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fix doc and remove useless code

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revert-4814-Add_sequence_project_op
chengduoZH 7 years ago
parent 154dbb4697
commit 4c6bccbe20
4 changed files with 71 additions and 75 deletions
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4
paddle/operators/math/CMakeLists.txt
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@ -7,7 +7,7 @@ if(WITH_GPU)
nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
nv_library(sequence_project SRCS sequence_project.cc sequence_project.cu DEPS device_context math_function)
nv_library(sequence_project SRCS sequence_project.cc sequence_project.cu DEPS device_context)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@ -15,7 +15,7 @@ else()
cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
cc_library(pooling SRCS pooling.cc DEPS device_context)
cc_library(vol2col SRCS vol2col.cc DEPS device_context)
cc_library(sequence_project SRCS sequence_project.cc DEPS device_context math_function)
cc_library(sequence_project SRCS sequence_project.cc DEPS device_context)
endif()
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)

91
paddle/operators/math/sequence_project.h
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@ -18,7 +18,6 @@ limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
@ -32,37 +31,59 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
/*
* \brief Converts the feature data of four dimensions(CDHW) into a colData of
* seven dimensions in the Vol2ColFunctor calculation,
* And in the Col2VolFunctor calculation, it is reversed.
* \brief SequenceProject projects features of context_length time-steps of each
* instance.
*
* \param volData Vol data.
* \param volShape The shape of volData,
* [input_channels, input_depth, input_height, input_width].
* \param colData Column data.
* \param colShape The shape of colData.
* \param in Input data.
* \param inShape The shape of Input data,
* [minibatch, number_of_input_features].
* \param inShape A float LoDTensor.
*
* The shape of colData is:
* [input_channels, filter_depth, filter_height, filter_width, output_depth,
* output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution
* calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the height is equal
* input_channels * filter_depth * filter_height * filter_width, and the width
* is equal output_depth * output_height * output_width.
* \param padding_data Padding data.
* \param inShape The shape of Padding data,
* [up_pad + down_pad, number_of_input_features].
* \param inShape A float LoDTensor.
*
* Reshape:
* shape of colData shape of convolution matrix
* [input_channels,
* filter_depth,
* filter_height,
* filter_width, ======> [height, width]
* output_depth,
* output_height,
* output_width]
* \param col Col data.
* \param inShape The shape of Col data,
* [minibatch, 1].
* \param inShape A float LoDTensor.
*
* For a mini-batch of 2 variable lengths sentences, containing 3, and 1
* time-steps:
*
* Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3,
* 4].
* Besides, for the sake of simplicity, we assume M=1 and N=2.
*
* X = [[a1, a2;
* b1, b2;
* c1, c2]
* [d1, d2]]
*
* This is to say that input (X) has 4 words and the dimension of each word
* representation is 2.
*
* - Case1:
* If context_start is -1 and padding_trainable is false, we use zero to pad
* instead of learned weight to pad,
* and the context_lenth is 3, the output (Out) is:
*
* Out =[[0, 0, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, 0, 0 ]
* [0, 0, d1, d2, 0, 0 ]]
*
* - Case2:
* If context_start is -1 and padding_trainable is true, we use learned weight
* to pad,
* and the context_lenth is 3, the output (Out) is:
*
* Out = [[w1, w2, a1, a2, b1, b2;
* a1, a2, b1, b2, c1, c2;
* b1, b2, c1, c2, w3, w4]
* [w1, w2, d1, d2, w3, w4]]
*
* \note The caller needs to ensure that volShape.inputChannels is equal to
* colShape.inputChannels.
*/
template <typename Place, typename T>
@ -96,14 +117,16 @@ class SequenceProjectFunctor {
sequence_height = static_cast<int>(out_t.dims()[0]);
std::vector<int64_t> output_shape(
{sequence_height, 1, 1, context_length,
sequence_width}); // output_height, output_width,
// input_channels, filter_height, filter_width
out_t.Resize(framework::make_ddim(output_shape));
if (input_row_begin < input_row_end) {
framework::Tensor in_t = in->Slice(input_row_begin, input_row_end);
std::vector<int64_t> output_shape(
{sequence_height, 1, 1, context_length,
sequence_width}); // output_height, output_width,
// input_channels, filter_height, filter_width
out_t.Resize(framework::make_ddim(output_shape));
std::vector<int64_t> input_shape(
{1, input_row_end - input_row_begin,
sequence_width}); // input_channels, input_height, input_width

45
paddle/operators/sequence_conv_op.cc
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@ -135,39 +135,18 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
.GreaterThan(0);
AddComment(R"DOC(
SequenceConvOp projects features of context_length time-steps of each instance.
For a mini-batch of 2 variable lengths sentences, containing 3, and 1 time-steps:
Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3, 4].
Besides, for the sake of simplicity, we assume M=1 and N=2.
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2]]
This is to say that input (X) has 4 words and the dimension of each word
representation is 2.
- Case1:
If context_start is -1 and padding_trainable is false, we use zero to pad instead of learned weight to pad,
and the context_lenth is 3, the output (Out) is:
Out =[[0, 0, a1, a2, b1, b2;
a1, a2, b1, b2, c1, c2;
b1, b2, c1, c2, 0, 0 ]
[0, 0, d1, d2, 0, 0 ]]
- Case2:
If context_start is -1 and padding_trainable is true, we use learned weight to pad,
and the context_lenth is 3, the output (Out) is:
Out = [[w1, w2, a1, a2, b1, b2;
a1, a2, b1, b2, c1, c2;
b1, b2, c1, c2, w3, w4]
[w1, w2, d1, d2, w3, w4]]
SequenceConvOp performs convolution operation on features of
context_length time-steps of each instance.
The convolution operation calculates the output based on the input, filter
and strides, paddings parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Example:
Input:
X shape: (minibatch, number_of_input_features)
Filter shape: (context_length, number_of_input_features)
Output:
Out shape: (minibatch, 1)
)DOC");
}
};

6
paddle/operators/sequence_conv_op.h
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@ -182,12 +182,6 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
functor(context.device_context(), padding_data_g, 0);
for (int i = 0; i < static_cast<int>(lod_g_level_0.size()) - 1; ++i) {
input_row_begin =
(context_start > 0)
? static_cast<int>(lod_g_level_0[i]) + context_start
: static_cast<int>(lod_g_level_0[i]);
input_row_end = static_cast<int>(lod_g_level_0[i + 1]);
Tensor col_t = col.Slice(static_cast<int>(lod_g_level_0[i]),
static_cast<int>(lod_g_level_0[i + 1]));

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