def model_predictions(model, data, vocab, DEVICE, BATCH_SIZE=16):
"""
model: an instance of BertSCLSTM
data: list of tuples, with each tuple consisting of correct and incorrect
sentence string (would be split at whitespaces)
"""
topk = 1
# print("###############################################")
inference_st_time = time.time()
final_sentences = []
VALID_BATCH_SIZE = BATCH_SIZE
# print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
for batch_id, (batch_labels, batch_sentences) in enumerate(data_iter):
# 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_idxs, batch_lengths_ = sclstm_tokenize(batch_sentences, vocab)
assert (batch_lengths_ == batch_lengths).all() == True
assert len(batch_bert_splits) == len(batch_idxs)
batch_idxs = [batch_idxs_.to(DEVICE) for batch_idxs_ in batch_idxs]
batch_lengths = batch_lengths.to(DEVICE)
batch_labels_ids = batch_labels_ids.to(DEVICE)
# forward
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_predictions = model(batch_idxs,
batch_lengths,
batch_bert_inp,
batch_bert_splits,
targets=batch_labels_ids,
topk=topk)
batch_predictions = untokenize_without_unks(batch_predictions,
batch_lengths, vocab,
batch_labels)
final_sentences.extend(batch_predictions)
# print("total inference time for this data is: {:4f} secs".format(time.time()-inference_st_time))
return final_sentences
def model_predictions(model, data, vocab, DEVICE, BATCH_SIZE=16):
"""
model: an instance of ElmoSCTransformer
data: list of tuples, with each tuple consisting of correct and incorrect
sentence string (would be split at whitespaces)
"""
topk = 1
print("###############################################")
inference_st_time = time.time()
final_sentences = []
VALID_BATCH_SIZE = BATCH_SIZE
print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
for batch_id, (batch_clean_sentences,
batch_corrupt_sentences) in enumerate(data_iter):
# set batch data
batch_labels, batch_lengths = labelize(batch_clean_sentences, vocab)
batch_idxs, batch_lengths_, inverted_mask = sctrans_tokenize(
batch_corrupt_sentences, vocab)
assert (batch_lengths_ == batch_lengths).all() == True
batch_idxs = [batch_idxs_.to(DEVICE) for batch_idxs_ in batch_idxs]
batch_lengths = batch_lengths.to(DEVICE)
batch_labels = batch_labels.to(DEVICE)
inverted_mask = inverted_mask.to(DEVICE)
batch_elmo_inp = elmo_batch_to_ids(
[line.split() for line in batch_corrupt_sentences]).to(DEVICE)
# forward
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_predictions = model(batch_idxs,
inverted_mask,
batch_lengths,
batch_elmo_inp,
targets=batch_labels,
topk=topk)
batch_predictions = untokenize_without_unks(batch_predictions,
batch_lengths, vocab,
batch_clean_sentences)
final_sentences.extend(batch_predictions)
print("total inference time for this data is: {:4f} secs".format(
time.time() - inference_st_time))
return final_sentences
def model_predictions(model, data, vocab, DEVICE, BATCH_SIZE=16):
"""
model: an instance of CharLSTMWordLSTMModel
data: list of tuples, with each tuple consisting of correct and incorrect
sentence string (would be split at whitespaces)
"""
topk = 1
print("###############################################")
inference_st_time = time.time()
final_sentences = []
VALID_BATCH_SIZE = BATCH_SIZE
print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
for batch_id, (batch_clean_sentences,
batch_corrupt_sentences) in tqdm(enumerate(data_iter)):
# set batch data
batch_labels, batch_lengths = labelize(batch_clean_sentences, vocab)
batch_idxs, batch_lengths_, batch_char_lengths = char_tokenize(
batch_corrupt_sentences, vocab, return_nchars=True)
assert (batch_lengths_ == batch_lengths).all() == True
batch_idxs = [batch_idxs_.to(DEVICE) for batch_idxs_ in batch_idxs]
batch_char_lengths = [
batch_char_lengths_.to(DEVICE)
for batch_char_lengths_ in batch_char_lengths
]
batch_lengths = batch_lengths.to(DEVICE)
batch_labels = batch_labels.to(DEVICE)
# forward
with torch.no_grad():
# because topk=1, batch_predictions are of shape (batch_size,batch_max_seq_len)
_, batch_predictions = model(batch_idxs,
batch_char_lengths,
batch_lengths,
targets=batch_labels,
topk=topk)
batch_predictions = untokenize_without_unks(batch_predictions,
batch_lengths, vocab,
batch_clean_sentences)
final_sentences.extend(batch_predictions)
print("total inference time for this data is: {:4f} secs".format(
time.time() - inference_st_time))
return final_sentences
def model_inference(model, data, topk, DEVICE, BATCH_SIZE=16):
"""
model: an instance of ElmoSCTransformer
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
"""
print("###############################################")
inference_st_time = time.time()
_corr2corr, _corr2incorr, _incorr2corr, _incorr2incorr = 0, 0, 0, 0
_mistakes = []
VALID_BATCH_SIZE = BATCH_SIZE
valid_loss = 0.
valid_acc = 0.
print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
for batch_id, (batch_clean_sentences,
batch_corrupt_sentences) in tqdm(enumerate(data_iter)):
torch.cuda.empty_cache()
st_time = time.time()
# set batch data
batch_labels, batch_lengths = labelize(batch_clean_sentences, vocab)
batch_idxs, batch_lengths_, inverted_mask = sctrans_tokenize(
batch_corrupt_sentences, vocab)
assert (batch_lengths_ == batch_lengths).all() == True
batch_idxs = [batch_idxs_.to(DEVICE) for batch_idxs_ in batch_idxs]
batch_lengths = batch_lengths.to(DEVICE)
batch_labels = batch_labels.to(DEVICE)
inverted_mask = inverted_mask.to(DEVICE)
batch_elmo_inp = elmo_batch_to_ids(
[line.split() for line in batch_corrupt_sentences]).to(DEVICE)
# forward
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_idxs,
inverted_mask,
batch_lengths,
batch_elmo_inp,
targets=batch_labels,
topk=topk)
except RuntimeError:
print(
f"batch_idxs:{len(batch_idxs)},batch_lengths:{batch_lengths.shape},batch_elmo_inp:{batch_elmo_inp.shape},batch_labels:{batch_labels.shape}"
)
raise Exception("")
valid_loss += batch_loss
# compute accuracy in numpy
batch_labels = batch_labels.cpu().detach().numpy()
batch_lengths = batch_lengths.cpu().detach().numpy()
# based on topk, obtain either strings of batch_predictions or list of tokens
if topk == 1:
batch_predictions = untokenize_without_unks(
batch_predictions, batch_lengths, vocab,
batch_corrupt_sentences)
else:
batch_predictions = untokenize_without_unks2(
batch_predictions,
batch_lengths,
vocab,
batch_corrupt_sentences,
topk=None)
#corr2corr, corr2incorr, incorr2corr, incorr2incorr, mistakes = \
# get_metrics(batch_clean_sentences,batch_corrupt_sentences,batch_predictions,check_until_topk=topk,return_mistakes=True)
#_mistakes.extend(mistakes)
batch_clean_sentences = [
line.lower() for line in batch_clean_sentences
]
batch_corrupt_sentences = [
line.lower() for line in batch_corrupt_sentences
]
batch_predictions = [line.lower() for line in batch_predictions]
corr2corr, corr2incorr, incorr2corr, incorr2incorr = \
get_metrics(batch_clean_sentences,batch_corrupt_sentences,batch_predictions,check_until_topk=topk,return_mistakes=False)
_corr2corr += corr2corr
_corr2incorr += corr2incorr
_incorr2corr += incorr2corr
_incorr2incorr += incorr2incorr
# delete
del batch_loss
del batch_predictions
del batch_labels, batch_lengths, batch_idxs, batch_lengths_, batch_elmo_inp
torch.cuda.empty_cache()
'''
# 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("")
#for mistake in _mistakes:
# print(mistake)
print("")
print("total token count: {}".format(_corr2corr + _corr2incorr +
_incorr2corr + _incorr2incorr))
print(
f"_corr2corr:{_corr2corr}, _corr2incorr:{_corr2incorr}, _incorr2corr:{_incorr2corr}, _incorr2incorr:{_incorr2incorr}"
)
print(
f"accuracy is {(_corr2corr+_incorr2corr)/(_corr2corr+_corr2incorr+_incorr2corr+_incorr2incorr)}"
)
print(
f"word correction rate is {(_incorr2corr)/(_incorr2corr+_incorr2incorr)}"
)
print("###############################################")
return
def model_inference(model, data, topk, DEVICE, BATCH_SIZE=16, vocab_=None):
"""
model: an instance of SCLSTM
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_
# list of dicts with keys {"id":, "original":, "noised":, "predicted":, "topk":, "topk_prediction_probs":, "topk_reranker_losses":,}
results = []
line_index = 0
print("###############################################")
inference_st_time = time.time()
_corr2corr, _corr2incorr, _incorr2corr, _incorr2incorr = 0, 0, 0, 0
_mistakes = []
VALID_BATCH_SIZE = BATCH_SIZE
valid_loss = 0.
valid_acc = 0.
print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
for batch_id, (batch_clean_sentences,
batch_corrupt_sentences) in tqdm(enumerate(data_iter)):
st_time = time.time()
# set batch data
batch_labels, batch_lengths = labelize(batch_clean_sentences, vocab)
batch_idxs, batch_lengths_ = sclstm_tokenize(batch_corrupt_sentences,
vocab)
assert (batch_lengths_ == batch_lengths).all() == True
batch_idxs = [batch_idxs_.to(DEVICE) for batch_idxs_ in batch_idxs]
batch_lengths = batch_lengths.to(DEVICE)
batch_labels = batch_labels.to(DEVICE)
# forward
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_idxs,
batch_lengths,
targets=batch_labels,
topk=topk)
valid_loss += batch_loss
# compute accuracy in numpy
batch_labels = batch_labels.cpu().detach().numpy()
batch_lengths = batch_lengths.cpu().detach().numpy()
# based on topk, obtain either strings of batch_predictions or list of tokens
if topk == 1:
batch_predictions = untokenize_without_unks(
batch_predictions, batch_lengths, vocab,
batch_corrupt_sentences)
else:
batch_predictions = untokenize_without_unks2(
batch_predictions,
batch_lengths,
vocab,
batch_corrupt_sentences,
topk=None)
batch_clean_sentences = [
line.lower() for line in batch_clean_sentences
]
batch_corrupt_sentences = [
line.lower() for line in batch_corrupt_sentences
]
batch_predictions = [line.lower() for line in batch_predictions]
corr2corr, corr2incorr, incorr2corr, incorr2incorr, mistakes = \
get_metrics(batch_clean_sentences,batch_corrupt_sentences,batch_predictions,check_until_topk=topk,return_mistakes=True)
_corr2corr += corr2corr
_corr2incorr += corr2incorr
_incorr2corr += incorr2corr
_incorr2incorr += incorr2incorr
_mistakes.extend(mistakes)
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("")
# for mistake in _mistakes:
# print(mistake)
# print("")
print("total token count: {}".format(_corr2corr + _corr2incorr +
_incorr2corr + _incorr2incorr))
print(
f"_corr2corr:{_corr2corr}, _corr2incorr:{_corr2incorr}, _incorr2corr:{_incorr2corr}, _incorr2incorr:{_incorr2incorr}"
)
print(
f"accuracy is {(_corr2corr+_incorr2corr)/(_corr2corr+_corr2incorr+_incorr2corr+_incorr2incorr)}"
)
print(
f"word correction rate is {(_incorr2corr)/(_incorr2corr+_incorr2incorr)}"
)
print("###############################################")
return results
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.
valid_acc = 0.
print("data size: {}".format(len(data)))
data_iter = batch_iter(data, batch_size=VALID_BATCH_SIZE, shuffle=False)
model.eval()
model.to(DEVICE)
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)
# forward
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
# compute accuracy in numpy
batch_labels_ids = batch_labels_ids.cpu().detach().numpy()
batch_lengths = batch_lengths.cpu().detach().numpy()
# based on topk, obtain either strings of batch_predictions or list of tokens
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)
#corr2corr, corr2incorr, incorr2corr, incorr2incorr, mistakes = \
# get_metrics(batch_labels,batch_sentences,batch_predictions,check_until_topk=topk,return_mistakes=True)
#_mistakes.extend(mistakes)
# batch_labels = [line.lower() for line in batch_labels]
# batch_sentences = [line.lower() for line in batch_sentences]
# batch_predictions = [line.lower() for line in batch_predictions]
corr2corr, corr2incorr, incorr2corr, incorr2incorr = \
get_metrics(batch_labels,batch_sentences,batch_predictions,check_until_topk=topk,return_mistakes=False)
_corr2corr += corr2corr
_corr2incorr += corr2incorr
_incorr2corr += incorr2corr
_incorr2incorr += incorr2incorr
# delete
del batch_loss
del batch_predictions
del batch_labels, batch_lengths, batch_bert_inp
torch.cuda.empty_cache()
'''
# 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("")
#for mistake in _mistakes:
# print(mistake)
print("")
print("total token count: {}".format(_corr2corr + _corr2incorr +
_incorr2corr + _incorr2incorr))
print(
f"_corr2corr:{_corr2corr}, _corr2incorr:{_corr2incorr}, _incorr2corr:{_incorr2corr}, _incorr2incorr:{_incorr2incorr}"
)
print(
f"accuracy is {(_corr2corr+_incorr2corr)/(_corr2corr+_corr2incorr+_incorr2corr+_incorr2incorr)}"
)
print(
f"word correction rate is {(_incorr2corr)/(_incorr2corr+_incorr2incorr)}"
)
print("###############################################")
return