Yolov5 classification model support (#1082)
* updated gen_wts script to support yolov5 classfication model export * updated yolov5-s architecture to support classification head. * updated yolov5_trt infer script to support yolov5 classification model alongwith existing detection model. * added imagenet_classes file to load list of 1k classes required for the infer script. * final conv block doesn't require dynamic scale factor for out channel, hence hardcoded value provided to support all model varients * yolov5.cpp reverted back to original state, with explicit code changes * python infer script reverted back to prior state, with explicit code changes * seperate cpp file added for yolov5 classification task * cmake updated for yolo5 classification file * seperate python infer script for classification task * cmake updated by replacing cuda_add_executable with add_executable * default value added to the type argument * post-processing removed from yolov5 clasisifcation module * pre-processing for yolov5 classification inferencing * classification macro removed from the original yolov5 detection cpp file * reverted some extremely minor formatting changes. * reverted back to prior state by removing all formatting changes.
This commit is contained in:
Xiang Shin Wuu
GitHub
parent
8112542b5f
commit
fdd6a76ec2
@ -37,6 +37,13 @@ target_link_libraries(yolov5 cudart)
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target_link_libraries(yolov5 myplugins)
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target_link_libraries(yolov5 ${OpenCV_LIBS})
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add_executable(yolov5-cls calibrator.cpp yolov5_cls.cpp)
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target_link_libraries(yolov5-cls nvinfer)
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target_link_libraries(yolov5-cls cudart)
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target_link_libraries(yolov5-cls myplugins)
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target_link_libraries(yolov5-cls ${OpenCV_LIBS})
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if(UNIX)
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add_definitions(-O2 -pthread)
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endif(UNIX)
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@ -8,8 +8,12 @@ from utils.torch_utils import select_device
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def parse_args():
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parser = argparse.ArgumentParser(description='Convert .pt file to .wts')
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
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parser.add_argument('-o', '--output', help='Output (.wts) file path (optional)')
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parser.add_argument('-w', '--weights', required=True,
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help='Input weights (.pt) file path (required)')
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parser.add_argument(
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'-o', '--output', help='Output (.wts) file path (optional)')
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parser.add_argument(
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'-t', '--type', type=str, default='', help='determines the model is detection/classification')
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args = parser.parse_args()
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if not os.path.isfile(args.weights):
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raise SystemExit('Invalid input file')
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@ -19,10 +23,10 @@ def parse_args():
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args.output = os.path.join(
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args.output,
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os.path.splitext(os.path.basename(args.weights))[0] + '.wts')
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return args.weights, args.output
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return args.weights, args.output, args.type
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pt_file, wts_file = parse_args()
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pt_file, wts_file, m_type = parse_args()
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# Initialize
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device = select_device('cpu')
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@ -30,11 +34,14 @@ device = select_device('cpu')
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model = torch.load(pt_file, map_location=device) # load to FP32
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model = model['ema' if model.get('ema') else 'model'].float()
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# update anchor_grid info
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anchor_grid = model.model[-1].anchors * model.model[-1].stride[...,None,None]
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# model.model[-1].anchor_grid = anchor_grid
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delattr(model.model[-1], 'anchor_grid') # model.model[-1] is detect layer
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model.model[-1].register_buffer("anchor_grid",anchor_grid) #The parameters are saved in the OrderDict through the "register_buffer" method, and then saved to the weight.
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if m_type == "detect":
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# update anchor_grid info
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anchor_grid = model.model[-1].anchors * \
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model.model[-1].stride[..., None, None]
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# model.model[-1].anchor_grid = anchor_grid
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delattr(model.model[-1], 'anchor_grid') # model.model[-1] is detect layer
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# The parameters are saved in the OrderDict through the "register_buffer" method, and then saved to the weight.
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model.model[-1].register_buffer("anchor_grid", anchor_grid)
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model.to(device).eval()
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@ -45,5 +52,5 @@ with open(wts_file, 'w') as f:
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f.write('{} {} '.format(k, len(vr)))
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for vv in vr:
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f.write(' ')
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f.write(struct.pack('>f' ,float(vv)).hex())
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f.write(struct.pack('>f', float(vv)).hex())
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f.write('\n')
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296
yolov5/yolov5_cls.cpp
Normal file
296
yolov5/yolov5_cls.cpp
Normal file
@ -0,0 +1,296 @@
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#include <iostream>
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#include <chrono>
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#include <cmath>
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#include "cuda_utils.h"
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#include "logging.h"
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#include "common.hpp"
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#include "utils.h"
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#include "calibrator.h"
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#define USE_FP32 // set USE_INT8 or USE_FP16 or USE_FP32
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#define DEVICE 0 // GPU id
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#define NMS_THRESH 0.4
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#define CONF_THRESH 0.5
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#define BATCH_SIZE 1
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#define MAX_IMAGE_INPUT_SIZE_THRESH 3000 * 3000 // ensure it exceed the maximum size in the input images !
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 224;
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static const int INPUT_W = 224;
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static const int CLASS_NUM = 1000;
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static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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static Logger gLogger;
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static int get_width(int x, float gw, int divisor = 8) {
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return int(ceil((x * gw) / divisor)) * divisor;
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}
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static int get_depth(int x, float gd) {
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if (x == 1) return 1;
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int r = round(x * gd);
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if (x * gd - int(x * gd) == 0.5 && (int(x * gd) % 2) == 0) {
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--r;
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}
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return std::max<int>(r, 1);
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}
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ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights(wts_name);
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/* ------ yolov5 backbone------ */
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auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
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assert(conv0);
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auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
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auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
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auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
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auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
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auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
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auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
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auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
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auto bottleneck_csp8 = C3(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
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/* ------ yolov5 classification head ------ */
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auto conv_class = convBlock(network, weightMap, *bottleneck_csp8->getOutput(0), 1280, 1, 1, 1, "model.9.conv");
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IPoolingLayer* pool2 = network->addPoolingNd(*conv_class->getOutput(0), PoolingType::kAVERAGE, DimsHW{7, 7});
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assert(pool2);
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IFullyConnectedLayer* yolo = network->addFullyConnected(*pool2->getOutput(0), CLASS_NUM, weightMap["model.9.linear.weight"], weightMap["model.9.linear.bias"]);
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assert(yolo);
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yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*yolo->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
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#if defined(USE_FP16)
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config->setFlag(BuilderFlag::kFP16);
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#elif defined(USE_INT8)
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std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
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assert(builder->platformHasFastInt8());
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config->setFlag(BuilderFlag::kINT8);
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Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
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config->setInt8Calibrator(calibrator);
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#endif
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std::cout << "Building engine, please wait for a while..." << std::endl;
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ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
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std::cout << "Build engine successfully!" << std::endl;
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// Don't need the network any more
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network->destroy();
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// Release host memory
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for (auto& mem : weightMap)
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{
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free((void*)(mem.second.values));
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}
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return engine;
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}
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void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, float& gd, float& gw, std::string& wts_name) {
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// Create builder
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IBuilder* builder = createInferBuilder(gLogger);
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IBuilderConfig* config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an engine
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ICudaEngine *engine = nullptr;
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engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
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assert(engine != nullptr);
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// Serialize the engine
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(*modelStream) = engine->serialize();
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// Close everything down
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engine->destroy();
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builder->destroy();
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config->destroy();
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}
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void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* output, int batchSize) {
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// infer on the batch asynchronously, and DMA output back to host
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context.enqueue(batchSize, buffers, stream, nullptr);
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CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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}
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bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, float& gd, float& gw, std::string& img_dir) {
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if (argc < 4) return false;
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if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
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wts = std::string(argv[2]);
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engine = std::string(argv[3]);
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auto net = std::string(argv[4]);
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if (net[0] == 'n') {
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gd = 0.33;
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gw = 0.25;
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} else if (net[0] == 's') {
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gd = 0.33;
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gw = 0.50;
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} else if (net[0] == 'm') {
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gd = 0.67;
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gw = 0.75;
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} else if (net[0] == 'l') {
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gd = 1.0;
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gw = 1.0;
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} else if (net[0] == 'x') {
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gd = 1.33;
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gw = 1.25;
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} else if (net[0] == 'c' && argc == 7) {
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gd = atof(argv[5]);
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gw = atof(argv[6]);
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} else {
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return false;
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}
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} else if (std::string(argv[1]) == "-d" && argc == 4) {
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engine = std::string(argv[2]);
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img_dir = std::string(argv[3]);
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} else {
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return false;
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}
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return true;
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}
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int main(int argc, char** argv) {
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cudaSetDevice(DEVICE);
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std::string wts_name = "";
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std::string engine_name = "";
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float gd = 0.0f, gw = 0.0f;
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std::string img_dir;
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if (!parse_args(argc, argv, wts_name, engine_name, gd, gw, img_dir)) {
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std::cerr << "arguments not right!" << std::endl;
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std::cerr << "./yolov5 -s [.wts] [.engine] [n/s/m/l/x or c gd gw] // serialize model to plan file" << std::endl;
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std::cerr << "./yolov5 -d [.engine] ../samples // deserialize plan file and run inference" << std::endl;
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return -1;
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}
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// create a model using the API directly and serialize it to a stream
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if (!wts_name.empty()) {
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IHostMemory* modelStream{ nullptr };
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APIToModel(BATCH_SIZE, &modelStream, gd, gw, wts_name);
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assert(modelStream != nullptr);
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std::ofstream p(engine_name, std::ios::binary);
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if (!p) {
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std::cerr << "could not open plan output file" << std::endl;
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return -1;
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}
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p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
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modelStream->destroy();
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return 0;
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}
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// deserialize the .engine and run inference
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std::ifstream file(engine_name, std::ios::binary);
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if (!file.good()) {
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std::cerr << "read " << engine_name << " error!" << std::endl;
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return -1;
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}
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char *trtModelStream = nullptr;
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size_t size = 0;
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file.seekg(0, file.end);
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size = file.tellg();
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file.seekg(0, file.beg);
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trtModelStream = new char[size];
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assert(trtModelStream);
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file.read(trtModelStream, size);
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file.close();
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std::vector<std::string> file_names;
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if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
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std::cerr << "read_files_in_dir failed." << std::endl;
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return -1;
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}
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static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
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static float prob[BATCH_SIZE * OUTPUT_SIZE];
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IRuntime* runtime = createInferRuntime(gLogger);
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assert(runtime != nullptr);
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ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
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assert(engine != nullptr);
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IExecutionContext* context = engine->createExecutionContext();
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assert(context != nullptr);
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delete[] trtModelStream;
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assert(engine->getNbBindings() == 2);
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float* buffers[2];
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// In order to bind the buffers, we need to know the names of the input and output tensors.
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// Note that indices are guaranteed to be less than IEngine::getNbBindings()
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const int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME);
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const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
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assert(inputIndex == 0);
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assert(outputIndex == 1);
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// Create GPU buffers on device
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CUDA_CHECK(cudaMalloc((void**)&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
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CUDA_CHECK(cudaMalloc((void**)&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
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// Create stream
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cudaStream_t stream;
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CUDA_CHECK(cudaStreamCreate(&stream));
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uint8_t* img_host = nullptr;
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uint8_t* img_device = nullptr;
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// prepare input data cache in pinned memory
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CUDA_CHECK(cudaMallocHost((void**)&img_host, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
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// prepare input data cache in device memory
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CUDA_CHECK(cudaMalloc((void**)&img_device, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
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int fcount = 0;
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std::vector<cv::Mat> imgs_buffer(BATCH_SIZE);
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for (int f = 0; f < (int)file_names.size(); f++) {
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fcount++;
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if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
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//auto start = std::chrono::system_clock::now();
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float* buffer_idx = (float*)buffers[inputIndex];
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for (int b = 0; b < fcount; b++) {
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cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
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if (img.empty()) continue;
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size_t size_image = img.cols * img.rows * 3;
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size_t size_image_dst = INPUT_H * INPUT_W * 3;
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cv::Mat pr_img;
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cv::resize(img, pr_img, cv::Size(INPUT_W, INPUT_H));
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int i = 0;
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for (int row = 0; row < INPUT_H; ++row) {
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uchar* uc_pixel = pr_img.data + row * pr_img.step;
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for (int col = 0; col < INPUT_W; ++col) {
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data[b * 3 * INPUT_H * INPUT_W + i] = ((float)uc_pixel[2] / 255.0 - 0.485) / 0.229; // R-0.485
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data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = ((float)uc_pixel[1] / 255.0 - 0.456) / 0.224;
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data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = ((float)uc_pixel[0] / 255.0 - 0.406) / 0.225;
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uc_pixel += 3;
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++i;
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}
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}
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//copy data to pinned memory
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memcpy(img_host,data,size_image);
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//copy data to device memory
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CUDA_CHECK(cudaMemcpyAsync(img_device,img_host,size_image,cudaMemcpyHostToDevice,stream));
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buffer_idx += size_image_dst;
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}
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// Run inference
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auto start = std::chrono::system_clock::now();
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doInference(*context, stream, (void**)buffers, prob, BATCH_SIZE);
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auto end = std::chrono::system_clock::now();
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std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
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fcount = 0;
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}
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CUDA_CHECK(cudaFree(img_device));
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CUDA_CHECK(cudaFreeHost(img_host));
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CUDA_CHECK(cudaFree(buffers[inputIndex]));
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CUDA_CHECK(cudaFree(buffers[outputIndex]));
|
||||
// Destroy the engine
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
|
||||
return 0;
|
||||
}
|
||||
249
yolov5/yolov5_cls_trt.py
Normal file
249
yolov5/yolov5_cls_trt.py
Normal file
@ -0,0 +1,249 @@
|
||||
"""
|
||||
An example that uses TensorRT's Python api to make inferences.
|
||||
"""
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
import pycuda.autoinit
|
||||
import pycuda.driver as cuda
|
||||
import tensorrt as trt
|
||||
|
||||
|
||||
def get_img_path_batches(batch_size, img_dir):
|
||||
ret = []
|
||||
batch = []
|
||||
for root, dirs, files in os.walk(img_dir):
|
||||
for name in files:
|
||||
if len(batch) == batch_size:
|
||||
ret.append(batch)
|
||||
batch = []
|
||||
batch.append(os.path.join(root, name))
|
||||
if len(batch) > 0:
|
||||
ret.append(batch)
|
||||
return ret
|
||||
|
||||
|
||||
with open("imagenet_classes.txt") as f:
|
||||
classes = [line.strip() for line in f.readlines()]
|
||||
|
||||
|
||||
class YoLov5TRT(object):
|
||||
"""
|
||||
description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops.
|
||||
"""
|
||||
|
||||
def __init__(self, engine_file_path):
|
||||
# Create a Context on this device,
|
||||
self.ctx = cuda.Device(0).make_context()
|
||||
stream = cuda.Stream()
|
||||
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
|
||||
runtime = trt.Runtime(TRT_LOGGER)
|
||||
|
||||
# Deserialize the engine from file
|
||||
with open(engine_file_path, "rb") as f:
|
||||
engine = runtime.deserialize_cuda_engine(f.read())
|
||||
context = engine.create_execution_context()
|
||||
|
||||
host_inputs = []
|
||||
cuda_inputs = []
|
||||
host_outputs = []
|
||||
cuda_outputs = []
|
||||
bindings = []
|
||||
self.mean = (0.485, 0.456, 0.406)
|
||||
self.std = (0.229, 0.224, 0.225)
|
||||
|
||||
for binding in engine:
|
||||
print('binding:', binding, engine.get_binding_shape(binding))
|
||||
size = trt.volume(engine.get_binding_shape(
|
||||
binding)) * engine.max_batch_size
|
||||
dtype = trt.nptype(engine.get_binding_dtype(binding))
|
||||
# Allocate host and device buffers
|
||||
host_mem = cuda.pagelocked_empty(size, dtype)
|
||||
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
|
||||
# Append the device buffer to device bindings.
|
||||
bindings.append(int(cuda_mem))
|
||||
# Append to the appropriate list.
|
||||
if engine.binding_is_input(binding):
|
||||
self.input_w = engine.get_binding_shape(binding)[-1]
|
||||
self.input_h = engine.get_binding_shape(binding)[-2]
|
||||
host_inputs.append(host_mem)
|
||||
cuda_inputs.append(cuda_mem)
|
||||
else:
|
||||
host_outputs.append(host_mem)
|
||||
cuda_outputs.append(cuda_mem)
|
||||
|
||||
# Store
|
||||
self.stream = stream
|
||||
self.context = context
|
||||
self.engine = engine
|
||||
self.host_inputs = host_inputs
|
||||
self.cuda_inputs = cuda_inputs
|
||||
self.host_outputs = host_outputs
|
||||
self.cuda_outputs = cuda_outputs
|
||||
self.bindings = bindings
|
||||
self.batch_size = engine.max_batch_size
|
||||
|
||||
def infer(self, raw_image_generator):
|
||||
threading.Thread.__init__(self)
|
||||
# Make self the active context, pushing it on top of the context stack.
|
||||
self.ctx.push()
|
||||
# Restore
|
||||
stream = self.stream
|
||||
context = self.context
|
||||
engine = self.engine
|
||||
host_inputs = self.host_inputs
|
||||
cuda_inputs = self.cuda_inputs
|
||||
host_outputs = self.host_outputs
|
||||
cuda_outputs = self.cuda_outputs
|
||||
bindings = self.bindings
|
||||
# Do image preprocess
|
||||
batch_image_raw = []
|
||||
batch_input_image = np.empty(
|
||||
shape=[self.batch_size, 3, self.input_h, self.input_w])
|
||||
for i, image_raw in enumerate(raw_image_generator):
|
||||
batch_image_raw.append(image_raw)
|
||||
input_image = self.preprocess_cls_image(image_raw)
|
||||
np.copyto(batch_input_image[i], input_image)
|
||||
batch_input_image = np.ascontiguousarray(batch_input_image)
|
||||
|
||||
# Copy input image to host buffer
|
||||
np.copyto(host_inputs[0], batch_input_image.ravel())
|
||||
start = time.time()
|
||||
# Transfer input data to the GPU.
|
||||
cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
|
||||
# Run inference.
|
||||
context.execute_async(batch_size=self.batch_size,
|
||||
bindings=bindings, stream_handle=stream.handle)
|
||||
# Transfer predictions back from the GPU.
|
||||
cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
|
||||
# Synchronize the stream
|
||||
stream.synchronize()
|
||||
end = time.time()
|
||||
# Remove any context from the top of the context stack, deactivating it.
|
||||
self.ctx.pop()
|
||||
# Here we use the first row of output in that batch_size = 1
|
||||
output = host_outputs[0]
|
||||
# Do postprocess
|
||||
for i in range(self.batch_size):
|
||||
classes_ls, predicted_conf_ls, category_id_ls = self.postprocess_cls(
|
||||
output)
|
||||
cv2.putText(batch_image_raw[i], str(
|
||||
classes_ls), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA)
|
||||
return batch_image_raw, end - start
|
||||
|
||||
def destroy(self):
|
||||
# Remove any context from the top of the context stack, deactivating it.
|
||||
self.ctx.pop()
|
||||
|
||||
def get_raw_image(self, image_path_batch):
|
||||
"""
|
||||
description: Read an image from image path
|
||||
"""
|
||||
for img_path in image_path_batch:
|
||||
yield cv2.imread(img_path)
|
||||
|
||||
def get_raw_image_zeros(self, image_path_batch=None):
|
||||
"""
|
||||
description: Ready data for warmup
|
||||
"""
|
||||
for _ in range(self.batch_size):
|
||||
yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8)
|
||||
|
||||
def preprocess_cls_image(self, input_img):
|
||||
im = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
|
||||
im = cv2.resize(im, (self.input_h, self.input_w))
|
||||
im = np.float32(im)
|
||||
im /= 255.0
|
||||
im -= self.mean
|
||||
im /= self.std
|
||||
im = im.transpose(2, 0, 1)
|
||||
# prepare batch
|
||||
batch_data = np.expand_dims(im, axis=0)
|
||||
return batch_data
|
||||
|
||||
def postprocess_cls(self, output_data):
|
||||
classes_ls = []
|
||||
predicted_conf_ls = []
|
||||
category_id_ls = []
|
||||
output_data = output_data.reshape(self.batch_size, -1)
|
||||
output_data = torch.Tensor(output_data)
|
||||
p = torch.nn.functional.softmax(output_data, dim=1)
|
||||
score, index = torch.topk(output_data, 3)
|
||||
for ind in range(index.shape[0]):
|
||||
input_category_id = index[ind][0].item() # 716
|
||||
category_id_ls.append(input_category_id)
|
||||
predicted_confidence = score[ind][0].item()
|
||||
predicted_conf_ls.append(predicted_confidence)
|
||||
classes_ls.append(classes[input_category_id])
|
||||
return classes_ls, predicted_conf_ls, category_id_ls
|
||||
|
||||
|
||||
class inferThread(threading.Thread):
|
||||
def __init__(self, yolov5_wrapper, image_path_batch):
|
||||
threading.Thread.__init__(self)
|
||||
self.yolov5_wrapper = yolov5_wrapper
|
||||
self.image_path_batch = image_path_batch
|
||||
|
||||
def run(self):
|
||||
batch_image_raw, use_time = self.yolov5_wrapper.infer(
|
||||
self.yolov5_wrapper.get_raw_image(self.image_path_batch))
|
||||
for i, img_path in enumerate(self.image_path_batch):
|
||||
parent, filename = os.path.split(img_path)
|
||||
save_name = os.path.join('output', filename)
|
||||
# Save image
|
||||
cv2.imwrite(save_name, batch_image_raw[i])
|
||||
print('input->{}, time->{:.2f}ms, saving into output/'.format(
|
||||
self.image_path_batch, use_time * 1000))
|
||||
|
||||
|
||||
class warmUpThread(threading.Thread):
|
||||
def __init__(self, yolov5_wrapper):
|
||||
threading.Thread.__init__(self)
|
||||
self.yolov5_wrapper = yolov5_wrapper
|
||||
|
||||
def run(self):
|
||||
batch_image_raw, use_time = self.yolov5_wrapper.infer(
|
||||
self.yolov5_wrapper.get_raw_image_zeros())
|
||||
print(
|
||||
'warm_up->{}, time->{:.2f}ms'.format(batch_image_raw[0].shape, use_time * 1000))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# load custom plugin and engine
|
||||
engine_file_path = "build/yolov5s_cls.engine"
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
engine_file_path = sys.argv[1]
|
||||
if len(sys.argv) > 2:
|
||||
PLUGIN_LIBRARY = sys.argv[2]
|
||||
|
||||
if os.path.exists('output/'):
|
||||
shutil.rmtree('output/')
|
||||
os.makedirs('output/')
|
||||
# a YoLov5TRT instance
|
||||
yolov5_wrapper = YoLov5TRT(engine_file_path)
|
||||
try:
|
||||
print('batch size is', yolov5_wrapper.batch_size)
|
||||
|
||||
image_dir = "samples/"
|
||||
image_path_batches = get_img_path_batches(
|
||||
yolov5_wrapper.batch_size, image_dir)
|
||||
|
||||
for i in range(10):
|
||||
# create a new thread to do warm_up
|
||||
thread1 = warmUpThread(yolov5_wrapper)
|
||||
thread1.start()
|
||||
thread1.join()
|
||||
for batch in image_path_batches:
|
||||
# create a new thread to do inference
|
||||
thread1 = inferThread(yolov5_wrapper, batch)
|
||||
thread1.start()
|
||||
thread1.join()
|
||||
finally:
|
||||
# destroy the instance
|
||||
yolov5_wrapper.destroy()
|
||||
Loading…
Reference in New Issue
Block a user