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156 lines
6.0 KiB
156 lines
6.0 KiB
7 years ago
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/* 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 "paddle/fluid/operators/squeeze_op.h"
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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using framework::OpKernelType;
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using framework::Tensor;
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class SqueezeOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SqueezeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SqueezeOp should not be null.");
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const auto& x_dims = ctx->GetInputDim("X");
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// TODO(chenweihang): need check input tensor dims (<9).
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const auto& axes = ctx->Attrs().Get<std::vector<int>>("axes");
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// TODO(chenweihang): need check axes is valid.
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// PADDLE_ENFORCE();
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for (int a : axes) {
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PADDLE_ENFORCE_LT(a, x_dims.size(),
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"The axis must be less than input tensor's rank.");
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}
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auto out_dims = GetOutputShape(axes, x_dims);
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ctx->SetOutputDim("Out", out_dims);
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// TODO(chenweihang): need other check.
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}
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static framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
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const framework::DDim& in_dims) {
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int num_squeeze_dims = squeeze_dims.size();
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int cnt_squeezed_dims = 0;
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bool should_squeeze[9] = {false};
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// Determines number of dimensions of output tensor after squeeze.
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// Mark and count the dimensions need to be squeezed
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if (num_squeeze_dims == 0) {
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for (int idx = 0; idx < in_dims.size(); ++idx) {
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if (in_dims[idx] == 1) {
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should_squeeze[idx] = true;
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++cnt_squeezed_dims;
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}
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}
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} else {
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for (int idx = 0; idx < num_squeeze_dims; ++idx) {
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int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + in_dims.size()
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: squeeze_dims[idx];
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// TODO(chenweihang): shoude use PADALE_ENFORCE ? or if.
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PADDLE_ENFORCE_GE(current, 0, "Invalid axis is given.");
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PADDLE_ENFORCE_LT(current, in_dims.size(), "Invalid axis is given.");
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PADDLE_ENFORCE_EQ(in_dims[current], 1, "Invalid axis is given.");
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if (!(should_squeeze[current])) ++cnt_squeezed_dims;
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should_squeeze[current] = true;
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}
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}
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// Make output dimensions
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std::vector<int64_t> output_shape(in_dims.size() - cnt_squeezed_dims, 0);
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for (int in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
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if (!should_squeeze[in_idx]) {
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output_shape[out_idx++] = in_dims[in_idx];
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}
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}
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return framework::make_ddim(output_shape);
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}
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};
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class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor), Tensors with at least max(dims) dimensions.");
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AddOutput("Out", "(Tensor), Reshaped tensor with same data as input.");
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AddAttr<std::vector<int>>("axes",
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"List of positive integers,"
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" indicate the dimensions to squeeze.");
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AddAttr<bool>("inplace",
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"(default: false) Change the source tensor's shape without "
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"memory copy. When Attr(inplace) is set true, the output "
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"tensor shares memory with Input(X), otherwise, a new output "
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"tensor is created, and its data are copied from Input(x).")
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.SetDefault(false);
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AddComment(R"DOC(
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Squeeze Operator.
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Remove single-dimensional entries from the shape of a tensor.
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Takes a parameter axes with a list of axes to squeeze.
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If axes is not provided, all the single dimensions will be removed from the shape.
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If an axis is selected with shape entry not equal to one, an error is raised.
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)DOC");
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}
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};
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class SqueezeGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SqueezeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Output(Out@GRAD/) of SqueezeOp should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
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ctx.device_context());
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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_OPERATOR(squeeze, ops::SqueezeOp, ops::SqueezeOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(squeeze_grad, ops::SqueezeGradOp);
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REGISTER_OP_CPU_KERNEL(
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squeeze, ops::SqueezeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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squeeze_grad,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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