select GPU0 if multiple available
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14
train.py
14
train.py
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@ -43,11 +43,11 @@ def train(
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if resume:
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checkpoint = torch.load(latest, map_location='cpu')
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# Load weights to resume from
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model.load_state_dict(checkpoint['model'])
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if torch.cuda.device_count() > 1:
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raise Exception('Multi-GPU issue: https://github.com/ultralytics/yolov3/issues/21')
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# print('Using ', torch.cuda.device_count(), ' GPUs')
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# model = nn.DataParallel(model)
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# if torch.cuda.device_count() > 1:
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# model = nn.DataParallel(model)
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model.to(device).train()
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# # Transfer learning (train only YOLO layers)
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@ -72,10 +72,8 @@ def train(
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# Initialize model with darknet53 weights (optional)
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load_darknet_weights(model, os.path.join(weights, 'darknet53.conv.74'))
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if torch.cuda.device_count() > 1:
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raise Exception('Multi-GPU not currently supported: https://github.com/ultralytics/yolov3/issues/21')
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# print('Using ', torch.cuda.device_count(), ' GPUs')
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# model = nn.DataParallel(model)
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# if torch.cuda.device_count() > 1:
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# model = nn.DataParallel(model)
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model.to(device).train()
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# Set optimizer
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@ -6,7 +6,6 @@ def init_seeds(seed=0):
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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# torch.cuda.set_device(0) # OPTIONAL: Set your GPU if multiple available
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def select_device(force_cpu=False):
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@ -14,5 +13,11 @@ def select_device(force_cpu=False):
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device = torch.device('cpu')
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else:
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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if torch.cuda.device_count() > 1:
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print('WARNING Using GPU0 Only. Multi-GPU issue: https://github.com/ultralytics/yolov3/issues/21')
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torch.cuda.set_device(0) # OPTIONAL: Set your GPU if multiple available
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# print('Using ', torch.cuda.device_count(), ' GPUs')
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print('Using ' + str(device) + '\n')
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return device
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