd /usr/local
     git rebase master caffe
     # Fix any merge conflicts and commit to caffe branch

docs/model_zoo.md

@@ -3,30 +3,28 @@ title: Model Zoo
 ---
 # Caffe Model Zoo
 
-Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
-These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
-
-To help share these models, we introduce the model zoo framework:
+Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
+To lower the friction of sharing these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
-- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
+- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` parameters.
 - A central wiki page for sharing model info Gists.
 
-## Where to get trained models
+## BVLC Reference Models
 
-First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
-<br>
-See the [BVLC model license](#bvlc-model-license) for details.
+First of all, we provide some trained models out of the box.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
-- **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper. (Trained by Jeff Donahue @jeffdonahue)
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
+- **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
+
 
-**Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
-These models are subject to conditions of their respective authors such as citation and license.
-Thank you for sharing your models!
+## Community Models
+
+The publicly-editable [Caffe Model Zoo wiki](https://github.com/BVLC/caffe/wiki/Model-Zoo) catalogues user-made models.
+Refer to the model details for authorship and conditions -- please respect licenses and citations.
 
 ## Model info format
 
@@ -46,7 +44,7 @@ A caffe model is distributed as a directory containing:
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 
-`scripts/upload_model_to_gist.sh <dirname>` uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
+- `scripts/upload_model_to_gist.sh <dirname>`: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
 
 Try doing `scripts/upload_model_to_gist.sh models/bvlc_alexnet` to test the uploading (don't forget to delete the uploaded gist afterward).
 
@@ -58,13 +56,4 @@ It is up to the user where to host the `.caffemodel` file.
 We host our BVLC-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
-`scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
-
-## BVLC model license
-
-The Caffe models bundled by the BVLC are released for unrestricted use.
-
-These models are trained on data from the [ImageNet project](http://www.image-net.org/) and training data includes internet photos that may be subject to copyright.
-
-Our present understanding as researchers is that there is no restriction placed on the open release of these learned model weights, since none of the original images are distributed in whole or in part.
-To the extent that the interpretation arises that weights are derivative works of the original copyright holder and they assert such a copyright, UC Berkeley makes no representations as to what use is allowed other than to consider our present release in the spirit of fair use in the academic mission of the university to disseminate knowledge and tools as broadly as possible without restriction.
+- `scripts/download_model_binary.py <dirname>`: downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.

docs/performance_hardware.md

@@ -48,7 +48,7 @@ and then set the clock speed with
 
     sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
 
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
+but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to intialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
 
 ## NVIDIA Titan
 

docs/tutorial/data.md

@@ -10,15 +10,15 @@ New input types are supported by developing a new data layer -- the rest of the
 
 This data layer definition
 
-    layer {
+    layers {
       name: "mnist"
-      # Data layer loads leveldb or lmdb storage DBs for high-throughput.
-      type: "Data"
+      # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
+      type: DATA
       # the 1st top is the data itself: the name is only convention
       top: "data"
       # the 2nd top is the ground truth: the name is only convention
       top: "label"
-      # the Data layer configuration
+      # the DATA layer configuration
       data_param {
         # path to the DB
         source: "examples/mnist/mnist_train_lmdb"
@@ -46,9 +46,9 @@ The (data, label) pairing is a convenience for classification models.
 
 **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
 
-    layer {
+    layers {
       name: "data"
-      type: "Data"
+      type: DATA
       [...]
       transform_param {
         scale: 0.1