train.py
- 2 F541: f-string is missing placeholders
- 2 F821: undefined name 'process_outputs_functions'
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26
E501: line too long (106 > 79 characters)
(and 17 similar)
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3 from utils import save_model, save_pred, get_pred_prefix, get_model_prefix, detach_and_clone, collate_list -
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24 # Using enumerate(iterator) can sometimes leak memory in some environments (!) -
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27 iterator = tqdm(dataset['loader']) if config.progress_bar else dataset['loader'] -
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43 y_pred = process_outputs_functions[config.process_outputs_function](y_pred) -
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67 # log after updating the scheduler in case it needs to access the internal logs -
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79 def train(algorithm, datasets, general_logger, result_logger, config, epoch_offset, best_val_metric): -
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85 run_epoch(algorithm, datasets['train'], general_logger, epoch, config, train=True) -
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88 val_results, y_pred = run_epoch(algorithm, datasets['val'], general_logger, epoch, config, train=False) -
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90 general_logger.write(f'Validation {config.val_metric}: {curr_val_metric:.3f}\n') -
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102 general_logger.write(f'Epoch {epoch} has the best validation performance so far.\n') -
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106 save_model_if_needed(algorithm, datasets['val'], epoch, config, is_best, best_val_metric) -
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111 additional_splits = [split for split in datasets.keys() if split not in ['train', 'val']] -
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115 _, y_pred = run_epoch(algorithm, datasets[split], general_logger, epoch, config, train=False) -
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116 save_pred_if_needed(y_pred, datasets[split], epoch, config, is_best) -
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122 def evaluate(algorithm, datasets, epoch, general_logger, result_logger, config, is_best): -
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126 if (not config.evaluate_all_splits) and (split not in config.eval_splits): -
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131 iterator = tqdm(dataset['loader']) if config.progress_bar else dataset['loader'] -
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137 y_pred = process_outputs_functions[config.process_outputs_function](y_pred) -
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155 # Skip saving train preds, since the train loader generally shuffles the data -
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157 save_pred_if_needed(epoch_y_pred, dataset, epoch, config, is_best, force_save=True) -
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171 def save_pred_if_needed(y_pred, dataset, epoch, config, is_best, force_save=False): -
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174 if force_save or (config.save_step is not None and (epoch + 1) % config.save_step == 0): -
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182 def save_model_if_needed(algorithm, dataset, epoch, config, is_best, best_val_metric): -
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185 save_model(algorithm, epoch, best_val_metric, prefix, f'epoch-{epoch}_model.pth') -
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187 save_model(algorithm, epoch, best_val_metric, prefix, 'epoch-last_model.pth') -
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189 save_model(algorithm, epoch, best_val_metric, prefix, 'epoch-best_model.pth')
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4
E303: too many blank lines (2)
(and 1 similar)
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Too many blank lines (2):
36 # These tensors are already detached, but we need to clone them again -
Too many blank lines (2):
93 if best_val_metric is None: -
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106 save_model_if_needed(algorithm, datasets['val'], epoch, config, is_best, best_val_metric) -
Too many blank lines (3):
122 def evaluate(algorithm, datasets, epoch, general_logger, result_logger, config, is_best):
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- 4 W293: blank line contains whitespace