wehub-resource-sync
121 lines
4.2 KiB
Plaintext
121 lines
4.2 KiB
Plaintext
// 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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#include "paddle/phi/kernels/conv_transpose_kernel.h"
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#include "paddle/common/ddim.h"
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#include "paddle/common/layout.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cpu/conv_util.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/gpu/depthwise_conv.h"
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#include "paddle/phi/kernels/impl/conv_transpose_kernel_impl.h"
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namespace phi {
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template <typename T, typename Context>
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void DepthwiseConv2dTransposeKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& filter,
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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>& output_padding,
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const IntArray& output_size,
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const std::string& padding_algorithm,
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int groups,
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const std::vector<int>& dilations,
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const std::string& data_format,
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DenseTensor* out) {
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if (x.numel() == 0 || filter.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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const DataLayout data_layout = StringToDataLayout(data_format);
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DenseTensor filter_ = filter;
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dev_ctx.template Alloc<T>(out);
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PADDLE_ENFORCE_EQ(
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groups,
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filter_.dims()[0],
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errors::InvalidArgument(
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"groups should be error to the 1st dimension of filter_. But "
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"received groups is %d and filter dimension[0] is %d",
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groups,
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filter_.dims()[0]));
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std::vector<int> paddings_ = paddings;
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std::vector<int> dilations_ = dilations;
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for (auto v : dilations_) {
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PADDLE_ENFORCE_EQ(
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v,
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1,
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errors::InvalidArgument("dilations should be 1 in depthwise conv. "
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"But received dilations is %d",
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v));
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}
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auto x_dims = x.dims();
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auto filter_dims = filter_.dims();
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DDim in_data_dims;
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if (data_layout != DataLayout::NHWC) {
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in_data_dims = slice_ddim(x_dims, 2, x_dims.size());
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} else {
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in_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
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}
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DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
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std::vector<int> ksize = vectorize<int>(filter_data_dims);
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UpdatePaddingAndDilation(
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&paddings_, &dilations_, padding_algorithm, in_data_dims, strides, ksize);
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dev_ctx.template Alloc<T>(out);
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funcs::SetConstant<Context, T> set_zero;
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set_zero(dev_ctx, out, static_cast<T>(0));
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math::DepthwiseConvInputGradFunctor<Context, T> depthwiseConvInputGrad;
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depthwiseConvInputGrad(
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dev_ctx,
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*out,
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filter,
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x,
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strides,
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std::vector<int>{paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
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dilations_,
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out,
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data_layout);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(conv2d_transpose,
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GPU,
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ALL_LAYOUT,
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phi::Conv2dTransposeKernel,
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float,
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double) {}
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PD_REGISTER_KERNEL(conv3d_transpose,
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GPU,
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ALL_LAYOUT,
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phi::Conv3dTransposeKernel,
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float,
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double) {}
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PD_REGISTER_KERNEL(depthwise_conv2d_transpose,
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GPU,
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ALL_LAYOUT,
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phi::DepthwiseConv2dTransposeKernel,
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float,
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double) {}
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