def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser( (ModelArguments, DataTrainingArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() if (os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1), training_args.fp16, ) logger.info("Training/evaluation parameters %s", training_args) # Set seed set_seed(training_args.seed) try: processor = processors[data_args.task_name]() label_list = processor.get_labels() num_labels = len(label_list) except KeyError: raise ValueError("Task not found: %s" % (data_args.task_name)) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. tokenizer = BertTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, ) model = AlbertForJustice.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), cache_dir=model_args.cache_dir, ) # Get datasets train_dataset = (MultipleChoiceDataset( data_dir=data_args.data_dir, tokenizer=tokenizer, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, ) if training_args.do_train else None) eval_dataset = (MultipleChoiceDataset( data_dir=data_args.data_dir, tokenizer=tokenizer, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, ) if training_args.do_eval else None) def compute_metrics(p: EvalPrediction) -> Dict: preds = p.predictions ac = (np.argmax(preds, axis=1) == p.label_ids).mean() # preds = np.argmax(p.predictions, axis=1) return {"acc": np.array(ac).mean()} # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics, ) # Training if training_args.do_train: trainer.train(model_path=model_args.model_name_or_path if os.path. isdir(model_args.model_name_or_path) else None) trainer.save_model() # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) if trainer.is_world_master(): tokenizer.save_pretrained(training_args.output_dir) # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") result = trainer.evaluate() output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt") if trainer.is_world_master(): with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key, value in result.items(): logger.info(" %s = %s", key, value) writer.write("%s = %s\n" % (key, value)) results.update(result) return results
dataset.get_train_batch = lambda epochs: get_batch(args.dataset, args.train_split, cfg.TFRECORD_DIR, num_classes= NUM_TRAIN_CLASSES, is_training=True, epochs=epochs) dataset.get_val_batch = lambda: get_batch(args.dataset, args.val_split, cfg.TFRECORD_DIR, num_classes=NUM_TRAIN_CLASSES, is_training=False, epochs=1) trainer = Trainer(resnetv1, dataset, pretrained_model='data/models/resnet_v1_50.ckpt', ckpt_dir=ckpt_dir, tbdir=log_dir) if args.mode == 'trainval': trainer.train_val(zip(cfg.TRAIN.EPOCHS, cfg.TRAIN.LEARNING_RATES)) elif args.mode == 'train': trainer.train_val(zip(cfg.TRAIN.EPOCHS, cfg.TRAIN.LEARNING_RATES), val=False) elif args.mode == 'val': trainer.evaluate() else: raise NotImplementedError( "--mode must be one of (trainval, train, val)")