def _inference_score(self, hyps, refs, ids):
hypotheses, references = dict(), dict()
for key, pred, tgt in zip(ids, hyps, refs):
hypotheses[key] = [pred]
references[key] = tgt if isinstance(tgt, list) else [tgt]
bleu, rouge_l, meteor = summarization_metrics.eval_accuracies(hypotheses, references)
return bleu, rouge_l, meteor
def _inference_score(self, hyps, refs, ids):
hypotheses, references = dict(), dict()
for key, pred, tgt in zip(ids, hyps, refs):
hypotheses[key] = [pred]
references[key] = tgt if isinstance(tgt, list) else [tgt]
bleu, rouge_l, meteor = eval_accuracies(hypotheses,
references,
filename='pred.txt')
return bleu, rouge_l, meteor
def _inference_score(self, hyps, refs, ids):
hypotheses, references = dict(), dict()
for key, pred, tgt in zip(ids, hyps, refs):
# avoid the bug of bleu evaluation, replace '' with '0'
hypotheses[key] = ['0'] if len(pred) == 0 else [pred]
references[key] = tgt if isinstance(tgt, list) else [tgt]
bleu, rouge_l, meteor = summarization_metrics.eval_accuracies(
hypotheses, references)
return bleu, rouge_l, meteor
def _inference_score(self, hyps, refs, ids):
hypotheses, references = dict(), dict()
for key, pred, tgt in zip(ids, hyps, refs):
hypotheses[key] = [pred]
references[key] = tgt if isinstance(tgt, list) else [tgt]
# if self.args['model']['arch'] in ['codenn']:
# bleu, rouge_l, meteor = eval_utils.eval_accuracies(hypotheses, references, mode='test')
# else:
# bleu, rouge_l, meteor = eval_utils.eval_accuracies(hypotheses, references)
bleu, rouge_l, meteor = summarization_metrics.eval_accuracies(
hypotheses, references)
return bleu, rouge_l, meteor
def main(args, out_file=None):
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args['dataset']['gen_subset'])
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _ = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None
if args['eval']['no_beamable_mm'] else args['eval']['beam'],
need_attn=args['eval']['print_alignment'],
)
if use_cuda:
device = os.environ.get('CUDA_VISIBALE_DEVICES',
[0])[0] # get first device as default
torch.cuda.set_device(f'cuda:{device}')
model = model.cuda()
if args['common']['fp16'] and use_cuda:
model.half()
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args['dataset']['gen_subset']),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['eval']['max_sentences'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]),
ignore_invalid_inputs=args['dataset']
['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=args['dataset']
['required_batch_size_multiple'],
num_shards=args['dataset']['num_shards'],
shard_id=args['dataset']['shard_id'],
num_workers=args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=args['common']['log_format'],
log_interval=args['common']['log_interval'],
default_log_format=('tqdm' if not args['common']['no_progress_bar']
else 'none'),
)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(models, args)
sources, hypotheses, references = dict(), dict(), dict()
for sample in progress:
torch.cuda.empty_cache()
sample = move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
gen_timer.start()
hypos = task.inference_step(generator,
models,
sample,
bos_token=tgt_dict.bos())
num_generated_tokens = sum(len(h[0]['tokens'])
for h in hypos) # TODO: warning
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :],
tgt_dict.pad()).int().cpu()
hypos_tokens = utils.strip_eos(hypos[i][0]['tokens'],
tgt_dict.eos()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if src_dict is not None:
src_str = src_dict.string(src_tokens,
args['eval']['remove_bpe'])
else:
src_str = "0"
if has_target:
target_str = tgt_dict.string(target_tokens,
args['eval']['remove_bpe'],
escape_unk=True)
hypo_str = tgt_dict.string(hypos_tokens,
args['eval']['remove_bpe'])
sources[sample_id] = [src_str]
hypotheses[sample_id] = [hypo_str]
references[sample_id] = [target_str]
bleu, rouge_l, meteor = \
summarization_metrics.eval_accuracies(hypotheses, references, filename=out_file, mode='test')
LOGGER.info('BLEU: {:.2f}\t ROUGE-L: {:.2f}\t METEOR: {:.2f}'.format(
bleu, rouge_l, meteor))