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207 lines
7.7 KiB
207 lines
7.7 KiB
/* Copyright (c) 2018 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 <utility>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/strided_memcpy.h"
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namespace paddle {
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namespace operators { // Internal
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template <typename T, size_t D, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
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using framework::Tensor;
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static std::vector<int> GetOffsets(const framework::ExecutionContext& ctx) {
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std::vector<int> res;
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int rank = ctx.Input<Tensor>("X")->dims().size();
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if (ctx.HasInput("Offsets")) {
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PADDLE_ENFORCE_EQ(ctx.Attr<std::vector<int>>("offsets").empty(), true,
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platform::errors::InvalidArgument(
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"Input 'Offsets' and attribute 'offsets' "
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"should not be used at the same time for CropOp."));
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const auto* offsets_tensor = ctx.Input<Tensor>("Offsets");
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PADDLE_ENFORCE_EQ(offsets_tensor->dims().size(), 1,
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platform::errors::InvalidArgument(
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"The number of dimensions of input 'Offsets' for "
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"CropOp must be 1, but the value received is %d.",
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offsets_tensor->dims().size()));
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PADDLE_ENFORCE_EQ(
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rank, offsets_tensor->dims()[0],
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platform::errors::InvalidArgument("The number of elements (%d) for "
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"input 'Offsets' must be equal to "
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"the number of dimensions (%d) "
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"of the input tensor.",
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offsets_tensor->dims()[0], rank));
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const int* offsets_data;
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framework::Tensor cpu_tmp_tensor;
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if (platform::is_cpu_place(offsets_tensor->place())) {
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offsets_data = offsets_tensor->data<int>();
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} else {
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framework::TensorCopySync(*offsets_tensor, platform::CPUPlace(),
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&cpu_tmp_tensor);
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offsets_data = cpu_tmp_tensor.data<int>();
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}
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res = std::vector<int>(offsets_data, offsets_data + rank);
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} else {
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res = ctx.Attr<std::vector<int>>("offsets");
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PADDLE_ENFORCE_EQ(
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rank, static_cast<int>(res.size()),
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platform::errors::InvalidArgument("The number of elements (%d) for "
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"input 'Offsets' must be equal to "
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"the number of dimensions (%d) "
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"of the input tensor.",
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res.size(), rank));
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}
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return res;
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}
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template <typename DeviceContext, typename T, size_t D>
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void CropFunction(const framework::ExecutionContext& context) {
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auto* x = context.Input<Tensor>("X");
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auto* out = context.Output<Tensor>("Out");
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auto out_dims = out->dims();
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if (out_dims[0] == -1) {
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out_dims[0] = x->dims()[0];
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}
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out->mutable_data<T>(out_dims, context.GetPlace());
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auto x_stride = framework::stride(x->dims());
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auto offsets = GetOffsets(context);
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int64_t offset = 0;
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for (size_t i = 0; i < offsets.size(); ++i) {
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offset += (x_stride[i] * offsets[i]);
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}
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auto x_tensor = EigenTensor<T, D>::From(*x);
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auto out_tensor = EigenTensor<T, D>::From(*out);
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Eigen::array<int, D> e_offsets;
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Eigen::array<int, D> e_shape;
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for (size_t i = 0; i < D; ++i) {
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e_offsets[i] = offsets[i];
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e_shape[i] = out->dims()[i];
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}
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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out_tensor.device(place) = x_tensor.slice(e_offsets, e_shape);
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}
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template <typename DeviceContext, typename T>
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class CropKernel : 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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int rank = context.Input<Tensor>("X")->dims().size();
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PADDLE_ENFORCE_GE(
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rank, 1,
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platform::errors::InvalidArgument(
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"The number of dimensions of the Input(X) for CropOp must be "
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"greater than or equal to 1, but the value received is %d.",
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rank));
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PADDLE_ENFORCE_LE(
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rank, 6,
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platform::errors::InvalidArgument(
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"The number of dimensions of the Input(X) for CropOp must be "
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"less than or equal to 6, but the value received is %d.",
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rank));
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switch (rank) {
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case 1:
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CropFunction<DeviceContext, T, 1>(context);
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break;
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case 2:
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CropFunction<DeviceContext, T, 2>(context);
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break;
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case 3:
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CropFunction<DeviceContext, T, 3>(context);
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break;
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case 4:
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CropFunction<DeviceContext, T, 4>(context);
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break;
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case 5:
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CropFunction<DeviceContext, T, 5>(context);
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break;
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case 6:
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CropFunction<DeviceContext, T, 6>(context);
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break;
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}
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}
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};
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template <typename DeviceContext, typename T, size_t D>
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void CropGradFunction(const framework::ExecutionContext& context) {
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auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
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auto* x = context.Input<Tensor>("X");
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if (d_x != nullptr) {
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auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
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d_x->mutable_data<T>(x->dims(), context.GetPlace());
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auto offsets = GetOffsets(context);
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Eigen::array<std::pair<int, int>, D> paddings;
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for (size_t i = 0; i < D; ++i) {
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paddings[i].first = offsets[i];
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paddings[i].second = d_x->dims()[i] - d_out->dims()[i] - offsets[i];
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}
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auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
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auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
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d_x_tensor.device(
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*context.template device_context<DeviceContext>().eigen_device()) =
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d_out_tensor.pad(paddings, 0);
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}
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}
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template <typename DeviceContext, typename T>
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class CropGradKernel : 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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size_t rank =
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context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
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PADDLE_ENFORCE_GE(
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rank, 1, platform::errors::InvalidArgument(
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"The number of dimensions of the input 'Out@GRAD' for "
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"CropGrad must be greater than or equal "
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"to 1, but the value received is %d.",
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rank));
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PADDLE_ENFORCE_LE(
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rank, 6, platform::errors::InvalidArgument(
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"The number of dimensions of the input 'Out@GRAD' for "
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"CropGrad must be less than or equal "
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"to 6, but the value received is %d.",
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rank));
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switch (rank) {
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case 1:
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CropGradFunction<DeviceContext, T, 1>(context);
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break;
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case 2:
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CropGradFunction<DeviceContext, T, 2>(context);
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break;
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case 3:
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CropGradFunction<DeviceContext, T, 3>(context);
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break;
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case 4:
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CropGradFunction<DeviceContext, T, 4>(context);
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break;
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case 5:
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CropGradFunction<DeviceContext, T, 5>(context);
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break;
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case 6:
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CropGradFunction<DeviceContext, T, 6>(context);
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break;
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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