def just_korquad_eval(args):
expected_version = 'KorQuAD_v1.0'
with open(args.predict_file) as dataset_file:
dataset_json = json.load(dataset_file)
read_version = "_".join(dataset_json['version'].split("_")[:-1])
if (read_version != expected_version):
logger.info('Evaluation expects ' + expected_version +
', but got dataset with ' + read_version,
file=sys.stderr)
dataset = dataset_json['data']
with open(
os.path.join(args.output_dir, "predictions_{}.json".format(
args.eda_type))) as prediction_file:
predictions = json.load(prediction_file)
logger.info(json.dumps(korquad_eval(dataset, predictions)))
def evaluate(args, model, eval_examples, eval_features):
""" Eval """
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_example_index)
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset,
sampler=sampler,
batch_size=args.batch_size)
logger.info("***** Evaluating *****")
logger.info(" Num features = %d", len(dataset))
logger.info(" Batch size = %d", args.batch_size)
model.eval()
all_results = []
set_seed(
args) # Added here for reproductibility (even between python 2 and 3)
logger.info("Start evaluating!")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
dataloader, desc="Evaluating"):
input_ids = input_ids.to(args.device)
input_mask = input_mask.to(args.device)
segment_ids = segment_ids.to(args.device)
with torch.no_grad():
batch_start_logits, batch_end_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
write_predictions(eval_examples, eval_features, all_results,
args.n_best_size, args.max_answer_length, False,
output_prediction_file, output_nbest_file, None, False,
False, 0.0)
expected_version = 'KorQuAD_v1.0'
with open(args.predict_file) as dataset_file:
dataset_json = json.load(dataset_file)
read_version = "_".join(dataset_json['version'].split("_")[:-1])
if (read_version != expected_version):
logger.info('Evaluation expects ' + expected_version +
', but got dataset with ' + read_version,
file=sys.stderr)
dataset = dataset_json['data']
with open(os.path.join(args.output_dir,
"predictions.json")) as prediction_file:
predictions = json.load(prediction_file)
logger.info(json.dumps(korquad_eval(dataset, predictions)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint",
default='output/korquad_3.bin',
type=str,
help="checkpoint")
parser.add_argument(
"--output_dir",
default='debug',
type=str,
help=
"The output directory where the model checkpoints and predictions will be written."
)
parser.add_argument("--model_config", type=str)
parser.add_argument("--vocab", type=str)
## Other parameters
parser.add_argument(
"--predict_file",
default='data/KorQuAD_v1.0_dev.json',
type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
)
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=64,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=96,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--batch_size",
default=16,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json output file."
)
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16',
action='store_true',
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"
)
parser.add_argument('--ensemble', default='false', type=str)
args = parser.parse_args()
args.ensemble = args.ensemble.lower() == 'true'
# Setup CUDA, GPU & distributed training
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
args.device = device
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger.info("device: %s, n_gpu: %s, 16-bits training: %s", device,
args.n_gpu, args.fp16)
# Set seed
set_seed(args)
tokenizer = BertTokenizer(vocab_file=args.vocab,
do_basic_tokenize=True,
max_len=args.max_seq_length)
config = Config.from_json_file(args.model_config)
model = QuestionAnswering(config)
# Evaluate
examples, features = load_and_cache_examples(args, tokenizer)
if not args.ensemble:
logger.info(" Load Model: %s ", args.checkpoint)
model.load_state_dict(torch.load(args.checkpoint))
num_params = count_parameters(model)
logger.info("Total Parameter: %d" % num_params)
if args.fp16:
model.half()
model.to(args.device)
logger.info("Evaluation parameters %s", args)
results = evaluate(args, model, examples, features)
else:
list_ckpts = []
with open(os.path.join(args.output_dir, "ckpt_list.txt"), 'r') as f:
for line in f:
list_ckpts.append(line.strip())
list_results = []
for i, ckpt in enumerate(list_ckpts):
fn = os.path.join(args.output_dir, ckpt)
logger.info(" Load Model: %s ", fn)
model.load_state_dict(torch.load(fn))
num_params = count_parameters(model)
logger.info("Total Parameter: %d" % num_params)
if args.fp16:
model.half()
model.to(args.device)
logger.info("Evaluation parameters %s", args)
results = evaluate(args, model, examples, features)
list_results.append(results)
list_qid = []
for example in examples:
list_qid.append(example.qas_id)
all_predictions = collections.OrderedDict()
for qid in list_qid:
max_prob, answer = 0.0, ""
for results in list_results:
prob, text = 0.0, None
for output in results[qid]:
if output["text"]:
prob = output["probability"]
text = output["text"]
break
if prob > max_prob:
max_prob = prob
answer = text
all_predictions[qid] = answer
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
expected_version = 'KorQuAD_v1.0'
with open(args.predict_file) as dataset_file:
dataset_json = json.load(dataset_file)
read_version = "_".join(dataset_json['version'].split("_")[:-1])
if (read_version != expected_version):
logger.info('Evaluation expects ' + expected_version +
', but got dataset with ' + read_version,
file=sys.stderr)
dataset = dataset_json['data']
with open(os.path.join(args.output_dir,
"predictions.json")) as prediction_file:
predictions = json.load(prediction_file)
logger.info(json.dumps(korquad_eval(dataset, predictions)))
