wehub-resource-sync
192 lines
8.9 KiB
C++
192 lines
8.9 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <string>
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#include <vector>
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/core/dense_tensor.h"
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namespace phi {
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template <typename T, typename Context>
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void Pool2dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& dout,
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const IntArray& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* dx);
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template <typename T, typename Context>
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void LPPool2dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& dout,
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const IntArray& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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const float norm_type,
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DenseTensor* dx);
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template <typename T, typename Context>
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void Pool2dGradGPUDNNKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& dout,
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const IntArray& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* dx);
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template <typename T, typename Context>
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void Pool2dDoubleGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* out);
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template <typename T, typename Context>
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void Pool2dDoubleGradGPUDNNKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* out);
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template <typename T, typename Context>
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void MaxPool2dWithIndexGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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const DenseTensor& dout,
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const std::vector<int>& kernel_size,
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const std::vector<int>& strides,
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const std::vector<int>& paddings,
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const std::vector<int>& dilations,
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bool global_pooling,
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bool adaptive,
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bool ceil_mode,
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DenseTensor* dx);
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template <typename T, typename Context>
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void Pool3dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& dout,
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const std::vector<int64_t>& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* dx);
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template <typename T, typename Context>
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void Pool3dGradGPUDNNKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& dout,
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const std::vector<int64_t>& kernel_size,
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const std::vector<int64_t>& strides,
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const std::vector<int64_t>& paddings,
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bool ceil_mode,
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bool exclusive,
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const std::string& data_format,
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const std::string& pooling_type,
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bool global_pooling,
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bool adaptive,
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const std::string& padding_algorithm,
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DenseTensor* dx);
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template <typename T, typename Context>
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void MaxPool3dWithIndexGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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const DenseTensor& dout,
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const std::vector<int>& kernel_size,
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const std::vector<int>& strides,
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const std::vector<int>& paddings,
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const std::vector<int>& dilations,
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bool global_pooling,
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bool adaptive,
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bool ceil_mode,
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DenseTensor* dx);
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template <typename T, typename Context>
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void FractionalMaxPool2dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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const DenseTensor& dout,
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const std::vector<int>& output_size,
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const std::vector<int>& kernel_size,
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float random_u,
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bool return_mask,
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DenseTensor* dx);
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template <typename T, typename Context>
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void FractionalMaxPool3dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mask,
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const DenseTensor& dout,
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const std::vector<int>& output_size,
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const std::vector<int>& kernel_size,
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float random_u,
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bool return_mask,
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DenseTensor* dx);
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} // namespace phi
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