You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
324 lines
14 KiB
324 lines
14 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#include "paddle/fluid/operators/conv_transpose_op.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
|
|
PADDLE_ENFORCE(ctx->HasInput("Input"),
|
|
"Input(Input) of ConvTransposeOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Filter"),
|
|
"Input(Filter) of ConvTransposeOp should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("Output"),
|
|
"Output(Output) of ConvTransposeOp should not be null.");
|
|
|
|
auto in_dims = ctx->GetInputDim("Input");
|
|
auto filter_dims = ctx->GetInputDim("Filter");
|
|
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
|
|
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
|
|
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
|
|
|
|
PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
|
|
"ConvTransposeOp intput should be 4-D or 5-D tensor.");
|
|
PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
|
|
"ConvTransposeOp input dimension and filter dimension "
|
|
"should be the same.");
|
|
PADDLE_ENFORCE(in_dims.size() - strides.size() == 2U,
|
|
"ConvTransposeOp input dimension and strides dimension should "
|
|
"be consistent.");
|
|
PADDLE_ENFORCE_EQ(paddings.size(), strides.size(),
|
|
"ConvTransposeOp paddings dimension and strides "
|
|
"dimension should be the same.");
|
|
PADDLE_ENFORCE_EQ(paddings.size(), dilations.size(),
|
|
"ConvTransposeOp paddings dimension and dilations "
|
|
"dimension should be the same.");
|
|
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
|
|
"In ConvTransposeOp, The input channel should be the same "
|
|
"as the number of filters.");
|
|
|
|
std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
|
|
for (size_t i = 0; i < strides.size(); ++i) {
|
|
auto filter_extent = dilations[i] * (filter_dims[i + 2] - 1) + 1;
|
|
output_shape.push_back((in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] +
|
|
filter_extent);
|
|
}
|
|
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
|
|
}
|
|
|
|
framework::OpKernelType ConvTransposeOp::GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const {
|
|
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
|
|
use_cudnn &= platform::is_gpu_place(ctx.GetPlace());
|
|
#ifdef PADDLE_WITH_CUDA
|
|
if (platform::is_gpu_place(ctx.GetPlace())) {
|
|
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
|
|
use_cudnn &= dev_ctx.cudnn_handle() != nullptr;
|
|
}
|
|
#endif
|
|
framework::LibraryType library_;
|
|
if (use_cudnn) {
|
|
library_ = framework::LibraryType::kCUDNN;
|
|
} else {
|
|
library_ = framework::LibraryType::kPlain;
|
|
}
|
|
|
|
std::string data_format = ctx.Attr<std::string>("data_format");
|
|
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
|
|
return framework::OpKernelType(
|
|
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
|
|
layout_, library_);
|
|
}
|
|
|
|
Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(OpProto* proto,
|
|
OpAttrChecker* op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput(
|
|
"Input",
|
|
"(Tensor) The input tensor of convolution transpose operator. "
|
|
"The format of input tensor is NCHW. Where N is batch size, C is the "
|
|
"number of input channels, H is the height of the feature, and "
|
|
"W is the width of the feature.");
|
|
AddInput(
|
|
"Filter",
|
|
"(Tensor) The filter tensor of convolution transpose operator. "
|
|
"The format of the filter tensor is MCHW, where M is the number of "
|
|
"input feature channels, C is the number of "
|
|
"output feature channels,"
|
|
"H is the height of the filter, and W is the width of the filter. "
|
|
"We enforce groups number == 1 in the convolution transpose scenario.");
|
|
AddOutput("Output",
|
|
"(Tensor) The output tensor of convolution transpose operator. "
|
|
"The format of output tensor is also NCHW.");
|
|
|
|
AddAttr<std::vector<int>>("dilations",
|
|
"(vector<int> default:{1, 1}), the "
|
|
"dilations(h_dilation, w_dilation) of convolution "
|
|
"transpose operator.")
|
|
.SetDefault({1, 1});
|
|
AddAttr<std::vector<int>>(
|
|
"strides",
|
|
"(vector<int> default:{1, 1}), the strides(h_stride, w_stride) of "
|
|
"convolution transpose operator.")
|
|
.SetDefault({1, 1});
|
|
AddAttr<std::vector<int>>(
|
|
"paddings",
|
|
"(vector<int> default:{0, 0}), the paddings(h_pad, w_pad) of convolution "
|
|
"transpose operator.")
|
|
.SetDefault({0, 0});
|
|
AddAttr<bool>(
|
|
"use_cudnn",
|
|
"(bool, default false) Only used in cudnn kernel, need install cudnn")
|
|
.SetDefault(false);
|
|
AddAttr<std::string>(
|
|
"data_format",
|
|
"(string, default NCHW) Only used in "
|
|
"An optional string from: \"NHWC\", \"NCHW\". "
|
|
"Defaults to \"NHWC\". Specify the data format of the output data, "
|
|
"the input will be transformed automatically. ")
|
|
.SetDefault("AnyLayout");
|
|
// TODO(dzhwinter): need to registered layout transform function
|
|
AddAttr<int>("workspace_size_MB",
|
|
"Used in cudnn kernel only. workspace size for cudnn, in MB, "
|
|
"workspace is a section of GPU memory which will be "
|
|
"allocated/freed each time the operator runs, larger "
|
|
"workspace size can increase performance but also requires "
|
|
"better hardward. This size should be carefully setted.")
|
|
.SetDefault(4096);
|
|
AddComment(R"DOC(
|
|
Convolution2D Transpose Operator.
|
|
|
|
The convolution transpose operation calculates the output based on the input, filter
|
|
and dilations, strides, paddings, groups parameters. The size of each dimension of the
|
|
parameters is checked in the infer-shape.
|
|
Input(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the
|
|
number of channels, H is the height of the feature, and W is the width of the feature.
|
|
Filter(Input) is in MCHW format. Where M is the number of input feature channels,
|
|
C is the number of output feature channels, H is the height of the filter,
|
|
and W is the width of the filter.
|
|
Parameters(strides, paddings) are two elements. These two elements represent height
|
|
and width, respectively.
|
|
The input(X) size and output(Out) size may be different.
|
|
|
|
Example:
|
|
Input:
|
|
Input shape: $(N, C_{in}, H_{in}, W_{in})$
|
|
Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
|
|
Output:
|
|
Output shape: $(N, C_{out}, H_{out}, W_{out})$
|
|
Where
|
|
$$
|
|
H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\
|
|
W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
|
|
$$
|
|
)DOC");
|
|
}
|
|
|
|
Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(OpProto* proto,
|
|
OpAttrChecker* op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput("Input",
|
|
"(Tensor) The input tensor of convolution transpose operator."
|
|
"The format of input tensor is NCDHW. Where N is batch size, C is "
|
|
"the number of channels, D is the depth of the feature, H is the "
|
|
"height of the feature, and "
|
|
"W is the width of the feature.");
|
|
AddInput("Filter",
|
|
"(Tensor) The filter tensor of convolution transpose operator."
|
|
"The format of the filter tensor is MCDHW, where M is the number of "
|
|
"input feature channels, C is the number of "
|
|
"output feature channels, D "
|
|
"is the depth of the filter, H is the height of the filter, and "
|
|
"W is the width of the filter."
|
|
"We enforce groups number == 1 and padding == 0 in "
|
|
"the convolution3d transpose scenario.");
|
|
AddOutput("Output",
|
|
"(Tensor) The output tensor of convolution transpose operator."
|
|
"The format of output tensor is also NCDHW."
|
|
"Where N is batch size, C is "
|
|
"the number of channels, D is the depth of the feature, H is the "
|
|
"height of the feature, and W is the width of the feature.");
|
|
|
|
AddAttr<std::vector<int>>(
|
|
"dilations",
|
|
"(vector<int> default:{1, 1, 1}), the "
|
|
"dilations(d_dilation,h_dilation, w_dilation) of convolution "
|
|
"transpose operator.")
|
|
.SetDefault({1, 1, 1});
|
|
AddAttr<std::vector<int>>("strides",
|
|
"(vector<int> default:{1, 1, 1}), the "
|
|
"strides{d_stride, h_stride, w_stride} of "
|
|
"convolution transpose operator.")
|
|
.SetDefault({1, 1, 1});
|
|
AddAttr<std::vector<int>>("paddings",
|
|
"(vector<int> default:{0, 0, 0}), paddings(d_pad, "
|
|
"h_pad, w_pad) of convolution transpose operator.")
|
|
.SetDefault({0, 0, 0});
|
|
AddAttr<bool>(
|
|
"use_cudnn",
|
|
"(bool, default false) Only used in cudnn kernel, need install cudnn")
|
|
.SetDefault(false);
|
|
AddAttr<std::string>(
|
|
"data_format",
|
|
"(string, default NCHW) Only used in "
|
|
"An optional string from: \"NHWC\", \"NCHW\". "
|
|
"Defaults to \"NHWC\". Specify the data format of the output data, "
|
|
"the input will be transformed automatically. ")
|
|
.SetDefault("AnyLayout");
|
|
// TODO(dzhwinter): need to registered layout transform function
|
|
AddAttr<int>("workspace_size_MB",
|
|
"Used in cudnn kernel only. workspace size for cudnn, in MB, "
|
|
"workspace is a section of GPU memory which will be "
|
|
"allocated/freed each time the operator runs, larger "
|
|
"workspace size can increase performance but also requires "
|
|
"better hardward. This size should be carefully setted.")
|
|
.SetDefault(4096);
|
|
AddComment(R"DOC(
|
|
Convolution3D Transpose Operator.
|
|
|
|
The convolution transpose operation calculates the output based on the input, filter
|
|
and dilations, strides, paddings, groups parameters. The size of each dimension of the
|
|
parameters is checked in the infer-shape.
|
|
Input(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the
|
|
number of channels, D is the depth of the feature, H is the height of the feature,
|
|
and W is the width of the feature.
|
|
Filter(Input) is in MCDHW format. Where M is the number of input feature channels,
|
|
C is the number of output feature channels, D is the depth of the filter,H is the
|
|
height of the filter, and W is the width of the filter.
|
|
Parameters(strides, paddings) are three elements. These three elements represent
|
|
depth, height and width, respectively.
|
|
The input(X) size and output(Out) size may be different.
|
|
|
|
Example:
|
|
Input:
|
|
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
|
|
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
|
|
Output:
|
|
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
|
|
Where
|
|
$$
|
|
D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\
|
|
H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\
|
|
W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
|
|
$$
|
|
)DOC");
|
|
}
|
|
|
|
void ConvTransposeOpGrad::InferShape(framework::InferShapeContext* ctx) const {
|
|
auto in_dims = ctx->GetInputDim("Input");
|
|
auto filter_dims = ctx->GetInputDim("Filter");
|
|
if (ctx->HasOutput(framework::GradVarName("Input"))) {
|
|
ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
|
|
}
|
|
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
|
|
ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
|
|
}
|
|
}
|
|
|
|
framework::OpKernelType ConvTransposeOpGrad::GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const {
|
|
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
|
|
use_cudnn &= platform::is_gpu_place(ctx.GetPlace());
|
|
#ifdef PADDLE_WITH_CUDA
|
|
if (platform::is_gpu_place(ctx.GetPlace())) {
|
|
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
|
|
use_cudnn &= dev_ctx.cudnn_handle() != nullptr;
|
|
}
|
|
#endif
|
|
framework::LibraryType library_;
|
|
if (use_cudnn) {
|
|
library_ = framework::LibraryType::kCUDNN;
|
|
} else {
|
|
library_ = framework::LibraryType::kPlain;
|
|
}
|
|
|
|
std::string data_format = ctx.Attr<std::string>("data_format");
|
|
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
|
|
return framework::OpKernelType(
|
|
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
|
|
layout_, library_);
|
|
}
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
|
|
REGISTER_OP(conv2d_transpose, ops::ConvTransposeOp, ops::Conv2DTransposeOpMaker,
|
|
conv2d_transpose_grad, ops::ConvTransposeOpGrad);
|
|
|
|
REGISTER_OP_CPU_KERNEL(
|
|
conv2d_transpose,
|
|
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
conv2d_transpose_grad,
|
|
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
|
|
double>);
|
|
|
|
REGISTER_OP(conv3d_transpose, ops::ConvTransposeOp, ops::Conv3DTransposeOpMaker,
|
|
conv3d_transpose_grad, ops::ConvTransposeOpGrad);
|
|
|
|
REGISTER_OP_CPU_KERNEL(
|
|
conv3d_transpose,
|
|
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
conv3d_transpose_grad,
|
|
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
|
|
double>);
|