parent
4a025ae97f
commit
c5360f6e70
6
train.py
6
train.py
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@ -36,7 +36,7 @@ from utils.autoanchor import check_anchors
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from utils.datasets import create_dataloader
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from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
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strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
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check_yaml, check_suffix, print_mutation, set_logging, one_cycle, colorstr, methods
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check_file, check_yaml, check_suffix, print_mutation, set_logging, one_cycle, colorstr, methods
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from utils.downloads import attempt_download
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from utils.loss import ComputeLoss
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from utils.plots import plot_labels, plot_evolve
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@ -105,6 +105,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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is_coco = data.endswith('coco.yaml') and nc == 80 # COCO dataset
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# Model
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check_suffix(weights, '.pt') # check weights
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pretrained = weights.endswith('.pt')
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if pretrained:
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with torch_distributed_zero_first(RANK):
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@ -484,8 +485,7 @@ def main(opt, callbacks=Callbacks()):
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opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
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LOGGER.info(f'Resuming training from {ckpt}')
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else:
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check_suffix(opt.weights, '.pt') # check weights
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opt.data, opt.cfg, opt.hyp = check_yaml(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp) # check YAMLs
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opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp) # check YAMLs
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assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
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if opt.evolve:
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opt.project = 'runs/evolve'
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