updates
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4
train.py
4
train.py
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@ -103,7 +103,7 @@ def train():
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best_fitness = float('inf')
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best_fitness = float('inf')
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attempt_download(weights)
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attempt_download(weights)
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if weights.endswith('.pt'): # pytorch format
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if weights.endswith('.pt'): # pytorch format
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# possible weights are 'last.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
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# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
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if opt.bucket:
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if opt.bucket:
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os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket
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os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket
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chkpt = torch.load(weights, map_location=device)
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chkpt = torch.load(weights, map_location=device)
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@ -129,7 +129,7 @@ def train():
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del chkpt
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del chkpt
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elif len(weights) > 0: # darknet format
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elif len(weights) > 0: # darknet format
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# possible weights are 'yolov3.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
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# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
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cutoff = load_darknet_weights(model, weights)
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cutoff = load_darknet_weights(model, weights)
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if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers
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if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers
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