def load_dataset(main_file, args, processor, tokenizer, output_mode, data_type = None):
# Prepare data loader
id_file = main_file + "_ids_nocls.pt"
mask_file = main_file + "_mask_nocls.pt"
label_file = main_file + "_labels.pt"
#case1 tensor files exist
file_exist_count = sum([isfile(id_file), isfile(label_file), isfile(mask_file)])
if 0 < file_exist_count < 3:
exit("Only part of the data is saved as tensor files. Delete those files and try again.")
elif file_exist_count == 3:
#import pdb; pdb.set_trace()
all_input_ids = torch.load(id_file)
all_masks = torch.load(mask_file)
all_label_ids = torch.load(label_file)
return TensorDataset(all_input_ids, all_masks, all_label_ids)
#load the mega object
if data_type == "train":
features = convert_examples_to_features(
processor.get_train_examples(args.data_dir),
processor.get_labels(),
tokenizer,
args.max_tokens)
elif data_type == "test":
features = convert_examples_to_features(
processor.get_dev_examples(args.data_dir),
processor.get_labels(),
tokenizer,
args.max_tokens)
elif data_type == "val":
features = convert_examples_to_features(
processor.get_val_examples(args.data_dir),
processor.get_labels(),
tokenizer,
args.max_tokens)
else:
exit(f"invalid data_type {data_type}")
#parse it carefully into tensor files
torch.save(torch.tensor([f.input_ids for f in features], dtype=torch.long), id_file)
torch.save(torch.tensor([f.input_mask for f in features], dtype=torch.long), mask_file)
torch.save(torch.tensor([f.label_id for f in features], dtype=torch.long), label_file)
#call this function again!
return load_dataset(main_file, args, processor, tokenizer, output_mode, data_type=data_type)
def run(data):
data_loaded = json.loads(data)
input_sentence = data_loaded["input"]
input_example = ("PREDICT_0", input_sentence, "None")
max_seq_len = configs["max_sequence_length"]
feats = convert_examples_to_features([input_example], classes_list, max_seq_len, tokenizer)[0]
feats = [torch.tensor(x).unsqueeze(0) for x in feats]
model_out = model(input_ids=feats[0], attention_mask=feats[1], token_type_ids=feats[2], class_label_ids=None, input_ids_masked=feats[4])
logits_softmaxed = torch.nn.functional.softmax(model_out[1][0], dim=-1).detach().cpu().numpy()
return {"country_prediction": str(classes_list[np.argmax(logits_softmaxed)]), "province_prediction": "None"}
def load_and_cache_examples(args, task, tokenizer, evaluate=False):
processor = processors[task]()
output_mode = output_modes[task]
# Load data features from cache or dataset file
cached_features_file = os.path.join(
args.data_dir, 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train',
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length), str(task)))
if os.path.exists(cached_features_file):
logger.info("Loading features from cached file %s",
cached_features_file)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", args.data_dir)
label_list = processor.get_labels()
examples = processor.get_dev_examples(
args.data_dir) if evaluate else processor.get_train_examples(
args.data_dir)
features = convert_examples_to_features(
examples,
label_list,
args.max_seq_length,
tokenizer,
output_mode,
cls_token_at_end=bool(args.model_type in ['xlnet']
), # xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
sep_token=tokenizer.sep_token,
cls_token_segment_id=2 if args.model_type in ['xlnet'] else 1,
pad_on_left=bool(
args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0)
if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s",
cached_features_file)
torch.save(features, cached_features_file)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features],
dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label_ids)
return dataset
def load_and_cache_examples(args, folder, task, tokenizer):
processor = processors[task]()
output_mode = output_modes[task]
cached_features_file = os.path.join(
folder, 'cached_{}_{}_{}_{}_{}'.format(
'test',
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length), str(task),
str(args.similarity_threshold)))
if os.path.exists(cached_features_file):
#if os.path.exists(cached_features_file):
features = torch.load(cached_features_file)
else:
label_list = processor.get_labels()
examples = torch.load(folder + '/example')
features = convert_examples_to_features(
examples,
label_list,
args.max_seq_length,
tokenizer,
output_mode,
cls_token_at_end=bool(args.model_type in ['xlnet']
), # xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
sep_token=tokenizer.sep_token,
cls_token_segment_id=2 if args.model_type in ['xlnet'] else 1,
pad_on_left=bool(
args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0)
if args.local_rank in [-1, 0]:
#logger.info("Saving features into cached file %s", cached_features_file)
torch.save(features, cached_features_file)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features],
dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label_ids)
return dataset
def return_logits(in_example):
input_example = ("PREDICT_0", in_example, "None")
max_seq_len = configs["max_sequence_length"]
feats = convert_examples_to_features([input_example], classes_list,
max_seq_len, tokenizer)[0]
feats = [torch.tensor(x).unsqueeze(0) for x in feats]
model_out = model(input_ids=feats[0],
attention_mask=feats[1],
token_type_ids=feats[2],
class_label_ids=None,
input_ids_masked=feats[4])
logits_softmaxed = torch.nn.functional.softmax(
model_out[1][0], dim=-1).detach().cpu().numpy()
return logits_softmaxed
def load_and_cache_examples_randomized(args,
task,
tokenizer,
random_smooth,
epoch,
evaluate=False):
processor = processors[task]()
output_mode = output_modes[task]
# Load data features from cache or dataset file
cached_features_file = os.path.join(
args.data_dir, 'cached_{}_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train',
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length), str(task),
str(args.similarity_threshold)))
##############################################################################
if os.path.exists(cached_features_file + '_' + str(epoch)):
##############################################################################
print('Randomize dataset: cached features exists')
logger.info("Loading features from cached file %s",
cached_features_file + '_' + str(epoch))
features = torch.load(cached_features_file + '_' + str(epoch))
else:
print('Randomize dataset: cached features NOT exists')
if os.path.exists(cached_features_file + '_example'):
print('Randomize dataset: cached examples exists')
examples = torch.load(cached_features_file + '_example')
else:
print('Randomize dataset: cached examples NOT exists')
logger.info("Creating features from dataset file at %s",
args.data_dir)
examples = processor.get_dev_examples(
args.data_dir) if evaluate else processor.get_train_examples(
args.data_dir)
# save examples before perturbe it
logger.info("Saving examples into cached file %s",
cached_features_file + '_example')
torch.save(examples, cached_features_file + '_example')
for example in examples:
if example.text_a:
example.text_a = str(
random_smooth.get_perturbed_batch(
np.array([[example.text_a]]))[0][0])
if example.text_b:
example.text_b = str(
random_smooth.get_perturbed_batch(
np.array([[example.text_b]]))[0][0])
label_list = processor.get_labels()
features = convert_examples_to_features(
examples,
label_list,
args.max_seq_length,
tokenizer,
output_mode,
cls_token_at_end=bool(args.model_type in ['xlnet']
), # xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
sep_token=tokenizer.sep_token,
cls_token_segment_id=2 if args.model_type in ['xlnet'] else 1,
pad_on_left=bool(
args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0)
if args.local_rank in [-1, 0]:
#logger.info("Saving examples into cached file %s", cached_features_file + '_example')
#torch.save(examples, cached_features_file + '_example')
logger.info("Saving features into cached file %s",
cached_features_file + '_' + str(epoch))
torch.save(features, cached_features_file + '_' + str(epoch))
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features],
dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label_ids)
return dataset
