mirror of
https://github.com/we0091234/yolov7_plate.git
synced 2025-09-26 21:01:13 +08:00
80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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import sys
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import time
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from pathlib import Path
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sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories
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import onnx
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import torch
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import torch.nn as nn
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from torch.utils.mobile_optimizer import optimize_for_mobile
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import cv2
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import numpy as np
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import models
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from models.experimental import attempt_load
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from utils.activations import Hardswish, SiLU
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from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging
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from utils.torch_utils import select_device
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='runs/train/yolov714/weights/best.pt', help='weights path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
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parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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opt = parser.parse_args()
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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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print(opt)
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set_logging()
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t = time.time()
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# Load PyTorch model
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device = select_device(opt.device)
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model = attempt_load(opt.weights, map_location=device) # load FP32 model
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labels = model.names
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# Checks
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gs = int(max(model.stride)) # grid size (max stride)
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opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
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# Input
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img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
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# Update model
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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if isinstance(m.act, nn.Hardswish):
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m.act = Hardswish()
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elif isinstance(m.act, nn.SiLU):
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m.act = SiLU()
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# elif isinstance(m, models.yolo.Detect):
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# m.forward = m.forward_export # assign forward (optional)
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model.model[-1].export = True # set Detect() layer grid export
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for _ in range(2):
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y = model(img) # dry runs
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output_names = None
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print(f'starting export with onnx {onnx.__version__}...')
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f = opt.weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['data'],
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output_names=['stride_' + str(int(x)) for x in model.stride])
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# print(onnx.helper.printable_graph(model_onnx.graph)) # print
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# Finish
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print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
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