experiments/STT-tensorflow
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A. Unique TensorFlower 2016-08-26 12:50:48 -08:00 committed by TensorFlower Gardener
parent 0f867ebf83
commit b0bdff4827
65 changed files with 1374 additions and 159 deletions
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4
README.md
View File

@ -34,9 +34,9 @@ and discussion.**
People who are a little more adventurous can also try our nightly binaries:
* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/))
* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
* [Android](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/lastSuccessfulBuild/artifact/bazel-out/local_linux/bin/tensorflow/examples/android/tensorflow_demo.apk) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/))
#### *Try your first TensorFlow program*

10
configure vendored
View File

@ -98,7 +98,7 @@ while true; do
fi
fi
if [ -e "$GCC_HOST_COMPILER_PATH" ]; then
export CC=$GCC_HOST_COMPILER_PATH
export GCC_HOST_COMPILER_PATH
break
fi
echo "Invalid gcc path. ${GCC_HOST_COMPILER_PATH} cannot be found" 1>&2
@ -142,7 +142,7 @@ while true; do
if [ -e "${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH}" ]; then
export CUDA_TOOLKIT_PATH
export CUDA_VERSION=$TF_CUDA_VERSION
export TF_CUDA_VERSION
break
fi
echo "Invalid path to CUDA $TF_CUDA_VERSION toolkit. ${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH} cannot be found"
@ -203,7 +203,7 @@ while true; do
fi
if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" -o -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then
export CUDNN_VERSION=$TF_CUDNN_VERSION
export TF_CUDNN_VERSION
export CUDNN_INSTALL_PATH
break
fi
@ -211,7 +211,7 @@ while true; do
if [ "$OSNAME" == "Linux" ]; then
CUDNN_PATH_FROM_LDCONFIG="$(ldconfig -p | sed -n 's/.*libcudnn.so .* => \(.*\)/\1/p')"
if [ -e "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}" ]; then
export CUDNN_VERSION=$TF_CUDNN_VERSION
export TF_CUDNN_VERSION
export CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
break
fi
@ -263,7 +263,7 @@ EOF
exit 1
fi
else
export CUDA_COMPUTE_CAPABILITIES=$TF_CUDA_COMPUTE_CAPABILITIES
export TF_CUDA_COMPUTE_CAPABILITIES
break
fi
TF_CUDA_COMPUTE_CAPABILITIES=""

9
tensorflow/BUILD
View File

@ -174,6 +174,15 @@ cc_binary(
],
)
cc_binary(
name = "libtensorflow_c.so",
linkshared = 1,
deps = [
"//tensorflow/c:c_api",
"//tensorflow/core:tensorflow",
],
)
cc_binary(
name = "libtensorflow_cc.so",
linkshared = 1,

2
tensorflow/cc/framework/cc_op_gen.cc
View File

@ -232,7 +232,7 @@ string PrintAttrValue(string op, const AttrValue& attr_value) {
string ToCamelCase(const string& str) {
string result;
const char joiner = '_';
int i = 0;
size_t i = 0;
bool cap = true;
while (i < str.size()) {
const char c = str[i++];

3
tensorflow/contrib/cmake/CMakeLists.txt
View File

@ -51,7 +51,8 @@ include(highwayhash)
# Let's get to work!
include(tf_core_framework.cmake)
include(tf_stream_executor.cmake)
# NOTE: Disabled until issue #3996 is fixed.
# include(tf_stream_executor.cmake)
include(tf_core_cpu.cmake)
include(tf_models.cmake)
include(tf_core_ops.cmake)

2
tensorflow/contrib/cmake/tf_core_kernels.cmake
View File

@ -13,6 +13,8 @@ file(GLOB_RECURSE tf_core_kernels_exclude_srcs
"${tensorflow_source_dir}/tensorflow/core/kernels/*testutil.cc"
"${tensorflow_source_dir}/tensorflow/core/kernels/*main.cc"
"${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc"
"${tensorflow_source_dir}/tensorflow/core/kernels/debug_ops.h"
"${tensorflow_source_dir}/tensorflow/core/kernels/debug_ops.cc"
)
list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_exclude_srcs})

3
tensorflow/contrib/ios_examples/README.md
View File

@ -72,6 +72,9 @@ rundown:
unused because no other code references the variables, but in fact their
constructors have the important side effect of registering the class.
- You'll need to include the Accelerate framework in the "Link Binary with
Libraries" build phase of your project.
- C++11 support (or later) should be enabled by setting `C++ Language Dialect` to
`GNU++11` (or `GNU++14`), and `C++ Standard Library` to `libc++`.

4
tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
View File

@ -9,6 +9,7 @@
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590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D871D02091F00DF5523 /* libprotobuf.a */; };
5993C7701D5D4E7F0048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */; };
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@ -25,6 +26,7 @@
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59A3CFF11CF4E68100C4259F /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
59A3CFF21CF4E68100C4259F /* AppDelegate.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = AppDelegate.mm; sourceTree = "<group>"; };
59A3CFF41CF4E68100C4259F /* cropped_panda.jpg */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = cropped_panda.jpg; sourceTree = "<group>"; };
@ -50,6 +52,7 @@
isa = PBXFrameworksBuildPhase;
buildActionMask = 2147483647;
files = (
5993C7701D5D4E7F0048CE6A /* Accelerate.framework in Frameworks */,
590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */,
590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */,
59A3D0181CF4E86100C4259F /* UIKit.framework in Frameworks */,
@ -63,6 +66,7 @@
591157921CF4011C00C31E3A = {
isa = PBXGroup;
children = (
5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */,
590E7D861D02091F00DF5523 /* libprotobuf-lite.a */,
590E7D871D02091F00DF5523 /* libprotobuf.a */,
59A3D0171CF4E86100C4259F /* UIKit.framework */,

4
tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
View File

@ -24,6 +24,7 @@
592FF90D18EDD0DA00C164F8 /* MainStoryboard_iPhone.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 592FF90A18EDD0DA00C164F8 /* MainStoryboard_iPhone.storyboard */; };
592FF92518EE240200C164F8 /* CameraExampleAppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 592FF92218EE240200C164F8 /* CameraExampleAppDelegate.m */; };
592FF92618EE240200C164F8 /* CameraExampleViewController.mm in Sources */ = {isa = PBXBuildFile; fileRef = 592FF92418EE240200C164F8 /* CameraExampleViewController.mm */; };
5993C7721D5D4E980048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C7711D5D4E980048CE6A /* Accelerate.framework */; };
/* End PBXBuildFile section */
/* Begin PBXFileReference section */
@ -52,6 +53,7 @@
592FF92218EE240200C164F8 /* CameraExampleAppDelegate.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = CameraExampleAppDelegate.m; sourceTree = SOURCE_ROOT; };
592FF92318EE240200C164F8 /* CameraExampleViewController.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = CameraExampleViewController.h; sourceTree = SOURCE_ROOT; };
592FF92418EE240200C164F8 /* CameraExampleViewController.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = CameraExampleViewController.mm; sourceTree = SOURCE_ROOT; };
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/* End PBXFileReference section */
/* Begin PBXFrameworksBuildPhase section */
@ -59,6 +61,7 @@
isa = PBXFrameworksBuildPhase;
buildActionMask = 2147483647;
files = (
5993C7721D5D4E980048CE6A /* Accelerate.framework in Frameworks */,
591D3EDF1CFFAD230059011C /* libprotobuf-lite.a in Frameworks */,
591D3EE01CFFAD230059011C /* libprotobuf.a in Frameworks */,
591D3ECF1CFF7FCE0059011C /* ImageIO.framework in Frameworks */,
@ -103,6 +106,7 @@
592FF8B718ECBD7600C164F8 /* Frameworks */ = {
isa = PBXGroup;
children = (
5993C7711D5D4E980048CE6A /* Accelerate.framework */,
591D3EDD1CFFAD230059011C /* libprotobuf-lite.a */,
591D3EDE1CFFAD230059011C /* libprotobuf.a */,
591D3ECE1CFF7FCE0059011C /* ImageIO.framework */,

4
tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
View File

@ -9,6 +9,7 @@
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590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D871D02091F00DF5523 /* libprotobuf.a */; };
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59A3D0011CF4E68100C4259F /* AppDelegate.mm in Sources */ = {isa = PBXBuildFile; fileRef = 59A3CFF21CF4E68100C4259F /* AppDelegate.mm */; };
59A3D0031CF4E68100C4259F /* grace_hopper.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF51CF4E68100C4259F /* grace_hopper.jpg */; };
59A3D0051CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF71CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt */; };
@ -25,6 +26,7 @@
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59A3CFF11CF4E68100C4259F /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
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@ -50,6 +52,7 @@
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buildActionMask = 2147483647;
files = (
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590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */,
590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */,
59A3D0181CF4E86100C4259F /* UIKit.framework in Frameworks */,
@ -63,6 +66,7 @@
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59A3D0171CF4E86100C4259F /* UIKit.framework */,

1
tensorflow/contrib/layers/__init__.py
View File

@ -27,6 +27,7 @@ common machine learning algorithms.
@@convolution2d_transpose
@@flatten
@@fully_connected
@@layer_norm
@@max_pool2d
@@one_hot_encoding
@@repeat

109
tensorflow/contrib/layers/python/layers/layers.py
View File

@ -52,6 +52,7 @@ __all__ = ['avg_pool2d',
'dropout',
'flatten',
'fully_connected',
'layer_norm',
'linear',
'max_pool2d',
'one_hot_encoding',
@ -276,7 +277,8 @@ def batch_norm(inputs,
outputs.set_shape(inputs_shape)
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope
@ -328,7 +330,8 @@ def bias_add(inputs,
outputs = nn.bias_add(inputs, biases)
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope
@ -441,7 +444,8 @@ def convolution2d(inputs,
outputs = nn.bias_add(outputs, biases)
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope
@ -541,7 +545,8 @@ def convolution2d_in_plane(
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope
@ -668,7 +673,8 @@ def convolution2d_transpose(
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope
@ -845,7 +851,95 @@ def fully_connected(inputs,
# Reshape back outputs
outputs = array_ops.reshape(outputs, array_ops.pack(out_shape))
outputs.set_shape(static_shape)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope
def layer_norm(inputs,
center=True,
scale=True,
activation_fn=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
scope=None):
"""Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450.
"Layer Normalization"
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
Can be used as a normalizer function for conv2d and fully_connected.
Args:
inputs: a tensor with 2 or more dimensions. The normalization
occurs over all but the first dimension.
center: If True, subtract `beta`. If False, `beta` is ignored.
scale: If True, multiply by `gamma`. If False, `gamma` is
not used. When the next layer is linear (also e.g. `nn.relu`), this can be
disabled since the scaling can be done by the next layer.
activation_fn: Optional activation function.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
variables_collections: optional collections for the variables.
outputs_collections: collections to add the outputs.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional scope for `variable_op_scope`.
Returns:
A `Tensor` representing the output of the operation.
Raises:
ValueError: if rank or last dimension of `inputs` is undefined.
"""
with variable_scope.variable_scope(scope, 'LayerNorm', [inputs],
reuse=reuse) as sc:
inputs = ops.convert_to_tensor(inputs)
inputs_shape = inputs.get_shape()
inputs_rank = inputs_shape.ndims
if inputs_rank is None:
raise ValueError('Inputs %s has undefined rank.' % inputs.name)
dtype = inputs.dtype.base_dtype
axis = list(range(1, inputs_rank))
params_shape = inputs_shape[-1:]
if not params_shape.is_fully_defined():
raise ValueError('Inputs %s has undefined last dimension %s.' % (
inputs.name, params_shape))
# Allocate parameters for the beta and gamma of the normalization.
beta, gamma = None, None
if center:
beta_collections = utils.get_variable_collections(variables_collections,
'beta')
beta = variables.model_variable('beta',
shape=params_shape,
dtype=dtype,
initializer=init_ops.zeros_initializer,
collections=beta_collections,
trainable=trainable)
if scale:
gamma_collections = utils.get_variable_collections(variables_collections,
'gamma')
gamma = variables.model_variable('gamma',
shape=params_shape,
dtype=dtype,
initializer=init_ops.ones_initializer,
collections=gamma_collections,
trainable=trainable)
# Calculate the moments on the last axis (layer activations).
mean, variance = nn.moments(inputs, axis, keep_dims=True)
# Compute layer normalization using the batch_normalization function.
variance_epsilon = 1E-12
outputs = nn.batch_normalization(
inputs, mean, variance, beta, gamma, variance_epsilon)
outputs.set_shape(inputs_shape)
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope,
outputs)
@add_arg_scope
@ -1094,7 +1188,8 @@ def separable_convolution2d(
if activation_fn:
outputs = activation_fn(outputs)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
return utils.collect_named_outputs(outputs_collections,
sc.original_name_scope, outputs)
@add_arg_scope

104
tensorflow/contrib/layers/python/layers/layers_test.py
View File

@ -203,6 +203,16 @@ class Convolution2dTest(tf.test.TestCase):
scope='conv1')
self.assertEquals(output.op.name, 'conv1/Relu')
def testCreateConvWithCollection(self):
height, width = 3, 3
images = tf.random_uniform((5, height, width, 3), seed=1)
with tf.name_scope('fe'):
conv = tf.contrib.layers.convolution2d(
images, 32, [3, 3], outputs_collections='outputs',
scope='Conv')
namedOutputs = tf.get_collection('outputs')[0]
self.assertEquals(namedOutputs.name, 'fe/Conv')
def testCreateConvWithoutActivation(self):
height, width = 3, 3
with self.test_session():
@ -989,6 +999,16 @@ class FCTest(tf.test.TestCase):
output = tf.contrib.layers.fully_connected(inputs, 32, scope='fc1')
self.assertEquals(output.op.name, 'fc1/Relu')
def testCreateFCWithCollection(self):
height, width = 3, 3
inputs = tf.random_uniform((5, height * width * 3), seed=1)
with tf.name_scope('fe'):
fc = tf.contrib.layers.fully_connected(
inputs, 7, outputs_collections='outputs',
scope='fc')
namedOutputs = tf.get_collection('outputs')[0]
self.assertEquals(namedOutputs.name, 'fe/fc')
def testCreateFcCreatesWeightsAndBiasesVars(self):
height, width = 3, 3
inputs = tf.random_uniform((5, height * width * 3), seed=1)
@ -1542,6 +1562,90 @@ class BatchNormTest(tf.test.TestCase):
self.assertAllClose(moving_variance.eval(), expected_var)
class LayerNormTest(tf.test.TestCase):
def testUnknownShape(self):
with tf.Graph().as_default() as g, self.test_session(g):
inputs = tf.placeholder(dtype=tf.float32)
with self.assertRaisesRegexp(ValueError, 'undefined rank'):
tf.contrib.layers.layer_norm(inputs)
def testUnknownLastDim(self):
with tf.Graph().as_default() as g, self.test_session(g):
inputs = tf.placeholder(dtype=tf.float32)
inputs.set_shape(tf.TensorShape((5, 3, 3, None)))
with self.assertRaisesRegexp(ValueError, 'undefined last dimension'):
tf.contrib.layers.layer_norm(inputs)
def testCreateOp(self):
height, width = 3, 3
with self.test_session():
images = np.random.uniform(size=(5, height, width, 3))
output = tf.contrib.layers.layer_norm(images)
self.assertTrue(output.op.name.startswith('LayerNorm/batchnorm'))
self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
tf.contrib.layers.layer_norm(images)
beta = tf.contrib.framework.get_variables_by_name('beta')[0]
gamma = tf.contrib.framework.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'LayerNorm/beta')
self.assertEquals(gamma.op.name, 'LayerNorm/gamma')
def testReuseVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
tf.contrib.layers.layer_norm(images, scope='ln')
tf.contrib.layers.layer_norm(images, scope='ln', reuse=True)
beta = tf.contrib.framework.get_variables_by_name('beta')
gamma = tf.contrib.framework.get_variables_by_name('gamma')
self.assertEquals(len(beta), 1)
self.assertEquals(len(gamma), 1)
def testReuseVars(self):
height, width = 3, 3
with self.test_session() as sess:
image_shape = (10, height, width, 3)
image_values = np.random.rand(*image_shape)
images = tf.constant(image_values, shape=image_shape, dtype=tf.float32)
output_train = tf.contrib.layers.layer_norm(images, scope='LN')
output_eval = tf.contrib.layers.layer_norm(images,
scope='LN',
reuse=True)
# Initialize all variables
sess.run(tf.initialize_all_variables())
# output_train and output_eval should be the same.
self.assertAllClose(sess.run([output_train]), sess.run([output_eval]))
def doOutputTest(self, input_shape):
with self.test_session() as sess:
input_values = np.random.rand(*input_shape)
inputs = tf.constant(input_values, shape=input_shape, dtype=tf.float32)
output_op = tf.contrib.layers.layer_norm(inputs, scope='LN')
# Initialize all variables
sess.run(tf.initialize_all_variables())
# The mean and variance of the output should be close to 0 and 1
# respectively.
moments_axis = tuple([i for i in range(1, len(input_shape))])
outputs = sess.run(output_op)
expected_mean = np.zeros(input_shape[0])
expected_var = np.ones(input_shape[0])
mean = np.mean(outputs, axis=moments_axis)
var = np.var(outputs, axis=moments_axis)
tol = 1e-5
self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
self.assertAllClose(var, expected_var, rtol=tol, atol=tol)
def testOutput2DInput(self):
self.doOutputTest((10, 300))
def testOutput4DInput(self):
self.doOutputTest((100, 10, 10, 3))
class MaxPool2DTest(tf.test.TestCase):
def testCreateMaxPool(self):

3
tensorflow/contrib/learn/BUILD
View File

@ -684,6 +684,9 @@ py_test(
size = "small",
srcs = ["python/learn/utils/export_test.py"],
srcs_version = "PY2AND3",
tags = [
"manual", # http://b/31032996
],
deps = [
":learn",
"//tensorflow:tensorflow_py",

10
tensorflow/contrib/makefile/Makefile
View File

@ -285,6 +285,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
-arch armv7 \
-D__thread= \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-mno-thumb \
-DTF_LEAN_BINARY \
@ -295,6 +296,7 @@ ifeq ($(TARGET),IOS)
${IPHONEOS_SYSROOT}
LDFLAGS := -arch armv7 \
-miphoneos-version-min=${MIN_SDK_VERSION} \
-framework Accelerate \
-Xlinker -S \
-Xlinker -x \
-Xlinker -dead_strip \
@ -306,6 +308,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
-arch armv7s \
-D__thread= \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-mno-thumb \
-DTF_LEAN_BINARY \
@ -316,6 +319,7 @@ ifeq ($(TARGET),IOS)
${IPHONEOS_SYSROOT}
LDFLAGS := -arch armv7s \
-miphoneos-version-min=${MIN_SDK_VERSION} \
-framework Accelerate \
-Xlinker -S \
-Xlinker -x \
-Xlinker -dead_strip \
@ -327,6 +331,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
-arch arm64 \
-D__thread= \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-DTF_LEAN_BINARY \
-D__ANDROID_TYPES_SLIM__ \
@ -336,6 +341,7 @@ ifeq ($(TARGET),IOS)
${IPHONEOS_SYSROOT}
LDFLAGS := -arch arm64 \
-miphoneos-version-min=${MIN_SDK_VERSION} \
-framework Accelerate \
-Xlinker -S \
-Xlinker -x \
-Xlinker -dead_strip \
@ -347,6 +353,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
-arch i386 \
-D__thread= \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-DTF_LEAN_BINARY \
-D__ANDROID_TYPES_SLIM__ \
@ -356,6 +363,7 @@ ifeq ($(TARGET),IOS)
${IPHONESIMULATOR_SYSROOT}
LDFLAGS := -arch i386 \
-mios-simulator-version-min=${MIN_SDK_VERSION} \
-framework Accelerate \
-Xlinker -S \
-Xlinker -x \
-Xlinker -dead_strip \
@ -367,6 +375,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
-arch x86_64 \
-D__thread= \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-DTF_LEAN_BINARY \
-D__ANDROID_TYPES_SLIM__ \
@ -376,6 +385,7 @@ ifeq ($(TARGET),IOS)
${IPHONESIMULATOR_SYSROOT}
LDFLAGS := -arch x86_64 \
-mios-simulator-version-min=${MIN_SDK_VERSION} \
-framework Accelerate \
-Xlinker -S \
-Xlinker -x \
-Xlinker -dead_strip \

4
tensorflow/contrib/makefile/README.md
View File

@ -260,15 +260,17 @@ For other variations of valid optimization flags, see [clang optimization levels
## Raspberry Pi
Building on the Raspberry Pi is similar to a normal Linux system. First
download the dependencies and build protobuf:
download the dependencies, install the required packages and build protobuf:
```bash
tensorflow/contrib/makefile/download_dependencies.sh
sudo apt-get install autoconf automake libtool
cd tensorflow/contrib/makefile/downloads/protobuf/
./autogen.sh
./configure
make
sudo make install
sudo ldconfig # refresh shared library cache
cd ../../../../..
```

1
tensorflow/contrib/makefile/tf_op_files.txt
View File

@ -99,6 +99,7 @@ tensorflow/core/kernels/cwise_op_equal_to.cc
tensorflow/core/kernels/cwise_op_div.cc
tensorflow/core/kernels/cwise_op_add.cc
tensorflow/core/kernels/ctc_decoder_ops.cc
tensorflow/core/kernels/conv_ops_using_gemm.cc
tensorflow/core/kernels/conv_ops.cc
tensorflow/core/kernels/conv_grad_ops.cc
tensorflow/core/kernels/control_flow_ops.cc

2
tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
View File

@ -59,7 +59,7 @@ def confusion_matrix(predictions, labels, num_classes=None,
name: Scope name.
Returns:
A l X l matrix represeting the confusion matrix, where l in the number of
A k X k matrix represeting the confusion matrix, where k is the number of
possible labels in the classification task.
Raises:

3
tensorflow/core/common_runtime/gpu/gpu_device.cc
View File

@ -787,7 +787,8 @@ struct CudaVersion {
};
std::vector<CudaVersion> supported_cuda_compute_capabilities = {
TF_CUDA_CAPABILITIES,};
TF_CUDA_CAPABILITIES,
};
std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
auto cuda_caps = supported_cuda_compute_capabilities;

2
tensorflow/core/debug/debug_graph_utils.cc
View File

@ -154,7 +154,7 @@ Status DebugNodeInserter::InsertNodes(
// Create all requested debug nodes and their edges to the Copy node.
std::vector<Node*> node_added_debug_nodes;
for (int i = 0; i < tensor_watches[tensor_name].size(); ++i) {
for (size_t i = 0; i < tensor_watches[tensor_name].size(); ++i) {
const string& debug_op_name = tensor_watches[tensor_name][i];
Node* debug_node;

2
tensorflow/core/framework/log_memory.cc
View File

@ -30,7 +30,7 @@ namespace {
template <typename T>
void OutputToLog(const T& proto) {
string type_name = proto.GetTypeName();
const int index = type_name.find_last_of(".");
const size_t index = type_name.find_last_of(".");
if (index != string::npos) type_name = type_name.substr(index + 1);
LOG(INFO) << LogMemory::kLogMemoryLabel << " " << type_name << " { "
<< ProtoShortDebugString(proto) << " }";

4
tensorflow/core/framework/op.cc
View File

@ -156,7 +156,7 @@ string OpRegistry::DebugString(bool include_internal) const {
bool OpRegistry::MustCallDeferred() const {
if (initialized_) return false;
initialized_ = true;
for (int i = 0; i < deferred_.size(); ++i) {
for (size_t i = 0; i < deferred_.size(); ++i) {
TF_QCHECK_OK(RegisterAlreadyLocked(deferred_[i]));
}
deferred_.clear();
@ -166,7 +166,7 @@ bool OpRegistry::MustCallDeferred() const {
Status OpRegistry::CallDeferred() const {
if (initialized_) return Status::OK();
initialized_ = true;
for (int i = 0; i < deferred_.size(); ++i) {
for (size_t i = 0; i < deferred_.size(); ++i) {
Status s = RegisterAlreadyLocked(deferred_[i]);
if (!s.ok()) {
return s;

2
tensorflow/core/framework/shape_inference.h
View File

@ -57,7 +57,7 @@ class DimensionHandle {
const Dimension* ptr_ = nullptr;
friend class DimensionOrConstant;
friend struct DimensionOrConstant;
friend class InferenceContext;
friend class ShapeInferenceTest;
friend class ShapeInferenceTestutil;

2
tensorflow/core/framework/tensor.cc
View File

@ -740,7 +740,7 @@ string Tensor::SummarizeValue(int64 max_entries) const {
string ret;
// TODO(irving): Don't call flat every time around this
// loop.
for (int64 i = 0; i < limit; ++i) {
for (size_t i = 0; i < limit; ++i) {
if (i > 0) strings::StrAppend(&ret, " ");
switch (dtype()) {
case DT_STRING:

4
tensorflow/core/kernels/conv_ops.cc
View File

@ -242,8 +242,12 @@ class Conv2DOp : public BinaryOp<T> {
Name("Conv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
Conv2DOp<CPUDevice, T>);
// If we're using the alternative GEMM-based implementation of Conv2D for the
// CPU implementation, don't register this EigenTensor-based version.
#if !defined(USE_GEMM_FOR_CONV)
TF_CALL_half(REGISTER_CPU);
TF_CALL_float(REGISTER_CPU);
#endif // USE_GEMM_FOR_CONV
// To be used inside depthwise_conv_op.cc.
template class LaunchConv2DOp<CPUDevice, float>;

622
tensorflow/core/kernels/conv_ops_using_gemm.cc Normal file
View File

@ -0,0 +1,622 @@
/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file contains a set of different implementations of the two-dimensional
// convolution operation. The standard TensorFlow Conv2d kernel uses EigenTensor
// to implement the computation, but this module has a variety of different ways
// of producing the same result. These methods are designed to be easier to
// understand and connect to other libraries, so that we can take advantage of
// platforms that have specialized implementations of GEMM for example.
//
// The basic interface is a Conv functor object that's templated by the types
// of the data it will be operating on, and is passed in the arguments needed to
// calculate the convolution. The simplest implementation of this functor is
// ReferenceConvFunctor, which is a readable but slow reference version.
//
// A faster version uses the approach of packing image patches into a matrix
// before calling a matrix multiply, the Im2ColConvFunctor. In turn, this can
// use a variety of different methods to calculate the matrix multiplication,
// or GEMM. The simplest but slowest is the ReferenceGemmFunctor, but the
// FastGemmFunctor will use whatever optimized libraries are available. By
// default it uses Eigen, but on Apple platforms it will take advantage of the
// system's Accelerate BLAS library to get better performance than the standard
// TensorFlow convolution kernel.
//
// The version actually used is defined at the bottom of this file using the
// REGISTER_KERNEL_BUILDER() macro. To try out different implementations (for
// example to switch to a reference one for easier debugging) you can swap out
// the default functors in that call.
//
// The registration itself is guarded with the USE_GEMM_FOR_CONV macro. The iOS
// makefile build defines this, but if you want to enable this implementation
// and disable the standard EigenTensor one in other build setups, you'll need
// to define it there too.
#include <string.h>
#include <map>
#include <vector>
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/kernels/bounds_check.h"
#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"
#if defined(__APPLE__)
#include <Accelerate/Accelerate.h>
#define USE_ACCELERATE_GEMM
#endif // __APPLE__
namespace tensorflow {
namespace {
// This function implements the convolution operation in as simple a form as
// possible. It won't give great performance, but it is very useful for
// stepping through and instrumenting for debugging, creating minimal benchmarks
// to prototype with, and sharing with teams that want to run this outside of
// our environment.
// With that in mind, I've avoided using anything except pretty standard C++
// types. This is especially noticeable in the data access through raw array
// indexing. It's deliberate in this case though, since it makes the underlying
// memory order very explicit, which is important for both inspecting memory
// contents during debugging and for specifying what we expect to others.
// The memory layout of the data is, from biggest stride to smallest:
// input_data = [input_batches, input_height, input_width, input_depth]
// filter_data = [filter_height, filter_width, input_depth, filter_count]
// output_data = [input_batches, output_height, output_width, filter_count]
template <class T1, class T2, class T3>
class ReferenceConvFunctor {
public:
void operator()(OpKernelContext* context, const T1* input_data,
int input_batches, int input_height, int input_width,
int input_depth, const T2* filter_data, int filter_height,
int filter_width, int filter_count, int stride_rows,
int stride_cols, Padding padding, T3* output_data,
int output_height, int output_width) {
// The two different padding modes we support can be a bit confusing. SAME
// means we're trying to produce an output image that's the same size as the
// input. It's complicated by stride, which shrinks the output image by a
// a factor, but it means we end up sampling from outside the borders of the
// input. These out-of-bounds values are read as zeroes. VALID means only
// produce output values where the filters can read all their values from
// within the input image. It effectively removes the margins of the output
// image compared to the one produced by SAME. Stride complicates this
// definition though, because it can result in the right and bottom filter
// patches sampling from outside the borders if it's greater than 1.
// Most of the logic for sorting this all out is done before this function,
// when we calculate the output size, but the positioning of the origin of
// the filters is different between the two modes, since SAME positions the
// first filter off the edge of the input.
int filter_left_offset;
int filter_top_offset;
if (padding == VALID) {
filter_left_offset =
((output_width - 1) * stride_cols + filter_width - input_width + 1) /
2;
filter_top_offset = ((output_height - 1) * stride_rows + filter_height -
input_height + 1) /
2;
} else {
filter_left_offset =
((output_width - 1) * stride_cols + filter_width - input_width) / 2;
filter_top_offset =
((output_height - 1) * stride_rows + filter_height - input_height) /
2;
}
// If we've got multiple images in our input, work through each of them.
for (int batch = 0; batch < input_batches; ++batch) {
// Walk through all the output image values, sliding the filter to
// different positions in the input.
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
// Each filter kernel produces one output channel.
for (int out_channel = 0; out_channel < filter_count; ++out_channel) {
// We're going to calculate a single output value, which means we
// need to multiply a three dimensional kernel of weights against
// the current location within the input image.
/*
*-------------------------------...
|\ ^
| \in_depth
| \ v
| *-------------------------------...
| | ^
| | in_y_origin
| | v \
| |<in_x_origin>*---*^
| | \| |filter_height
. | *---*v
. | <--->
. filter_width
.
*/
const int in_x_origin = (out_x * stride_cols) - filter_left_offset;
const int in_y_origin = (out_y * stride_rows) - filter_top_offset;
T3 total(0);
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
for (int in_channel = 0; in_channel < input_depth;
++in_channel) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
T1 input_value;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height)) {
input_value =
input_data[(batch * input_height * input_width *
input_depth) +
(in_y * input_width * input_depth) +
(in_x * input_depth) + in_channel];
} else {
input_value = T1(0);
}
const T2 filter_value =
filter_data[(filter_y * filter_width * input_depth *
filter_count) +
(filter_x * input_depth * filter_count) +
(in_channel * filter_count) + out_channel];
total += (input_value * filter_value);
}
}
}
output_data[(batch * output_height * output_width * filter_count) +
(out_y * output_width * filter_count) +
(out_x * filter_count) + out_channel] = total;
}
}
}
}
}
};
// A readable but slow implementation of matrix multiplication, useful for
// debugging and understanding the algorithm. Use instead of FastGemmFunctor in
// the Im2ColConvFunctor template definition inside the op registration to
// enable. Assumes row-major ordering of the values in memory.
template <class T1, class T2, class T3>
class ReferenceGemmFunctor {
public:
void operator()(size_t m, size_t n, size_t k, const T1* a, size_t lda,
const T2* b, size_t ldb, T3* c, size_t ldc) {
const size_t a_i_stride = lda;
const size_t a_l_stride = 1;
const size_t b_j_stride = 1;
const size_t b_l_stride = ldb;
const size_t c_i_stride = ldc;
const size_t c_j_stride = 1;
size_t i, j, l;
for (j = 0; j < n; j++) {
for (i = 0; i < m; i++) {
T3 total(0);
for (l = 0; l < k; l++) {
const size_t a_index = ((i * a_i_stride) + (l * a_l_stride));
const T1 a_value = a[a_index];
const size_t b_index = ((j * b_j_stride) + (l * b_l_stride));
const T2 b_value = b[b_index];
total += (a_value * b_value);
}
const size_t c_index = ((i * c_i_stride) + (j * c_j_stride));
c[c_index] = total;
}
}
}
};
// Uses the optimized Eigen library to implement the matrix multiplication
// required by the Im2ColConvFunctor class. We supply the two input and one
// output types so that the accumulator can potentially be higher-precision than
// the inputs, even though we don't currently take advantage of this.
template <class T1, class T2, class T3>
class FastGemmFunctor {
public:
// Convenience wrappers for the Eigen matrix types we'll be using.
typedef Eigen::Map<
const Eigen::Matrix<T1, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
ConstMatrixT1;
typedef Eigen::Map<
const Eigen::Matrix<T2, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
ConstMatrixT2;
typedef Eigen::Map<
Eigen::Matrix<T3, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
MatrixT3;
void operator()(size_t m, size_t n, size_t k, const T1* a, size_t lda,
const T2* b, size_t ldb, T3* c, size_t ldc) {
ConstMatrixT1 a_matrix(a, m, k);
ConstMatrixT2 b_matrix(b, k, n);
MatrixT3 c_matrix(c, m, n);
c_matrix.noalias() = a_matrix * b_matrix;
}
};
// If we have Apple's Accelerate framework, use their implementation of GEMM to
// get a performance boost for float.
#if defined(USE_ACCELERATE_GEMM)
template <>
class FastGemmFunctor<float, float, float> {
public:
void operator()(size_t m, size_t n, size_t k, const float* a, size_t lda,
const float* b, size_t ldb, float* c, size_t ldc) {
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, 1.0f, a,
lda, b, ldb, 0.0f, c, ldc);
}
};
#endif // USE_ACCELERATE_GEMM
// Used to keep track of persistent memory buffers used within the op.
template <class T, size_t size>
struct Im2ColBufferResource : public ResourceBase {
mutex mu;
T data[size];
string DebugString() { return "Im2ColBufferResource"; }
};
// Implements convolution as a two stage process, first packing the patches of
// the input image into columns (im2col) and then running GEMM to produce the
// final result.
template <class T1, class T2, class T3, class TGemmFunctor>
class Im2ColConvFunctor {
public:
void operator()(OpKernelContext* context, const T1* input_data,
int input_batches, int input_height, int input_width,
int input_depth, const T2* filter_data, int filter_height,
int filter_width, int filter_count, int stride_rows,
int stride_cols, Padding padding, T3* output_data,
int output_height, int output_width) {
if ((input_batches <= 0) || (input_width <= 0) || (input_height <= 0) ||
(input_depth <= 0)) {
LOG(WARNING) << "Conv2D was called with bad input dimensions: "
<< input_batches << ", " << input_height << ", "
<< input_width << ", " << input_depth;
return;
}
if ((filter_width <= 0) || (filter_height <= 0) || (filter_count <= 0)) {
LOG(WARNING) << "Conv2D was called with bad filter dimensions: "
<< filter_width << ", " << filter_height << ", "
<< filter_count;
return;
}
if ((output_width <= 0) || (output_height <= 0)) {
LOG(WARNING) << "Conv2D was called with bad output width or height: "
<< output_width << ", " << output_height;
return;
}
// These calculations define how the patches will be positioned within the
// input image. The actual definitions are quite complex, and rely on the
// previously-calculated output size.
int filter_left_offset;
int filter_top_offset;
if (padding == VALID) {
filter_left_offset =
((output_width - 1) * stride_cols + filter_width - input_width + 1) /
2;
filter_top_offset = ((output_height - 1) * stride_rows + filter_height -
input_height + 1) /
2;
} else {
filter_left_offset =
((output_width - 1) * stride_cols + filter_width - input_width) / 2;
filter_top_offset =
((output_height - 1) * stride_rows + filter_height - input_height) /
2;
}
// The im2col buffer has # of patches rows, and # of filters cols.
// It's laid out like this, in row major order in memory:
// < filter value count >
// ^ +---------------------+
// patch | |
// count | |
// v +---------------------+
// Each patch row contains a filter_width x filter_height patch of the
// input, with the depth channel as the most contiguous in memory, followed
// by the width, then the height. This is the standard memory order in the
// image world if it helps to visualize it.
const int filter_value_count = filter_width * filter_height * input_depth;
// We don't want to allocate a buffer to hold all the patches if the size is
// going to be extremely large, so break it into chunks if it's bigger than
// a limit. Each chunk will be processed serially, so we can refill the
// buffer for the next chunk and reuse it, keeping maximum memory size down.
// In this case, we've picked 16 megabytes as a reasonable limit.
const size_t max_chunk_size = (16 * 1024 * 1024);
OP_REQUIRES(context, (filter_value_count * sizeof(T1)) <= max_chunk_size,
errors::InvalidArgument("Im2Col patch too large for buffer"));
const size_t patches_per_chunk =
max_chunk_size / (filter_value_count * sizeof(T1));
// Because memory allocation is very expensive on mobile platforms, try to
// allocate a persistent buffer that will be kept around between calls. We
// use TensorFlow's resource management to ensure that the memory will be
// released when the session is over.
Im2ColBufferResource<T1, max_chunk_size>* im2col_buffer_resource;
std::function<Status(Im2ColBufferResource<T1, max_chunk_size>**)> creator =
[](Im2ColBufferResource<T1, max_chunk_size>** resource) {
*resource = new Im2ColBufferResource<T1, max_chunk_size>();
return Status::OK();
};
OP_REQUIRES_OK(context, context->resource_manager()->LookupOrCreate(
"Conv2d", "im2col_buffer",
&im2col_buffer_resource, creator));
// This means that multiple ops can't be run simultaneously on different
// threads, because we have a single shared resource. The platforms this is
// aimed at have intra-op parallelism as their focus though, so it shouldn't
// be an issue.
mutex_lock lock_buffer(im2col_buffer_resource->mu);
core::ScopedUnref unref_buffer(im2col_buffer_resource);
T1* im2col_buffer = im2col_buffer_resource->data;
for (int batch = 0; batch < input_batches; ++batch) {
const T1* input_batch_start =
input_data + (batch * input_height * input_width * input_depth);
for (int out_y = 0; out_y < output_height; ++out_y) {
const int in_y_origin = (out_y * stride_rows) - filter_top_offset;
for (int out_x = 0; out_x < output_width; ++out_x) {
const int in_x_origin = (out_x * stride_cols) - filter_left_offset;
const int patch_index = (batch * output_width * output_height) +
(out_y * output_width) + out_x;
const int patch_index_within_chunk = patch_index % patches_per_chunk;
T1* im2col_patch_start =
im2col_buffer + (patch_index_within_chunk * filter_value_count);
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
const int in_y = in_y_origin + filter_y;
T1* im2col_row_start =
im2col_patch_start + (filter_y * filter_width * input_depth);
// If we're off the top or the bottom of the input, fill the whole
// row with zeroes.
if ((in_y < 0) || (in_y >= input_height)) {
T1* im2col_row_end =
im2col_row_start + (filter_width * input_depth);
std::fill(im2col_row_start, im2col_row_end, T1(0));
} else {
// What we're doing here is trying to copy and fill the im2col
// buffer as efficiently as possible, using functions to set or
// duplicate values en masse. We know we don't have to worry about
// vertical edges because we dealt with that case above, so we
// just need to handle filters that overlap the left or right
// edges. Here's what that looks like:
//
// < left_zero_count > < center_copy_count > < right_zero_count >
// +------------------+---------------------+--------------------+
// | (filter) | (image) | (filter) |
// +------------------+---------------------+--------------------+
// in_x_origin 0 input_width in_x_end
//
// In reality it's unlikely that a filter patch will be wider
// than an input, but this shows all the edge cases.
// We use std::fill() to set the left and right sections to zeroes
// and std::copy() to copy over the input data for the center.
const int in_x_end = in_x_origin + filter_width;
const int left_zero_count = std::max(0, 0 - in_x_origin);
const int right_zero_count = std::max(0, in_x_end - input_width);
const int center_copy_count =
filter_width - (left_zero_count + right_zero_count);
if (left_zero_count > 0) {
T1* im2col_left_start = im2col_row_start;
T1* im2col_left_end =
im2col_left_start + (left_zero_count * input_depth);
std::fill(im2col_left_start, im2col_left_end, T1(0));
}
if (center_copy_count > 0) {
const T1* input_row_start =
input_batch_start + (in_y * input_width * input_depth) +
(std::max(0, in_x_origin) * input_depth);
const T1* input_row_end =
input_row_start + (center_copy_count * input_depth);
T1* im2col_center_start =
im2col_row_start + (left_zero_count * input_depth);
std::copy(input_row_start, input_row_end, im2col_center_start);
}
if (right_zero_count > 0) {
T1* im2col_right_start =
im2col_row_start +
((left_zero_count + center_copy_count) * input_depth);
T1* im2col_right_end =
im2col_right_start + (right_zero_count * input_depth);
std::fill(im2col_right_start, im2col_right_end, T1(0));
}
}
}
const bool is_last_in_chunk =
(patch_index_within_chunk == (patches_per_chunk - 1));
const bool is_last_overall =
((batch == (input_batches - 1)) &&
(out_y == (output_height - 1)) && (out_x == (output_width - 1)));
if (is_last_in_chunk || is_last_overall) {
// Now we've assembled a set of image patches into a matrix, apply a
// GEMM matrix multiply of the patches as rows, times the filter
// weights in columns, to get partial results in the output matrix.
const int how_many_patches = patch_index_within_chunk + 1;
const int m = how_many_patches;
const int n = filter_count;
const int k = filter_value_count;
const int lda = filter_value_count;
const int ldb = filter_count;
const int ldc = filter_count;
const size_t start_patch_index =
patch_index - (how_many_patches - 1);
T3* chunk_output_data =
output_data + (start_patch_index * filter_count);
TGemmFunctor gemm_functor;
gemm_functor(m, n, k, im2col_buffer, lda, filter_data, ldb,
chunk_output_data, ldc);
}
}
}
}
}
};
} // namespace
// This TensorFlow kernel class handles all of the IO and housekeeping for the
// functors that actually implement the underlying algorithm. To swap in
// different implementations of the main calculations, use a different
// TConvFunctor parameter when instantiating the template.
template <class T, class TConvFunctor>
class Conv2DUsingGemmOp : public BinaryOp<T> {
public:
explicit Conv2DUsingGemmOp(OpKernelConstruction* context)
: BinaryOp<T>(context) {
OP_REQUIRES_OK(context, context->GetAttr("strides", &strides_));
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
OP_REQUIRES(context, data_format_ == FORMAT_NHWC,
errors::InvalidArgument(
"Data format not supported by this kernel", data_format));
OP_REQUIRES(context, strides_.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
const int64 stride_n = GetTensorDim(strides_, data_format_, 'N');
const int64 stride_c = GetTensorDim(strides_, data_format_, 'C');
OP_REQUIRES(
context, stride_n == 1 && stride_c == 1,
errors::InvalidArgument("Current implementation does not yet support "
"strides in the batch and depth dimensions."));
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
}
void Compute(OpKernelContext* context) override {
// Input tensor is of the following dimensions:
// [ batch, in_rows, in_cols, in_depth ]
const Tensor& input = context->input(0);
// Input filter is of the following dimensions:
// [ filter_rows, filter_cols, in_depth, out_depth]
const Tensor& filter = context->input(1);
// For 2D convolution, there should be 4 dimensions.
OP_REQUIRES(context, input.dims() == 4,
errors::InvalidArgument("input must be 4-dimensional",
input.shape().DebugString()));
OP_REQUIRES(context, filter.dims() == 4,
errors::InvalidArgument("filter must be 4-dimensional: ",
filter.shape().DebugString()));
for (int i = 0; i < 3; i++) {
OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i),
std::numeric_limits<int>::max()),
errors::InvalidArgument("filter too large"));
}
// The last dimension for input is in_depth. It must be the same as the
// filter's in_depth.
const int64 in_depth = GetTensorDim(input, data_format_, 'C');
OP_REQUIRES(
context, in_depth == filter.dim_size(2),
errors::InvalidArgument("input and filter must have the same depth: ",
in_depth, " vs ", filter.dim_size(2)));
// The last dimension for filter is out_depth.
const int out_depth = static_cast<int>(filter.dim_size(3));
// The second dimension for input is rows/height.
// The first dimension for filter is rows/height.
const int64 input_rows_raw = GetTensorDim(input, data_format_, 'H');
OP_REQUIRES(context, FastBoundsCheck(input_rows_raw,
std::numeric_limits<int>::max()),
errors::InvalidArgument("Input rows too large"));
const int input_rows = static_cast<int>(input_rows_raw);
const int filter_rows = static_cast<int>(filter.dim_size(0));
// The third dimension for input is columns/width.
// The second dimension for filter is columns/width.
const int64 input_cols_raw = GetTensorDim(input, data_format_, 'W');
OP_REQUIRES(context, FastBoundsCheck(input_cols_raw,
std::numeric_limits<int>::max()),
errors::InvalidArgument("Input cols too large"));
const int input_cols = static_cast<int>(input_cols_raw);
const int filter_cols = static_cast<int>(filter.dim_size(1));
// The first dimension for input is batch.
const int64 batch_raw = GetTensorDim(input, data_format_, 'N');
OP_REQUIRES(context,
FastBoundsCheck(batch_raw, std::numeric_limits<int>::max()),
errors::InvalidArgument("batch is too large"));
const int batch = static_cast<int>(batch_raw);
// For now we take the stride from the second and third dimensions only (we
// do not support striding on the batch or depth dimension).
const int stride_rows = GetTensorDim(strides_, data_format_, 'H');
const int stride_cols = GetTensorDim(strides_, data_format_, 'W');
int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0;
OP_REQUIRES_OK(context,
GetWindowedOutputSize(input_rows, filter_rows, stride_rows,
padding_, &out_rows, &pad_rows));
OP_REQUIRES_OK(context,
GetWindowedOutputSize(input_cols, filter_cols, stride_cols,
padding_, &out_cols, &pad_cols));
TensorShape out_shape =
ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
// Output tensor is of the following dimensions:
// [ in_batch, out_rows, out_cols, out_depth ]
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
VLOG(2) << "Conv2D: in_depth = " << in_depth
<< ", input_cols = " << input_cols
<< ", filter_cols = " << filter_cols
<< ", input_rows = " << input_rows
<< ", filter_rows = " << filter_rows
<< ", stride_rows = " << stride_rows
<< ", stride_cols = " << stride_cols
<< ", out_depth = " << out_depth;
// If there is nothing to compute, return.
if (out_shape.num_elements() == 0) {
return;
}
TConvFunctor conv_functor;
conv_functor(context, input.flat<T>().data(), batch, input_rows, input_cols,
in_depth, filter.flat<T>().data(), filter_rows, filter_cols,
out_depth, stride_rows, stride_cols, padding_,
output->flat<T>().data(), out_rows, out_cols);
}
private:
std::vector<int32> strides_;
Padding padding_;
TensorFormat data_format_;
TF_DISALLOW_COPY_AND_ASSIGN(Conv2DUsingGemmOp);
};
#define REGISTER_CPU(T) \
REGISTER_KERNEL_BUILDER( \
Name("Conv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
Conv2DUsingGemmOp< \
T, Im2ColConvFunctor<T, T, T, FastGemmFunctor<T, T, T>>>);
// Only register this GEMM-based implementation of Conv2d if the compiler flags
// request the implementation explicitly, since otherwise it will clash with the
// default EigenTensor-based kernel.
#if defined(USE_GEMM_FOR_CONV)
TF_CALL_half(REGISTER_CPU);
TF_CALL_float(REGISTER_CPU);
#endif // USE_GEMM_FOR_CONV
} // namespace tensorflow

2
tensorflow/core/kernels/reduce_join_op.cc
View File

@ -105,7 +105,7 @@ void MakeUnreducedIndices(gtl::InlinedVector<bool, 8> index_is_reduced,
TensorShape GetOutputShape(gtl::InlinedVector<bool, 8> index_is_reduced,
const TensorShape& input_shape, bool keep_dims) {
TensorShape output_shape;
for (int32 index = 0; index < index_is_reduced.size(); ++index) {
for (size_t index = 0; index < index_is_reduced.size(); ++index) {
if (index_is_reduced[index]) {
if (keep_dims) output_shape.AddDim(1);
} else {

86
tensorflow/core/kernels/reverse_sequence_op.cc
View File

@ -40,19 +40,19 @@ namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
template <typename Device>
template <typename Device, typename Tlen>
void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) {
const Tensor& input = context->input(0);
const Tensor& seq_lens = context->input(1);
auto seq_lens_t = seq_lens.vec<int64>();
auto seq_lens_t = seq_lens.vec<Tlen>();
std::vector<int64> seq_lens_vec(seq_lens_t.size());
std::vector<Tlen> seq_lens_vec(seq_lens_t.size());
// Copy seq_len info down for validity checks
context->eigen_device<Device>().memcpyDeviceToHost(
seq_lens_vec.data(), seq_lens_t.data(),
sizeof(int64) * seq_lens_t.size());
sizeof(Tlen) * seq_lens_t.size());
OP_REQUIRES(context, batch_dim != seq_dim,
errors::InvalidArgument("batch_dim == seq_dim == ", seq_dim));
@ -76,8 +76,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) {
}
}
template <>
void CheckErrors<GPUDevice>(OpKernelContext* context, int batch_dim,
void CheckErrorsGPU(OpKernelContext* context, int batch_dim,
int seq_dim) {
const Tensor& input = context->input(0);
const Tensor& seq_lens = context->input(1);
@ -97,7 +96,19 @@ void CheckErrors<GPUDevice>(OpKernelContext* context, int batch_dim,
" vs. ", input.dim_size(batch_dim)));
}
template <typename Device, typename T>
template <>
void CheckErrors<GPUDevice, int32>(OpKernelContext* context, int batch_dim,
int seq_dim) {
CheckErrorsGPU(context, batch_dim, seq_dim);
}
template <>
void CheckErrors<GPUDevice, int64>(OpKernelContext* context, int batch_dim,
int seq_dim) {
CheckErrorsGPU(context, batch_dim, seq_dim);
}
template <typename Device, typename T, typename Tlen>
class ReverseSequenceOp : public OpKernel {
public:
explicit ReverseSequenceOp(OpKernelConstruction* context)
@ -115,9 +126,9 @@ class ReverseSequenceOp : public OpKernel {
errors::InvalidArgument("seq_lens input must be 1-dim, not ",
seq_lens.dims()));
auto seq_lens_t = seq_lens.vec<int64>();
auto seq_lens_t = seq_lens.vec<Tlen>();
CheckErrors<Device>(context, batch_dim_, seq_dim_);
CheckErrors<Device, Tlen>(context, batch_dim_, seq_dim_);
const int input_dims = input.dims();
@ -127,7 +138,7 @@ class ReverseSequenceOp : public OpKernel {
#define HANDLE_DIM(NDIM) \
case NDIM: \
functor::ReverseSequence<Device, T, NDIM>::Compute( \
functor::ReverseSequence<Device, T, Tlen, NDIM>::Compute( \
context->eigen_device<Device>(), input.tensor<T, NDIM>(), batch_dim_, \
seq_dim_, seq_lens_t, output->tensor<T, NDIM>()); \
break;
@ -153,42 +164,57 @@ class ReverseSequenceOp : public OpKernel {
TF_DISALLOW_COPY_AND_ASSIGN(ReverseSequenceOp);
};
#define REGISTER_REVERSE_SEQUENCE(type) \
#define REGISTER_REVERSE_SEQUENCE(type, len_type) \
REGISTER_KERNEL_BUILDER( \
Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
ReverseSequenceOp<CPUDevice, type>);
Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"). \
TypeConstraint<len_type>("Tlen"), \
ReverseSequenceOp<CPUDevice, type, len_type>);
TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE);
#define REGISTER_REVERSE_SEQUENCE_LEN(type) \
REGISTER_REVERSE_SEQUENCE(type, int32); \
REGISTER_REVERSE_SEQUENCE(type, int64);
TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_LEN);
#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T, Dims) \
template <> \
void ReverseSequence<GPUDevice, T, Dims>::Compute( \
const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input, \
int32 batch_dim, int32 seq_dim, TTypes<int64>::ConstVec seq_lens, \
typename TTypes<T, Dims>::Tensor output); \
extern template struct ReverseSequence<GPUDevice, T, Dims>;
#define DECLARE_GPU_SPEC(T, Tlen, Dims) \
template <> \
void ReverseSequence<GPUDevice, T, Tlen, Dims>::Compute( \
const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input, \
int32 batch_dim, int32 seq_dim, \
typename TTypes<Tlen>::ConstVec seq_lens, \
typename TTypes<T, Dims>::Tensor output); \
extern template struct ReverseSequence<GPUDevice, T, Tlen, Dims>;
#define DECLARE_GPU_SPECS(T) \
DECLARE_GPU_SPEC(T, 2); \
DECLARE_GPU_SPEC(T, 3); \
DECLARE_GPU_SPEC(T, 4); \
DECLARE_GPU_SPEC(T, 5);
#define DECLARE_GPU_SPEC_LEN(T, Dims) \
DECLARE_GPU_SPEC(T, int32, Dims); \
DECLARE_GPU_SPEC(T, int64, Dims);
#define DECLARE_GPU_SPECS(T) \
DECLARE_GPU_SPEC_LEN(T, 2); \
DECLARE_GPU_SPEC_LEN(T, 3); \
DECLARE_GPU_SPEC_LEN(T, 4); \
DECLARE_GPU_SPEC_LEN(T, 5);
TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
} // namespace functor
// Registration of the GPU implementations.
#define REGISTER_REVERSE_SEQUENCE_GPU(type) \
#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type) \
REGISTER_KERNEL_BUILDER( \
Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
ReverseSequenceOp<GPUDevice, type>);
Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"). \
TypeConstraint<len_type>("Tlen"), \
ReverseSequenceOp<GPUDevice, type, len_type>);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU);
#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type) \
REGISTER_REVERSE_SEQUENCE_GPU(type, int32); \
REGISTER_REVERSE_SEQUENCE_GPU(type, int64);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU_LEN);
#undef REGISTER_REVERSE_SEQUENCE_GPU

15
tensorflow/core/kernels/reverse_sequence_op.h
View File

@ -25,12 +25,12 @@ namespace tensorflow {
namespace generator {
template <typename T, size_t Dims>
template <typename T, typename Tlen, size_t Dims>
class ReverseGenerator {
public:
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
ReverseGenerator(typename TTypes<T, Dims>::ConstTensor input, int32 batch_dim,
int32 seq_dim, TTypes<int64>::ConstVec seq_lengths)
int32 seq_dim, typename TTypes<Tlen>::ConstVec seq_lengths)
: input_(input),
batch_dim_(batch_dim),
seq_dim_(seq_dim),
@ -51,21 +51,22 @@ class ReverseGenerator {
typename TTypes<T, Dims>::ConstTensor input_;
int32 batch_dim_;
int32 seq_dim_;
TTypes<int64>::ConstVec seq_lengths_;
typename TTypes<Tlen>::ConstVec seq_lengths_;
};
} // namespace generator
namespace functor {
template <typename Device, typename T, size_t Dims>
template <typename Device, typename T, typename Tlen, size_t Dims>
struct ReverseSequence {
EIGEN_ALWAYS_INLINE static void Compute(
const Device& d, typename TTypes<T, Dims>::ConstTensor input,
int32 batch_dim, int32 seq_dim, TTypes<int64>::ConstVec seq_lengths,
int32 batch_dim, int32 seq_dim,
typename TTypes<Tlen>::ConstVec seq_lengths,
typename TTypes<T, Dims>::Tensor output) {
generator::ReverseGenerator<T, Dims> generator(input, batch_dim, seq_dim,
seq_lengths);
generator::ReverseGenerator<T, Tlen, Dims> generator(input, batch_dim,
seq_dim, seq_lengths);
output.device(d) = input.generate(generator);
}
};

18
tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
View File

@ -24,15 +24,19 @@ namespace tensorflow {
typedef Eigen::GpuDevice GPUDevice;
#define DEFINE_GPU_SPEC(T, dims) \
template class generator::ReverseGenerator<T, dims>; \
template struct functor::ReverseSequence<GPUDevice, T, dims>;
#define DEFINE_GPU_SPEC(T, Tlen, dims) \
template class generator::ReverseGenerator<T, Tlen, dims>; \
template struct functor::ReverseSequence<GPUDevice, T, Tlen, dims>;
#define DEFINE_GPU_SPEC_LEN(T, dims) \
DEFINE_GPU_SPEC(T, int32, dims); \
DEFINE_GPU_SPEC(T, int64, dims);
#define DEFINE_GPU_SPECS(T) \
DEFINE_GPU_SPEC(T, 2); \
DEFINE_GPU_SPEC(T, 3); \
DEFINE_GPU_SPEC(T, 4); \
DEFINE_GPU_SPEC(T, 5);
DEFINE_GPU_SPEC_LEN(T, 2); \
DEFINE_GPU_SPEC_LEN(T, 3); \
DEFINE_GPU_SPEC_LEN(T, 4); \
DEFINE_GPU_SPEC_LEN(T, 5);
TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS);

2
tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
View File

@ -91,7 +91,7 @@ class SparseDenseBinaryOpShared : public OpKernel {
auto VecGreaterEq = [](ArraySlice<int64> lhs, ArraySlice<int64> rhs) {
if (lhs.size() > rhs.size()) return true;
if (lhs.size() < rhs.size()) return false;
for (int i = 0; i < lhs.size(); ++i) {
for (size_t i = 0; i < lhs.size(); ++i) {
if (lhs[i] < rhs[i]) return false;
}
return true;

3
tensorflow/core/ops/array_ops.cc
View File

@ -1471,11 +1471,12 @@ This operation returns N 1-D integer tensors representing shape of `input[i]s`.
// --------------------------------------------------------------------------
REGISTER_OP("ReverseSequence")
.Input("input: T")
.Input("seq_lengths: int64")
.Input("seq_lengths: Tlen")
.Output("output: T")
.Attr("seq_dim: int")
.Attr("batch_dim: int = 0")
.Attr("T: type")
.Attr("Tlen: {int32, int64} = DT_INT64")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle input = c->input(0);
ShapeHandle seq_lens_shape;

36
tensorflow/core/ops/ops.pbtxt
View File

@ -4711,6 +4711,42 @@ op {
summary: "Decode a PNG-encoded image to a uint8 or uint16 tensor."
description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n* 0: Use the number of channels in the PNG-encoded image.\n* 1: output a grayscale image.\n* 3: output an RGB image.\n* 4: output an RGBA image.\n\nIf needed, the PNG-encoded image is transformed to match the requested number\nof color channels."
}
op {
name: "DecodeGif"
input_arg {
name: "contents"
description: "0-D. The GIF-encoded image."
type: DT_STRING
}
output_arg {
name: "image"
description: "3-D with shape `[height, width, channels]`."
type_attr: "dtype"
}
attr {
name: "channels"
type: "int"
default_value {
i: 0
}
description: "Number of color channels for the decoded image."
}
attr {
name: "dtype"
type: "type"
default_value {
type: DT_UINT8
}
allowed_values {
list {
type: DT_UINT8
type: DT_UINT16
}
}
}
summary: "Decode a GIF-encoded image to a uint8 or uint16 tensor."
description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n* 0: Use the number of channels in the GIF-encoded image.\n* 1: output a grayscale image.\n* 3: output an RGB image.\n* 4: output an RGBA image.\n\nIf needed, the GIF-encoded image is transformed to match the requested number\nof color channels."
}
op {
name: "DecodeRaw"
input_arg {

12
tensorflow/core/util/example_proto_helper.cc
View File

@ -222,7 +222,7 @@ Status SingleExampleProtoToTensors(
const auto& feature_dict = features.feature();
// Handle dense features.
for (int d = 0; d < fixed_len_features.size(); ++d) {
for (size_t d = 0; d < fixed_len_features.size(); ++d) {
const FixedLenFeature& feature_config = fixed_len_features[d];
const string& key = feature_config.key;
const DataType& dtype = feature_config.dtype;
@ -263,7 +263,7 @@ Status SingleExampleProtoToTensors(
}
// Handle sparse features.
for (int d = 0; d < var_len_features.size(); ++d) {
for (size_t d = 0; d < var_len_features.size(); ++d) {
const VarLenFeature& feature_config = var_len_features[d];
const string& key = feature_config.key;
const DataType& dtype = feature_config.dtype;
@ -338,7 +338,7 @@ Status BatchExampleProtoToTensors(
fixed_len_features.size());
// Preallocate dense_values, since we know their sizes.
for (int d = 0; d < fixed_len_features.size(); ++d) {
for (size_t d = 0; d < fixed_len_features.size(); ++d) {
const FixedLenFeature& config = fixed_len_features[d];
TensorShape out_shape;
out_shape.AddDim(batch_size);
@ -352,11 +352,11 @@ Status BatchExampleProtoToTensors(
// Temporary vector to hold sparse values.
std::vector<std::vector<Tensor>> sparse_values_tmp(var_len_features.size());
for (int d = 0; d < var_len_features.size(); ++d) {
for (size_t d = 0; d < var_len_features.size(); ++d) {
sparse_values_tmp[d] = std::vector<Tensor>(batch_size);
}
for (int b = 0; b < examples.size(); ++b) {
for (size_t b = 0; b < examples.size(); ++b) {
const Example& ex = *(examples[b]);
const string& example_name = (has_names) ? names[b] : "<unknown>";
SingleExampleProtoToTensors(
@ -364,7 +364,7 @@ Status BatchExampleProtoToTensors(
&output_dense_values_tensor_ptrs, &sparse_values_tmp);
}
for (int d = 0; d < var_len_features.size(); ++d) {
for (size_t d = 0; d < var_len_features.size(); ++d) {
const VarLenFeature& feature_config = var_len_features[d];
const DataType& dtype = feature_config.dtype;
const std::vector<Tensor>& sparse_values_tensor = sparse_values_tmp[d];

2
tensorflow/core/util/sparse/sparse_tensor.h
View File

@ -283,7 +283,7 @@ void SparseTensor::Reorder(const VarDimArray& order) {
// permutation (the inverse). This can be calculated with O(1)
// additional
// and O(n) time (INVPERM) but we just do the simple thing here.
std::vector<int64> permutation(reorder.size());
std::vector<size_t> permutation(reorder.size());
for (std::size_t n = 0; n < reorder.size(); ++n) {
permutation[reorder[n]] = n;
}

2
tensorflow/examples/image_retraining/retrain.py
View File

@ -703,7 +703,7 @@ def variable_summaries(var, name):
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))

2
tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
View File

@ -75,7 +75,7 @@ def train():
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))

10
tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
View File

@ -184,9 +184,9 @@ applies gradients.
### Gating Gradients
Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
argument that controls the degree of parallelism during the application of
the gradients.
Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
that controls the degree of parallelism during the application of the
gradients.
The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
@ -258,7 +258,3 @@ Use `get_slot_names()` to get the list of slot names created by the
- - -
#### `tf.train.Optimizer.get_name()` {#Optimizer.get_name}

2
tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
View File

@ -35,7 +35,7 @@ the same shape in order for this function to work.
##### Returns:
A l X l matrix represeting the confusion matrix, where l in the number of
A k X k matrix represeting the confusion matrix, where k is the number of
possible labels in the classification task.
##### Raises:

8
tensorflow/g3doc/api_docs/python/train.md
View File

@ -204,9 +204,9 @@ applies gradients.
### Gating Gradients
Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
argument that controls the degree of parallelism during the application of
the gradients.
Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
that controls the degree of parallelism during the application of the
gradients.
The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
@ -3771,5 +3771,3 @@ Generates a checkpoint state proto.
CheckpointState proto with model_checkpoint_path and
all_model_checkpoint_paths updated to either absolute paths or
relative paths to the current save_dir.

7
tensorflow/g3doc/get_started/os_setup.md
View File

@ -8,9 +8,10 @@ github source.
The TensorFlow Python API supports Python 2.7 and Python 3.3+.
The GPU version (Linux & Mac OS X only) works best with Cuda Toolkit 7.5 and
cuDNN v4. other versions are supported (Cuda toolkit >= 7.0 and cuDNN 6.5(v2),
7.0(v3), v5) only when installing from sources. Please see [Cuda installation]
(#optional-install-cuda-gpus-on-linux) for details.
cuDNN v4. other versions are supported (Cuda toolkit >= 7.0 and
cuDNN 6.5(v2), 7.0(v3), v5) only when installing from sources.
Please see [Cuda installation](#optional-install-cuda-gpus-on-linux)
for details.
## Overview

2
tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
View File

@ -86,7 +86,7 @@ def variable_summaries(var, name):
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))

28
tensorflow/python/BUILD
View File

@ -71,6 +71,34 @@ tf_py_test(
],
)
tf_py_test(
name = "flags_test",
size = "small",
srcs = ["platform/flags_test.py"],
additional_deps = [
":platform",
":platform_test",
],
tags = [
"manual",
"notap",
],
)
tf_py_test(
name = "app_test",
size = "small",
srcs = ["platform/app_test.py"],
additional_deps = [
":platform",
":platform_test",
],
tags = [
"manual",
"notap",
],
)
cc_library(
name = "numpy_lib",
srcs = ["lib/core/numpy.cc"],

40
tensorflow/python/kernel_tests/clip_ops_test.py
View File

@ -58,7 +58,12 @@ class ClipTest(tf.test.TestCase):
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
clip_tensor = tf.constant(4.0)
ans = tf.clip_by_norm(x, clip_norm)
tf_ans_tensor = ans.eval()
self.assertAllClose(np_ans, tf_ans)
self.assertAllClose(np_ans, tf_ans_tensor)
def testClipByNormNotClipped(self):
# No norm clipping when clip_norm >= 5
@ -148,6 +153,28 @@ class ClipTest(tf.test.TestCase):
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormClippedTensor(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = tf.constant(4.0)
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormSupportsNone(self):
# Norm clipping when clip_norm < 5
with self.test_session():
@ -259,6 +286,19 @@ class ClipTest(tf.test.TestCase):
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormClippedTensor(self):
# Norm clipping when average clip_norm < 0.83333333
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-2.88, 0.0, 0.0],
[3.84, 0.0, 0.0]]
clip_norm = tf.constant(0.8)
ans = tf.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormNotClipped(self):
# No norm clipping when average clip_norm >= 0.83333333
with self.test_session():

7
tensorflow/python/kernel_tests/reverse_sequence_op_test.py
View File

@ -47,7 +47,7 @@ class ReverseSequenceTest(tf.test.TestCase):
self._testReverseSequence(x, batch_dim, seq_dim, seq_lengths,
truth, False, expected_err_re)
def _testBasic(self, dtype):
def _testBasic(self, dtype, len_dtype=np.int64):
x = np.asarray([
[[1, 2, 3, 4], [5, 6, 7, 8]],
[[9, 10, 11, 12], [13, 14, 15, 16]],
@ -56,7 +56,7 @@ class ReverseSequenceTest(tf.test.TestCase):
x = x.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2
# reverse dim 2 up to (0:3, none, 0:4) along dim=0
seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)
seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype)
truth_orig = np.asarray(
[[[3, 2, 1, 4], [7, 6, 5, 8]], # reverse 0:3
@ -70,6 +70,9 @@ class ReverseSequenceTest(tf.test.TestCase):
batch_dim = 2
self._testBothReverseSequence(x, batch_dim, seq_dim, seq_lengths, truth)
def testSeqLenghtInt32(self):
self._testBasic(np.float32, np.int32)
def testFloatBasic(self):
self._testBasic(np.float32)

23
tensorflow/python/kernel_tests/sparse_ops_test.py
View File

@ -743,5 +743,28 @@ class SparseMinimumMaximumTest(test_util.TensorFlowTestCase):
tf.sparse_maximum(sp_zero, sp_one).eval()
class SparseTransposeTest(tf.test.TestCase):
def _SparseTensorPlaceholder(self):
return tf.SparseTensor(
tf.placeholder(tf.int64),
tf.placeholder(tf.float64),
tf.placeholder(tf.int64))
def testTranspose(self):
with self.test_session(use_gpu=False) as sess:
np.random.seed(1618)
shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float32, np.float64]:
dn_input = np.random.randn(*shape).astype(dtype)
rank = tf.rank(dn_input).eval()
perm = np.random.choice(rank, rank, False)
sp_input, unused_a_nnz = _sparsify(dn_input)
sp_trans = tf.sparse_transpose(sp_input, perm=perm)
dn_trans = tf.sparse_tensor_to_dense(sp_trans).eval()
expected_trans = tf.transpose(dn_input, perm=perm).eval()
self.assertAllEqual(dn_trans, expected_trans)
if __name__ == "__main__":
googletest.main()

4
tensorflow/python/ops/clip_ops.py
View File

@ -206,7 +206,7 @@ def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None):
# Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
scale = clip_norm * math_ops.minimum(
1.0 / use_norm,
constant_op.constant(1.0 / clip_norm, dtype=use_norm.dtype))
constant_op.constant(1.0, dtype=use_norm.dtype) / clip_norm)
values = [
ops.convert_to_tensor(
@ -268,7 +268,7 @@ def clip_by_average_norm(t, clip_norm, name=None):
math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
tclip = array_ops.identity(
t * clip_norm * math_ops.minimum(
l2norm_inv * n_element, constant_op.constant(1.0 / clip_norm)),
l2norm_inv * n_element, constant_op.constant(1.0) / clip_norm),
name=name)
return tclip

1
tensorflow/python/ops/io_ops.py
View File

@ -80,6 +80,7 @@ Queues](../../how_tos/threading_and_queues/index.md).
@@FIFOQueue
@@PaddingFIFOQueue
@@RandomShuffleQueue
@@PriorityQueue
## Dealing with the filesystem

4
tensorflow/python/ops/nn_ops.py
View File

@ -19,6 +19,8 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numbers
import numpy as np
from tensorflow.python.framework import common_shapes
@ -1131,7 +1133,7 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):
"""
with ops.name_scope(name, "dropout", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1:
raise ValueError("keep_prob must be a scalar tensor or a float in the "
"range (0, 1], got %g" % keep_prob)
keep_prob = ops.convert_to_tensor(keep_prob,

47
tensorflow/python/ops/sparse_ops.py
View File

@ -40,6 +40,7 @@ dimension, and dense along all other dimensions.
@@sparse_retain
@@sparse_reset_shape
@@sparse_fill_empty_rows
@@sparse_transpose
## Reduction
@@sparse_reduce_sum
@ -1582,3 +1583,49 @@ def _SparseSparseMaximumMinimumShape(op): # pylint: disable=invalid-name
op.inputs[4].get_shape().assert_has_rank(1) # b_values
op.inputs[5].get_shape().assert_has_rank(1) # b_shape
return [tensor_shape.unknown_shape(2), tensor_shape.unknown_shape(1)]
def sparse_transpose(sp_input, perm=None, name=None):
"""Transposes a `SparseTensor`
The returned tensor's dimension i will correspond to the input dimension
`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
the rank of the input tensor. Hence by default, this operation performs a
regular matrix transpose on 2-D input Tensors.
For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:
[0, 3]: b
[0, 1]: a
[3, 1]: d
[2, 0]: c
then the output will be a `SparseTensor` of shape `[5, 4]` and
`indices` / `values`:
[0, 2]: c
[1, 0]: a
[1, 3]: d
[3, 0]: b
Args:
sp_input: The input `SparseTensor`.
perm: A permutation of the dimensions of `sp_input`.
name: A name prefix for the returned tensors (optional)
Returns:
A transposed `SparseTensor`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
with ops.op_scope([sp_input], name, "SparseTranspose") as name:
if perm is None:
rank = array_ops.rank(sp_input)
perm = (rank - 1) - math_ops.range(0, rank, 1)
indices = sp_input.indices
transposed_indices = array_ops.transpose(array_ops.gather(array_ops.transpose(indices), perm))
dense_shape = sp_input.shape
transposed_dense_shape = array_ops.gather(dense_shape, perm)
transposed_st = ops.SparseTensor(transposed_indices, sp_input.values, transposed_dense_shape)
transposed_st = sparse_reorder(transposed_st)
return transposed_st

4
tensorflow/python/platform/app.py
View File

@ -25,6 +25,6 @@ from tensorflow.python.platform import flags
def run(main=None):
f = flags.FLAGS
f._parse_flags()
flags_passthrough = f._parse_flags()
main = main or sys.modules['__main__'].main
sys.exit(main(sys.argv))
sys.exit(main(sys.argv[:1] + flags_passthrough))

45
tensorflow/python/platform/app_test.py Normal file
View File

@ -0,0 +1,45 @@
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for our flags implementation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
from tensorflow.python.platform import app
from tensorflow.python.platform import flags
FLAGS = flags.FLAGS
flags.DEFINE_boolean('myflag', False, '')
def main(argv):
if (len(argv) != 3):
print("Length of argv was not 3: ", argv)
sys.exit(-1)
if argv[1] != "--passthrough":
print("--passthrough argument not in argv")
sys.exit(-1)
if argv[2] != "extra":
print("'extra' argument not in argv")
sys.exit(-1)
if __name__ == '__main__':
sys.argv.extend(["--myflag", "--passthrough", "extra"])
app.run()

3
tensorflow/python/platform/flags.py
View File

@ -30,10 +30,11 @@ class _FlagValues(object):
self.__dict__['__parsed'] = False
def _parse_flags(self):
result, _ = _global_parser.parse_known_args()
result, unparsed = _global_parser.parse_known_args()
for flag_name, val in vars(result).items():
self.__dict__['__flags'][flag_name] = val
self.__dict__['__parsed'] = True
return unparsed
def __getattr__(self, name):
"""Retrieves the 'value' attribute of the flag --name."""

2
tensorflow/python/training/device_setter.py
View File

@ -150,7 +150,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps",
else:
cluster_spec = server_lib.ClusterSpec(cluster).as_dict()
# Get ps_job_name from ps_device by striping "/job:".
ps_job_name = ps_device.lstrip("/job:")
ps_job_name = pydev.DeviceSpec.from_string(ps_device).job
if ps_job_name not in cluster_spec or cluster_spec[ps_job_name] is None:
return None
ps_tasks = len(cluster_spec[ps_job_name])

17
tensorflow/python/training/device_setter_test.py
View File

@ -78,6 +78,23 @@ class DeviceSetterTest(tf.test.TestCase):
self.assertDeviceEqual("/job:moon/task:1", w.initializer.device)
self.assertDeviceEqual("/job:sun", a.device)
def testPS2TasksWithCPUConstraint(self):
cluster_spec = tf.train.ClusterSpec({
"sun": ["sun0:2222", "sun1:2222", "sun2:2222"],
"moon": ["moon0:2222", "moon1:2222"]})
with tf.device(tf.train.replica_device_setter(
ps_device="/job:moon/cpu:0", worker_device="/job:sun",
cluster=cluster_spec.as_cluster_def())):
v = tf.Variable([1, 2])
w = tf.Variable([2, 1])
a = v + w
self.assertDeviceEqual("/job:moon/task:0/cpu:0", v.device)
self.assertDeviceEqual("/job:moon/task:0/cpu:0", v.initializer.device)
self.assertDeviceEqual("/job:moon/task:1/cpu:0", w.device)
self.assertDeviceEqual("/job:moon/task:1/cpu:0", w.initializer.device)
self.assertDeviceEqual("/job:sun", a.device)
if __name__ == "__main__":
tf.test.main()

10
tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
View File

@ -53,7 +53,7 @@ limitations under the License.
}
.card .card-bottom-row {
position: absolute;
left: 50px;
left: 75px;
bottom: 0;
padding-right: 10px;
}
@ -71,6 +71,14 @@ limitations under the License.
display: block;
}
.log-option-button {
position: absolute;
left: 25px;
bottom: 0px;
color: #2196F3;
display: block;
}
#content-container{
display: block;
}

20
tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
View File

@ -145,6 +145,13 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
icon="fullscreen"
on-tap="toggleSelected"
></paper-icon-button>
<paper-icon-button
class="log-option-button"
icon="line-weight"
on-tap="toggleYScale"
title="Toggle y-axis scale (log, linear)"
></paper-icon-button>
</div>
<template is="dom-if" if="[[_showDownloadLinks]]">
<div class="card-bottom-row">
@ -242,6 +249,19 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
chartScaffold.chart().redraw();
}
},
toggleYScale: function(e) {
var currentTarget = Polymer.dom(e.currentTarget);
var b = currentTarget.parentNode.querySelector('.log-option-button');
var c = currentTarget.parentNode.querySelector('vz-line-chart');
if (c !== null) {
c.yScaleType = c.yScaleType === 'log' ? 'linear' : 'log';
b.icon = c.yScaleType === 'log' ? 'line-weight' : 'reorder';
c.redraw();
}
},
validRuns: function(tag, runsChange, run2tagChange) {
var _this = this;
var result = this.selectedRuns.filter(function(r) {

16
tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
View File

@ -191,6 +191,16 @@ such as different X scales (linear and temporal), tooltips and smoothing.
value: 'step'
},
/**
* The scale for the y-axis. Allows:
* - "linear" - linear scale (Plottable.Scales.Linear)
* - "log" - modified-log scale (Plottable.Scales.ModifiedLog)
*/
yScaleType: {
type: String,
value: 'linear'
},
/**
* Change how the tooltip is sorted. Allows:
* - "default" - Sort the tooltip by input order.
@ -228,7 +238,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
}
},
observers: [
"_makeChart(xType, colorScale, _attached)",
"_makeChart(xType, yScaleType, colorScale, _attached)",
"_reloadFromCache(_chart)",
"_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)",
"_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)",
@ -284,7 +294,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
this.scopeSubtree(this.$.tooltip, true);
this.scopeSubtree(this.$.chartsvg, true);
},
_makeChart: function(xType, colorScale, _attached) {
_makeChart: function(xType, yScaleType, colorScale, _attached) {
if (this._makeChartAsyncCallbackId === null) {
this.cancelAsync(this._makeChartAsyncCallbackId);
}
@ -294,7 +304,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
if (!this._attached) return;
if (this._chart) this._chart.destroy();
var tooltip = d3.select(this.$.tooltip);
var chart = new VZ.LineChart(xType, colorScale, tooltip);
var chart = new VZ.LineChart(xType, yScaleType, colorScale, tooltip);
var svg = d3.select(this.$.chartsvg);
chart.renderTo(svg);
this._chart = chart;

18
tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
View File

@ -49,7 +49,7 @@ module VZ {
private targetSVG: d3.Selection<any>;
constructor(
xType: string, colorScale: Plottable.Scales.Color,
xType: string, yScaleType: string, colorScale: Plottable.Scales.Color,
tooltip: d3.Selection<any>) {
this.seriesNames = [];
this.name2datasets = {};
@ -63,10 +63,10 @@ module VZ {
// need to do a single bind, so we can deregister the callback from
// old Plottable.Datasets. (Deregistration is done by identity checks.)
this.onDatasetChanged = this._onDatasetChanged.bind(this);
this.buildChart(xType);
this.buildChart(xType, yScaleType);
}
private buildChart(xType: string) {
private buildChart(xType: string, yScaleType: string) {
if (this.outer) {
this.outer.destroy();
}
@ -75,7 +75,7 @@ module VZ {
this.xScale = xComponents.scale;
this.xAxis = xComponents.axis;
this.xAxis.margin(0).tickLabelPadding(3);
this.yScale = new Plottable.Scales.Linear();
this.yScale = LineChart.getYScaleFromType(yScaleType);
this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
let yFormatter = VZ.ChartHelpers.multiscaleFormatter(
VZ.ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
@ -450,6 +450,16 @@ module VZ {
return this.name2datasets[name];
}
static getYScaleFromType(yScaleType: string): Plottable.QuantitativeScale<number> {
if (yScaleType === 'log') {
return new Plottable.Scales.ModifiedLog();
} else if (yScaleType === 'linear') {
return new Plottable.Scales.Linear();
} else {
throw new Error('Unrecognized yScale type ' + yScaleType);
}
}
/**
* Update the selected series on the chart.
*/

7
tensorflow/tensorboard/tensorboard.py
View File

@ -138,7 +138,12 @@ def main(unused_argv=None):
status_bar.SetupStatusBarInsideGoogle('TensorBoard %s' % tag, FLAGS.port)
print('Starting TensorBoard %s on port %d' % (tag, FLAGS.port))
print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
if FLAGS.host == "0.0.0.0":
print('(You can navigate to http://%s:%d)' % (socket.gethostbyname(socket.gethostname()), FLAGS.port))
else:
print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
tb_server.serve_forever()

2
tensorflow/tools/ci_build/Dockerfile.gpu
View File

@ -24,4 +24,4 @@ ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
ENV CUDA_TOOLKIT_PATH /usr/local/cuda
ENV CUDNN_INSTALL_PATH /usr/lib/x86_64-linux-gnu
ENV TF_NEED_CUDA 1
ENV CUDA_COMPUTE_CAPABILITIES 3.0,5.2
ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0,5.2

16
tensorflow/tools/docker/parameterized_docker_build.sh
View File

@ -323,7 +323,21 @@ fi
# Apply the final image name and tag
FINAL_IMG="${FINAL_IMAGE_NAME}:${FINAL_TAG}"
docker tag -f "${IMG}" "${FINAL_IMG}" || \
DOCKER_VER=$(docker version | grep Version | head -1 | awk '{print $NF}')
if [[ -z "${DOCKER_VER}" ]]; then
die "ERROR: Failed to determine docker version"
fi
DOCKER_MAJOR_VER=$(echo "${DOCKER_VER}" | cut -d. -f 1)
DOCKER_MINOR_VER=$(echo "${DOCKER_VER}" | cut -d. -f 2)
FORCE_TAG=""
if [[ "${DOCKER_MAJOR_VER}" -le 1 ]] && \
[[ "${DOCKER_MINOR_VER}" -le 9 ]]; then
FORCE_TAG="--force"
fi
docker tag ${FORCE_TAG} "${IMG}" "${FINAL_IMG}" || \
die "Failed to tag intermediate docker image ${IMG} as ${FINAL_IMG}"
echo ""

40
tensorflow/workspace.bzl
View File

@ -2,12 +2,14 @@
load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
# If TensorFlow is linked as a submodule, path_prefix is TensorFlow's directory
# within the workspace (e.g. "tensorflow/"), and tf_repo_name is the name of the
# local_repository rule (e.g. "@tf").
# If TensorFlow is linked as a submodule.
# path_prefix and tf_repo_name are no longer used.
def tf_workspace(path_prefix = "", tf_repo_name = ""):
cuda_configure(name = "local_config_cuda")
if path_prefix:
print("path_prefix was specified to tf_workspace but is no longer used and will be removed in the future.")
if tf_repo_name:
print("tf_repo_name was specified to tf_workspace but is no longer used and will be removed in the future.")
# These lines need to be changed when updating Eigen. They are parsed from
# this file by the cmake and make builds to determine the eigen version and hash.
eigen_version = "9e1b48c333aa"
@ -18,7 +20,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
url = "https://bitbucket.org/eigen/eigen/get/" + eigen_version + ".tar.gz",
sha256 = eigen_sha256,
strip_prefix = "eigen-eigen-" + eigen_version,
build_file = path_prefix + "eigen.BUILD",
build_file = str(Label("//:eigen.BUILD")),
)
native.git_repository(
@ -37,7 +39,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
name = "farmhash_archive",
url = "https://github.com/google/farmhash/archive/34c13ddfab0e35422f4c3979f360635a8c050260.zip",
sha256 = "e3d37a59101f38fd58fb799ed404d630f0eee18bfc2a2433910977cc8fea9c28",
build_file = path_prefix + "farmhash.BUILD",
build_file = str(Label("//:farmhash.BUILD")),
)
native.bind(
@ -56,28 +58,28 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
name = "jpeg_archive",
url = "http://www.ijg.org/files/jpegsrc.v9a.tar.gz",
sha256 = "3a753ea48d917945dd54a2d97de388aa06ca2eb1066cbfdc6652036349fe05a7",
build_file = path_prefix + "jpeg.BUILD",
build_file = str(Label("//:jpeg.BUILD")),
)
native.new_http_archive(
name = "png_archive",
url = "https://github.com/glennrp/libpng/archive/v1.2.53.zip",
sha256 = "c35bcc6387495ee6e757507a68ba036d38ad05b415c2553b3debe2a57647a692",
build_file = path_prefix + "png.BUILD",
build_file = str(Label("//:png.BUILD")),
)
native.new_http_archive(
name = "gif_archive",
url = "http://ufpr.dl.sourceforge.net/project/giflib/giflib-5.1.4.tar.gz",
sha256 = "34a7377ba834397db019e8eb122e551a49c98f49df75ec3fcc92b9a794a4f6d1",
build_file = path_prefix + "gif.BUILD",
build_file = str(Label("//:gif.BUILD")),
)
native.new_http_archive(
name = "six_archive",
url = "https://pypi.python.org/packages/source/s/six/six-1.10.0.tar.gz#md5=34eed507548117b2ab523ab14b2f8b55",
sha256 = "105f8d68616f8248e24bf0e9372ef04d3cc10104f1980f54d57b2ce73a5ad56a",
build_file = path_prefix + "six.BUILD",
build_file = str(Label("//:six.BUILD")),
)
native.bind(
@ -95,7 +97,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
name = "gmock_archive",
url = "http://pkgs.fedoraproject.org/repo/pkgs/gmock/gmock-1.7.0.zip/073b984d8798ea1594f5e44d85b20d66/gmock-1.7.0.zip",
sha256 = "26fcbb5925b74ad5fc8c26b0495dfc96353f4d553492eb97e85a8a6d2f43095b",
build_file = path_prefix + "gmock.BUILD",
build_file = str(Label("//:gmock.BUILD")),
)
native.bind(
@ -110,7 +112,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
native.bind(
name = "python_headers",
actual = tf_repo_name + "//util/python:python_headers",
actual = str(Label("//util/python:python_headers")),
)
# grpc expects //external:protobuf_clib and //external:protobuf_compiler
@ -130,7 +132,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
commit = "d7ff4ff40071d2b486a052183e3e9f9382afb745",
init_submodules = True,
remote = "https://github.com/grpc/grpc.git",
build_file = path_prefix + "grpc.BUILD",
build_file = str(Label("//:grpc.BUILD")),
)
# protobuf expects //external:grpc_cpp_plugin to point to grpc's
@ -149,7 +151,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
name = "jsoncpp_git",
remote = "https://github.com/open-source-parsers/jsoncpp.git",
commit = "11086dd6a7eba04289944367ca82cea71299ed70",
build_file = path_prefix + "jsoncpp.BUILD",
build_file = str(Label("//:jsoncpp.BUILD")),
)
native.bind(
@ -167,7 +169,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
name = "nanopb_git",
commit = "1251fa1",
remote = "https://github.com/nanopb/nanopb.git",
build_file = path_prefix + "nanopb.BUILD",
build_file = str(Label("//:nanopb.BUILD")),
)
native.bind(
@ -179,26 +181,26 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
name = "avro_archive",
url = "http://www-us.apache.org/dist/avro/avro-1.8.0/cpp/avro-cpp-1.8.0.tar.gz",
sha256 = "ec6e2ec957e95ca07f70cc25f02f5c416f47cb27bd987a6ec770dcbe72527368",
build_file = path_prefix + "avro.BUILD",
build_file = str(Label("//:avro.BUILD")),
)
native.new_http_archive(
name = "boost_archive",
url = "http://pilotfiber.dl.sourceforge.net/project/boost/boost/1.61.0/boost_1_61_0.tar.gz",
sha256 = "a77c7cc660ec02704c6884fbb20c552d52d60a18f26573c9cee0788bf00ed7e6",
build_file = path_prefix + "boost.BUILD",
build_file = str(Label("//:boost.BUILD")),
)
native.new_http_archive(
name = "bzip2_archive",
url = "http://www.bzip.org/1.0.6/bzip2-1.0.6.tar.gz",
sha256 = "a2848f34fcd5d6cf47def00461fcb528a0484d8edef8208d6d2e2909dc61d9cd",
build_file = path_prefix + "bzip2.BUILD",
build_file = str(Label("//:bzip2.BUILD")),
)
native.new_http_archive(
name = "zlib_archive",
url = "http://zlib.net/zlib-1.2.8.tar.gz",
sha256 = "36658cb768a54c1d4dec43c3116c27ed893e88b02ecfcb44f2166f9c0b7f2a0d",
build_file = path_prefix + "zlib.BUILD",
build_file = str(Label("//:zlib.BUILD")),
)

40
third_party/gpus/cuda_configure.bzl vendored
View File

@ -3,19 +3,25 @@
`cuda_configure` depends on the following environment variables:
* `ENABLE_CUDA`: Whether to enable building with CUDA.
* `CC`: The GCC host compiler path
* `TF_NEED_CUDA`: Whether to enable building with CUDA.
* `GCC_HOST_COMPILER_PATH`: The GCC host compiler path
* `CUDA_TOOLKIT_PATH`: The path to the CUDA toolkit. Default is
`/usr/local/cuda`.
* `CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
* `TF_CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
use the system default.
* `CUDNN_VERSION`: The version of the cuDNN library.
* `TF_CUDNN_VERSION`: The version of the cuDNN library.
* `CUDNN_INSTALL_PATH`: The path to the cuDNN library. Default is
`/usr/local/cuda`.
* `CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
* `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
`3.5,5.2`.
"""
_GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH"
_CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH"
_TF_CUDA_VERSION = "TF_CUDA_VERSION"
_TF_CUDNN_VERSION = "TF_CUDNN_VERSION"
_CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH"
_TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES"
_DEFAULT_CUDA_VERSION = ""
_DEFAULT_CUDNN_VERSION = ""
@ -30,8 +36,8 @@ _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"]
def find_cc(repository_ctx):
"""Find the C++ compiler."""
cc_name = "gcc"
if "CC" in repository_ctx.os.environ:
cc_name = repository_ctx.os.environ["CC"].strip()
if _GCC_HOST_COMPILER_PATH in repository_ctx.os.environ:
cc_name = repository_ctx.os.environ[_GCC_HOST_COMPILER_PATH].strip()
if not cc_name:
cc_name = "gcc"
if cc_name.startswith("/"):
@ -93,8 +99,8 @@ def _enable_cuda(repository_ctx):
def _cuda_toolkit_path(repository_ctx):
"""Finds the cuda toolkit directory."""
cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH
if "CUDA_TOOLKIT_PATH" in repository_ctx.os.environ:
cuda_toolkit_path = repository_ctx.os.environ["CUDA_TOOLKIT_PATH"].strip()
if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ:
cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip()
if not repository_ctx.path(cuda_toolkit_path).exists:
fail("Cannot find cuda toolkit path.")
return cuda_toolkit_path
@ -103,8 +109,8 @@ def _cuda_toolkit_path(repository_ctx):
def _cudnn_install_basedir(repository_ctx):
"""Finds the cudnn install directory."""
cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH
if "CUDNN_INSTALL_PATH" in repository_ctx.os.environ:
cudnn_install_path = repository_ctx.os.environ["CUDNN_INSTALL_PATH"].strip()
if _CUDNN_INSTALL_PATH in repository_ctx.os.environ:
cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip()
if not repository_ctx.path(cudnn_install_path).exists:
fail("Cannot find cudnn install path.")
return cudnn_install_path
@ -112,25 +118,25 @@ def _cudnn_install_basedir(repository_ctx):
def _cuda_version(repository_ctx):
"""Detects the cuda version."""
if "CUDA_VERSION" in repository_ctx.os.environ:
return repository_ctx.os.environ["CUDA_VERSION"].strip()
if _TF_CUDA_VERSION in repository_ctx.os.environ:
return repository_ctx.os.environ[_TF_CUDA_VERSION].strip()
else:
return ""
def _cudnn_version(repository_ctx):
"""Detects the cudnn version."""
if "CUDNN_VERSION" in repository_ctx.os.environ:
return repository_ctx.os.environ["CUDNN_VERSION"].strip()
if _TF_CUDNN_VERSION in repository_ctx.os.environ:
return repository_ctx.os.environ[_TF_CUDNN_VERSION].strip()
else:
return ""
def _compute_capabilities(repository_ctx):
"""Returns a list of strings representing cuda compute capabilities."""
if "CUDA_COMPUTE_CAPABILITIES" not in repository_ctx.os.environ:
if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ:
return _DEFAULT_CUDA_COMPUTE_CAPABILITIES
capabilities_str = repository_ctx.os.environ["CUDA_COMPUTE_CAPABILITIES"]
capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES]
capabilities = capabilities_str.split(",")
for capability in capabilities:
# Workaround for Skylark's lack of support for regex. This check should