updates
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6
train.py
6
train.py
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@ -137,7 +137,7 @@ def train():
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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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nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
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nf = [int(model.module_defs[x - 1]['filters']) for x in model.yolo_layers] # yolo layer size (i.e. 255)
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if opt.prebias:
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for p in optimizer.param_groups:
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@ -147,9 +147,9 @@ def train():
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p['momentum'] = 0.9
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for p in model.parameters():
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if opt.prebias and p.numel() == nf: # train (yolo biases)
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if opt.prebias and p.numel() in nf: # train (yolo biases)
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p.requires_grad = True
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elif opt.transfer and p.shape[0] == nf: # train (yolo biases+weights)
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elif opt.transfer and p.shape[0] in nf: # train (yolo biases+weights)
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p.requires_grad = True
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else: # freeze layer
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p.requires_grad = False
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