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2026-07-13 12:40:42 +08:00

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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include <vector>
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
template <typename T, typename Context>
void ChannelShuffleKernel(const Context& dev_ctx,
const DenseTensor& x,
int groups,
const std::string& data_format,
DenseTensor* out) {
auto* in = &x;
dev_ctx.template Alloc<T>(out);
if (out && out->numel() == 0) {
return;
}
bool channel_last = (data_format == "NHWC");
const auto& in_dims = in->dims();
const auto& o_dims = out->dims();
DenseTensor t(*in);
if (!channel_last) {
t.Resize({in_dims[0], groups, in_dims[1] / groups, in_dims[2], in_dims[3]});
} else {
t.Resize({in_dims[0], in_dims[1], in_dims[2], groups, in_dims[3] / groups});
}
auto axis = !channel_last ? std::vector<int>{0, 2, 1, 3, 4}
: std::vector<int>{0, 1, 2, 4, 3};
DenseTensor o(*out);
if (!channel_last) {
o.Resize({in_dims[0], in_dims[1] / groups, groups, in_dims[2], in_dims[3]});
} else {
o.Resize({in_dims[0], in_dims[1], in_dims[2], in_dims[3] / groups, groups});
}
funcs::Transpose<Context, T, 5> trans;
trans(dev_ctx, t, &o, axis);
out->Resize(o_dims);
}
} // namespace phi