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train_SeqLabel.py
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train_SeqLabel.py
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import argparse
import torch
import torch.nn as nn
from t5_model import *
from config import Config
import os, sys, math
from checkpointing import CheckpointManager
import random
from pre_train_dataset import *
import utils
from tqdm import tqdm
from seqeval.metrics import f1_score, precision_score, recall_score, classification_report
def get_label_dict(label_path):
label_dict = {}
with open(label_path) as out:
for l in out.readlines():
l = l.strip()
label_dict[l] = len(label_dict)
return label_dict
def evaluation(config, eval_data, model, label_map, device, show_detail=False, output_path=None):
model.eval()
model = model.to(device)
preds = None
pad_token_label_id = -100
input_words = []
scores = []
softmax = torch.nn.Softmax(dim=-1)
with torch.no_grad():
for batch in tqdm(eval_data):
for n in batch:
if batch[n] is not None and n not in ['gt_x']:
batch[n] = batch[n].to(device)
outputs = model(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask']
)
logits = outputs[0]
logits = softmax(logits)
max_score, _ = torch.max(logits, dim=-1)
mean_score = torch.sum(max_score * batch['attention_mask'], dim=1) / torch.sum(batch['attention_mask'], dim=1)
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = batch["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, batch["labels"].detach().cpu().numpy(), axis=0)
input_words += batch['gt_x']
scores += mean_score.detach().cpu().numpy().tolist()
preds = np.argmax(preds, axis=2)
preds_list = [[] for _ in range(out_label_ids.shape[0])]
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != pad_token_label_id:
preds_list[i].append(label_map[preds[i][j]])
out_label_list[i].append(label_map[out_label_ids[i][j]])
new_F = f1_score(out_label_list, preds_list) * 100
print("all data Performance %.2f" % new_F)
selected_words, selected_tags = [], []
if config.score_top_ratio > 0:
selected_gt, selected_pred = [], []
rank_list = [(w, t, gt, s) for (w, t, gt, s) in zip(input_words, preds_list, out_label_list, scores)]
sorted_rank_list = sorted(rank_list, key=lambda x: x[3], reverse=True)
select_num = int(len(sorted_rank_list) * config.score_top_ratio)
for input_word, tag, gt_tag, _ in sorted_rank_list[:select_num]:
selected_words.append(input_word)
selected_tags.append(tag)
selected_pred.append(tag)
selected_gt.append(gt_tag)
F = f1_score(selected_gt, selected_pred) * 100
print("Selected Performance %.2f" % F)
else:
for input_word, gt_label, pred_label in zip(input_words, out_label_list, preds_list):
if config.enable_consistency_filtering:
if len(input_word) > config.filter_by_min_length and ' '.join(gt_label) == ' '.join(pred_label):
selected_words.append(input_word)
selected_tags.append(pred_label)
else:
selected_words.append(input_word)
selected_tags.append(pred_label)
if output_path is not None:
with open(output_path, 'w') as out:
for gen, labels in zip(selected_words, selected_tags):
for g, l in zip(gen, labels):
out.write("%s %s\n" % (g, l))
out.write("\n")
return new_F
parser = argparse.ArgumentParser("Train a MT5 for Machine Translation")
parser.add_argument(
"--config", required=True, help="Path to a config file with all configuration parameters."
)
parser.add_argument(
"--config-override",
default=[],
nargs="*",
help="A sequence of key-value pairs specifying certain config arguments (with dict-like "
"nesting) using a dot operator. The actual config will be updated and recorded in "
"the serialization directory.",
)
parser.add_argument(
"--serialization-dir",
default=None,
help="Path to a (non-existent) directory for serializing checkpoints and tensorboard logs.",
)
parser.add_argument(
"--start-from-checkpoint",
default=None,
help="Path to load checkpoint and continue training [only supported for module_training].",
)
parser.add_argument(
"--output-path",
default=None,
help="Path to save output captions",
)
parser.add_argument(
"--pre-compute",
action='store_true',
help="Pre Compute",
)
group = parser.add_mutually_exclusive_group()
group.add_argument('--train', action='store_true')
group.add_argument('--validation', action='store_true')
group.add_argument('--test', action='store_true')
if __name__ == "__main__":
_A = parser.parse_args()
_C = Config(_A.config, _A.config_override)
np.random.seed(_C.random_seed)
random.seed(_C.random_seed)
torch.manual_seed(_C.random_seed)
torch.cuda.manual_seed_all(_C.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
label_dict = get_label_dict(_C.label_path)
label_map = {v:k for (k,v) in label_dict.items()}
tokenizer, model = get_bert_model(_C, len(label_dict))
model = model.to(device)
total_parameter_count = 0
trainable_parameter_count = 0
for p in model.parameters():
total_parameter_count += p.numel()
if p.requires_grad:
trainable_parameter_count += p.numel()
print('Total Parameter Count %d' % total_parameter_count)
print('Trainable Parameter Count %d' % trainable_parameter_count)
print(_C)
for arg in vars(_A):
print("{:<20}: {}".format(arg, getattr(_A, arg)))
dev_data = SeqLabelDataset(_C, _C.dev_path, label_dict, tokenizer)
dev_loader = get_seq_data_loader(_C, dev_data, _C.batch_size if _C.val_batch_size < 0 else _C.val_batch_size)
test_data = SeqLabelDataset(_C, _C.test_path, label_dict, tokenizer)
test_loader = get_seq_data_loader(_C, test_data, _C.batch_size if _C.val_batch_size < 0 else _C.val_batch_size)
if _A.validation or _A.test:
if torch.cuda.is_available():
model.load_state_dict(torch.load(os.path.join(_A.start_from_checkpoint, 'model-best.pth'))['model'], strict=False)
else:
model.load_state_dict(torch.load(os.path.join(_A.start_from_checkpoint, 'model-best.pth'), map_location=torch.device('cpu'))['model'], strict=False)
selected_data = dev_loader if _A.validation else test_loader
evaluation(_C, selected_data, model, label_map, device, show_detail=True, output_path=_A.output_path)
if _A.train:
train_data = SeqLabelDataset(_C, _C.train_path, label_dict, tokenizer, is_training=True)
train_loader = get_seq_data_loader(_C, train_data, _C.batch_size, shuffle=True)
train_iter = iter(train_loader)
if _C.num_training_steps == 0:
_C.num_training_steps = int(len(train_iter) * _C.max_epoch / _C.gradient_accumulation_steps)
epoch_num = math.ceil(_C.num_training_steps / _C.checkpoint_every_step)
if _C.warmup_step == 0 and _C.warmup_ratio == 0:
optimizer = utils.build_optimizer(_C, model)
else:
optimizer = utils.build_warmup_optimizer(_C, model)
os.makedirs(_A.serialization_dir, exist_ok=True)
_C.dump(os.path.join(_A.serialization_dir, "config.yml"))
checkpoint_manager = CheckpointManager(model, _A.serialization_dir, mode="max")
eval_every = _C.checkpoint_every_step * _C.gradient_accumulation_steps
total_step = 0
best_test_performance = 0
for epoch in range(epoch_num):
print('EPOCH %d / %d' % (epoch + 1, epoch_num))
run_step = eval_every if total_step + eval_every < _C.num_training_steps * _C.gradient_accumulation_steps else _C.num_training_steps * _C.gradient_accumulation_steps - total_step
model.train()
with tqdm(total=math.ceil(run_step / _C.gradient_accumulation_steps), file=sys.stdout) as pbar:
for step in range(run_step):
try:
batch = next(train_iter)
except:
train_iter = iter(train_loader)
batch = next(train_iter)
for n in batch:
if batch[n] is not None and n not in ['gt_x']:
batch[n] = batch[n].to(device)
total_step += 1
outputs = model(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask'],
labels=batch['labels']
)
loss = outputs.loss
loss = loss / _C.gradient_accumulation_steps
loss.backward()
if (step + 1) % _C.gradient_accumulation_steps == 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), _C.max_grad_norm)
optimizer.step()
if torch.cuda.is_initialized():
torch.cuda.synchronize()
pbar.set_description("loss %.2f" % (loss.item() * _C.gradient_accumulation_steps))
pbar.update(1)
optimizer.zero_grad()
_score = evaluation(_C, dev_loader, model, label_map, device)
update_test = checkpoint_manager.step(_score)
if update_test:
best_test_performance = evaluation(_C, test_loader, model, label_map, device)
print("best test performance %.2f" % best_test_performance)