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PaddleDetection
提交 f2ccef26 编写于 作者: C chengduoZH
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Add sequence_conv_op

上级 0ab2c436
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Showing with 158 addition and 384 deletion
paddle/operators/CMakeLists.txt
浏览文件 @ f2ccef26
......@@ -115,7 +115,8 @@ set(DEPS_OPS
softmax_with_cross_entropy_op
sum_op
pool_op
pool_with_index_op)
pool_with_index_op
sequence_conv_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
......@@ -126,6 +127,8 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(sum_op DEPS net_op)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
op_library(sequence_conv_op DEPS sequence_project)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
paddle/operators/sequence_project_op.cc → paddle/operators/sequence_conv_op.cc
浏览文件 @ f2ccef26
......@@ -12,34 +12,41 @@ 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. */
#include "paddle/operators/sequence_project_op.h"
#include "paddle/operators/sequence_conv_op.h"
namespace paddle {
namespace operators {
class SequenceProjectOp : public framework::OperatorWithKernel {
class SequenceConvOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceProjectOp should not be null.");
"Input(X) of SequenceConvOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of SequenceConvOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceProjectOp should not be null.");
"Output(Out) of SequenceConvOp should not be null.");
// PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() >
// 0 failed, 0 <= 0)
PADDLE_ENFORCE(
ctx->HasInput("PaddingData"),
"Input(PaddingData) of SequenceProjectOp should not be null.");
auto in_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(in_dims.size() == 2, "Input(X) should be 2-D tensor.");
PADDLE_ENFORCE(ctx->HasInput("PaddingData"),
"Input(PaddingData) of SequenceConvOp should not be null.");
int context_length = ctx->Attrs().Get<int>("context_length");
bool padding_trainable = ctx->Attrs().Get<bool>("padding_trainable");
int context_start = ctx->Attrs().Get<int>("context_start");
auto in_dims = ctx->GetInputDim("X");
auto filter_dims = ctx->GetInputDim("Filter");
PADDLE_ENFORCE(in_dims.size() == 2 && filter_dims.size() == 2,
"Input(X, Filter) should be 2-D tensor.");
PADDLE_ENFORCE(
filter_dims[0] == context_length && filter_dims[1] == in_dims[1],
"Filter's shape should be (context_length x "
"number_of_input_features).");
if (padding_trainable) {
framework::DDim padding_dim = ctx->GetInputDim("PaddingData");
int up_pad = std::max(0, -context_start);
......@@ -60,12 +67,12 @@ class SequenceProjectOp : public framework::OperatorWithKernel {
"and 'context_length'.");
}
in_dims[1] = in_dims[1] * context_length;
in_dims[1] = 1;
ctx->SetOutputDim("Out", in_dims);
}
};
class SequenceProjectGradOp : public framework::OperatorWithKernel {
class SequenceConvGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -77,60 +84,66 @@ class SequenceProjectGradOp : public framework::OperatorWithKernel {
if (ctx->Attrs().Get<bool>("padding_trainable") &&
ctx->HasOutput(framework::GradVarName("PaddingData"))) {
auto padding_dims = ctx->GetInputDim("PaddingData");
ctx->SetOutputDim(framework::GradVarName("PaddingData"), padding_dims);
ctx->SetOutputDim(framework::GradVarName("PaddingData"),
ctx->GetInputDim("PaddingData"));
}
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"),
ctx->GetInputDim("Filter"));
}
}
};
class SequenceProjectOpMaker : public framework::OpProtoAndCheckerMaker {
class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceProjectOpMaker(framework::OpProto* proto,
SequenceConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(A float LoDTensor) the input of SequenceProjectOp, a vector of "
"(A float LoDTensor) the input of SequenceConvOp, a vector of "
"2-D matrix of size (minibatch, number_of_input_features).");
AddOutput("Out",
"(A float LoDTensor) the output of SequenceProjectOp, a vector "
"of 2-D matrix of size (minibatch, number_of_input_features x "
"context_length).");
AddInput("PaddingData",
"(A float LoDTensor) the input of SequenceProjectOp, a vector of "
"(A float LoDTensor) the input of SequenceConvOp, a vector of "
"2-D matrix of size (up_pad + down_pad, "
"number_of_input_features). ");
AddInput("Filter",
"(A float LoDTensor) the input of SequenceConvOp, a vector of "
"2-D matrix of size (context_length x number_of_input_features).");
AddOutput("Out",
"(A float LoDTensor) the output of SequenceConvOp, a vector "
"of 2-D matrix of size (minibatch, 1).");
AddAttr<bool>("padding_trainable",
"(bool, default false) the padding data of SequenceProjectOp "
"(bool, default false) the padding data of SequenceConvOp "
"is trainable or not.")
.SetDefault(false);
AddAttr<int>("context_length",
"(int, default 3) the context_length of SequenceProjectOp.")
"(int, default 3) the context_length of SequenceConvOp.")
.SetDefault(3)
.GreaterThan(0);
AddAttr<int>("context_start",
"(int, default 0) the context_start of SequenceProjectOp.")
"(int, default 0) the context_start of SequenceConvOp.")
.SetDefault(0);
AddAttr<int>("context_stride",
"(int, default 1) the context_stride of SequenceProjectOp. "
"(int, default 1) the context_stride of SequenceConvOp. "
"Currently, sequence_project_op only support "
"context_stride=1.")
.SetDefault(1)
.GreaterThan(0);
AddComment(R"DOC(
SequenceProjectOp projects features of context_length time-steps of each instance.
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.
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2]]
......@@ -141,19 +154,19 @@ class SequenceProjectOpMaker : public framework::OpProtoAndCheckerMaker {
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;
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]
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;
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]
b1, b2, c1, c2, w3, w4]
[w1, w2, d1, d2, w3, w4]]
)DOC");
}
......@@ -163,13 +176,11 @@ class SequenceProjectOpMaker : public framework::OpProtoAndCheckerMaker {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sequence_project, ops::SequenceProjectOp,
ops::SequenceProjectOpMaker, sequence_project_grad,
ops::SequenceProjectGradOp);
REGISTER_OP(sequence_conv, ops::SequenceConvOp, ops::SequenceConvOpMaker,
sequence_conv_grad, ops::SequenceConvGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_project,
ops::SequenceProjectKernel<paddle::platform::CPUPlace, float>);
sequence_conv, ops::SequenceConvKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sequence_project_grad,
ops::SequenceProjectGradKernel<paddle::platform::CPUPlace, float>);
sequence_conv_grad,
ops::SequenceConvGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sequence_project_op.cu → paddle/operators/sequence_conv_op.cu
浏览文件 @ f2ccef26
......@@ -14,12 +14,11 @@
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_project_op.h"
#include "paddle/operators/sequence_conv_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_project,
ops::SequenceProjectKernel<paddle::platform::GPUPlace, float>);
sequence_conv, ops::SequenceConvKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
sequence_project_grad,
ops::SequenceProjectGradKernel<paddle::platform::GPUPlace, float>);
sequence_conv_grad,
ops::SequenceConvGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/sequence_project_op.h → paddle/operators/sequence_conv_op.h
浏览文件 @ f2ccef26
......@@ -15,46 +15,39 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/strided_memcpy.h"
#include "paddle/operators/math/sequence_project.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
// template <typename T, int MajorType = Eigen::RowMajor,
// typename IndexType = Eigen::DenseIndex>
// using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class SequenceProjectKernel : public framework::OpKernel<T> {
class SequenceConvKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
out->mutable_data<T>(context.GetPlace());
// Because if padding_trainable is false, padding data should be zeros.
auto temp = framework::EigenVector<T>::Flatten(*out);
temp.device(context.GetEigenDevice<Place>()) =
temp.constant(static_cast<T>(0));
auto filter = *context.Input<LoDTensor>("Filter");
auto place = context.GetEigenDevice<Place>();
out->mutable_data<T>(context.GetPlace());
int context_start = context.Attr<int>("context_start");
int context_length = context.Attr<int>("context_length");
bool padding_trainable = context.Attr<bool>("padding_trainable");
int context_stride = context.Attr<int>("context_stride");
bool padding_trainable = context.Attr<bool>("padding_trainable");
// InferShape by in_lod
PADDLE_ENFORCE_EQ(in->lod().size(), 1UL,
"Only support one level sequence now.");
auto lod_level_0 = in->lod()[0];
const LoDTensor* padding_data = nullptr;
if (padding_trainable) {
......@@ -63,117 +56,51 @@ class SequenceProjectKernel : public framework::OpKernel<T> {
int up_pad = std::max(0, -context_start);
int down_pad = std::max(0, context_start + context_length - 1);
int sequence_height, sequence_width;
int input_row_begin, input_row_end;
int sequence_width;
sequence_width = static_cast<int>(in->dims()[1]);
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
im2col_ocf;
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
input_row_begin = (context_start > 0)
? static_cast<int>(lod_level_0[i]) + context_start
: static_cast<int>(lod_level_0[i]);
input_row_end = static_cast<int>(lod_level_0[i + 1]);
Tensor out_t = out->Slice(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
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) {
Tensor in_t = in->Slice(input_row_begin, input_row_end);
std::vector<int64_t> input_shape(
{1, input_row_end - input_row_begin,
sequence_width}); // input_channels, input_height, input_width
in_t.Resize(framework::make_ddim(input_shape));
im2col_ocf(context.device_context(), in_t, out_t,
/*stride_height*/ context_stride, /*stride_width*/ 0, up_pad,
down_pad);
}
// use col_shape in the im2col calculation
framework::DDim col_shape = {in->dims()[0],
sequence_width * context_length};
LoDTensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// Because if padding_trainable is false, padding data should be zeros.
auto temp = framework::EigenVector<T>::Flatten(col);
temp.device(context.GetEigenDevice<Place>()) =
temp.constant(static_cast<T>(0));
if (padding_trainable) {
// add up trainable data
out_t.Resize(framework::make_ddim(
{sequence_height * context_length, sequence_width}));
paddle::operators::math::SequenceProjectFunctor<Place, T>
seq_project_functor;
if (up_pad > 0) { // add up pad
int padding_rows = std::min(
up_pad, static_cast<int>(lod_level_0[i + 1] - lod_level_0[i]));
seq_project_functor(context.device_context(), in, padding_data, &col,
padding_trainable, context_start, context_length,
context_stride, up_pad, down_pad);
for (int k = 0; k < padding_rows; ++k) {
int padding_size =
k + context_length < up_pad ? context_length : up_pad - k;
Tensor out_t_sub = out_t.Slice(k * context_length,
k * context_length + padding_size);
Tensor w_sub = padding_data->Slice(k, k + padding_size);
// in this block, using EigenVector<T>::Flatten is ok too.
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
auto w_sub_e = EigenMatrix<T>::From(w_sub);
out_t_sub_e.device(place) = w_sub_e;
}
}
if (down_pad > 0) { // add down pad
int down_pad_begin_row =
std::max(0,
(sequence_height - context_start - context_length) + 1) +
1;
int padding_begin = std::max(0, context_start - sequence_height);
int padding_size =
sequence_height - context_start >= context_length
? 1
: context_length - (sequence_height - context_start);
if (context_start >= sequence_height) padding_size = context_length;
int padding_idx = padding_begin;
for (int t = 0; t + down_pad_begin_row <= sequence_height;
++t, ++padding_size) {
if (context_start >= sequence_height) padding_size = context_length;
if (padding_size > context_length) {
padding_size = context_length;
padding_idx++;
}
if (padding_begin > 0 || sequence_height == context_start)
padding_idx = padding_begin + t;
Tensor out_t_sub = out_t.Slice(
(down_pad_begin_row + t) * context_length - padding_size,
(down_pad_begin_row + t) * context_length);
Tensor w_sub = padding_data->Slice(
up_pad + padding_idx, up_pad + padding_idx + padding_size);
auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
auto w_sub_e = EigenMatrix<T>::From(w_sub);
out_t_sub_e.device(place) = w_sub_e;
}
}
}
out_t.Resize(framework::make_ddim(
{sequence_height, context_length * sequence_width}));
}
filter.Resize(framework::make_ddim({context_length * sequence_width, 1}));
math::matmul<Place, T>(context.device_context(), col, false, filter, false,
T(1.0), out, T(0.0));
}
};
template <typename Place, typename T>
class SequenceProjectGradKernel : public framework::OpKernel<T> {
class SequenceConvGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* filter_g =
context.Output<LoDTensor>(framework::GradVarName("Filter"));
auto* padding_data_g =
context.Output<LoDTensor>(framework::GradVarName("PaddingData"));
auto* in = context.Input<LoDTensor>("X");
auto* filter = context.Input<LoDTensor>("Filter");
auto place = context.GetEigenDevice<Place>();
int context_start = context.Attr<int>("context_start");
int context_length = context.Attr<int>("context_length");
bool padding_trainable = context.Attr<bool>("padding_trainable");
int context_stride = context.Attr<int>("context_stride");
bool padding_trainable = context.Attr<bool>("padding_trainable");
// InferShape by in_lod
PADDLE_ENFORCE_EQ(in->lod().size(), 1UL,
......@@ -187,15 +114,31 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
sequence_width = static_cast<int>(in->dims()[1]);
paddle::operators::math::Col2ImFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
col2im_ocf;
// use col_shape in the im2col calculation
framework::DDim col_shape = {in->dims()[0],
sequence_width * context_length};
LoDTensor col;
if (in_g || filter_g || (padding_trainable && padding_data_g)) {
col.mutable_data<T>(col_shape, context.GetPlace());
// Because if padding_trainable is false, padding data should be zeros.
auto temp = framework::EigenVector<T>::Flatten(col);
temp.device(context.GetEigenDevice<Place>()) =
temp.constant(static_cast<T>(0));
math::matmul<Place, T>(context.device_context(), *out_g, false, *filter,
true, T(1.0), &col, T(1.0));
}
if (in_g) {
in_g->mutable_data<T>(context.GetPlace());
math::SetConstant<Place, T> functor;
functor(context.device_context(), in_g, 0);
paddle::operators::math::Col2ImFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
col2im_ocf;
for (int i = 0; i < static_cast<int>(lod_g_level_0.size()) - 1; ++i) {
input_row_begin =
(context_start > 0)
......@@ -203,10 +146,10 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
: static_cast<int>(lod_g_level_0[i]);
input_row_end = static_cast<int>(lod_g_level_0[i + 1]);
Tensor out_g_t = out_g->Slice(static_cast<int>(lod_g_level_0[i]),
Tensor col_t = col.Slice(static_cast<int>(lod_g_level_0[i]),
static_cast<int>(lod_g_level_0[i + 1]));
sequence_height = static_cast<int>(out_g_t.dims()[0]);
sequence_height = static_cast<int>(col_t.dims()[0]);
if (input_row_begin < input_row_end) {
Tensor in_t = in_g->Slice(input_row_begin, input_row_end);
......@@ -215,18 +158,18 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
{sequence_height, 1, 1, context_length,
sequence_width}); // output_height, output_width,
// input_channels, filter_height, filter_width
out_g_t.Resize(framework::make_ddim(output_shape));
col_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
in_t.Resize(framework::make_ddim(input_shape));
col2im_ocf(context.device_context(), in_t, out_g_t,
col2im_ocf(context.device_context(), in_t, col_t,
/*stride_height*/ context_stride, /*stride_width*/ 0,
up_pad, down_pad);
}
out_g_t.Resize(framework::make_ddim(
col_t.Resize(framework::make_ddim(
{sequence_height, context_length * sequence_width}));
}
}
......@@ -244,12 +187,12 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
: static_cast<int>(lod_g_level_0[i]);
input_row_end = static_cast<int>(lod_g_level_0[i + 1]);
Tensor out_g_t = out_g->Slice(static_cast<int>(lod_g_level_0[i]),
Tensor col_t = col.Slice(static_cast<int>(lod_g_level_0[i]),
static_cast<int>(lod_g_level_0[i + 1]));
sequence_height = static_cast<int>(out_g_t.dims()[0]);
sequence_height = static_cast<int>(col_t.dims()[0]);
out_g_t.Resize(framework::make_ddim(
col_t.Resize(framework::make_ddim(
{sequence_height * context_length, sequence_width}));
if (up_pad > 0) { // add up pad
......@@ -260,7 +203,7 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
for (int k = 0; k < padding_rows; ++k) {
int padding_size =
k + context_length < up_pad ? context_length : up_pad - k;
Tensor out_t_sub = out_g_t.Slice(k * context_length,
Tensor out_t_sub = col_t.Slice(k * context_length,
k * context_length + padding_size);
Tensor w_sub = padding_data_g->Slice(k, k + padding_size);
// in this block, using EigenVector<T>::Flatten is ok too.
......@@ -290,7 +233,7 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
}
if (padding_begin > 0 || sequence_height == context_start)
padding_idx = padding_begin + t;
Tensor out_t_sub = out_g_t.Slice(
Tensor out_t_sub = col_t.Slice(
(down_pad_begin_row + t) * context_length - padding_size,
(down_pad_begin_row + t) * context_length);
Tensor w_sub = padding_data_g->Slice(
......@@ -300,10 +243,40 @@ class SequenceProjectGradKernel : public framework::OpKernel<T> {
w_sub_e.device(place) = w_sub_e + out_t_sub_e;
}
}
out_g_t.Resize(framework::make_ddim(
col_t.Resize(framework::make_ddim(
{sequence_height, context_length * sequence_width}));
}
}
if (filter_g) {
filter_g->mutable_data<T>(context.GetPlace());
math::SetConstant<Place, T> functor;
functor(context.device_context(), filter_g, 0);
Tensor filter_grad_ = *filter_g;
Tensor out_grad_ = *out_g;
const LoDTensor* padding_data = nullptr;
if (padding_trainable) {
padding_data = context.Input<LoDTensor>("PaddingData");
}
sequence_width = static_cast<int>(in->dims()[1]);
paddle::operators::math::SequenceProjectFunctor<Place, T>
seq_project_functor;
seq_project_functor(context.device_context(), in, padding_data, &col,
padding_trainable, context_start, context_length,
context_stride, up_pad, down_pad);
filter_grad_.Resize(
framework::make_ddim({context_length * sequence_width, 1}));
math::matmul<Place, T>(context.device_context(), col, true, out_grad_,
false, T(1.0), &filter_grad_, T(1.0));
}
}
};
......
python/paddle/v2/framework/tests/test_seq_project.py 已删除 100644 → 0
浏览文件 @ 0ab2c436
import unittest
import numpy as np
import random
from op_test import OpTest
class TestSeqProject(OpTest):
def setUp(self):
self.init_test_case()
self.op_type = 'sequence_project'
if self.context_length == 1 and self.context_start == 0 and self.padding_trainable:
print "If context_start is 0 and context_length is 1, padding_trainable should be false."
return
# one level, batch size
x = np.random.uniform(
0.1, 1, [self.input_size[0], self.input_size[1]]).astype('float32')
self.begin_pad = np.max([0, -self.context_start])
self.end_pad = np.max([0, self.context_start + self.context_length - 1])
self.total_pad = self.begin_pad + self.end_pad
if self.total_pad == 0:
self.total_pad = 1
# PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0)
padding_data = np.random.uniform(
0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32')
self.inputs = {
'X': (x, self.lod),
'PaddingData': (padding_data, [[0, self.total_pad]])
}
self.attrs = {
'context_start': self.context_start,
'context_length': self.context_length,
'padding_trainable': self.padding_trainable,
'context_stride': self.context_stride
}
out = np.zeros((self.input_size[0], self.input_size[1] *
self.context_length)).astype('float32')
self.outputs = {'Out': out}
self.compute()
def compute(self):
x, lod = self.inputs['X']
pading_data, _ = self.inputs['PaddingData']
out = self.outputs['Out']
lod = lod[0]
begin_pad = np.max([0, -self.context_start])
for i in range(len(lod) - 1):
for j in range(self.context_length):
in_begin = lod[i] + self.context_start + j
in_end = lod[i + 1] + self.context_start + j
out_begin = lod[i]
out_end = lod[i + 1]
if in_begin < lod[i]:
pad_size = np.min([lod[i] - in_begin, lod[i + 1] - lod[i]])
if self.padding_trainable:
sub_w = pading_data[j:j + pad_size, :]
out[lod[i]:lod[i] + pad_size, j * self.input_size[1]:(
j + 1) * self.input_size[1]] = sub_w
out_begin = lod[i] + pad_size
in_begin = lod[i]
if in_end > lod[i + 1]:
pad_size = np.min(
[in_end - lod[i + 1], lod[i + 1] - lod[i]])
if self.padding_trainable:
sub_w = pading_data[begin_pad + self.context_start + j -
pad_size:begin_pad +
self.context_start + j, :]
out[lod[i + 1] - pad_size:lod[i + 1], j * self.
input_size[1]:(j + 1) * self.input_size[1]] = sub_w
in_end = lod[i + 1]
out_end = lod[i + 1] - pad_size
if in_end <= in_begin:
continue
in_sub = x[in_begin:in_end, :]
out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
self.input_size[1]] += in_sub
def test_check_output(self):
self.check_output()
def test_check_grad(self):
if self.padding_trainable:
self.check_grad(
set(['X', 'PaddingData']), 'Out', max_relative_error=0.05)
def test_check_grad_no_filter(self):
self.check_grad(
['X'],
'Out',
max_relative_error=0.05,
no_grad_set=set(['PaddingData']))
def test_check_grad_no_input(self):
if self.padding_trainable:
self.check_grad(
['PaddingData'],
'Out',
max_relative_error=0.05,
no_grad_set=set(['X']))
def init_test_case(self):
self.op_type = "sequence_project"
self.input_row = 11
self.context_start = 0
self.context_length = 1
self.padding_trainable = False
self.context_stride = 1
self.input_size = [self.input_row, 23]
self.lod = [[0, 4, 5, 8, self.input_row]]
class TestSeqProjectCase1(TestSeqProject):
def init_test_case(self):
self.op_type = "sequence_project"
self.input_row = 11
self.context_start = -1
self.context_length = 3
self.padding_trainable = True
self.context_stride = 1
self.input_size = [self.input_row, 23]
self.lod = [[0, 4, 5, 8, self.input_row]]
class TestSeqProjectCase2(TestSeqProject):
def init_test_case(self):
self.op_type = "sequence_project"
self.input_row = 25
self.context_start = 2
self.context_length = 3
self.padding_trainable = True
self.context_stride = 1
self.input_size = [self.input_row, 23]
idx = range(self.input_size[0])
del idx[0]
self.lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +
[self.input_size[0]]]
'''
class TestSeqProjectCases(TestSeqProject):
def setUp(self):
self.init_test_case()
self.op_type = 'sequence_project'
num = 0
for context_start in [-5, -3, -1, 0, 3]:
for context_length in [1, 2, 5, 7]:
for batch_size in [1, 2, 5, 7]:
for padding_trainable in [False, True]:
if context_length == 1 and context_start == 0 and padding_trainable:
continue
self.context_start = context_start
self.context_length = context_length
self.padding_trainable = padding_trainable
self.input_size = [batch_size, 23]
x = np.random.uniform(0.1, 1,
self.input_size).astype('float32')
self.lod = [[0, self.input_size[0]]]
if self.input_size[0] > 2:
idx = range(self.input_size[0])
del idx[0]
self.lod = [
[0] + np.sort(random.sample(idx, 2)).tolist() +
[self.input_size[0]]
]
self.begin_pad = np.max([0, -self.context_start])
self.end_pad = np.max([0, self.context_start + self.context_length - 1])
self.total_pad = self.begin_pad + self.end_pad
if self.total_pad == 0:
self.total_pad = 1
# PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0)
padding_data = np.random.uniform(
0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32')
self.inputs = {
'X': (x, self.lod),
'PaddingData': (padding_data, [[0, self.total_pad]])
}
self.attrs = {
'context_start': self.context_start,
'context_length': self.context_length,
'padding_trainable': self.padding_trainable,
'context_stride': self.context_stride
}
out = np.zeros((self.input_size[0], self.input_size[1] *
self.context_length)).astype('float32')
self.outputs = {'Out': out}
print num
print self.attrs
print batch_size
print padding_trainable
print "$$$$$$$$$$$$$"
self.compute()
self.test_check_output()
num += 1
'''
if __name__ == '__main__':
unittest.main()
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