def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default="glue_data/STS-B",
type=str,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_model", default="bert-base-uncased", type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default="sts",
type=str,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default="sts",
type=str,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--tagger_path", default=None, type=str,
help="tagger_path for predictions if needing real-time tagging. Default: None, by loading pre-tagged data")
## Other parameters
parser.add_argument("--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
processors = {
"sts": STSProcessor,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval and not args.do_predict:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
#num_labels = num_labels_task[task_name]
label_list = None
# num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
if args.tagger_path != None:
srl_predictor = SRLPredictor(args.tagger_path)
else:
srl_predictor = None
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
train_features = None
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer,srl_predictor=srl_predictor )
#TagTokenizer.make_tag_vocab("tag_vocab", tag_vocab)
tag_tokenizer = TagTokenizer("tag_vocab")
vocab_size = len(tag_tokenizer.ids_to_tags)
print("tokenizer vocab size: ", str(vocab_size))
tag_config = TagConfig(tag_vocab_size=vocab_size,
hidden_size=10,
layer_num=1,
output_dim=10,
dropout_prob=0.1,
num_aspect=3)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank))
model = BertForSequenceScoreTag.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),tag_config=tag_config)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
best_epoch = 0
best_result = 0.0
if args.do_train:
train_features = transform_tag_features(train_features, tag_tokenizer, args.max_seq_length)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
all_start_end_idx = torch.tensor([f.orig_to_token_split_idx for f in train_features], dtype=torch.long)
all_input_tag_ids = torch.tensor([f.input_tag_ids for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_end_idx, all_input_tag_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
eval_dataloader = None
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer,srl_predictor=srl_predictor )
eval_features = transform_tag_features(eval_features, tag_tokenizer, args.max_seq_length)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
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_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
all_start_end_idx = torch.tensor([f.orig_to_token_split_idx for f in eval_features], dtype=torch.long)
all_input_tag_ids = torch.tensor([f.input_tag_ids for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_end_idx, all_input_tag_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, start_end_idx, input_tag_ids, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses
lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, str(epoch)+"_pytorch_model.bin")
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
model_state_dict = torch.load(output_model_file)
predict_model = BertForSequenceScoreTag.from_pretrained(args.bert_model, state_dict=model_state_dict,tag_config=tag_config)
predict_model.to(device)
predict_model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
total_pred, total_labels = [],[]
total_TP, total_FP, total_FN, total_TN = 0, 0, 0, 0
for input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
start_end_idx = start_end_idx.to(device)
input_tag_ids = input_tag_ids.to(device)
with torch.no_grad():
tmp_eval_loss = predict_model(input_ids, segment_ids, input_mask, start_end_idx, input_tag_ids, label_ids)
logits = predict_model(input_ids, segment_ids, input_mask, start_end_idx, input_tag_ids, None)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
eval_loss += tmp_eval_loss.mean().item()
total_pred.extend(logits.squeeze().tolist())
total_labels.extend(label_ids.squeeze().tolist())
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
del predict_model
eval_loss = eval_loss / nb_eval_steps
spear = spearmanr(total_pred, total_labels)
pear = pearsonr(total_pred, total_labels)
mse = mean_squared_error(total_pred, total_labels)
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {'eval_loss': eval_loss,
'global_step': global_step,
'spearson':spear,
'pearson':pear,
'mse':mse,
'loss': loss}
if pear[0] > best_result:
best_epoch = epoch
best_result = pear[0]
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info("Epoch: %s, %s = %s", str(epoch), key, str(result[key]))
writer.write("Epoch: %s, %s = %s\n" % (str(epoch), key, str(result[key])))
logger.info("best epoch: %s, result: %s", str(best_epoch), str(best_result))
if args.do_predict:
eval_examples = processor.get_test_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer,srl_predictor=srl_predictor )
eval_features = transform_tag_features(eval_features, tag_tokenizer, args.max_seq_length)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
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_start_end_idx = torch.tensor([f.orig_to_token_split_idx for f in eval_features], dtype=torch.long)
all_input_tag_ids = torch.tensor([f.input_tag_ids for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_end_idx,
all_input_tag_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
output_model_file = os.path.join(args.output_dir, str(best_epoch)+ "_pytorch_model.bin")
model_state_dict = torch.load(output_model_file)
predict_model = BertForSequenceScoreTag.from_pretrained(args.bert_model, state_dict=model_state_dict,tag_config=tag_config)
predict_model.to(device)
predict_model.eval()
predictions = []
for input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
start_end_idx = start_end_idx.to(device)
input_tag_ids = input_tag_ids.to(device)
with torch.no_grad():
logits = predict_model(input_ids, segment_ids, input_mask, start_end_idx, input_tag_ids, None)
logits = logits.detach().cpu().numpy()
for (i, prediction) in enumerate(logits):
predict_label = prediction.squeeze()
predictions.append(predict_label)
output_test_file = os.path.join(args.output_dir, "pred_results.txt")
index = 0
with open(output_test_file, "a") as writer:
writer.write("index" + "\t" + "prediction" + "\n")
for pred in predictions:
writer.write(str(index) + "\t" + str(pred) + "\n")
index += 1
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default="glue_data/MNLI/",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default="mnli",
type=str,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default="base_mnli",
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--tagger_path",
default=None,
type=str,
help=
"tagger_path for predictions if needing real-time tagging. Default: None, by loading pre-tagged data"
"For example, the trained models by AllenNLP")
parser.add_argument("--max_num_aspect",
default=3,
type=int,
help="max_num_aspect")
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
processors = {
"snli": SnliProcessor,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval and not args.do_predict:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.tagger_path != None:
srl_predictor = SRLPredictor(args.tagger_path)
else:
srl_predictor = None
tag_tokenizer = TagTokenizer()
vocab_size = len(tag_tokenizer.ids_to_tags)
print("tokenizer vocab size: ", str(vocab_size))
tag_config = TagConfig(tag_vocab_size=vocab_size,
hidden_size=10,
layer_num=1,
output_dim=10,
dropout_prob=0.1,
num_aspect=args.max_num_aspect)
# Prepare optimizer
if args.do_eval:
# for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples,
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
eval_features = transform_tag_features(args.max_num_aspect,
eval_features, tag_tokenizer,
args.max_seq_length)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
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_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
all_start_end_idx = torch.tensor(
[f.orig_to_token_split_idx for f in eval_features],
dtype=torch.long)
all_input_tag_ids = torch.tensor(
[f.input_tag_ids for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_end_idx,
all_input_tag_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# epoch = 1
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
model_state_dict = torch.load(output_model_file)
predict_model = BertForSequenceClassificationTag.from_pretrained(
args.bert_model,
state_dict=model_state_dict,
num_labels=num_labels,
tag_config=tag_config)
predict_model.to(device)
predict_model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
start_end_idx = start_end_idx.to(device)
input_tag_ids = input_tag_ids.to(device)
with torch.no_grad():
tmp_eval_loss = predict_model(input_ids, segment_ids,
input_mask, start_end_idx,
input_tag_ids, label_ids)
logits = predict_model(input_ids, segment_ids, input_mask,
start_end_idx, input_tag_ids, None)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info("Result: %s = %s", key, str(result[key]))
writer.write("Result: %s = %s\n" % (key, str(result[key])))
logger.info("result: %s", str(result))
if args.do_predict:
eval_examples = processor.get_test_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples,
label_list,
args.max_seq_length,
tokenizer,
srl_predictor=srl_predictor)
eval_features = transform_tag_features(args.max_num_aspect,
eval_features, tag_tokenizer,
args.max_seq_length)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
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_start_end_idx = torch.tensor(
[f.orig_to_token_split_idx for f in eval_features],
dtype=torch.long)
all_input_tag_ids = torch.tensor(
[f.input_tag_ids for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_end_idx,
all_input_tag_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
model_state_dict = torch.load(output_model_file)
predict_model = BertForSequenceClassificationTag.from_pretrained(
args.bert_model,
state_dict=model_state_dict,
num_labels=num_labels,
tag_config=tag_config)
predict_model.to(device)
predict_model.eval()
predictions = []
for input_ids, input_mask, segment_ids, start_end_idx, input_tag_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
start_end_idx = start_end_idx.to(device)
input_tag_ids = input_tag_ids.to(device)
with torch.no_grad():
logits = predict_model(input_ids, segment_ids, input_mask,
start_end_idx, input_tag_ids, None)
logits = logits.detach().cpu().numpy()
for (i, prediction) in enumerate(logits):
predict_label = np.argmax(prediction)
predictions.append(predict_label)
output_test_file = os.path.join(args.output_dir, "_pred_results.tsv")
index = 0
with open(output_test_file, "w") as writer:
writer.write("index" + "\t" + "prediction" + "\n")
for pred in predictions:
writer.write(
str(index) + "\t" + str(label_list[int(pred)]) + "\n")
index += 1