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105 lines
3.6 KiB
105 lines
3.6 KiB
/* Copyright (c) 2019 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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#pragma once
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/operators/math/pooling.h"
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#include "paddle/fluid/platform/device_context.h"
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namespace paddle {
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namespace operators {
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framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
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const framework::DDim &in_dims, bool is_runtime);
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template <typename DeviceContext, typename T>
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class SqueezeKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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auto *in = context.Input<framework::LoDTensor>("X");
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auto *out = context.Output<framework::LoDTensor>("Out");
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auto &axes = context.Attr<std::vector<int>>("axes");
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auto x_dims = in->dims();
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auto out_dims = GetOutputShape(axes, x_dims, true);
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out->mutable_data(context.GetPlace(), in->type());
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framework::TensorCopy(
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*in, context.GetPlace(),
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context.template device_context<platform::DeviceContext>(), out);
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out->Resize(out_dims);
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}
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};
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template <typename DeviceContext, typename T>
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class SqueezeGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *d_out =
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
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auto *d_x = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
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auto in_dims = ctx.Input<framework::LoDTensor>("X")->dims();
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d_x->mutable_data(ctx.GetPlace(), d_out->type());
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framework::TensorCopySync(*d_out, ctx.GetPlace(), d_x);
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d_x->Resize(in_dims);
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}
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};
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template <typename DeviceContext, typename T>
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class Squeeze2Kernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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auto *out = context.Output<framework::LoDTensor>("Out");
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auto *in = context.Input<framework::LoDTensor>("X");
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auto &axes = context.Attr<std::vector<int>>("axes");
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auto x_dims = in->dims();
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auto out_dims = GetOutputShape(axes, x_dims, true);
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out->mutable_data(context.GetPlace(), in->type());
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framework::TensorCopy(
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*in, context.GetPlace(),
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context.template device_context<platform::DeviceContext>(), out);
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out->Resize(out_dims);
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}
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};
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template <typename DeviceContext, typename T>
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class Squeeze2GradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *d_out =
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
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auto *d_x = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
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// auto in_dims = d_x->dims();
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auto xshape_dims = ctx.Input<framework::LoDTensor>("XShape")->dims();
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auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
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d_x->mutable_data(ctx.GetPlace(), d_out->type());
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framework::TensorCopySync(*d_out, ctx.GetPlace(), d_x);
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d_x->Resize(x_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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