mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
d74e1209ae
* Update Inpaint pipeline * Update concat * Add GaussianRandomKernel * Update GaussianRandom * Add vae endoder * Add unet infer * Add vae decoder predict * add PrepareMaskAndMaskedImage * Add imwrite * Add time counter * Fix pipeline * use FDTensor move * Fix scaled_linear dpm solver * Add RGB2BGR
119 lines
3.9 KiB
C++
119 lines
3.9 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.
|
|
|
|
#include "fastdeploy/function/concat.h"
|
|
|
|
#include "fastdeploy/utils/utils.h"
|
|
#include <cstring>
|
|
#include <limits>
|
|
#include <set>
|
|
#include <sstream>
|
|
|
|
namespace fastdeploy {
|
|
namespace function {
|
|
|
|
std::vector<int64_t>
|
|
ComputeAndCheckConcatOutputShape(const std::vector<FDTensor>& input, int axis) {
|
|
const size_t n = input.size();
|
|
auto out_dims = input[0].shape;
|
|
size_t in_zero_dims_size = out_dims.size();
|
|
for (size_t i = 1; i < n; ++i) {
|
|
FDASSERT(input[i].shape.size() == out_dims.size(),
|
|
"The shape of input[0] and input[%d] is expected to be equal. But "
|
|
"received input[0]'s shape = %s, input[%d]'s shape = %s.",
|
|
i, Str(out_dims).c_str(), i, Str(input[i].shape).c_str());
|
|
for (size_t j = 0; j < in_zero_dims_size; j++) {
|
|
if (j == axis) {
|
|
out_dims[axis] += input[i].shape[axis];
|
|
} else {
|
|
FDASSERT(
|
|
input[0].shape[j] == input[i].shape[j],
|
|
"The %d-th dimension of input[0] and input[%d] is expected to be "
|
|
"equal."
|
|
"But received input[0]'s shape = %s, input[%d]'s shape = %s.",
|
|
j, i, Str(input[0].shape).c_str(), i, Str(input[i].shape).c_str());
|
|
}
|
|
}
|
|
}
|
|
return out_dims;
|
|
}
|
|
|
|
template <typename T> struct ConcatFunctor {
|
|
void operator()(const std::vector<FDTensor>& input, int axis,
|
|
FDTensor* output) {
|
|
size_t num = input.size();
|
|
|
|
int64_t rows = 1;
|
|
auto dim_0 = input[0].shape;
|
|
for (int i = 0; i < axis; ++i) {
|
|
rows *= dim_0[i];
|
|
}
|
|
int64_t out_rows = rows, out_cols = 0;
|
|
|
|
std::vector<int64_t> input_cols(num);
|
|
for (size_t i = 0; i < num; ++i) {
|
|
int64_t t_cols = input[i].Numel() / rows;
|
|
out_cols += t_cols;
|
|
input_cols[i] = t_cols;
|
|
}
|
|
|
|
// computation
|
|
T* output_data = reinterpret_cast<T*>(output->Data());
|
|
int64_t col_idx = 0;
|
|
for (size_t j = 0; j < num; ++j) {
|
|
int64_t col_len = input_cols[j];
|
|
const T* input_data = reinterpret_cast<const T*>(input[j].Data());
|
|
for (int64_t k = 0; k < out_rows; ++k) {
|
|
FDTensor::CopyBuffer(output_data + k * out_cols + col_idx,
|
|
input_data + k * col_len, sizeof(T) * col_len,
|
|
input[j].device, input[j].is_pinned_memory);
|
|
}
|
|
col_idx += col_len;
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
void ConcatKernel(const std::vector<FDTensor>& input, FDTensor* output,
|
|
int axis) {
|
|
auto output_shape = ComputeAndCheckConcatOutputShape(input, axis);
|
|
FDTensor output_tmp;
|
|
output_tmp.Resize(output_shape, TypeToDataType<T>::dtype, output->name,
|
|
input[0].device);
|
|
|
|
ConcatFunctor<T> functor;
|
|
functor(input, axis, &output_tmp);
|
|
*output = std::move(output_tmp);
|
|
}
|
|
|
|
void Concat(const std::vector<FDTensor>& x, FDTensor* out, int axis) {
|
|
FDASSERT(x.size() > 0,
|
|
"The number of FDTensor array should be larger than 0, but the size "
|
|
"of input is %d",
|
|
x.size());
|
|
int64_t rank = x[0].shape.size();
|
|
FDASSERT(axis >= -rank && axis < rank,
|
|
"The axis is expected to be in range of [%d, %d), but got %d", -rank,
|
|
rank, axis);
|
|
if (axis < 0) {
|
|
axis += rank;
|
|
}
|
|
|
|
FD_VISIT_ALL_TYPES(x[0].dtype, "Concat",
|
|
([&] { ConcatKernel<data_t>(x, out, axis); }));
|
|
}
|
|
|
|
} // namespace function
|
|
} // namespace fastdeploy
|