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[Backend] Add AdaptivePool2d for TensorRT plugin (PaddlePaddle#668)
* add adaptivepool2d for tensorrt plugin * update code * update code * update code to fix bug
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35 changes: 35 additions & 0 deletions
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fastdeploy/backends/op_cuda_kernels/adaptive_pool2d_kernel.h
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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. | ||
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| #pragma once | ||
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| #include <cuda.h> | ||
| #include <cuda_runtime.h> | ||
| #include <cstdint> | ||
| #include <iostream> | ||
| #include <vector> | ||
| #include <math.h> | ||
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| namespace fastdeploy { | ||
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| void CudaAdaptivePool(const std::vector<int64_t>& input_dims, | ||
| const std::vector<int64_t>& output_dims, | ||
| float* output, | ||
| const float* input, | ||
| void* compute_stream, | ||
| const std::string& pooling_type); | ||
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| } // namespace fastdeploy |
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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. | ||
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| #include "adaptive_pool2d.h" | ||
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| namespace fastdeploy { | ||
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| nvinfer1::PluginFieldCollection AdaptivePool2dPluginCreator::mFC{}; | ||
| std::vector<nvinfer1::PluginField> AdaptivePool2dPluginCreator::mPluginAttributes; | ||
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| pluginStatus_t AdaptivePool2dInference(cudaStream_t stream, int32_t n, const void* input, void* output); | ||
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| AdaptivePool2d::AdaptivePool2d(std::vector<int32_t> output_size, std::string pooling_type) { | ||
| output_size_ = output_size; | ||
| pooling_type_ = pooling_type; | ||
| } | ||
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| AdaptivePool2d::AdaptivePool2d(const void* buffer, size_t length) { | ||
| const char *d = reinterpret_cast<const char*>(buffer), *a = d; | ||
| output_size_.resize(4); | ||
| for(int64_t i =0 ; i < 4; i++){ | ||
| output_size_[i] =read<int32_t>(d); | ||
| } | ||
| if(read<int32_t>(d) == 0){ | ||
| pooling_type_ = "avg"; | ||
| }else{ | ||
| pooling_type_ = "max"; | ||
| } | ||
| FDASSERT(d == a + length, "deserialize failed."); | ||
| } | ||
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| int AdaptivePool2d::getNbOutputs() const noexcept { | ||
| return 1; | ||
| } | ||
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| nvinfer1::DimsExprs AdaptivePool2d::getOutputDimensions( | ||
| int outputIndex, const nvinfer1::DimsExprs* inputs, | ||
| int nbInputs, nvinfer1::IExprBuilder& exprBuilder) noexcept { | ||
| try { | ||
| nvinfer1::DimsExprs output(inputs[0]); | ||
| output.d[2] = exprBuilder.constant(static_cast<int32_t>(output_size_[2])); | ||
| output.d[3] = exprBuilder.constant(static_cast<int32_t>(output_size_[3])); | ||
| return output; | ||
| } | ||
| catch (const std::exception& e) { | ||
| FDASSERT(false, "getOutputDimensions failed: %s.",e.what()); | ||
| } | ||
| return nvinfer1::DimsExprs{}; | ||
| } | ||
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| int AdaptivePool2d::enqueue(const nvinfer1::PluginTensorDesc* inputDesc, | ||
| const nvinfer1::PluginTensorDesc* outputDesc, | ||
| const void* const* inputs, | ||
| void* const* outputs, | ||
| void* workspace, | ||
| cudaStream_t stream) noexcept { | ||
| if (inputDesc[0].type != nvinfer1::DataType::kFLOAT) { | ||
| return -1; | ||
| } | ||
| auto const* data = static_cast<float const*>(inputs[0]); | ||
| auto* result = static_cast<float*>(outputs[0]); | ||
| int nums = outputDesc[0].dims.d[0] * outputDesc[0].dims.d[1] * outputDesc[0].dims.d[2]* outputDesc[0].dims.d[3]; | ||
| std::vector<int64_t> input_size, output_size; | ||
| for(int i =0; i< 4; i++){ | ||
| input_size.push_back(inputDesc[0].dims.d[i]); | ||
| output_size.push_back(outputDesc[0].dims.d[i]); | ||
| } | ||
| CudaAdaptivePool(input_size, output_size, result, data, stream, pooling_type_); | ||
| return cudaPeekAtLastError(); | ||
| } | ||
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| size_t AdaptivePool2d::getSerializationSize() const noexcept { | ||
| return 5 * sizeof(int32_t) ; | ||
| } | ||
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| void AdaptivePool2d::serialize(void* buffer) const noexcept { | ||
| char *d = reinterpret_cast<char*>(buffer), *a = d; | ||
| for(int64_t i=0; i< 4; i++){ | ||
| write(d, output_size_[i]); | ||
| } | ||
| int32_t pooling_type_val = 0; | ||
| if(pooling_type_ != "avg"){ | ||
| pooling_type_val = 1; | ||
| } | ||
| write(d, pooling_type_val); | ||
| FDASSERT(d == a + getSerializationSize(), "d == a + getSerializationSize()"); | ||
| } | ||
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| nvinfer1::DataType AdaptivePool2d::getOutputDataType( | ||
| int index, const nvinfer1::DataType* inputType, int nbInputs) const noexcept { | ||
| return inputType[0]; | ||
| } | ||
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| bool AdaptivePool2d::supportsFormatCombination( | ||
| int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) noexcept { | ||
| return (inOut[pos].format == nvinfer1::PluginFormat::kLINEAR); | ||
| } | ||
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| int AdaptivePool2d::initialize() noexcept { | ||
| return 0; | ||
| } | ||
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| void AdaptivePool2d::terminate() noexcept { | ||
| return; | ||
| } | ||
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| size_t AdaptivePool2d::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, | ||
| int nbInputs, | ||
| const nvinfer1::PluginTensorDesc* outputs, | ||
| int nbOutputs) const noexcept { | ||
| return 0; | ||
| } | ||
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| const char* AdaptivePool2d::getPluginType() const noexcept { | ||
| return "AdaptivePool2d"; | ||
| } | ||
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| const char* AdaptivePool2d::getPluginVersion() const noexcept { | ||
| return "1"; | ||
| } | ||
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| void AdaptivePool2d::destroy() noexcept { | ||
| return; | ||
| } | ||
| void AdaptivePool2d::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, | ||
| const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) noexcept { | ||
| return; | ||
| } | ||
| nvinfer1::IPluginV2DynamicExt* AdaptivePool2d::clone() const noexcept { | ||
| try{ | ||
| nvinfer1::IPluginV2DynamicExt* plugin = new AdaptivePool2d(output_size_, pooling_type_); | ||
| plugin->setPluginNamespace(mNamespace.c_str()); | ||
| return plugin; | ||
| } | ||
| catch (std::exception const& e){ | ||
| FDASSERT(false, "clone failed: %s.",e.what()); | ||
| } | ||
| return nullptr; | ||
| } | ||
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| AdaptivePool2dPluginCreator::AdaptivePool2dPluginCreator() { | ||
| mPluginAttributes.clear(); | ||
| mPluginAttributes.emplace_back(nvinfer1::PluginField("output_size", nullptr, nvinfer1::PluginFieldType::kINT32, 4)); | ||
| mPluginAttributes.emplace_back(nvinfer1::PluginField("pooling_type", nullptr, nvinfer1::PluginFieldType::kCHAR, 3)); | ||
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| mFC.nbFields = mPluginAttributes.size(); | ||
| mFC.fields = mPluginAttributes.data(); | ||
| } | ||
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| const char* AdaptivePool2dPluginCreator::getPluginName() const noexcept { | ||
| return "AdaptivePool2d"; | ||
| } | ||
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| const char* AdaptivePool2dPluginCreator::getPluginVersion() const noexcept { | ||
| return "1"; | ||
| } | ||
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| const nvinfer1::PluginFieldCollection* AdaptivePool2dPluginCreator::getFieldNames() noexcept { | ||
| return &mFC; | ||
| } | ||
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| nvinfer1::IPluginV2DynamicExt* AdaptivePool2dPluginCreator::createPlugin(const char* name, | ||
| const nvinfer1::PluginFieldCollection* fc) noexcept { | ||
| try{ | ||
| const nvinfer1::PluginField* fields = fc->fields; | ||
| auto const dims = static_cast<int32_t const*>(fields[0].data); | ||
| output_size_.resize(4); | ||
| for(int64_t i = 0; i < 4; i++){ | ||
| output_size_[i] = dims[i]; | ||
| } | ||
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| const char* pooling_type_ptr = (static_cast<char const*>(fields[1].data)); | ||
| std::string pooling_type(pooling_type_ptr, 3); | ||
| pooling_type_ = pooling_type; | ||
| return new AdaptivePool2d(output_size_, pooling_type_); | ||
| } | ||
| catch (std::exception const& e){ | ||
| FDASSERT(false, "createPlugin failed: %s.",e.what()); | ||
| } | ||
| return nullptr; | ||
| } | ||
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| nvinfer1::IPluginV2DynamicExt* AdaptivePool2dPluginCreator::deserializePlugin(const char* name, | ||
| const void* serialData, | ||
| size_t serialLength) noexcept { | ||
| try{ | ||
| return new AdaptivePool2d(serialData, serialLength); | ||
| } | ||
| catch (std::exception const& e){ | ||
| FDASSERT(false, "deserializePlugin failed: %s.",e.what()); | ||
| } | ||
| return nullptr; | ||
| } | ||
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| } // namespace fastdeploy |
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