Fix pylint: do not use bare 'except' (#5025)
* Fix E722, do not use bare 'except' * Remove used codes * Add FileNotFoundError in LoadImagesAndLabels * Remove AssertionError * Ignore LoadImagesAndLabels * Ignore downloads.py * Ignore torch_utils.py * Ignore train.py * Ignore datasets.py * Enable utils/download.py * Fixing exception in thop * Remove unused code * Fixing exception in LoadImagesAndLabels * Fixing exception in exif_size * Fixing exception in parse_model * Ignore exceptions in requests * Revert the exception as suggested * Revert the exception as suggested
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@ -268,7 +268,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
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for j, a in enumerate(args):
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try:
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args[j] = eval(a) if isinstance(a, str) else a # eval strings
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except:
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except NameError:
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pass
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n = max(round(n * gd), 1) if n > 1 else n # depth gain
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@ -233,7 +233,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
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for j, a in enumerate(args):
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try:
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args[j] = eval(a) if isinstance(a, str) else a # eval strings
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except:
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except NameError:
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pass
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n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
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1
train.py
1
train.py
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@ -499,7 +499,6 @@ def main(opt, callbacks=Callbacks()):
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# DDP mode
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device = select_device(opt.device, batch_size=opt.batch_size)
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if LOCAL_RANK != -1:
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from datetime import timedelta
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assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
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assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
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assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
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@ -152,7 +152,7 @@ def is_colab():
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try:
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import google.colab
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return True
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except Exception as e:
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except ImportError:
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return False
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@ -160,6 +160,7 @@ def is_pip():
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# Is file in a pip package?
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return 'site-packages' in Path(__file__).resolve().parts
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def is_ascii(s=''):
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# Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
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s = str(s) # convert list, tuple, None, etc. to str
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@ -741,11 +742,11 @@ def print_mutation(results, hyp, save_dir, bucket):
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data = pd.read_csv(evolve_csv)
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data = data.rename(columns=lambda x: x.strip()) # strip keys
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i = np.argmax(fitness(data.values[:, :7])) #
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f.write(f'# YOLOv5 Hyperparameter Evolution Results\n' +
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f.write('# YOLOv5 Hyperparameter Evolution Results\n' +
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f'# Best generation: {i}\n' +
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f'# Last generation: {len(data)}\n' +
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f'# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
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f'# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
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'# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
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'# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
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yaml.safe_dump(hyp, f, sort_keys=False)
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if bucket:
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