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109 lines
4.1 KiB
109 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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 <string>
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#include <vector>
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#include "paddle/fluid/operators/conv_op.h"
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#ifdef PADDLE_WITH_CUDA
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#include "paddle/fluid/platform/cudnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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// This fused conv follows the equation:
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// y = act ( alpha1 * conv(x) + alpha2 * z + bias ).
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// here, y is Output,
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// x is Input,
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// z is ResidualData,
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// bias is Bias
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// When `split_channels` is set, y will be splitted into multiple outputs,
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// each output has split_channels[i] number of channels.
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class Conv2DFusionOpMaker : public Conv2DOpMaker {
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protected:
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void Apply() override {
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AddAttr<std::string>(
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"activation",
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"The activation type can be 'identity', 'sigmoid', 'relu', 'relu6' "
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"'relux' , 'tanh', 'band_pass'")
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.SetDefault("relu");
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AddAttr<std::vector<int>>(
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"split_channels",
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"When `split_channels` are set, there will be multiple outputs, the "
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"output size is equal to the number of `split_channels`.")
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.SetDefault({});
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AddOutput("Outputs",
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"This Outputs is used when setting `split_channels`."
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"Usually used to fuse conv with same input and same filter size, "
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"padding, stride, dilation size.")
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.AsDuplicable()
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.AsDispensable();
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AddInput("AlgoCache",
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"The cache of convolution algorithm, a RAW type variable.")
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.AsDispensable();
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AddAttr<int>(
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"search_times",
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"The number of exhaustive search times for convolution algorithm.")
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.SetDefault(-1);
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}
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};
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class Conv2DFusionOpInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext* ctx) const override {
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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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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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std::vector<int> dilations =
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ctx->Attrs().Get<std::vector<int>>("dilations");
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std::vector<int64_t> oshape({in_dims[0], filter_dims[0]});
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for (size_t i = 0; i < strides.size(); ++i) {
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oshape.push_back(ConvOutputSize(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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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of ConvOp should not be null.");
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ctx->SetOutputDim("Output", framework::make_ddim(oshape));
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std::vector<int> channels =
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ctx->Attrs().Get<std::vector<int>>("split_channels");
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if (channels.size()) {
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PADDLE_ENFORCE(ctx->HasOutputs("Outputs"),
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"Output(Outputs) of ConvOp should not be null.");
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std::vector<framework::DDim> oshapes;
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oshapes.reserve(channels.size());
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for (size_t i = 0; i < channels.size(); ++i) {
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oshapes.push_back({oshape[0], channels[i], oshape[2], oshape[3]});
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}
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ctx->SetOutputsDim("Outputs", oshapes);
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}
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}
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};
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// TODO(qingqing): add gradient operator for conv2d_fusion
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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_OPERATOR(conv2d_fusion, ops::ConvOp, ops::Conv2DFusionOpMaker,
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ops::Conv2DFusionOpInferShape, ops::ConvOpInferVarType,
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paddle::framework::EmptyGradOpMaker);
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