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| #!/usr/bin/env python3 -u | |
| #!/usr/bin/env python3 -u | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from fairseq import checkpoint_utils, options, progress_bar, utils | |
| def main(args, override_args=None): | |
| utils.import_user_module(args) | |
| use_fp16 = args.fp16 | |
| use_cuda = torch.cuda.is_available() and not args.cpu | |
| if override_args is not None: | |
| overrides = vars(override_args) | |
| overrides.update(eval(getattr(override_args, 'model_overrides', '{}'))) | |
| else: | |
| overrides = None | |
| # Load ensemble | |
| print('| loading model(s) from {}'.format(args.path)) | |
| models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( | |
| [args.path], | |
| arg_overrides=overrides, | |
| ) | |
| model = models[0] | |
| # Move models to GPU | |
| for model in models: | |
| if use_fp16: | |
| model.half() | |
| if use_cuda: | |
| model.cuda() | |
| # Print args | |
| print(model_args) | |
| # Build criterion | |
| criterion = task.build_criterion(model_args) | |
| criterion.eval() | |
| # Load valid dataset (we load training data below, based on the latest checkpoint) | |
| for subset in args.valid_subset.split(','): | |
| try: | |
| task.load_dataset(subset, combine=False, epoch=0) | |
| dataset = task.dataset(subset) | |
| except KeyError: | |
| raise Exception('Cannot find dataset: ' + subset) | |
| # Initialize data iterator | |
| itr = task.get_batch_iterator( | |
| dataset=dataset, | |
| max_tokens=args.max_tokens, | |
| max_sentences=args.max_sentences, | |
| max_positions=utils.resolve_max_positions( | |
| task.max_positions(), | |
| *[m.max_positions() for m in models], | |
| ), | |
| ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, | |
| required_batch_size_multiple=args.required_batch_size_multiple, | |
| seed=args.seed, | |
| num_workers=args.num_workers, | |
| ).next_epoch_itr(shuffle=False) | |
| progress = progress_bar.build_progress_bar( | |
| args, itr, | |
| prefix='valid on \'{}\' subset'.format(subset), | |
| no_progress_bar='simple' | |
| ) | |
| log_outputs = [] | |
| for i, sample in enumerate(progress): | |
| sample = utils.move_to_cuda(sample) if use_cuda else sample | |
| _loss, _sample_size, log_output = task.valid_step(sample, model, criterion) | |
| progress.log(log_output, step=i) | |
| log_outputs.append(log_output) | |
| log_output = task.aggregate_logging_outputs(log_outputs, criterion) | |
| progress.print(log_output, tag=subset, step=i) | |
| def cli_main(): | |
| parser = options.get_validation_parser() | |
| args = options.parse_args_and_arch(parser) | |
| # only override args that are explicitly given on the command line | |
| override_parser = options.get_validation_parser() | |
| override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True) | |
| main(args, override_args) | |
| if __name__ == '__main__': | |
| cli_main() | |