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115 lines
4.1 KiB
115 lines
4.1 KiB
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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/reshape_op.h"
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namespace paddle {
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namespace operators {
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class ReshapeOp : public framework::OperatorWithKernel {
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public:
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ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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protected:
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void InferShape(framework::InferShapeContextBase *ctx) const override {
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// input check
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of ReshapeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of ReshapeOp should not be null.");
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auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
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PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
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for (auto dim : shape) {
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PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
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}
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// capacity check
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int64_t capacity =
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std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
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auto x_dims = ctx->GetInputDim("X");
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int64_t in_size = framework::product(x_dims);
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PADDLE_ENFORCE_EQ(capacity, in_size,
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"The size of Input(X) mismatches with Attr(shape).");
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// resize output
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto out_dims = framework::make_ddim(shape_int64);
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ctx->SetOutputDim("Out", out_dims);
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if (shape[0] == x_dims[0]) {
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// Only pass LoD when the first dimension is equal between
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// output and input.
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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}
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};
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class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ReshapeOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The input tensor of reshape operator.");
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AddOutput("Out", "The output tensor of reshape operator.");
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AddAttr<std::vector<int>>("shape", "Target shape of reshape operator.");
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AddComment(R"DOC(Reshape operator
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Reshape Input(X) into the shape specified by Attr(shape).
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An example:
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Given a 2-D tensor X with 2 rows and 2 columns
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[[1, 2], [3, 4]]
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with target shape = [1, 4], the reshape operator will transform
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the tensor X into a 1-D tensor:
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[1, 2, 3, 4]
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)DOC");
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}
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};
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class ReshapeGradOp : public framework::OperatorWithKernel {
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public:
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ReshapeGradOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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protected:
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void InferShape(framework::InferShapeContextBase *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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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(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
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ops::ReshapeGradOp);
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REGISTER_OP_CPU_KERNEL(reshape,
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ops::ReshapeKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);
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