wehub-resource-sync
142 lines
4.8 KiB
C++
142 lines
4.8 KiB
C++
// 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 "glog/logging.h"
|
|
|
|
#include "paddle/phi/kernels/funcs/math_function.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename DeviceContext, typename T>
|
|
inline void ResizeToChannelFirst(const DeviceContext& dev_ctx,
|
|
const DenseTensor* input,
|
|
DenseTensor* transformed_input) {
|
|
int dim = input->dims().size() - 2;
|
|
if (dim == 3) {
|
|
// input
|
|
transformed_input->Resize(input->dims());
|
|
|
|
auto in_dims_vec = vectorize(input->dims());
|
|
in_dims_vec[1] = input->dims()[4];
|
|
in_dims_vec[2] = input->dims()[1];
|
|
in_dims_vec[3] = input->dims()[2];
|
|
in_dims_vec[4] = input->dims()[3];
|
|
transformed_input->Resize(in_dims_vec);
|
|
dev_ctx.template Alloc<T>(transformed_input);
|
|
} else if (dim == 2) {
|
|
// input
|
|
transformed_input->Resize(input->dims());
|
|
|
|
auto in_dims_vec = vectorize(input->dims());
|
|
in_dims_vec[1] = input->dims()[3];
|
|
in_dims_vec[2] = input->dims()[1];
|
|
in_dims_vec[3] = input->dims()[2];
|
|
transformed_input->Resize(in_dims_vec);
|
|
dev_ctx.template Alloc<T>(transformed_input);
|
|
} else if (dim == 1) {
|
|
transformed_input->Resize(input->dims());
|
|
|
|
auto in_dims_vec = vectorize(input->dims());
|
|
in_dims_vec[1] = input->dims()[2];
|
|
in_dims_vec[2] = input->dims()[1];
|
|
transformed_input->Resize(in_dims_vec);
|
|
dev_ctx.template Alloc<T>(transformed_input);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
inline void ResizeToChannelLast(const DeviceContext& dev_ctx,
|
|
const DenseTensor* input,
|
|
DenseTensor* transformed_input) {
|
|
int dim = input->dims().size() - 2;
|
|
if (dim == 3) {
|
|
// input
|
|
transformed_input->Resize(input->dims());
|
|
|
|
auto in_dims_vec = vectorize(input->dims());
|
|
in_dims_vec[1] = input->dims()[2];
|
|
in_dims_vec[2] = input->dims()[3];
|
|
in_dims_vec[3] = input->dims()[4];
|
|
in_dims_vec[4] = input->dims()[1];
|
|
transformed_input->Resize(in_dims_vec);
|
|
dev_ctx.template Alloc<T>(transformed_input);
|
|
|
|
} else if (dim == 2) {
|
|
// input
|
|
transformed_input->Resize(input->dims());
|
|
|
|
auto in_dims_vec = vectorize(input->dims());
|
|
in_dims_vec[1] = input->dims()[2];
|
|
in_dims_vec[2] = input->dims()[3];
|
|
in_dims_vec[3] = input->dims()[1];
|
|
transformed_input->Resize(in_dims_vec);
|
|
dev_ctx.template Alloc<T>(transformed_input);
|
|
} else if (dim == 1) {
|
|
transformed_input->Resize(input->dims());
|
|
|
|
auto in_dims_vec = vectorize(input->dims());
|
|
in_dims_vec[1] = input->dims()[2];
|
|
in_dims_vec[2] = input->dims()[1];
|
|
transformed_input->Resize(in_dims_vec);
|
|
dev_ctx.template Alloc<T>(transformed_input);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
inline void TransToChannelFirst(const DeviceContext& dev_ctx,
|
|
const DenseTensor* input,
|
|
DenseTensor* transformed_input) {
|
|
VLOG(5) << "Why am I called?";
|
|
int dim = input->dims().size() - 2;
|
|
if (dim == 3) {
|
|
std::vector<int> axis{0, 4, 1, 2, 3};
|
|
funcs::Transpose<DeviceContext, T, 5> trans5;
|
|
trans5(dev_ctx, *input, transformed_input, axis);
|
|
|
|
} else if (dim == 2) {
|
|
std::vector<int> axis{0, 3, 1, 2};
|
|
funcs::Transpose<DeviceContext, T, 4> trans4;
|
|
trans4(dev_ctx, *input, transformed_input, axis);
|
|
} else if (dim == 1) {
|
|
std::vector<int> axis{0, 2, 1};
|
|
funcs::Transpose<DeviceContext, T, 3> trans3;
|
|
trans3(dev_ctx, *input, transformed_input, axis);
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
inline void TransToChannelLast(const DeviceContext& dev_ctx,
|
|
const DenseTensor* input,
|
|
DenseTensor* transformed_input) {
|
|
int dim = input->dims().size() - 2;
|
|
if (dim == 3) {
|
|
std::vector<int> axis{0, 2, 3, 4, 1};
|
|
funcs::Transpose<DeviceContext, T, 5> trans5;
|
|
trans5(dev_ctx, *input, transformed_input, axis);
|
|
|
|
} else if (dim == 2) {
|
|
std::vector<int> axis{0, 2, 3, 1};
|
|
funcs::Transpose<DeviceContext, T, 4> trans4;
|
|
trans4(dev_ctx, *input, transformed_input, axis);
|
|
} else if (dim == 1) {
|
|
std::vector<int> axis{0, 2, 1};
|
|
funcs::Transpose<DeviceContext, T, 3> trans3;
|
|
trans3(dev_ctx, *input, transformed_input, axis);
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|