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251 lines
11 KiB
251 lines
11 KiB
/* 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/conv_op.h"
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namespace paddle {
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namespace operators {
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void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of ConvOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Filter"),
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"Input(Filter) of ConvOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of ConvOp 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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int groups = ctx->Attrs().Get<int>("groups");
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std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
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int input_channels = in_dims[1];
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int output_channels = filter_dims[0];
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PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
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"Conv intput should be 4-D or 5-D tensor.");
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PADDLE_ENFORCE_EQ(
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in_dims.size(), filter_dims.size(),
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"Conv input dimension and filter dimension should be the same.");
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PADDLE_ENFORCE(
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in_dims.size() - strides.size() == 2U,
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"Conv input dimension and strides dimension should be consistent.");
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PADDLE_ENFORCE_EQ(
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paddings.size(), strides.size(),
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"Conv paddings dimension and Conv strides dimension should be the same.");
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PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
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"The number of input channels should be equal to filter "
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"channels * groups.");
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PADDLE_ENFORCE_EQ(
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output_channels % groups, 0,
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"The number of output channels should be divided by groups.");
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std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
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for (size_t i = 0; i < strides.size(); ++i) {
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PADDLE_ENFORCE(in_dims[i + 2] + 2 * paddings[i] -
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(dilations[i] * (filter_dims[i + 2] - 1) + 1) >
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0,
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"Due to the settings of paddings, filter_dims and "
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"dilations, the output size is less than 0, please check "
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"again.");
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output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
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dilations[i], paddings[i], strides[i]));
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}
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ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
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}
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Conv2DOpMaker::Conv2DOpMaker(OpProto* proto, 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 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 channels, H is the height of the feature, "
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"and W is the width of the feature.");
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AddInput("Filter",
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"(Tensor) The filter tensor of convolution operator. "
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"The format of the filter tensor is MCHW, where M is the number of "
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"output image channels, C 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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"If the groups attribute is greater than 1, C equals the number of "
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"input image channels divided by the groups.");
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AddOutput("Output",
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"(Tensor) The output tensor of convolution 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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"(vector<int> default:{1, 1}), the "
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"strides(h_stride, w_stride) of "
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"convolution operator.")
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.SetDefault({1, 1});
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AddAttr<std::vector<int>>("paddings",
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"(vector<int> default:{0, 0}), the "
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"paddings(h_pad, w_pad) of "
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"convolution operator.")
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.SetDefault({0, 0});
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AddAttr<int>(
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"groups",
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"(int default:1), the groups number of the convolution operator. "
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"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
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"when group=2, the first half of the filters is only connected to the "
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"first half of the input channels, while the second half of the filters "
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"is only connected to the second half of the input channels.")
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.SetDefault(1);
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AddAttr<std::vector<int>>("dilations",
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"(vector<int> default:{1, 1}), the "
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"dilations(h_dilation, w_dilation) of "
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"convolution operator.")
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.SetDefault({1, 1});
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AddComment(R"DOC(
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Convolution Operator.
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The convolution operation calculates the output based on the input, filter
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and strides, paddings, dilations, groups parameters. The size of each dimension of the
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parameters is checked in the infer-shape.
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Input(Input) and Output(Output) are in NCHW format. Where N is batch
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size, C is the number of channels, H is the height of the feature, and W is
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the width of the feature.
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Filters(Input) is MCHW format. Where M is the number of output image channels, C is
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the number of input image channels, H is the height of the filter, and W
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is the width of the filter.
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Parameters(strides, paddings, dilations) are two elements. These two elements represent
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height and width, respectively.
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The input(X) size and output(Out) size may be different.
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Example:
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Input:
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Input shape: $(N, C_{in}, H_{in}, W_{in})$
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Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
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Output:
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Output shape: $(N, C_{out}, H_{out}, W_{out})$
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Where
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$$
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H_{out}= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
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W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
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$$
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)DOC");
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}
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Conv3DOpMaker::Conv3DOpMaker(OpProto* proto, 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 operator. "
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"The format of input tensor is NCDHW. Where N is batch size, C is the "
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"number of channels, D is the depth of the feature, H is the height of "
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"the feature, "
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"and W is the width of the feature.");
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AddInput("Filter",
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"(Tensor) The filter tensor of convolution operator. "
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"The format of the filter tensor is MCDHW, where M is the number of "
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"output image channels, C is the number of input image channels, "
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"D is the depth of the filter, H is the height of the filter, and W "
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"is the width of the filter."
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"If the groups attribute is greater than 1, C equals the number of "
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"input image channels divided by the groups.");
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AddOutput("Output",
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"(Tensor) The output tensor of convolution operator."
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"The format of output tensor is also NCDHW.");
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AddAttr<std::vector<int>>("strides",
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"(vector<int>, default:{1, 1, 1}), the "
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"strides(d_stride, h_stride, w_stride) of "
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"convolution operator.")
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.SetDefault({1, 1, 1});
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AddAttr<std::vector<int>>("paddings",
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"(vector<int>, default:{0, 0, 0}), the "
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"paddings(d_pad, h_pad, w_pad) of convolution "
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"operator.")
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.SetDefault({0, 0, 0});
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AddAttr<int>(
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"groups",
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"(int default:1), the groups number of the convolution operator. "
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"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
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"when group=2, the first half of the filters is only connected to the "
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"first half of the input channels, while the second half of the filters "
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"is only connected to the second half of the input channels.")
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.SetDefault(1);
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AddAttr<std::vector<int>>("dilations",
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"(vector<int> default:{1, 1, 1}), the "
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"dilations(d_dilation, h_dilation, w_dilation) of "
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"convolution operator.")
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.SetDefault({1, 1, 1});
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AddComment(R"DOC(
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Convolution3D Operator.
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The convolution operation calculates the output based on the input, filter
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and strides, paddings, dilations, groups parameters. The size of each dimension of the
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parameters is checked in the infer-shape.
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Input(Input) and output(Output) are in NCDHW format, where N is batch
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size, C is the number of channels,D is the depth of the feature, H is the height of
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the feature, and W is the width of the feature.
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Filters(Input) is MCDHW format, where M is the number of output image channels,
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C is the number of input image channels, D is the depth of the filter,
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H is the height of the filter, and W is the width of the filter.
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Parameters(strides, paddings, dilations) are three elements. These three elements
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represent depth, height and width, respectively.
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The input(X) size and output(Out) size may be different.
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Example:
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Input:
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Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
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Filter shape: $(C_{out}, C_{in}, D_f, H_f, W_f)$
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Output:
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Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
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Where
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$$
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D_{out}= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\
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H_{out}= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\
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W_{out}= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
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$$
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)DOC");
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}
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void ConvOpGrad::InferShape(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, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
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ops::ConvOpGrad);
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namespace ops = paddle::operators;
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REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
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ops::ConvOpGrad);
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REGISTER_OP_CPU_KERNEL(
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conv2d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
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ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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conv2d_grad,
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ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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conv3d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
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ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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conv3d_grad,
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ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
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