\ub2e4\uc591\ud55c \ud558\ub4dc\uc6e8\uc5b4 \uc9c0\uc6d0:<\/strong> TensorRT\ub294 NVIDIA GPU\uc5d0\uc11c \ucd5c\uc0c1\uc758 \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n4. YOLO \ubaa8\ub378\uc744 TensorRT\ub85c \ucd5c\uc801\ud654\ud558\uae30<\/h2>\n
TensorRT\ub97c \uc0ac\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc758 \ucd94\ub860 \uc18d\ub3c4\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\ub824\uba74 \uba87 \uac00\uc9c0 \ub2e8\uacc4\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc544\ub798\ub294 \uadf8 \uacfc\uc815\uc785\ub2c8\ub2e4:<\/p>\n
4.1 YOLO \ubaa8\ub378 \uc900\ube44<\/h3>\n
TensorRT\ub294 ONNX(Open Neural Network Exchange) \ubaa8\ub378 \ud615\uc2dd\uc744 \uc9c0\uc6d0\ud558\ubbc0\ub85c, YOLO \ubaa8\ub378\uc744 ONNX \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec YOLOv5 \ubaa8\ub378\uc744 ONNX\ub85c \ub0b4\ubcf4\ub0b4\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\n # YOLOv5 \ubaa8\ub378 \ub85c\ub4dc\n model = torch.hub.load('ultralytics\/yolov5', 'yolov5s')\n # \uc785\ub825 \ud150\uc11c \uc0dd\uc131\n dummy_input = torch.randn(1, 3, 640, 640)\n # ONNX\ub85c \ub0b4\ubcf4\ub0b4\uae30\n torch.onnx.export(model, dummy_input, \"yolov5s.onnx\", verbose=True, opset_version=11)<\/code><\/pre>\n4.2 TensorRT \ucd5c\uc801\ud654<\/h3>\n
\uc774\uc81c ONNX \ubaa8\ub378\uc774 \uc900\ube44\ub418\uc5c8\uc73c\ubbc0\ub85c TensorRT\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. TensorRT \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\ub294 \uc608\uc81c \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
import tensorrt as trt\n\n def build_engine(onnx_file_path):\n # TensorRT \ub85c\uac70 \uc0dd\uc131\n logger = trt.Logger(trt.Logger.WARNING)\n builder = trt.Builder(logger)\n network = builder.create_network()\n parser = trt.OnnxParser(network, logger)\n\n # ONNX \ubaa8\ub378 \uc77d\uae30\n with open(onnx_file_path, 'rb') as model:\n parser.parse(model.read())\n\n # \ucd5c\uc801\ud654 \uc124\uc815\n builder.max_batch_size = 1\n builder.max_workspace_size = 1 << 30 # 1GB\n engine = builder.build_cuda_engine(network)\n return engine\n\n # YOLO \ubaa8\ub378\uc5d0\uc11c \uc5d4\uc9c4 \uc0dd\uc131\n engine = build_engine(\"yolov5s.onnx\")<\/code><\/pre>\n4.3 \ucd94\ub860 \uc2e4\ud589<\/h3>\n
TensorRT \uc5d4\uc9c4\uc744 \uc0dd\uc131\ud55c \ud6c4, \uc774\uc81c \uc2e4\uc81c\ub85c YOLO \ubaa8\ub378\uc744 \ud1b5\ud574 \ucd94\ub860\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ucd94\ub860 \uc2e4\ud589\uc744 \uc704\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
import pycuda.driver as cuda\n import pycuda.autoinit\n\n # \uc5d4\uc9c4\uc73c\ub85c\ubd80\ud130 \ucee8\ud14d\uc2a4\ud2b8\uc640 \uc2a4\ud2b8\ub9bc \uc0dd\uc131\n context = engine.create_execution_context()\n stream = cuda.Stream()\n\n # \uc785\ub825\/\ucd9c\ub825 \ubc84\ud37c \ud560\ub2f9\n input_size = (3, 640, 640)\n output_size = (1, 25200, 85) # YOLOv5s\uc758 \ucd9c\ub825 \ud06c\uae30\n d_input = cuda.mem_alloc(trt.volume(input_size) * np.dtype(np.float32).itemsize)\n d_output = cuda.mem_alloc(trt.volume(output_size) * np.dtype(np.float32).itemsize)\n bindings = [int(d_input), int(d_output)]\n\n def infer(image):\n # \uc774\ubbf8\uc9c0 \uc804\ucc98\ub9ac \ubc0f \ubcf5\uc0ac\n cuda.memcpy_htod_async(d_input, image, stream)\n context.execute_async(batch_size=1, bindings=bindings, stream_handle=stream.handle)\n cuda.memcpy_dtoh_async(output, d_output, stream)\n stream.synchronize()\n return output\n\n # \uc774\ubbf8\uc9c0 \ucd94\ub860 \uc608\uc2dc\n output = infer(preprocessed_image)<\/code><\/pre>\n5. \uc131\ub2a5 \ube44\uad50<\/h2>\n
TensorRT\ub97c \ud65c\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc758 \ucd94\ub860 \uc18d\ub3c4\uac00 \uc5bc\ub9c8\ub098 \ud5a5\uc0c1\ub418\uc5c8\ub294\uc9c0 \ube44\uad50\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc131\ub2a5 \ube44\uad50\ub97c \uc704\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
import time\n\n # TensorFlow \ub610\ub294 PyTorch\ub85c \ucd94\ub860\ud558\ub294 \uc2dc\uac04 \uce21\uc815\n start_time = time.time()\n for _ in range(100):\n output_tf = model(image)\n tf_duration = time.time() - start_time\n\n # TensorRT\ub85c \ucd94\ub860\ud558\ub294 \uc2dc\uac04 \uce21\uc815\n start_time = time.time()\n for _ in range(100):\n output_trt = infer(image)\n trt_duration = time.time() - start_time\n\n print(f'TensorFlow\/PyTorch time: {tf_duration:.2f}s')\n print(f'TensorRT time: {trt_duration:.2f}s')<\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\ubcf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654 \ubc29\ubc95\uacfc TensorRT\ub97c \ud65c\uc6a9\ud55c \ucd94\ub860 \uc18d\ub3c4 \ud5a5\uc0c1\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. TensorRT\ub97c \uc0ac\uc6a9\ud558\uba74 YOLO \ubaa8\ub378\uc758 \uc2e4\uc2dc\uac04 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc5b4, \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd5c\uc801\ud654 \uacfc\uc815\uc744 \ud1b5\ud574 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc18d\ub3c4\uc640 \uc815\ud655\ub3c4\ub97c \uade0\ud615 \uc788\uac8c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 YOLO\uc640 \uac19\uc740 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud55c \uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c TensorRT\ub97c \ud3ec\ud568\ud55c \ub2e4\uc591\ud55c \ucd5c\uc801\ud654 \uae30\uc220\ub4e4\uc774 \ub354\uc6b1 \ubc1c\uc804\ud558\uae30\ub97c \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c YOLO(You Only Look Once) \ubaa8\ub378\uc740 \ub192\uc740 \uc815\ud655\ub3c4\uc640 \ube60\ub978 \uc18d\ub3c4\ub85c \uc778\uae30\ub97c \uc5bb\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098, \uace0\uae09 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c\ub294 \ub354\uc6b1 \ub192\uc740 \uc131\ub2a5\uacfc \ube60\ub978 \ucd94\ub860 \uc18d\ub3c4\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. NVIDIA\uc758 TensorRT\ub294 \ub525\ub7ec\ub2dd \ucd94\ub860 \ucd5c\uc801\ud654 \ub3c4\uad6c\ub85c, YOLO \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654 \ubc29\ubc95 \ubc0f TensorRT\ub97c \ud65c\uc6a9\ud55c \ucd94\ub860 \uc18d\ub3c4 \ud5a5\uc0c1\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. YOLO … \ub354 \ubcf4\uae30 “YOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654, \ud150\uc11cRT(TensorRT)\ub97c \ud65c\uc6a9\ud55c YOLO \ubaa8\ub378\uc758 \ucd94\ub860 \uc18d\ub3c4 \ud5a5\uc0c1”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[189],"tags":[],"class_list":["post-38895","post","type-post","status-publish","format-standard","hentry","category-yolo"],"yoast_head":"\nYOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654, \ud150\uc11cRT(TensorRT)\ub97c \ud65c\uc6a9\ud55c YOLO \ubaa8\ub378\uc758 \ucd94\ub860 \uc18d\ub3c4 \ud5a5\uc0c1 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/38895\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"YOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654, \ud150\uc11cRT(TensorRT)\ub97c \ud65c\uc6a9\ud55c YOLO \ubaa8\ub378\uc758 \ucd94\ub860 \uc18d\ub3c4 \ud5a5\uc0c1 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c YOLO(You Only Look Once) \ubaa8\ub378\uc740 \ub192\uc740 \uc815\ud655\ub3c4\uc640 \ube60\ub978 \uc18d\ub3c4\ub85c \uc778\uae30\ub97c \uc5bb\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098, \uace0\uae09 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c\ub294 \ub354\uc6b1 \ub192\uc740 \uc131\ub2a5\uacfc \ube60\ub978 \ucd94\ub860 \uc18d\ub3c4\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. NVIDIA\uc758 TensorRT\ub294 \ub525\ub7ec\ub2dd \ucd94\ub860 \ucd5c\uc801\ud654 \ub3c4\uad6c\ub85c, YOLO \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654 \ubc29\ubc95 \ubc0f TensorRT\ub97c \ud65c\uc6a9\ud55c \ucd94\ub860 \uc18d\ub3c4 \ud5a5\uc0c1\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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