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Commit ae11a987 by Ting PAN
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Add ChannelShuffleOp

1 parent 6b82cb26
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Showing with 718 additions and 315 deletions
.gitignore
## General
# Compiled Object files
*.slo
*.lo
......@@ -7,13 +5,15 @@
*.cuo
# Compiled Dynamic libraries
# *.so
*.so
*.dll
*.dylib
# Compiled Static libraries
*.lai
*.la
#*.a
*.a
*.lib
# Compiled python
*.pyc
......@@ -40,6 +40,9 @@ __pycache__
# QtCreator files
*.user
# VSCode files
.vscode
# PyCharm files
.idea
......
Docs/api/python/Makefile
......@@ -5,7 +5,7 @@
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = _build
BUILDDIR = _build/api
# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
......@@ -52,9 +52,9 @@ clean:
rm -rf $(BUILDDIR)/*
html:
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/python
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
@echo "Build finished. The HTML pages are in $(BUILDDIR)/python."
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
......
Docs/api/python/contents/core/tensor_utils.rst
......@@ -11,14 +11,12 @@ Quick Reference
============================== =============================================================================
List Brief
============================== =============================================================================
`FromShape`_ Create a Tensor from the shape.
`FromShape`_ Create a Tensor from the given shape.
`FromArray`_ Create a Tensor from an existing Array.
`ToArray`_ Create a Array from an existing Tensor.
`SetShape`_ Set a Tensor with the shape.
`FromPyArray`_ Create a Tensor from a existing Array.
`SetPyArray`_ Set a Tensor from a existing Array.
`ToPyArray`_ Create a Array from a existing Tensor.
`GetStorage`_ Get the storage of a existing Tensor.
`ToCPUTensor`_ Switch the storage of a existing Tensor on cpu memory.
`ToCUDATensor`_ Switch the storage of a existing Tensor on cuda memory.
`SetArray`_ Set a Tensor from an existing Array.
`GetStorage`_ Get the storage of an existing Tensor.
============================== =============================================================================
API Reference
......@@ -28,10 +26,8 @@ API Reference
:members:
.. _FromShape: #dragon.core.tensor_utils.FromShape
.. _FromArray: #dragon.core.tensor_utils.FromArray
.. _ToArray: #dragon.core.tensor_utils.ToArray
.. _SetShape: #dragon.core.tensor_utils.SetShape
.. _FromPyArray: #dragon.core.tensor_utils.FromPyArray
.. _SetPyArray: #dragon.core.tensor_utils.SetPyArray
.. _ToPyArray: #dragon.core.tensor_utils.ToPyArray
.. _GetStorage: #dragon.core.tensor_utils.GetStorage
.. _ToCPUTensor: #dragon.core.tensor_utils.ToCPUTensor
.. _ToCUDATensor: #dragon.core.tensor_utils.ToCUDATensor
\ No newline at end of file
.. _SetArray: #dragon.core.tensor_utils.SetArray
.. _GetStorage: #dragon.core.tensor_utils.GetStorage
\ No newline at end of file
Docs/api/python/contents/ops.rst
......@@ -132,37 +132,38 @@ List Brief
Array
-----
=============== ======================================================================
List Brief
=============== ======================================================================
`Where`_ Select elements from either *x* or *y*.
`IndexSelect`_ Select the elements according to the indices along the given axis.
`MaskedSelect`_ Select the the elements where *mask* is *1*.
`Reduce`_ Reduce the inputs along the axis in given axes.
`Sum`_ Compute the sum along the given axis.
`Mean`_ Compute the mean along the given axis.
`Max`_ Compute the values of maximum elements along the given axis.
`ArgMax`_ Compute the indices of maximum elements along the given axis.
`Min`_ Compute the values of minimum elements along the given axis.
`ArgMin`_ Compute the indices of minimum elements along the given axis.
`Slice`_ Slice the inputs into several parts along the given axis.
`Stack`_ Stack the inputs along the given axis.
`Concat`_ Concatenate the inputs along the given axis.
`Repeat`_ Repeat the input along the given axis.
`Transpose`_ Transpose the input according to the given permutations.
`Tile`_ Tile the input according to the given multiples.
`Pad`_ Pad the input according to the given sizes.
`Crop`_ Crop the input according to the given starts and sizes.
`OneHot`_ Generate the one-hot representation of inputs.
`Flatten`_ Flatten the input along the given axes.
`Reshape`_ Reshape the dimensions of input.
`Squeeze`_ Remove the dimensions with size 1.
`ExpandDims`_ Expand the new dimension with size 1 to specific axis.
`Shape`_ Get the dynamic shape of a Tensor.
`NonZero`_ Return the indices of non-zero elements.
`Arange`_ Return evenly spaced values within a given interval.
`Multinomial`_ Return indices sampled from the multinomial distribution.
=============== ======================================================================
================== ======================================================================
List Brief
================== ======================================================================
`Where`_ Select elements from either *x* or *y*.
`IndexSelect`_ Select the elements according to the indices along the given axis.
`MaskedSelect`_ Select the the elements where *mask* is *1*.
`Reduce`_ Reduce the inputs along the axis in given axes.
`Sum`_ Compute the sum along the given axis.
`Mean`_ Compute the mean along the given axis.
`Max`_ Compute the values of maximum elements along the given axis.
`ArgMax`_ Compute the indices of maximum elements along the given axis.
`Min`_ Compute the values of minimum elements along the given axis.
`ArgMin`_ Compute the indices of minimum elements along the given axis.
`Slice`_ Slice the inputs into several parts along the given axis.
`Stack`_ Stack the inputs along the given axis.
`Concat`_ Concatenate the inputs along the given axis.
`ChannelShuffle`_ Shuffle channels between groups along the given axis. `[Zhang et.al, 2017] <https://arxiv.org/abs/1707.01083>`_.
`Repeat`_ Repeat the input along the given axis.
`Transpose`_ Transpose the input according to the given permutations.
`Tile`_ Tile the input according to the given multiples.
`Pad`_ Pad the input according to the given sizes.
`Crop`_ Crop the input according to the given starts and sizes.
`OneHot`_ Generate the one-hot representation of inputs.
`Flatten`_ Flatten the input along the given axes.
`Reshape`_ Reshape the dimensions of input.
`Squeeze`_ Remove the dimensions with size 1.
`ExpandDims`_ Expand the new dimension with size 1 to specific axis.
`Shape`_ Get the dynamic shape of a Tensor.
`NonZero`_ Return the indices of non-zero elements.
`Arange`_ Return evenly spaced values within a given interval.
`Multinomial`_ Return indices sampled from the multinomial distribution.
================== ======================================================================
Control Flow
------------
......@@ -302,6 +303,7 @@ List Brief
.. _Slice: operators/array.html#dragon.operators.array.Slice
.. _Stack: operators/array.html#dragon.operators.array.Stack
.. _Concat: operators/array.html#dragon.operators.array.Concat
.. _ChannelShuffle: operators/array.html#dragon.operators.array.ChannelShuffle
.. _Transpose: operators/array.html#dragon.operators.array.Transpose
.. _Repeat: operators/array.html#dragon.operators.array.Repeat
.. _Tile: operators/array.html#dragon.operators.array.Tile
......
Docs/api/python/contents/vm/caffe/layer.rst
......@@ -14,8 +14,8 @@ Data
==================== =============================================================================
List Brief
==================== =============================================================================
`DataLayer`_ The implementation of ``DataLayer``.
`MemoryDataLayer`_ The implementation of ``MemoryDataLayer``.
`DataLayer`_ The implementation of *DataLayer*.
`MemoryDataLayer`_ The implementation of *MemoryDataLayer*.
==================== =============================================================================
Vision
......@@ -24,16 +24,16 @@ Vision
============================== =============================================================================
List Brief
============================== =============================================================================
`ConvolutionLayer`_ The implementation of ``ConvolutionLayer``.
`DepthwiseConvolutionLayer`_ The implementation of ``DepthwiseConvolutionLayer``.
`DeconvolutionLayer`_ The implementation of ``DeconvolutionLayer``.
`PoolingLayer`_ The implementation of ``PoolingLayer``.
`ROIPoolingLayer`_ The implementation of ``ROIPoolingLayer``.
`ROIAlignLayer`_ The implementation of ``ROIAlignLayer``.
`LRNLayer`_ The implementation of ``LRNLayer``.
`NNResizeLayer`_ The implementation of ``NNResizeLayer``.
`BilinearResizeLayer`_ The implementation of ``BilinearResizeLayer``.
`DropBlockLayer`_ The implementation of ``DropBlockLayer``.
`ConvolutionLayer`_ The implementation of *ConvolutionLayer*.
`DepthwiseConvolutionLayer`_ The implementation of *DepthwiseConvolutionLayer*.
`DeconvolutionLayer`_ The implementation of *DeconvolutionLayer*.
`PoolingLayer`_ The implementation of *PoolingLayer*.
`ROIPoolingLayer`_ The implementation of *ROIPoolingLayer*.
`ROIAlignLayer`_ The implementation of *ROIAlignLayer*.
`LRNLayer`_ The implementation of *LRNLayer*.
`NNResizeLayer`_ The implementation of *NNResizeLayer*.
`BilinearResizeLayer`_ The implementation of *BilinearResizeLayer*.
`DropBlockLayer`_ The implementation of *DropBlockLayer*.
============================== =============================================================================
......@@ -43,14 +43,14 @@ Neuron
==================== =============================================================================
List Brief
==================== =============================================================================
`ReLULayer`_ The implementation of ``ReLULayer``.
`PReLULayer`_ The implementation of ``PReLULayer``.
`ELULayer`_ The implementation of ``ELULayer``.
`SELULayer`_ The implementation of ``SELULayer``.
`SigmoidLayer`_ The implementation of ``SigmoidLayer``.
`TanHLayer`_ The implementation of ``TanHLayer``.
`DropoutLayer`_ The implementation of ``DropoutLayer``.
`PowerLayer`_ The implementation of ``PowerLayer``.
`ReLULayer`_ The implementation of *ReLULayer*.
`PReLULayer`_ The implementation of *PReLULayer*.
`ELULayer`_ The implementation of *ELULayer*.
`SELULayer`_ The implementation of *SELULayer*.
`SigmoidLayer`_ The implementation of *SigmoidLayer*.
`TanHLayer`_ The implementation of *TanHLayer*.
`DropoutLayer`_ The implementation of *DropoutLayer*.
`PowerLayer`_ The implementation of *PowerLayer*.
==================== =============================================================================
Common
......@@ -59,31 +59,30 @@ Common
======================== =============================================================================
List Brief
======================== =============================================================================
`InnerProductLayer`_ The implementation of ``InnerProductLayer``.
`AccuracyLayer`_ The implementation of ``AccuracyLayer``.
`PythonLayer`_ The implementation of ``PythonLayer``.
`EltwiseLayer`_ The implementation of ``EltwiseLayer``
`AddLayer`_ The extended implementation of ``EltwiseLayer``.
`ConcatLayer`_ The implementation of ``ConcatLayer``.
`SliceLayer`_ The implementation of ``SliceLayer``.
`CropLayer`_ The implementation of ``CropLayer``.
`ReshapeLayer`_ The implementation of ``ReshapeLayer``.
`PermuteLayer`_ The implementation of ``PermuteLayer``.
`FlattenLayer`_ The implementation of ``FlattenLayer``.
`GatherLayer`_ The extended implementation for ``GatherOp``.
`SoftmaxLayer`_ The implementation of ``SoftmaxLayer``.
`ArgMaxLayer`_ The implementation of ``ArgMaxLayer``.
`BatchNormLayer`_ The implementation of ``BatchNormLayer``.
`GroupNormLayer`_ The implementation of ``GroupNormLayer``.
`InstanceNormLayer`_ The implementation of ``InstanceNormLayer``.
`ScaleLayer`_ The implementation of ``ScaleLayer``.
`BNLayer`_ The implementation of ``BNLayer``.
`GNLayer`_ The implementation of ``GNLayer``.
`NormalizeLayer`_ The implementation of ``NormalizeLayer``.
`TileLayer`_ The extended implementation of ``TileLayer``.
`ExpandDimsLayer`_ The implementation of ``ExpandDimsLayer``.
`StopGradientLayer`_ The implementation of ``StopGradientLayer``.
`ProposalLayer`_ The implementation of ``ProposalLayer``.
`InnerProductLayer`_ The implementation of *InnerProductLayer*.
`AccuracyLayer`_ The implementation of *AccuracyLayer*.
`PythonLayer`_ The implementation of *PythonLayer*.
`EltwiseLayer`_ The implementation of *EltwiseLayer*
`AddLayer`_ The extended implementation of *EltwiseLayer*.
`ConcatLayer`_ The implementation of *ConcatLayer*.
`SliceLayer`_ The implementation of *SliceLayer*.
`CropLayer`_ The implementation of *CropLayer*.
`ReshapeLayer`_ The implementation of *ReshapeLayer*.
`PermuteLayer`_ The implementation of *PermuteLayer*.
`FlattenLayer`_ The implementation of *FlattenLayer*.
`GatherLayer`_ The extended implementation for *GatherOp*.
`SoftmaxLayer`_ The implementation of *SoftmaxLayer*.
`ArgMaxLayer`_ The implementation of *ArgMaxLayer*.
`BatchNormLayer`_ The implementation of *BatchNormLayer*.
`GroupNormLayer`_ The implementation of *GroupNormLayer*.
`ScaleLayer`_ The implementation of *ScaleLayer*.
`BNLayer`_ The implementation of *BNLayer*.
`GNLayer`_ The implementation of *GNLayer*.
`NormalizeLayer`_ The implementation of *NormalizeLayer*.
`TileLayer`_ The extended implementation of *TileLayer*.
`ExpandDimsLayer`_ The implementation of *ExpandDimsLayer*.
`StopGradientLayer`_ The implementation of *StopGradientLayer*.
`ProposalLayer`_ The implementation of *ProposalLayer*.
======================== =============================================================================
Loss
......@@ -92,12 +91,12 @@ Loss
================================= =============================================================================
List Brief
================================= =============================================================================
`SoftmaxWithLossLayer`_ The implementation of ``SoftmaxWithLossLayer``.
`SigmoidCrossEntropyLossLayer`_ The implementation of ``SigmoidCrossEntropyLossLayer``.
`L2LossLayer`_ The implementation of ``L2LossLayer``.
`SmoothL1LossLayer`_ The implementation of ``SmoothL1LossLayer``.
`SigmoidWithFocalLossLayer`_ The implementation of ``SigmoidWithFocalLossLayer``.
`SoftmaxWithFocalLossLayer`_ The implementation of ``SoftmaxWithFocalLossLayer``.
`SoftmaxWithLossLayer`_ The implementation of *SoftmaxWithLossLayer*.
`SigmoidCrossEntropyLossLayer`_ The implementation of *SigmoidCrossEntropyLossLayer*.
`L2LossLayer`_ The implementation of *L2LossLayer*.
`SmoothL1LossLayer`_ The implementation of *SmoothL1LossLayer*.
`SigmoidWithFocalLossLayer`_ The implementation of *SigmoidWithFocalLossLayer*.
`SoftmaxWithFocalLossLayer`_ The implementation of *SoftmaxWithFocalLossLayer*.
================================= =============================================================================
MPI
......@@ -106,8 +105,8 @@ MPI
================================= =============================================================================
List Brief
================================= =============================================================================
`MPIBroadcastLayer`_ The implementation of ``MPIBroadcastLayer``
`MPIGatherLayer`_ The implementation of ``MPIGatherLayer``
`MPIBroadcastLayer`_ The implementation of *MPIBroadcastLayer*
`MPIGatherLayer`_ The implementation of *MPIGatherLayer*
================================= =============================================================================
......@@ -188,7 +187,6 @@ API Reference
.. _ArgMaxLayer: #dragon.vm.caffe.layers.common.ArgMaxLayer
.. _BatchNormLayer: #dragon.vm.caffe.layers.common.BatchNormLayer
.. _GroupNormLayer: #dragon.vm.caffe.layers.common.GroupNormLayer
.. _InstanceNormLayer: #dragon.vm.caffe.layers.common.InstanceNormLayer
.. _ScaleLayer: #dragon.vm.caffe.layers.common.ScaleLayer
.. _BNLayer: #dragon.vm.caffe.layers.common.BNLayer
.. _GNLayer: #dragon.vm.caffe.layers.common.GNLayer
......
Dragon/CMakeLists.txt
......@@ -94,6 +94,9 @@ include_directories(${THIRD_PARTY_DIR}/eigen)
include_directories(${THIRD_PARTY_DIR}/protobuf/include)
include_directories(${PROJECT_SOURCE_DIR}/include)
include_directories(${PROJECT_SOURCE_DIR}/src)
if(APPLE)
include_directories(/usr/local/include)
endif()
if (BUILD_PYTHON_API)
include_directories(${PYTHON_INCLUDE_DIRS})
include_directories(${NUMPY_INCLUDE_DIR})
......@@ -112,6 +115,9 @@ endif()
# ---[ Lib Directories
list(APPEND THIRD_PARTY_LIBRARY_DIRS ${THIRD_PARTY_DIR}/protobuf/lib)
if(APPLE)
list(APPEND THIRD_PARTY_LIBRARY_DIRS /usr/local/lib)
endif()
if (WITH_CUDA)
list(APPEND THIRD_PARTY_LIBRARY_DIRS ${CUDA_TOOLKIT_ROOT_DIR}/lib)
list(APPEND THIRD_PARTY_LIBRARY_DIRS ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
......@@ -177,8 +183,13 @@ if(WIN32)
endif()
endif()
if(UNIX)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -s -fPIC")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -s -fPIC -O3 -m64 -std=c++11")
if(APPLE)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -w -fPIC")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -w -fPIC -O3 -m64 -std=c++11")
else()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -s -fPIC")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -s -fPIC -O3 -m64 -std=c++11")
endif()
if (WITH_OMP AND (NOT APPLE))
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fopenmp")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")
......
Dragon/include/core/context_cuda.h
......@@ -346,7 +346,7 @@ class CUDAContext {
void FinishDeviceCompution() { CUDA_NOT_COMPILED; }
/*! \brief Malloc the memory */
static void* New(size_t nbytes) { CUDA_NOT_COMPILED; }
static void* New(size_t nbytes) { return nullptr; }
/*! \brief Zero-Reset the memory */
static void Memset(
......
Dragon/include/core/graph.h
......@@ -21,23 +21,16 @@ namespace dragon {
class GraphBase {
public:
/*! \brief Default constructor */
GraphBase(
const GraphDef& def,
Workspace* ws);
GraphBase(const GraphDef&, Workspace*);
/*! \brief Default deconstructor */
virtual ~GraphBase() {}
/*! \brief Create a graph from the optimized def */
virtual bool Create(
const GraphDef& def,
Workspace* ws) = 0;
virtual bool Create(const GraphDef&, Workspace*) = 0;
/*! \brief Run the graph once synchronously */
virtual bool Run(
const string& include,
const string& exclude,
int stream_id = 0) = 0;
virtual bool Run(const string&, const string&, int = 0) = 0;
/*! \brief Return the graph name */
string name() const { return name_; }
......@@ -46,7 +39,7 @@ class GraphBase {
const string& phase() const { return phase_; }
/*! \brief Return the argument map */
const Map<std::string, const Argument*>& args() { return args_; }
const Map<string, const Argument*>& args() { return args_; }
/*! \brief Return the specified argument */
const Argument& arg(const string& name) { return *(args_[name]); }
......@@ -83,15 +76,10 @@ class Graph : public GraphBase {
virtual ~Graph() { for (auto* op : ops_) delete op; }
/*! \brief Create a graph from the optimized def */
bool Create(
const GraphDef& def,
Workspace* ws) override;
bool Create(const GraphDef&, Workspace*) override;
/*! \brief Run the graph once synchronously */
bool Run(
const string& include,
const string& exclude,
int stream_id = 0) override;
bool Run(const string&, const string&, int = 0) override;
protected:
/*! \brief Store the internal operators */
......@@ -99,9 +87,7 @@ class Graph : public GraphBase {
};
/*! \brief Create a graph from the raw def */
GraphBase* NewGraph(
const GraphDef& def,
Workspace* ws);
GraphBase* NewGraph(const GraphDef&, Workspace*);
/* Macros */
......
Dragon/include/core/operator.h
......@@ -31,7 +31,7 @@ class Workspace;
class OperatorBase {
public:
/*! \brief Default constructor */
OperatorBase(const OperatorDef& def, Workspace* ws);
OperatorBase(const OperatorDef&, Workspace*);
/*! \brief Default deconstructor */
virtual ~OperatorBase() {}
......@@ -49,7 +49,7 @@ class OperatorBase {
int YSize() { return (int)outputs_.size(); }
/*! \brief Modify operator according to the given def */
void UpdateFrom(const OperatorDef& def);
void UpdateFrom(const OperatorDef&);
/*! \brief Switch the internal running phase */
void SwitchToPhase(const string& phase) { phase_ = phase; }
......@@ -95,7 +95,7 @@ class OperatorBase {
vector<T> Args(const string& name);
/*! \brief Return the argument map */
const Map<std::string, const Argument*>& args() { return args_; }
const Map<string, const Argument*>& args() { return args_; }
/*! \brief Return the specified argument */
const Argument& arg(const string& name) { return *(args_[name]); }
......@@ -212,12 +212,11 @@ class Operator : public OperatorBase {
#ifndef WITH_MPI
return true;
#else
vec32_t allow_ranks =
OperatorBase::Args<int>("mpi_ranks");
if (allow_ranks.empty()) return true;
auto ranks = OperatorBase::Args<int>("mpi_ranks");
if (ranks.empty()) return true;
int cur_rank;
MPI_Comm_rank(MPI_COMM_WORLD, &cur_rank);
for (auto mpi_rank : allow_ranks)
for (auto mpi_rank : ranks)
if (cur_rank == mpi_rank) return true;
return false;
#endif
......@@ -225,10 +224,7 @@ class Operator : public OperatorBase {
};
/*! \brief New a operator from the raw def */
OperatorBase* NewOperator(
const OperatorDef& def,
Workspace* ws);
OperatorBase* NewOperator(const OperatorDef&, Workspace*);
/* Macros */
......
Dragon/include/core/typeid.h
......@@ -40,7 +40,7 @@ class TypeMeta {
TypeMeta(const TypeMeta& src)
: id_(src.id_), itemsize_(src.itemsize_),
ctor_(src.ctor_), copy_(src.copy_), dtor_(src.dtor_) {}
ctor_(src.ctor_), copy_(src.copy_), dtor_(src.dtor_) {}
TypeMeta& operator = (const TypeMeta& src) {
if (this == &src) return *this;
......
Dragon/include/operators/array/channel_shuffle_op.h 0 → 100644
/*!
* Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
*
* Licensed under the BSD 2-Clause License.
* You should have received a copy of the BSD 2-Clause License
* along with the software. If not, See,
*
* <https://opensource.org/licenses/BSD-2-Clause>
*
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_ARRAY_CHANNEL_SHUFFLE_OP_H_
#define DRAGON_OPERATORS_ARRAY_CHANNEL_SHUFFLE_OP_H_
#include "core/operator.h"
namespace dragon {
template <class Context>
class ChannelShuffleOp final: public Operator<Context> {
public:
ChannelShuffleOp(const OperatorDef& def, Workspace* ws)
: Operator<Context>(def, ws),
axis_(OpArg<int64_t>("axis", 0)),
group_(OpArg<int64_t>("group", 1)) {}
USE_OPERATOR_FUNCTIONS;
void RunOnDevice() override;
template <typename T> void RunImpl();
protected:
int64_t outer_dim_, inner_dim_;
int64_t axis_, axis_dim_, group_;
};
template <class Context>
class ChannelShuffleGradientOp final: public Operator<Context> {
public:
ChannelShuffleGradientOp(const OperatorDef& def, Workspace* ws)
: Operator<Context>(def, ws),
axis_(OpArg<int64_t>("axis", 0)),
group_(OpArg<int64_t>("group", 1)) {}
USE_OPERATOR_FUNCTIONS;
void RunOnDevice() override;
template <typename T> void RunImpl();
protected:
int64_t outer_dim_, inner_dim_;
int64_t axis_, axis_dim_, group_;
};
} // namespace dragon
#endif // DRAGON_OPERATORS_ARRAY_CHANNEL_SHUFFLE_OP_H_
\ No newline at end of file
Dragon/include/operators/array/slice_op.h
......@@ -30,7 +30,7 @@ class SliceOp final : public Operator<Context> {
template <typename T> void RunImpl();
protected:
vec64_t points_;
vec64_t points_, sections_;
int64_t outer_dim_, inner_dim_;
int64_t axis_, axis_dim_, slice_dim_, N_;
};
......@@ -48,7 +48,7 @@ class SliceGradientOp final : public Operator<Context> {
template <typename T> void RunImpl();
protected:
vec64_t points_;
vec64_t points_, sections_;
int64_t outer_dim_, inner_dim_;
int64_t axis_, axis_dim_, slice_dim_, N_;
};
......
Dragon/include/operators/misc/image_data_op.h
......@@ -27,13 +27,13 @@ class ImageDataOp final : public Operator<Context> {
if (mean_vec_.size() > 0) {
CHECK_EQ((int)mean_vec_.size(), 3);
auto* mean = mean_.Reshape({ 3 })
->mutable_data<float, CPUContext>();
->template mutable_data<float, CPUContext>();
for (int i = 0; i < 3; ++i) mean[i] = mean_vec_[i];
}
if (std_vec_.size() > 0) {
CHECK_EQ((int)std_vec_.size(), 3);
auto* std = std_.Reshape({ 3 })
->mutable_data<float, CPUContext>();
->template mutable_data<float, CPUContext>();
for (int i = 0; i < 3; ++i) std[i] = std_vec_[i];
}
}
......
Dragon/include/utils/op_kernel.h
......@@ -378,6 +378,18 @@ void ArgMin(
T* values,
Context* ctx);
/*! array.channel_shuffle */
template <typename T, class Context>
void ChannelShuffle(
const int outer_dim,
const int inner_dim,
const int axis_dim,
const int group,
const T* x,
T* y,
Context* ctx);
/*! array.concat */
template <typename T, class Context>
......
Dragon/modules/python/py_config.h
......@@ -10,7 +10,7 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_PYTHON_PY_CONIFG_H_
#ifndef DRAGON_PYTHON_PY_CONFIG_H_
#define DRAGON_PYTHON_PY_CONFIG_H_
#include "py_dragon.h"
......
Dragon/modules/python/py_tensor.h
......@@ -11,7 +11,7 @@
*/
#ifndef DRAGON_PYTHON_PY_TENSOR_H_
#define DRAGON_PYTHON_PY_TENOSR_H_
#define DRAGON_PYTHON_PY_TENSOR_H_
#include "py_dragon.h"
......
Dragon/python/dragon/config.py
......@@ -98,7 +98,7 @@ def EnableCNML(mlu_id=0):
Parameters
----------
device_id : int
mlu_id : int
The index of MLU to use.
Returns
......@@ -193,14 +193,14 @@ def SetGraphOptimizationLevel(level=3):
We have predefined four levels:
-O0(level=0): Do nothing.
*-O0*: Do nothing.
-O1(level=1): Prune the redundant nodes.
*-O1*: Prune the redundant nodes.
-O2(level=2): Add the inplace to outputs.
*-O2*: Add the inplace to outputs.
Note that the graph will no longer be a DAG.
-O3(level=3): Allocate the buffer for outputs.
*-O3*: Allocate the buffer for outputs.
This level is memory-efficient while debugging will be non-trivial.
Parameters
......
Dragon/python/dragon/core/helper.py
......@@ -33,7 +33,7 @@ class OperatorHelper(object):
# Following operators is the simplest case:
# Input(0) => Output(0), shape and data type unchanged.
'Relu', 'PRelu', 'Elu', 'SElu', 'Sigmoid', 'Tanh', 'Softmax',
'Dropout', 'DropPath', 'DropBlock2d',
'Dropout', 'DropPath', 'DropBlock2d', 'ChannelShuffle',
'Add', 'Sub', 'Mul', 'Div', 'Clip', 'Log', 'Exp', 'Pow', 'Square', 'Sqrt',
'Accumulate', 'Affine', 'Copy', 'StopGradient', 'MPIBroadcast',
'BatchNorm', 'GroupNorm', 'L2Norm', 'LRN', 'BiasAdd',
......
Dragon/python/dragon/operators/array.py
......@@ -52,7 +52,7 @@ def IndexSelect(inputs, indices, axis=0, **kwargs):
The input tensor.
indices : sequence or Tensor
The indices to select elements.
axis : int, optional
axis : int, optional, default=0
The axis of indices.
Returns
......@@ -156,25 +156,23 @@ def Crop(
@OpSchema.Inputs(1)
def Slice(inputs, axis=0, num_outputs=1, slice_points=None, **kwargs):
def Slice(inputs, axis=0, num_slices=1, slice_points=None, **kwargs):
"""Slice the inputs into several parts along the given axis.
All dimensions except the specified ``axis`` should be same.
The number of ``slice_points`` should be *len(X.shape) - 1*.
if ``slice_points`` is *None*, dimension of axis should be divided by ``num_outputs``.
**Type Constraints**: (*bool*, *int8*, *uint8*, *int32*, *int64*, *float16*, *float32*, *float64*)
Parameters
----------
inputs : Tensor
The input tensor.
axis : int, optional
axis : int, optional, default=0
The axis to slice, can be negative.
num_outputs : int, optional
The optional number number of slices.
num_slices: int, optional, default=1
The optional number of slices.
slice_points : sequence of int, optional
The optional slice points.
......@@ -184,9 +182,12 @@ def Slice(inputs, axis=0, num_outputs=1, slice_points=None, **kwargs):
The outputs.
"""
if slice_points is not None and len(slice_points) > 0:
num_outputs = len(slice_points) + 1
return Tensor.CreateOperator('Slice', **ParseArgs(locals()))
arguments = ParseArgs(locals())
if slice_points is not None:
arguments['num_outputs'] = len(slice_points) + 1
else:
arguments['num_outputs'] = num_slices
return Tensor.CreateOperator('Slice', **arguments)
@OpSchema.Inputs(1, INT_MAX)
......@@ -201,7 +202,7 @@ def Stack(inputs, axis=0, **kwargs):
----------
inputs : sequence of Tensor
The inputs.
axis : int
axis : int, optional, default=0
The axis to stack, can be negative.
Returns
......@@ -225,7 +226,7 @@ def Concat(inputs, axis=0, **kwargs):
----------
inputs : sequence of Tensor
The inputs.
axis : int
axis : int, optional, default=0
The axis to concatenate, can be negative.
Returns
......@@ -238,6 +239,30 @@ def Concat(inputs, axis=0, **kwargs):
@OpSchema.Inputs(1)
def ChannelShuffle(inputs, axis=0, group=1, **kwargs):
"""Shuffle channels between groups along the given axis. `[Zhang et.al, 2017] <https://arxiv.org/abs/1707.01083>`_.
**Type Constraints**: (*bool*, *int8*, *uint8*, *int32*, *int64*, *float16*, *float32*, *float64*)
Parameters
----------
inputs : Tensor
The inputs.
axis : int, optional, default=0
The axis of channels, can be negative.
group : int, optional, default=1
The number of groups.
Returns
-------
Tensor
The output tensor.
"""
return Tensor.CreateOperator('ChannelShuffle', **ParseArgs(locals()))
@OpSchema.Inputs(1)
def Reduce(inputs, axes=None, operation='SUM', keep_dims=False, **kwargs):
"""Reduce the inputs along the axis in given axes.
......@@ -411,9 +436,9 @@ def ArgMin(inputs, axis=None, top_k=1, keep_dims=False, **kwargs):
The input tensor.
axis : int, optional
The axis to compute, can be negative.
top_k : int, optional
top_k : int, optional, default=1
The top k results to keep.
keep_dims : bool, optional
keep_dims : bool, optional, default=False
Whether to keep dims after computing.
Returns
......@@ -439,9 +464,9 @@ def Min(inputs, axis=None, top_k=1, keep_dims=False, **kwargs):
The input tensor.
axis : int, optional
The axis to compute, can be negative.
top_k : int, optional
top_k : int, optional, default=1
The top k results to keep.
keep_dims : bool, optional
keep_dims : bool, optional, default=False
Whether to keep dims after computing.
Returns
......@@ -494,7 +519,7 @@ def Repeat(inputs, axis=None, repeats=1, **kwargs):
The input tensor.
axis : int, optional
The axis to repeat.
repeats : int or Tensor, optional
repeats : int or Tensor, optional, default=1
The magnitude of repeating.
Returns
......@@ -586,11 +611,11 @@ def OneHot(inputs, depth, on_value=1, off_value=0, **kwargs):
inputs : Tensor
The input tensor.
depth : int
The depth of one-hot representation.
on_value : int, optional
The value when ``indices[j] = i``.
off_value : int, optional
The value when ``indices[j] != i``.
The depth of representation.
on_value : int, optional, default=1
The value when *indices[j]* = *i*.
off_value : int, optional, default=0
The value when *indices[j]* != *i*.
Returns
-------
......@@ -613,12 +638,12 @@ def Flatten(inputs, axis=0, num_axes=-1, keep_axes=None, **kwargs):
----------
inputs : Tensor
The input tensor.
axis : int, optional
axis : int, optional, default=0
The start axis to flatten, can be negative.
num_axes : int, optional
The number of axes to flatten. Default is ``-1`` (Along all axes).
num_axes : int, optional, default=-1
The number of axes to flatten.
keep_axes : int, optional
The number of axes to keep. Default is ``None`` (Disabled).
The number of axes to keep.
Returns
-------
......
Dragon/python/dragon/operators/data.py
......@@ -27,20 +27,16 @@ def LMDBData(**kwargs):
The path of database.
shuffle : bool, optional, default=False
Whether to shuffle the data.
node_step: bool
Whether to split data for multiple parallel nodes.
num_chunks : int, optional, default=2048
The number of chunks to split.
chunk_size : int, optional, default=-1
The size(MB) of each chunk.
mean_values : list, optional
The mean value of each image channel.
scale : float, optional, default=1.
The scale performed after mean subtraction.
padding : int, optional, default=0
The zero-padding size.
fill_value : int or sequence, optional, default=127
The value(s) to fill for padding or cutout.
crop_size : int, optional, default=0
The cropping size.
cutout_size : int, optional, default=0
The square size to cutout.
mirror : bool, optional, default=False
Whether to mirror(flip horizontally) images.
color_augmentation : bool, optional, default=False
......
Dragon/python/dragon/ops.py
......@@ -126,6 +126,7 @@ ArgMin = _array_ops.ArgMin
Slice = _array_ops.Slice
Stack = _array_ops.Stack
Concat = _array_ops.Concat
ChannelShuffle = _array_ops.ChannelShuffle
Transpose = _array_ops.Transpose
Repeat = _array_ops.Repeat
Tile = _array_ops.Tile
......
Dragon/python/dragon/utils/vision/data_batch.py
......@@ -45,8 +45,8 @@ class DataBatch(object):
The number of chunks to split.
padding : int, optional, default=0
The zero-padding size.
fill_value : int, optional, default=127
The value to fill when padding is valid.
fill_value : int or sequence, optional, default=127
The value(s) to fill for padding or cutout.
crop_size : int, optional, default=0
The cropping size.
cutout_size : int, optional, default=0
......
Dragon/python/dragon/utils/vision/data_reader.py
......@@ -120,7 +120,7 @@ class DataReader(multiprocessing.Process):
self._cursor += 1
def next_chunk(self):
"""Step the cursor of shuffling chunks.
"""Step the cursor of chunks.
Returns
-------
......@@ -166,7 +166,7 @@ class DataReader(multiprocessing.Process):
# Search a optimal chunk size (Chunk-Wise)
min_size, max_size = \
1, self._db._total_size * 1.0 \
/ ((self._num_chunks * (1 << 20)))
/ (self._num_chunks * (1 << 20))
while min_size * 2 < max_size: min_size *= 2
self._perm_size = int(math.ceil(
self._db._total_size * 1.1 /
......
Dragon/python/dragon/utils/vision/data_transformer.py
......@@ -43,8 +43,8 @@ class DataTransformer(multiprocessing.Process):
----------
padding : int, optional, default=0
The zero-padding size.
fill_value : int, optional, default=127
The value to fill when padding is valid.
fill_value : int or sequence, optional, default=127
The value(s) to fill for padding or cutout.
crop_size : int, optional, default=0
The cropping size.
cutout_size : int, optional, default=0
......@@ -92,7 +92,7 @@ class DataTransformer(multiprocessing.Process):
The tuple image and labels.
"""
# decode
# Decode
datum = _proto_def.Datum()
datum.ParseFromString(serialized)
im = numpy.fromstring(datum.data, numpy.uint8)
......@@ -115,13 +115,14 @@ class DataTransformer(multiprocessing.Process):
# Padding
if self._padding > 0:
pad_img = numpy.empty((
pad_im = numpy.empty((
im.shape[0] + 2 * self._padding,
im.shape[1] + 2 * self._padding, im.shape[2]), dtype=im.dtype)
pad_img.fill(self._fill_value)
pad_img[self._padding : self._padding + im.shape[0],
self._padding : self._padding + im.shape[1], :] = im
im = pad_img
im.shape[1] + 2 * self._padding, im.shape[2]
), dtype=im.dtype)
pad_im[:] = self._fill_value
pad_im[self._padding : self._padding + im.shape[0],
self._padding : self._padding + im.shape[1], :] = im
im = pad_im
# Random crop
if self._crop_size > 0:
......
Dragon/python/dragon/vm/caffe/layers/__init__.py
......@@ -84,7 +84,6 @@ from .common import (
FlattenLayer,
ConcatLayer,
NormalizeLayer,
InstanceNormLayer,
TileLayer,
ReductionLayer,
ExpandDimsLayer,
......
Dragon/python/dragon/vm/caffe/layers/common.py
......@@ -370,28 +370,6 @@ class GroupNormLayer(_Layer):
return _ops.GroupNorm(inputs, **self.arguments)
class InstanceNormLayer(_Layer):
"""The implementation of *InstanceNormLayer*.
Introduced by `[Ulyanov et.al, 2016] <https://arxiv.org/abs/1607.08022>`_
Parameters
----------
eps : float
Refer ``InstanceNormParameter.eps``.
"""
def __init__(self, LayerParameter):
super(InstanceNormLayer, self).__init__(LayerParameter)
self.arguments = {
'axis': 1,
'eps': LayerParameter.instance_norm_param.eps,
}
def LayerSetup(self, bottom):
return _ops.InstanceNorm(bottom, **self.arguments)
class ScaleLayer(_Layer):
"""The implementation of *ScaleLayer*.
......
Dragon/python/dragon/vm/caffe/layers/data.py
......@@ -30,11 +30,15 @@ class DataLayer(_Layer):
The path of database. Refer `DataParameter.source`_.
prefetch: int
The prefetch count. Refer `DataParameter.prefetch`_.
shuffle : boolean
Whether to shuffle the data. Refer ``DataParameter.shuffle``.
nun_chunks : int
The number of chunks to shuffle. Refer ``DataParameter.num_chunks``.
batch_size : int
The size of a mini-batch. Refer `DataParameter.batch_size`_.
phase : Phase
The phase of layer. Refer `LayerParameter.phase`_.
mirrow : boolean
mirror : boolean
Whether to randomly mirror. Refer `TransformationParameter.mirror`_.
crop_size : int
The crop size. Refer `TransformationParameter.crop_size`_.
......@@ -62,11 +66,12 @@ class DataLayer(_Layer):
param = LayerParameter.data_param
memory_param = LayerParameter.memory_data_param
transform_param = LayerParameter.transform_param
parallel_param = LayerParameter.parallel_param
self.arguments = {
'source': param.source,
'prefetch': param.prefetch,
'shuffle': param.shuffle,
'num_chunks': param.num_chunks,
'batch_size': param.batch_size,
'phase': {0: 'TRAIN', 1: 'TEST'}[int(LayerParameter.phase)],
'mirror': transform_param.mirror,
......@@ -76,9 +81,6 @@ class DataLayer(_Layer):
'padding': transform_param.padding,
'min_random_scale': transform_param.min_random_scale,
'max_random_scale': transform_param.max_random_scale,
'shuffle': parallel_param.shuffle,
'multiple_nodes': parallel_param.multiple_nodes,
'partition': parallel_param.partition,
'dtype': {0: 'float32', 1: 'float16'}[memory_param.dtype],
'data_format': 'NCHW',
}
......
Dragon/python/dragon/vm/caffe/proto/caffe.proto
......@@ -416,18 +416,13 @@ message LayerParameter {
optional MPIParameter mpi_param = 153;
optional PermuteParameter permute_param = 154;
optional NormalizeParameter normalize_param = 155;
optional ParallelParameter parallel_param = 157;
optional ResizeParameter resize_param = 158;
optional ExpandDimsParameter expand_dims_param = 159;
optional ProposalParameter proposal_param = 160;
optional BatchRenormParameter batch_renorm_param = 161;
optional DenseConcatParameter dense_concat_param = 163;
optional FocalLossParameter focal_loss_param = 164;
optional GatherParameter gather_param = 165;
optional InstanceNormParameter instance_norm_param = 166;
optional GroupNormParameter group_norm_param = 167;
optional DropBlockParameter drop_block_param = 168;
optional CastParameter cast_param = 169;
optional ResizeParameter resize_param = 156;
optional ExpandDimsParameter expand_dims_param = 157;
optional ProposalParameter proposal_param = 158;
optional FocalLossParameter focal_loss_param = 159;
optional GroupNormParameter group_norm_param = 160;
optional DropBlockParameter drop_block_param = 161;
optional CastParameter cast_param = 163;
}
// Message that stores parameters used to apply transformation
......@@ -690,6 +685,10 @@ message DataParameter {
// Prefetch queue (Number of batches to prefetch to host memory, increase if
// data access bandwidth varies).
optional uint32 prefetch = 10 [default = 5];
// Whether to shuffle the data.
optional bool shuffle = 11 [default = false];
// The number of chunks to shuffle.
optional int32 num_chunks = 12 [default = 2048];
}
message DropoutParameter {
......@@ -1462,12 +1461,6 @@ message NormalizeParameter {
optional float eps = 4 [default = 1e-5];
}
message ParallelParameter {
optional bool multiple_nodes = 1 [default = false];
optional bool shuffle = 2 [default = false];
optional bool partition = 3 [default = false];
}
message ResizeParameter {
optional BlobShape shape = 1;
optional float fx = 2 [default = -1.0];
......@@ -1492,37 +1485,15 @@ message ProposalParameter {
optional int32 canonical_level = 11 [default = 4];
}
message BatchRenormParameter {
optional bool use_global_stats = 1;
optional float moving_average_fraction = 2 [default = 0.9];
optional float eps = 3 [default = 1e-5];
optional float r_max = 4 [default = 3.0];
optional float d_max = 5 [default = 5.0];
optional float t_delta = 6 [default = 0.001];
}
message DenseConcatParameter {
optional int32 axis = 1 [default = 1];
optional int32 growth_rate = 2 [default = 0];
}
message FocalLossParameter {
optional float alpha = 1 [default = 0.25];
optional float gamma = 2 [default = 2.0];
optional int32 neg_id = 3 [default = 0];
}
message GatherParameter {
optional int32 axis = 1 [default = 0];
}
message InstanceNormParameter {
optional float eps = 1 [default = 1e-5];
}
message GroupNormParameter {
optional float eps = 1 [default = 1e-5];
optional int32 group = 2 [default = 32]; // The group size
optional int32 group = 2 [default = 32];
}
message DropBlockParameter {
......
Dragon/python/dragon/vm/torch/ops/builtin.py
......@@ -34,9 +34,9 @@ from dragon.vm.torch.ops.modules.init import (
)
from dragon.vm.torch.ops.modules.array import (
Reshape, Squeeze, UnSqueeze, Permute,
Indexing, Repeat, Concat, Stack,
IndexSelect, MaskedSelect,
Reshape, Squeeze, UnSqueeze, Permute,
ChannelShuffle, Repeat, Concat, Stack, Chunk,
Indexing, IndexSelect, MaskedSelect,
Reduce, ArgReduce,
NonZero, Where,
OneHot, Multinomial,
......@@ -60,7 +60,8 @@ __all__ = [
'mean', 'sum', 'min', 'max', 'topk',
'nonzero', 'where', 'argmin', 'argmax',
'gt', 'lt', 'eq', 'ne', 'ge', 'le',
'cat', 'stack', 'narrow',
'cat', 'stack', 'chunk',
'narrow', 'channel_shuffle',
'index_select', 'masked_select',
'one_hot', 'multinomial',
'rand', 'randn',
......@@ -422,7 +423,7 @@ def _reshape(input, shape, shape_like=None):
def _permute(input, perm):
dev = MakeDevice(inputs=[input]); nperm = len(perm)
dev, nperm = MakeDevice([input]), len(perm)
key = 'Permute/{}/nperm:{}'.format(dev, nperm)
module = get_module(Permute, key, dev, nperm=nperm)
return module.forward(input, perm)
......@@ -576,7 +577,9 @@ def squeeze(input, dim=None, out=None):
Parameters
----------
dim : int
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The optional dim to remove.
out : dragon.vm.torch.Tensor, optional
The output tensor.
......@@ -598,6 +601,8 @@ def unsqueeze(input, dim, out=None):
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int
The dim to remove.
out : dragon.vm.torch.Tensor, optional
......@@ -856,7 +861,7 @@ def le(input, other, out=None):
----------
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
......@@ -877,7 +882,7 @@ def eq(input, other, out=None):
----------
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
......@@ -898,7 +903,7 @@ def ne(input, other, out=None):
----------
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
......@@ -930,7 +935,7 @@ def cat(seq, dim=0, out=None):
The output tensor.
"""
dev = MakeDevice(inputs=seq, outputs=[out] if out else [])
dev = MakeDevice(seq, [out] if out else [])
key = 'Concat/{}/dim:{}'.format(dev, dim)
module = get_module(Concat, key, dev, axis=dim)
return module.forward(seq, out)
......@@ -943,7 +948,7 @@ def stack(seq, dim=0, out=None):
----------
seq : sequence of dragon.vm.torch.Tensor
The sequence.
dim : int, optional
dim : int, optional, default=0
The dim to stack.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
......@@ -960,6 +965,60 @@ def stack(seq, dim=0, out=None):
return module.forward(seq, out)
def channel_shuffle(input, dim=0, group=1, out=None):
"""Shuffle channels between groups along the given axis.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional, default=0
The axis of channels.
group : int, optional, default=1
The number of groups.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The new tensor.
"""
dev = MakeDevice([input])
key = 'ChannelShuffle/{}/dim:{}/group:{}'.format(dev, dim, group)
module = get_module(
ChannelShuffle, key, dev,
axis=dim,
group=group,
)
return module.forward(input, out)
def chunk(tensor, chunks, dim=0):
"""Split the input into several parts along the given axis.
Parameters
----------
tensor : dragon.vm.torch.Tensor
The input to split.
chunks : int
The number of chunks to split.
dim : int, optional, default=0
The dim to split.
Returns
-------
sequence of dragon.vm.torch.Tensor
The output chunks.
"""
dev = MakeDevice([tensor])
key = 'Chunk/{}/chunks:{}/dim:{}'.format(dev, chunks, dim)
module = get_module(Chunk, key, dev, axis=dim, chunks=chunks)
return module.forward(tensor)
def index_select(input, dim, index, out=None):
"""Select the input values along the given axis using index.
......@@ -1047,7 +1106,7 @@ def nonzero(input, out=None):
Returns
-------
dragon.vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1069,7 +1128,7 @@ def one_hot(input, depth):
Returns
-------
dragon.vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1137,7 +1196,7 @@ def zeros(*sizes, **kwargs):
Returns
-------
vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1158,7 +1217,7 @@ def zeros_like(input, out=None, **kwargs):
Returns
-------
vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1180,7 +1239,7 @@ def ones(*sizes, **kwargs):
Returns
-------
vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1201,7 +1260,7 @@ def ones_like(input, out=None, **kwargs):
Returns
-------
vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1223,7 +1282,7 @@ def rand(*sizes, **kwargs):
Returns
-------
vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......@@ -1244,7 +1303,7 @@ def randn(*sizes, **kwargs):
Returns
-------
vm.torch.FloatTensor
dragon.vm.torch.Tensor
The output tensor.
"""
......
Dragon/python/dragon/vm/torch/ops/modules/array.py
......@@ -106,6 +106,32 @@ class Stack(BaseModule):
return self.run(inputs, outputs)
class Chunk(BaseModule):
"""This module imports the *SliceOp* from backend.
Slice the inputs into several parts along the given axis.
"""
def __init__(self, key, dev, **kwargs):
super(Chunk, self).__init__(key, dev, **kwargs)
self.axis = kwargs.get('axis', 0)
self.chunks = kwargs.get('chunks', 1)
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'Slice',
'arguments': {
'axis': self.axis,
},
}
def forward(self, x):
inputs = [x]; self.unify_devices(inputs)
outputs = [self.register_output() for _ in range(self.chunks)]
return self.run(inputs, outputs)
class IndexSelect(BaseModule):
"""This module imports the *IndexSelectOp* from backend.
......@@ -315,6 +341,28 @@ class Permute(BaseModule):
return self.run(inputs, outputs, callback=callback)
class ChannelShuffle(BaseModule):
def __init__(self, key, dev, **kwargs):
super(ChannelShuffle, self).__init__(key, dev, **kwargs)
self.axis = kwargs.get('axis', 0)
self.group = kwargs.get('group', 1)
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'ChannelShuffle',
'arguments': {
'axis': self.axis,
'group': self.group,
},
}
def forward(self, x, y):
inputs = [x]; self.unify_devices(inputs)
outputs = [y] if y else [self.register_output()]
return self.run(inputs, outputs)
class Repeat(BaseModule):
def __init__(self, key, dev, **kwargs):
super(Repeat, self).__init__(key, dev, **kwargs)
......
Dragon/python/dragon/vm/torch/ops/tensor.py
......@@ -23,7 +23,7 @@ from dragon.vm.torch.ops.builtin import (
_fundamental, _rfundamental,
log, exp, sqrt, clamp,
_reshape, squeeze, unsqueeze,
_permute, _repeat, narrow, _index,
_permute, _repeat, chunk, narrow, _index,
_assign, _masked_assign,
index_select, masked_select,
mean, sum, max, min,
......@@ -76,6 +76,7 @@ Tensor.view = lambda self, *shape: _reshape(self, shape)
Tensor.view_as = lambda *args, **kwargs: _reshape(*args, **kwargs)
Tensor.permute = lambda self, *dims: _permute(self, dims)
Tensor.repeat = lambda self, *args: _repeat(self, args)
Tensor.chunk = lambda *args, **kwargs: chunk(*args, **kwargs)
Tensor.mean = lambda *args, **kwargs: mean(*args, **kwargs)
Tensor.sum = lambda *args, **kwargs: sum(*args, **kwargs)
Tensor.max = lambda *args, **kwargs: max(*args, **kwargs)
......
Dragon/python/dragon/vm/torch/tensor.py
......@@ -355,7 +355,7 @@ class Tensor(object):
Parameters
----------
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
Returns
......@@ -371,7 +371,7 @@ class Tensor(object):
Parameters
----------
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
Returns
......@@ -387,7 +387,7 @@ class Tensor(object):
Parameters
----------
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
Returns
......@@ -403,7 +403,7 @@ class Tensor(object):
Parameters
----------
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
Returns
......@@ -419,7 +419,7 @@ class Tensor(object):
Parameters
----------
other : dragon.vm.torch.Tensor, number
other : dragon.vm.torch.Tensor or number
The other tensor.
Returns
......@@ -847,6 +847,24 @@ class Tensor(object):
"""
raise NotImplementedError('Refer torch.ops.tensor.repeat')
def chunk(self, chunks, dim=0):
"""Split self into several parts along the given axis.
Parameters
----------
chunks : int
The number of chunks to split.
dim : int, optional
The dim to split.
Returns
-------
sequence of dragon.vm.torch.Tensor
The output chunks.
"""
raise NotImplementedError('Refer torch.ops.tensor.chunk')
def nonzero(self):
"""Return the indices of non-zero elements.
......
Dragon/src/kernels/array/channel_shuffle_op_kernel.cc 0 → 100644
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
namespace dragon {
namespace kernel {
/* <T = ?, Device = CPU> */
template <typename T>
void _ChannelShuffle(
const int outer_dim,
const int inner_dim,
const int G,
const int K,
const T* x,
T* y,
CPUContext* ctx) {
int64_t x_ofs, y_ofs;
for (int n = 0; n < outer_dim; ++n) {
for (int gi = 0; gi < G; ++gi) {
for (int ki = 0; ki < K; ++ki) {
x_ofs = ((n * G + gi) * K + ki) * inner_dim;
y_ofs = ((n * K + ki) * G + gi) * inner_dim;
math::Copy(
inner_dim,
x + x_ofs,
y + y_ofs, ctx
);
}
}
}
}
/* Kernel Launchers */
#define DEFINE_SHUFFLE_KERNEL_LAUNCHER(T) \
template <> void ChannelShuffle<T, CPUContext>( \
const int outer_dim, \
const int inner_dim, \
const int axis_dim, \
const int group, \
const T* x, \
T* y, \
CPUContext* ctx) { \
_ChannelShuffle( \
outer_dim, \
inner_dim, \
group, \
axis_dim / group, \
x, y, ctx \
); \
}
DEFINE_SHUFFLE_KERNEL_LAUNCHER(bool);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(int8_t);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(uint8_t);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(int);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(int64_t);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(float16);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(float);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(double);
#undef DEFINE_SHUFFLE_KERNEL_LAUNCHER
} // namespace kernel
} // namepsace dragon
\ No newline at end of file
Dragon/src/kernels/array/channel_shuffle_op_kernel.cu 0 → 100644
#ifdef WITH_CUDA
#include "core/context_cuda.h"
#include "utils/op_kernel.h"
namespace dragon {
namespace kernel {
/* <T = ?, Device = CUDA> */
template <typename T>
__global__ void _ChannelShuffle(
const int nthreads,
const int inner_dim,
const int G,
const int K,
const T* x,
T* y) {
CUDA_1D_KERNEL_LOOP(yi, nthreads) {
const int inner_idx = yi % inner_dim;
const int gi = (yi / inner_dim) % G;
const int ki = (yi / inner_dim / G) % K;
const int outer_idx = yi / inner_dim / G / K;
y[yi] = x[((outer_idx * G + gi) * K + ki
) * inner_dim + inner_idx];
}
}
/* Kernel Launchers */
#define DEFINE_SHUFFLE_KERNEL_LAUNCHER(T) \
template <> void ChannelShuffle<T, CUDAContext>( \
const int outer_dim, \
const int inner_dim, \
const int axis_dim, \
const int group, \
const T* x, \
T* y, \
CUDAContext* ctx) { \
auto nthreads = outer_dim * axis_dim * inner_dim; \
_ChannelShuffle \
<<< CUDA_BLOCKS(nthreads), CUDA_THREADS, \
0, ctx->cuda_stream() >>>( \
nthreads, \
inner_dim, \
group, \
axis_dim / group, \
x, y \
); \
}
DEFINE_SHUFFLE_KERNEL_LAUNCHER(bool);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(int8_t);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(uint8_t);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(int);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(int64_t);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(float16);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(float);
DEFINE_SHUFFLE_KERNEL_LAUNCHER(double);
#undef DEFINE_SHUFFLE_KERNEL_LAUNCHER
} // namespace kernel
} // namepsace dragon
#endif // WITH_CUDA
\ No newline at end of file
Dragon/src/operators/array/channel_shuffle_op.cc 0 → 100644
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/array/channel_shuffle_op.h"
namespace dragon {
#define DETERMINE_RUNTIME_ARGS(X) \
axis_ = OpArg<int64_t>("axis", 0); \
axis_ = axis_ < 0 ? axis_ + X.ndim() : axis_; \
CHECK(axis_ >= 0 && axis_ < X.ndim()) \
<< "\nExcepted the axis in [-" << X.ndim() \
<< ", " << X.ndim() << "), got " \
<< OpArg<int64_t>("axis", 0) << ".";
template <class Context> template <typename T>
void ChannelShuffleOp<Context>::RunImpl() {
auto* x = X(0).template data<T, Context>();
auto* y = Y(0)->template mutable_data<T, Context>();
kernel::ChannelShuffle(
outer_dim_,
inner_dim_,
axis_dim_,
group_,
x, y, ctx()
);
}
template <class Context>
void ChannelShuffleOp<Context>::RunOnDevice() {
DETERMINE_RUNTIME_ARGS(X(0));
axis_dim_ = X(0).dim(axis_);
outer_dim_ = X(0).count(0, axis_);
inner_dim_ = X(0).count(axis_ + 1);
CHECK_EQ(axis_dim_ % group_, 0)
<< "\nThe " << axis_dim_ << " channels "
<< "can not be split into " << group_ << " groups.";
Y(0)->ReshapeLike(X(0));
DispatchHelper<TensorTypes
<bool, int8_t, uint8_t, int, int64_t,
float16, float, double>
>::Call(this, X(0));
}
template <class Context> template <typename T>
void ChannelShuffleGradientOp<Context>::RunImpl() {
auto* dy = X(1).template data<T, Context>();
auto* dx = Y(0)->template mutable_data<T, Context>();
kernel::ChannelShuffle(
outer_dim_,
inner_dim_,
axis_dim_,
axis_dim_ / group_,
dy, dx, ctx()
);
}
template <class Context>
void ChannelShuffleGradientOp<Context>::RunOnDevice() {
DETERMINE_RUNTIME_ARGS(X(0));
axis_dim_ = X(0).dim(axis_);
outer_dim_ = X(0).count(0, axis_);
inner_dim_ = X(0).count(axis_ + 1);
CHECK_EQ(axis_dim_ % group_, 0)
<< "\nThe " << axis_dim_ << " channels "
<< "can not be split into " << group_ << " groups.";
Y(0)->ReshapeLike(X(0));
DispatchHelper<TensorTypes
<bool, int8_t, uint8_t, int, int64_t,
float16, float, double>
>::Call(this, X(0));
}
DEPLOY_CPU(ChannelShuffle);
#ifdef WITH_CUDA
DEPLOY_CUDA(ChannelShuffle);
#endif
DEPLOY_CPU(ChannelShuffleGradient);
#ifdef WITH_CUDA
DEPLOY_CUDA(ChannelShuffleGradient);
#endif
OPERATOR_SCHEMA(ChannelShuffle)
/* X */
.NumInputs(1)
/* Y */
.NumOutputs(1);
OPERATOR_SCHEMA(ChannelShuffleGradient)
/* X, dY */
.NumInputs(2)
/* dX */
.NumOutputs(1);
REGISTER_GRADIENT(ChannelShuffle, SimpleGradientMaker);
#undef DETERMINE_RUNTIME_ARGS
} // namespace dragon
\ No newline at end of file
Dragon/src/operators/array/slice_op.cc
......@@ -12,9 +12,16 @@ namespace dragon {
<< ", " << Input.ndim() << "), got " \
<< OpArg<int64_t>("axis", 0) << "."; \
if (points_.empty()) { \
CHECK_EQ(X(0).dim(axis_) % N_, 0) \
<< "\nSelected dim is " << X(0).dim(axis_) \
<< ", can't be sliced into " << N_ << " parts."; \
auto dim = (X(0).dim(axis_) + N_ - 1) / N_; \
sections_ = vec64_t(N_, dim); \
sections_[N_ - 1] = X(0).dim(axis_) - dim * (N_ - 1); \
} else { \
int64_t slice_ofs = 0; \
sections_ = vec64_t(N_); \
for (int i = 0; i < N_; i++) \
sections_[i] = i < N_ - 1 ? \
points_[i] - slice_ofs : \
axis_dim_ - slice_ofs; \
}
template <class Context> template <typename T>
......@@ -24,16 +31,11 @@ void SliceOp<Context>::RunImpl() {
auto* x = X(0).template data<T, Context>();
for (int i = 0; i < N_; i++) {
if (!points_.empty()) {
slice_dim_ = i < N_ - 1 ?
points_[i] - slice_ofs :
axis_dim_ - slice_ofs;
}
slice_dim_ = sections_[i];
CHECK(slice_dim_ > 0 &&
slice_ofs + slice_dim_ <= axis_dim_)
<< "\nIllegal slicing points: "
<< Tensor::DimString(points_)
CHECK(slice_dim_ > 0)
<< "\nIllegal slicing sections: "
<< Tensor::DimString(sections_)
<< " for dimension: " << axis_dim_;
out_shape[axis_] = slice_dim_;
......@@ -77,16 +79,11 @@ void SliceGradientOp<Context>::RunImpl() {
auto* dx = Y(0)->template mutable_data<T, Context>();
for (int i = 0; i < N_; i++) {
if (!points_.empty()) {
slice_dim_ = i < N_ - 1 ?
points_[i] - slice_ofs :
axis_dim_ - slice_ofs;
}
slice_dim_ = sections_[i];
CHECK(slice_dim_ > 0 &&
slice_ofs + slice_dim_ <= axis_dim_)
CHECK(slice_dim_ > 0)
<< "\nIllegal slicing points: "
<< Tensor::DimString(points_)
<< Tensor::DimString(sections_)
<< " for dimension: " << axis_dim_;
if (X(i + 1).name() != "NULL") {
......
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