mirror of
https://github.com/facebookresearch/fairseq.git
synced 2024-09-17 12:07:09 +03:00
6ce55e4b01
Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/835 Differential Revision: D16904038 Pulled By: myleott fbshipit-source-id: 2c9d0b913f8d688297ac80fcabd905bd1397f66a
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
#!/usr/bin/env python3 -u
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#!/usr/bin/env python3 -u
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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from fairseq import checkpoint_utils, options, progress_bar, utils
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def main(args, override_args=None):
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utils.import_user_module(args)
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use_fp16 = args.fp16
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use_cuda = torch.cuda.is_available() and not args.cpu
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if override_args is not None:
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overrides = vars(override_args)
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overrides.update(eval(getattr(override_args, 'model_overrides', '{}')))
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else:
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overrides = None
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# Load ensemble
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print('| loading model(s) from {}'.format(args.path))
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models, model_args, task = checkpoint_utils.load_model_ensemble_and_task(
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[args.path],
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arg_overrides=overrides,
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)
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model = models[0]
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# Move models to GPU
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for model in models:
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if use_fp16:
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model.half()
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if use_cuda:
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model.cuda()
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# Print args
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print(model_args)
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# Build criterion
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criterion = task.build_criterion(model_args)
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criterion.eval()
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# Load valid dataset (we load training data below, based on the latest checkpoint)
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for subset in args.valid_subset.split(','):
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try:
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task.load_dataset(subset, combine=False, epoch=0)
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dataset = task.dataset(subset)
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except KeyError:
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raise Exception('Cannot find dataset: ' + subset)
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# Initialize data iterator
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itr = task.get_batch_iterator(
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dataset=dataset,
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max_tokens=args.max_tokens,
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max_sentences=args.max_sentences,
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max_positions=utils.resolve_max_positions(
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task.max_positions(),
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*[m.max_positions() for m in models],
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),
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ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
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required_batch_size_multiple=args.required_batch_size_multiple,
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seed=args.seed,
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num_workers=args.num_workers,
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).next_epoch_itr(shuffle=False)
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progress = progress_bar.build_progress_bar(
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args, itr,
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prefix='valid on \'{}\' subset'.format(subset),
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no_progress_bar='simple'
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)
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log_outputs = []
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for i, sample in enumerate(progress):
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sample = utils.move_to_cuda(sample) if use_cuda else sample
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_loss, _sample_size, log_output = task.valid_step(sample, model, criterion)
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progress.log(log_output, step=i)
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log_outputs.append(log_output)
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log_output = task.aggregate_logging_outputs(log_outputs, criterion)
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progress.print(log_output, tag=subset, step=i)
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def cli_main():
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parser = options.get_validation_parser()
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args = options.parse_args_and_arch(parser)
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# only override args that are explicitly given on the command line
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override_parser = options.get_validation_parser()
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override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True)
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main(args, override_args)
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if __name__ == '__main__':
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cli_main()
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