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https://github.com/PaddlePaddle/FastDeploy.git
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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
46 lines
1.5 KiB
C++
46 lines
1.5 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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#include "fastdeploy/function/gaussian_random.h"
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#include <memory>
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#include <random>
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#include <utility>
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namespace fastdeploy {
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namespace function {
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template <typename T>
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void GaussianRandomKernel(const std::vector<int64_t>& shape, float mean,
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float std, int seed, FDTensor* out) {
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std::normal_distribution<T> dist(mean, std);
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out->Allocate(shape, TypeToDataType<T>::dtype);
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int64_t size = out->Numel();
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T* data = reinterpret_cast<T*>(out->Data());
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std::mt19937_64 engine;
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engine.seed(seed);
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for (int64_t i = 0; i < size; ++i) {
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data[i] = dist(engine);
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}
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}
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void GaussianRandom(const std::vector<int64_t>& shape, FDTensor* out,
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FDDataType dtype, float mean, float std, int seed) {
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FD_VISIT_FLOAT_TYPES(dtype, "GaussianRandomKernel", [&]() {
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GaussianRandomKernel<data_t>(shape, mean, std, seed, out);
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});
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}
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} // namespace function
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} // namespace fastdeploy
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