def evaluate(model, args, dev_features, device, global_steps):
# Eval!
print("***** Running evaluation *****")
all_results = []
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
input_ids, input_mask, segment_ids, example_indices = batch
inputs = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': segment_ids
}
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = dev_features[example_index.item()]
unique_id = str(eval_feature.unique_id)
result = RawResult(unique_id=unique_id,
start_logits=to_list(outputs[0][i]),
end_logits=to_list(outputs[1][i]),
answer_type_logits=to_list(outputs[2][i]))
all_results.append(result)
pickle.dump(all_results,
open(os.path.join(args.output_dir, 'RawResults.pkl'), 'wb'))
# all_results = pickle.load(open(os.path.join(args.output_dir, 'RawResults.pkl'), 'rb'))
candidates_dict = read_candidates_from_one_split(args.predict_file)
nq_pred_dict = compute_pred_dict(candidates_dict, dev_features,
[r._asdict() for r in all_results],
args.n_best_size, args.max_answer_length)
output_prediction_file = os.path.join(
args.output_dir, 'predictions' + str(global_steps) + '.json')
with open(output_prediction_file, 'w') as f:
json.dump({'predictions': list(nq_pred_dict.values())}, f)
results = get_metrics_as_dict(args.predict_file, output_prediction_file)
print('Steps:{}'.format(global_steps))
print(json.dumps(results, indent=2))
model.train()
return results
answer_type_logits=to_list(outputs['answer_type_logits'][i]),
long_cls_logits=outputs['long_cls_logits'][i].cpu().numpy(),
short_cls_logits=outputs['short_cls_logits'][i].cpu().numpy())
all_results.append(result)
#
pickle.dump(
all_results,
open(os.path.join(args.output_dir, 'RawResults_test.pkl'), 'wb'))
# all_results = pickle.load(open(os.path.join(args.output_dir, 'RawResults_test.pkl'), 'rb'))
print("Going to candidates file")
candidates_dict = read_candidates_from_one_split(args.predict_file)
print("Compute_pred_dict")
nq_pred_dict = compute_pred_dict(candidates_dict,
features,
[r._asdict() for r in all_results],
args.n_best_size,
args.max_answer_length,
topk_pred=True,
long_n_top=5,
short_n_top=5)
output_prediction_file = os.path.join(args.output_dir,
'test_predictions.json')
print("Saving predictions to", output_prediction_file)
with open(output_prediction_file, 'w') as f:
json.dump({'predictions': list(nq_pred_dict.values())}, f)
make_submission(output_prediction_file, args.output_dir)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
result = RawResult(unique_id=unique_id,
start_logits=to_list(outputs[0][i]),
end_logits=to_list(outputs[1][i]),
answer_type_logits=to_list(outputs[2][i]))
all_results.append(result)
pickle.dump(all_results,
open(os.path.join(args.output_dir, 'RawResults.pkl'), 'wb'))
# all_results = pickle.load(open(os.path.join(args.output_dir, 'RawResults.pkl'), 'rb'))
print("Going to candidates file")
candidates_dict = read_candidates_from_one_split(args.predict_file)
print("Compute_pred_dict")
nq_pred_dict = compute_pred_dict(candidates_dict, features,
[r._asdict() for r in all_results],
args.n_best_size, args.max_answer_length)
output_prediction_file = os.path.join(args.output_dir, 'predictions.json')
print("Saving predictions to", output_prediction_file)
with open(output_prediction_file, 'w') as f:
json.dump({'predictions': list(nq_pred_dict.values())}, f)
print("Computing f1 score")
results = get_metrics_as_dict(args.predict_file, output_prediction_file)
print(json.dumps(results, indent=2))
def evaluate(model, args, dev_features, device, ei):
# Eval!
if os.path.exists(
os.path.join(args.output_dir,
'RawResults_ensemble{}.pkl'.format(ei))):
all_results = pickle.load(
open(
os.path.join(args.output_dir,
'RawResults_ensemble{}.pkl'.format(ei)), 'rb'))
else:
all_results = []
for batch in tqdm(eval_dataloader,
desc="Evaluating Ensemble-{}".format(ei)):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
input_ids, input_mask, segment_ids, example_indices = batch
inputs = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': segment_ids
}
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = dev_features[example_index.item()]
unique_id = str(eval_feature.unique_id)
result = RawResult(
unique_id=unique_id,
# [topk]
long_start_topk_logits=outputs['long_start_topk_logits']
[i].cpu().numpy(),
long_start_topk_index=outputs['long_start_topk_index']
[i].cpu().numpy(),
long_end_topk_logits=outputs['long_end_topk_logits']
[i].cpu().numpy(),
long_end_topk_index=outputs['long_end_topk_index']
[i].cpu().numpy(),
# [topk, topk]
short_start_topk_logits=outputs['short_start_topk_logits']
[i].cpu().numpy(),
short_start_topk_index=outputs['short_start_topk_index']
[i].cpu().numpy(),
short_end_topk_logits=outputs['short_end_topk_logits']
[i].cpu().numpy(),
short_end_topk_index=outputs['short_end_topk_index']
[i].cpu().numpy(),
answer_type_logits=to_list(
outputs['answer_type_logits'][i]),
long_cls_logits=outputs['long_cls_logits']
[i].cpu().numpy(),
short_cls_logits=outputs['short_cls_logits']
[i].cpu().numpy())
all_results.append(result)
pickle.dump(
all_results,
open(
os.path.join(args.output_dir,
'RawResults_ensemble{}.pkl'.format(ei)), 'wb'))
candidates_dict = read_candidates_from_one_split(args.predict_file)
nq_pred_dict = compute_pred_dict(candidates_dict,
dev_features,
[r._asdict() for r in all_results],
args.n_best_size,
args.max_answer_length,
topk_pred=True,
long_n_top=5,
short_n_top=5,
ensemble=True)
output_prediction_file = os.path.join(args.output_dir,
'predictions{}.json'.format(ei))
with open(output_prediction_file, 'w') as f:
json.dump({'predictions': list(nq_pred_dict.values())}, f)