chore: import upstream snapshot with attribution
Docker Image CI / build-ubuntu2004 (push) Has been cancelled
Docker Image CI / build-ubuntu2004 (push) Has been cancelled
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/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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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*/
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#include "multilevelCropAndResizePlugin.h"
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#include "common/plugin.h"
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#include <algorithm>
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#include <cuda_runtime_api.h>
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#include <string_view>
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#include <fstream>
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using namespace nvinfer1;
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using namespace plugin;
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using nvinfer1::plugin::MultilevelCropAndResize;
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using nvinfer1::plugin::MultilevelCropAndResizePluginCreator;
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namespace
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{
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char const* const kMULTILEVELCROPANDRESIZE_PLUGIN_VERSION{"1"};
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char const* const kMULTILEVELCROPANDRESIZE_PLUGIN_NAME{"MultilevelCropAndResize_TRT"};
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} // namespace
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MultilevelCropAndResizePluginCreator::MultilevelCropAndResizePluginCreator() noexcept
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{
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mPluginAttributes.clear();
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mPluginAttributes.emplace_back(PluginField("pooled_size", nullptr, PluginFieldType::kINT32, 1));
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mPluginAttributes.emplace_back(PluginField("image_size", nullptr, PluginFieldType::kINT32, 3));
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mFC.nbFields = mPluginAttributes.size();
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mFC.fields = mPluginAttributes.data();
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}
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char const* MultilevelCropAndResizePluginCreator::getPluginName() const noexcept
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{
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return kMULTILEVELCROPANDRESIZE_PLUGIN_NAME;
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}
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char const* MultilevelCropAndResizePluginCreator::getPluginVersion() const noexcept
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{
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return kMULTILEVELCROPANDRESIZE_PLUGIN_VERSION;
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}
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PluginFieldCollection const* MultilevelCropAndResizePluginCreator::getFieldNames() noexcept
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{
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return &mFC;
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}
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IPluginV2Ext* MultilevelCropAndResizePluginCreator::createPlugin(
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char const* name, PluginFieldCollection const* fc) noexcept
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{
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try
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{
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using namespace std::string_view_literals;
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plugin::validateRequiredAttributesExist({"pooled_size"}, fc);
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auto imageSize = TLTMaskRCNNConfig::IMAGE_SHAPE;
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PluginField const* fields = fc->fields;
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for (int32_t i = 0; i < fc->nbFields; ++i)
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{
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std::string_view const attrName = fields[i].name;
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if (attrName == "pooled_size"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
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mPooledSize = *(static_cast<int32_t const*>(fields[i].data));
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}
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if (attrName == "image_size"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
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auto const dims = static_cast<int32_t const*>(fields[i].data);
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std::copy_n(dims, 3, imageSize.d);
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}
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}
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return new MultilevelCropAndResize(mPooledSize, imageSize);
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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IPluginV2Ext* MultilevelCropAndResizePluginCreator::deserializePlugin(
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char const* name, void const* data, size_t length) noexcept
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{
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try
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{
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return new MultilevelCropAndResize(data, length);
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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MultilevelCropAndResize::MultilevelCropAndResize(int32_t pooled_size, nvinfer1::Dims const& imageSize)
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: mPooledSize({pooled_size, pooled_size})
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{
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PLUGIN_VALIDATE(pooled_size > 0);
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PLUGIN_VALIDATE(imageSize.nbDims == 3);
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PLUGIN_VALIDATE(imageSize.d[0] > 0 && imageSize.d[1] > 0 && imageSize.d[2] > 0);
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// shape
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mInputHeight = imageSize.d[1];
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mInputWidth = imageSize.d[2];
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// Threshold to P3: Smaller -> P2
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mThresh = (224 * 224) / (4.0F);
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}
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int32_t MultilevelCropAndResize::getNbOutputs() const noexcept
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{
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return 1;
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}
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int32_t MultilevelCropAndResize::initialize() noexcept
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{
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return 0;
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}
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void MultilevelCropAndResize::terminate() noexcept {}
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void MultilevelCropAndResize::destroy() noexcept
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{
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delete this;
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}
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size_t MultilevelCropAndResize::getWorkspaceSize(int32_t) const noexcept
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{
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return 0;
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}
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bool MultilevelCropAndResize::supportsFormat(DataType type, PluginFormat format) const noexcept
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{
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return ((type == DataType::kFLOAT || type == DataType::kHALF) && format == PluginFormat::kLINEAR);
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}
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char const* MultilevelCropAndResize::getPluginType() const noexcept
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{
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return "MultilevelCropAndResize_TRT";
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}
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char const* MultilevelCropAndResize::getPluginVersion() const noexcept
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{
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return "1";
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}
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IPluginV2Ext* MultilevelCropAndResize::clone() const noexcept
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{
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try
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{
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return new MultilevelCropAndResize(*this);
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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void MultilevelCropAndResize::setPluginNamespace(char const* libNamespace) noexcept
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{
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mNameSpace = libNamespace;
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}
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char const* MultilevelCropAndResize::getPluginNamespace() const noexcept
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{
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return mNameSpace.c_str();
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}
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void MultilevelCropAndResize::check_valid_inputs(nvinfer1::Dims const* inputs, int32_t nbInputDims) noexcept
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{
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// to be compatible with tensorflow node's input:
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// roi: [N, anchors, 4],
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// feature_map list(5 maps): p2, p3, p4, p5, p6
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PLUGIN_ASSERT(nbInputDims == 1 + mFeatureMapCount);
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nvinfer1::Dims rois = inputs[0];
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PLUGIN_ASSERT(rois.nbDims == 2);
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PLUGIN_ASSERT(rois.d[1] == 4);
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for (int32_t i = 1; i < nbInputDims; ++i)
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{
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nvinfer1::Dims dims = inputs[i];
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// CHW with the same #C
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PLUGIN_ASSERT(dims.nbDims == 3 && dims.d[0] == inputs[1].d[0]);
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}
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}
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Dims MultilevelCropAndResize::getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept
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{
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check_valid_inputs(inputs, nbInputDims);
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PLUGIN_ASSERT(index == 0);
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nvinfer1::Dims result{};
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result.nbDims = 4;
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// mROICount
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result.d[0] = inputs[0].d[0];
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// mFeatureLength
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result.d[1] = inputs[1].d[0];
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// height
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result.d[2] = mPooledSize.y;
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// width
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result.d[3] = mPooledSize.x;
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return result;
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}
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int32_t MultilevelCropAndResize::enqueue(
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int32_t batch_size, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
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{
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void* pooled = outputs[0];
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cudaError_t status = roiAlignHalfCenter(stream, batch_size, mFeatureLength, mROICount, mThresh,
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mInputHeight, mInputWidth, inputs[0], &inputs[1], mFeatureSpatialSize,
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pooled, mPooledSize, mPrecision);
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PLUGIN_ASSERT(status == cudaSuccess);
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return 0;
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}
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size_t MultilevelCropAndResize::getSerializationSize() const noexcept
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{
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return sizeof(int32_t) * 2 + sizeof(int32_t) * 4 + sizeof(float) + sizeof(int32_t) * 2 * mFeatureMapCount
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+ sizeof(DataType);
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}
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void MultilevelCropAndResize::serialize(void* buffer) const noexcept
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{
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char *d = reinterpret_cast<char*>(buffer), *a = d;
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write(d, mPooledSize.y);
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write(d, mPooledSize.x);
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write(d, mFeatureLength);
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write(d, mROICount);
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write(d, mInputHeight);
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write(d, mInputWidth);
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write(d, mThresh);
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for (int32_t i = 0; i < mFeatureMapCount; i++)
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{
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write(d, mFeatureSpatialSize[i].y);
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write(d, mFeatureSpatialSize[i].x);
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}
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write(d, mPrecision);
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PLUGIN_ASSERT(d == a + getSerializationSize());
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}
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MultilevelCropAndResize::MultilevelCropAndResize(void const* data, size_t length)
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{
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deserialize(static_cast<int8_t const*>(data), length);
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}
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void MultilevelCropAndResize::deserialize(int8_t const* data, size_t length)
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{
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auto const* d{data};
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mPooledSize = {read<int32_t>(d), read<int32_t>(d)};
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mFeatureLength = read<int32_t>(d);
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mROICount = read<int32_t>(d);
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mInputHeight = read<int32_t>(d);
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mInputWidth = read<int32_t>(d);
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mThresh = read<float>(d);
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for (int32_t i = 0; i < mFeatureMapCount; i++)
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{
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mFeatureSpatialSize[i].y = read<int32_t>(d);
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mFeatureSpatialSize[i].x = read<int32_t>(d);
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}
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mPrecision = read<DataType>(d);
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PLUGIN_VALIDATE(d == static_cast<int8_t const*>(data) + length);
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}
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// Return the DataType of the plugin output at the requested index
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DataType MultilevelCropAndResize::getOutputDataType(
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int32_t index, nvinfer1::DataType const* inputTypes, int32_t nbInputs) const noexcept
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{
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// Only DataType::kFLOAT is acceptable by the plugin layer
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// return DataType::kFLOAT;
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// Align output types with the input feature map data types
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if ((inputTypes[1] == DataType::kFLOAT) || (inputTypes[1] == DataType::kHALF))
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return inputTypes[1];
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return DataType::kFLOAT;
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}
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// Configure the layer with input and output data types.
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void MultilevelCropAndResize::configurePlugin(Dims const* inputDims, int32_t nbInputs, Dims const* outputDims,
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int32_t nbOutputs, DataType const* inputTypes, DataType const* outputTypes, bool const* inputIsBroadcast,
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bool const* outputIsBroadcast, PluginFormat floatFormat, int32_t maxBatchSize) noexcept
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{
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PLUGIN_ASSERT(supportsFormat(inputTypes[0], floatFormat));
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check_valid_inputs(inputDims, nbInputs);
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PLUGIN_ASSERT(nbOutputs == 1);
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PLUGIN_ASSERT(nbInputs == 1 + mFeatureMapCount);
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try
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{
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mROICount = dimToInt32(inputDims[0].d[0]);
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mFeatureLength = dimToInt32(inputDims[1].d[0]);
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for (size_t layer = 0; layer < mFeatureMapCount; ++layer)
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{
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mFeatureSpatialSize[layer] = {dimToInt32(inputDims[layer + 1].d[1]), dimToInt32(inputDims[layer + 1].d[2])};
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}
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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mPrecision = inputTypes[1];
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}
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// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
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void MultilevelCropAndResize::attachToContext(
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cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept
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{
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}
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// Detach the plugin object from its execution context.
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void MultilevelCropAndResize::detachFromContext() noexcept {}
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