updates
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8
train.py
8
train.py
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@ -253,6 +253,10 @@ def train(
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t = time.time()
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print(s)
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# Report time
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dt = (time.time() - t0) / 3600
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print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, dt))
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# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
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if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
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with torch.no_grad():
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@ -292,8 +296,6 @@ def train(
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# Delete checkpoint
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del chkpt
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dt = (time.time() - t0) / 3600
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print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, dt))
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return results
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@ -313,7 +315,7 @@ if __name__ == '__main__':
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parser.add_argument('--batch-size', type=int, default=8, help='batch size')
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parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco_1000img.data', help='coco.data file path')
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parser.add_argument('--single-scale', action='store_true', help='train at fixed size (no multi-scale)')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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