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main.py
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main.py
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import os
from tqdm import tqdm
import re
import time
import torch
import utils
from helpers import load_vocab_dict
from helpers import batch_iter, labelize, bert_tokenize_for_valid_examples
from helpers import untokenize_without_unks, untokenize_without_unks2, get_model_nparams
from hazm import Normalizer
from models import SubwordBert
from utils import get_sentences_splitters
def model_inference(model, data, topk, DEVICE, BATCH_SIZE=16, vocab_=None):
"""
model: an instance of SubwordBert
data: list of tuples, with each tuple consisting of correct and incorrect
sentence string (would be split at whitespaces)
topk: how many of the topk softmax predictions are considered for metrics calculations
"""
if vocab_ is not None:
vocab = vocab_
print("###############################################")
inference_st_time = time.time()
_corr2corr, _corr2incorr, _incorr2corr, _incorr2incorr = 0, 0, 0, 0
_mistakes = []
VALID_BATCH_SIZE = BATCH_SIZE
valid_loss = 0.
print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
results = []
line_index = 0
for batch_id, (batch_labels, batch_sentences) in tqdm(enumerate(data_iter)):
torch.cuda.empty_cache()
st_time = time.time()
# set batch data for bert
batch_labels_, batch_sentences_, batch_bert_inp, batch_bert_splits = bert_tokenize_for_valid_examples(
batch_labels, batch_sentences)
if len(batch_labels_) == 0:
print("################")
print("Not predicting the following lines due to pre-processing mismatch: \n")
print([(a, b) for a, b in zip(batch_labels, batch_sentences)])
print("################")
continue
else:
batch_labels, batch_sentences = batch_labels_, batch_sentences_
batch_bert_inp = {k: v.to(DEVICE) for k, v in batch_bert_inp.items()}
# set batch data for others
batch_labels_ids, batch_lengths = labelize(batch_labels, vocab)
batch_lengths = batch_lengths.to(DEVICE)
batch_labels_ids = batch_labels_ids.to(DEVICE)
try:
with torch.no_grad():
"""
NEW: batch_predictions can now be of shape (batch_size,batch_max_seq_len,topk) if topk>1, else (batch_size,batch_max_seq_len)
"""
batch_loss, batch_predictions = model(batch_bert_inp, batch_bert_splits, targets=batch_labels_ids,
topk=topk)
except RuntimeError:
print(f"batch_bert_inp:{len(batch_bert_inp.keys())},batch_labels_ids:{batch_labels_ids.shape}")
raise Exception("")
valid_loss += batch_loss
batch_lengths = batch_lengths.cpu().detach().numpy()
if topk == 1:
batch_predictions = untokenize_without_unks(batch_predictions, batch_lengths, vocab, batch_sentences)
else:
batch_predictions = untokenize_without_unks2(batch_predictions, batch_lengths, vocab, batch_sentences,
topk=None)
batch_clean_sentences = [line for line in batch_labels]
batch_corrupt_sentences = [line for line in batch_sentences]
batch_predictions = [line for line in batch_predictions]
for i, (a, b, c) in enumerate(zip(batch_clean_sentences, batch_corrupt_sentences, batch_predictions)):
results.append({"id": line_index + i, "original": a, "noised": b, "predicted": c, "topk": [],
"topk_prediction_probs": [], "topk_reranker_losses": []})
line_index += len(batch_clean_sentences)
'''
# update progress
progressBar(batch_id+1,
int(np.ceil(len(data) / VALID_BATCH_SIZE)),
["batch_time","batch_loss","avg_batch_loss","batch_acc","avg_batch_acc"],
[time.time()-st_time,batch_loss,valid_loss/(batch_id+1),None,None])
'''
print(f"\nEpoch {None} valid_loss: {valid_loss / (batch_id + 1)}")
print("total inference time for this data is: {:4f} secs".format(time.time() - inference_st_time))
print("###############################################")
print("###############################################")
return results
def load_model(vocab):
model = SubwordBert(3*len(vocab["chartoken2idx"]),vocab["token2idx"][ vocab["pad_token"] ],len(vocab["token_freq"]))
print(model)
print( get_model_nparams(model) )
return model
def load_pretrained(model, checkpoint_path, optimizer=None, device='cuda'):
if torch.cuda.is_available() and device != "cpu":
map_location = lambda storage, loc: storage.cuda()
else:
map_location = 'cpu'
print(f"Loading model params from checkpoint dir: {checkpoint_path}")
checkpoint_data = torch.load(checkpoint_path, map_location=map_location)
model.load_state_dict(checkpoint_data['model_state_dict'])
if optimizer is not None:
optimizer.load_state_dict(checkpoint_data['optimizer_state_dict'])
max_dev_acc, argmax_dev_acc = checkpoint_data["max_dev_acc"], checkpoint_data["argmax_dev_acc"]
if optimizer is not None:
return model, optimizer, max_dev_acc, argmax_dev_acc
return model
def load_pre_model(vocab_path, model_checkpoint_path):
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
print(f"loading vocab from {vocab_path}")
vocab = load_vocab_dict(vocab_path)
model = load_model(vocab)
model = load_pretrained(model, model_checkpoint_path)
return model, vocab, DEVICE
def spell_checking_on_sents(model, vocab, device, normalizer, txt):
sents, splitters = get_sentences_splitters(txt)
sents = [utils.space_special_chars(s) for s in sents]
sents = list(filter(lambda txt: (txt != '' and txt != ' '), sents))
test_data = [(normalizer.normalize(t), normalizer.normalize(t)) for t in sents]
print('inputs:')
for t in test_data:
print(t)
greedy_results = model_inference(model, test_data, topk=1, DEVICE=device, BATCH_SIZE=1,
vocab_=vocab)
out = []
for i, line in enumerate(greedy_results):
ls = [(n, p) if n == p else ("**" + n + "**", "**" + p + "**") for n, p in
zip(line["noised"].split(), line["predicted"].split())]
y, z = map(list, zip(*ls))
try:
z = ' '.join(z)
z = re.sub(r'\*\*(\w+)\*\*', r'** \1 **', z)
z = re.sub(r'\*\* (\w+) \*\*', r'**\1**', z)
except:
z = ' '.join(z)
out.append((" ".join(y), z))
new_out = []
for i, sent in enumerate(out):
new_out.append( (utils.de_space_special_chars(out[i][0]), utils.de_space_special_chars(out[i][1])))
return new_out, splitters
if __name__ == '__main__':
normalizer = Normalizer(punctuation_spacing=False, remove_extra_spaces=False)
vocab_path = os.path.join('model', 'vocab.pkl')
model_checkpoint_path = os.path.join('model', 'model.pth.tar')
model, vocab, device = load_pre_model(vocab_path=vocab_path, model_checkpoint_path=model_checkpoint_path)
#test
sample_input = 'این یک مثالل صاده برالی ازرابی این سامانح اسصت'
output = spell_checking_on_sents(model, vocab, device, normalizer, sample_input)
print(output)