qingqing01
8 years ago
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MOBILE
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======
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.. toctree::
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:maxdepth: 1
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cross_compiling_for_android_cn.md
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cross_compiling_for_ios_cn.md
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cross_compiling_for_raspberry_cn.md
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MOBILE
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======
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.. toctree::
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:maxdepth: 1
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cross_compiling_for_android_en.md
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cross_compiling_for_raspberry_en.md
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@ -1,111 +0,0 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/conv2d_transpose_op.h"
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namespace paddle {
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namespace operators {
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void Conv2DTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of Conv2DTransposeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Filter"),
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"Input(Filter) of Conv2DTransposeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of Conv2DTransposeOp should not be null.");
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auto in_dims = ctx->GetInputDim("Input");
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auto filter_dims = ctx->GetInputDim("Filter");
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std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
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std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
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for (size_t i = 0; i < paddings.size(); ++i) {
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PADDLE_ENFORCE_EQ(paddings[i], 0,
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"No Padding allowed in conv transpose op.");
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}
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PADDLE_ENFORCE_EQ(in_dims.size(), 4,
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"Conv2DTransposeOp input should be 4-D tensor.");
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PADDLE_ENFORCE_EQ(filter_dims.size(), 4,
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"Conv2DTransposeOp filter should be 4-D tensor.");
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PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
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"input and kernel input dimension should be equal.");
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auto output_height = (in_dims[2] - 1) * strides[0] + filter_dims[2];
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auto output_width = (in_dims[3] - 1) * strides[1] + filter_dims[3];
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ctx->SetOutputDim("Output",
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{in_dims[0], filter_dims[1], output_height, output_width});
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}
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Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
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framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"Input",
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"(Tensor) The input tensor of convolution transpose operator. "
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"The format of input tensor is NCHW, where N is batch size, C is the "
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"number of input channels, H is the height of the image, and "
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"W is the width of the image.");
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AddInput("Filter",
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"(Tensor) The filter tensor of convolution transpose operator."
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"The format of the filter tensor is CMHW, where C is the number of "
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"output image channels, M is the number of input image channels, "
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"H is the height of the filter, and W is the width of the filter. "
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"We enforce groups number == 1 and padding == 0 in "
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"the convolution transpose scenario.");
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AddOutput("Output",
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"(Tensor) The output tensor of convolution transpose operator."
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"The format of output tensor is also NCHW.");
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AddAttr<std::vector<int>>("strides",
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"strides of convolution transpose operator.")
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.SetDefault({1, 1});
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AddAttr<std::vector<int>>("paddings",
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"paddings of convolution transpose operator.")
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.SetDefault({0, 0});
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AddComment(R"DOC(
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Convolution Transpose Operator.
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The convolution transpose operation calculates the output based on the input,
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filter, strides, paddings, and groups parameters. The size of each dimension
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of the parameters is checked in the infer-shape method.
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)DOC");
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}
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void Conv2DTransposeOpGrad::InferShape(
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framework::InferShapeContext* ctx) const {
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auto in_dims = ctx->GetInputDim("Input");
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auto filter_dims = ctx->GetInputDim("Filter");
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if (ctx->HasOutput(framework::GradVarName("Input"))) {
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ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
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}
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if (ctx->HasOutput(framework::GradVarName("Filter"))) {
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ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
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}
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}
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(conv2d_transpose, ops::Conv2DTransposeOp,
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ops::Conv2DTransposeOpMaker, conv2d_transpose_grad,
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ops::Conv2DTransposeOpGrad);
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REGISTER_OP_CPU_KERNEL(
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conv2d_transpose,
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ops::GemmConv2DTransposeKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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conv2d_transpose_grad,
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ops::GemmConv2DTransposeGradKernel<paddle::platform::CPUPlace, float>);
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