updates
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7
train.py
7
train.py
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@ -39,7 +39,7 @@ def train():
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cfg = opt.cfg
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data = opt.data
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img_size = opt.img_size
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epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 16, 117263 images = 273 epochs
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epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
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batch_size = opt.batch_size
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accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
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weights = opt.weights # initial training weights
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@ -348,7 +348,9 @@ def train():
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# end epoch ----------------------------------------------------------------------------------------------------
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# Report time
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# end training
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if len(opt.name):
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os.rename('results.txt', 'results_%s.txt' % opt.name)
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plot_results() # save as results.png
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print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
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dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
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@ -377,6 +379,7 @@ if __name__ == '__main__':
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parser.add_argument('--weights', type=str, default='', help='initial weights') # i.e. weights/darknet.53.conv.74
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parser.add_argument('--arc', type=str, default='defaultpw', help='yolo architecture') # defaultpw, uCE, uBCE
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parser.add_argument('--prebias', action='store_true', help='transfer-learn yolo biases prior to training')
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parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
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parser.add_argument('--var', type=float, help='debug variable')
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opt = parser.parse_args()
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opt.weights = 'weights/last.pt' if opt.resume else opt.weights
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