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Create_LM_input_output.py
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# checking with BERT
from unittest.util import _MAX_LENGTH
from torchnlp.nn import attention
from transformers import BertTokenizer, BertTokenizerFast, RobertaTokenizer, RobertaTokenizerFast
import torch
import random
import torch.nn as nn
tokenizer, tokenizerFast = None, None
baseline = None
def initialize_tokenizer(lm_model, pretrained_data):
global tokenizer, tokenizerFast, baseline
if lm_model == "bert":
tokenizer = BertTokenizer.from_pretrained(pretrained_data)
tokenizerFast = BertTokenizerFast.from_pretrained(pretrained_data)
baseline = lm_model
elif lm_model == "roberta":
tokenizer = RobertaTokenizer.from_pretrained(pretrained_data)
tokenizerFast = RobertaTokenizerFast.from_pretrained(pretrained_data)
baseline = lm_model
def boolean_classification(model, input_text, q_type = None, candidate = [] ,correct_labels = None, other = None, device = "cuda:0", dataset = None, multi_task = False):
"""
all of the questions with the same task are passed
first we concat the questions and text and rules
and then create the input_ids and token_types
"""
# TODO changed
encoding = tokenizer(input_text, return_attention_mask = True, return_tensors="pt", padding=True, max_length = 512)
# encoding = tokenizer(input_text, max_length=512, pad_to_max_length = True, return_attention_mask = True, return_tensors="pt")
# if candidate: max_len = max([len(tokenizing(opt)) for opt in candidate])
input_ids, token_type_ids = [], []
input_ids = encoding["input_ids"].to(device)
# if baseline == "roberta":
# token_type_ids = None
# else:
# token_type_ids = encoding["token_type_ids"].to(device)
# for k in range(len(token_type_ids)):
# key = list(input_ids[k]).index(102)
# for i in range(key):
# token_type_ids[k][i] = 1
labels = []
for correct_label in correct_labels:
if q_type == 'FR':
label = torch.tensor([0]*(len(candidate)), device = device).long()
for opt in correct_label:
if dataset in ["spartqa", "babi"]: # answer is the id of correct label
label[opt] = 1
else:
label[candidate.index(opt.lower())] = 1
elif q_type == 'YN' and (other == "noDK" or dataset in[ "sprlqa", "babi", "boolq","spartun"]):
if correct_label == ['Yes']: label = torch.tensor([1,0], device = device).long()
elif correct_label == ['No']: label = torch.tensor([0,1], device = device).long()
elif q_type == 'YN': #spartqa,
if correct_label == ['Yes']: label = torch.tensor([1,0,0], device = device).long()
elif correct_label == ['No']: label = torch.tensor([0,1,0], device = device).long()
else: label = torch.tensor([0,0,1], device = device).long()
labels += [label]
if labels == [] : labels = None
else: labels = torch.stack(labels)
outputs = model(input_ids, labels=labels, task = q_type, multi_task = multi_task, attention_mask = encoding["attention_mask"].to(device))
# outputs = model(input_ids, labels=labels, task = q_type, multi_task = multi_task, attention_mask = encoding["attention_mask"].to(device))
loss, logits = outputs[:2]
logits = torch.transpose(logits, 0, 1)
outs = []
for logit in logits:
out = []
if q_type == 'FR':
#Do some inference
# remove those relations that cannot happened at the same time and they are both true.
out_logit = [torch.argmax(log) for log in logit]
out = Prune_FR_answer_based_on_constraints(out_logit, logit, candidate)
if dataset not in ["spartqa", "babi"]: out = [candidate[i] for i in out]
elif q_type == 'YN' and (len(candidate) == 2 or other == "noDK"):
max_arg = torch.argmax(logit[:, 1])
# print("2", max_arg)
if max_arg.item() == 0: out = ['Yes']
elif max_arg.item() == 1: out = ['No']
elif q_type == 'YN': #has three candidate labels
max_arg = torch.argmax(logit[:, 1])
# print(correct_label, logits, max_arg)
if max_arg.item() == 0: out = ['Yes']
elif max_arg.item() == 1: out = ['No']
else: out = ['DK']
outs += [out]
return loss, outs #, out_logit
def multiple_classification(model, input_text, q_type, candidate ,correct_labels , other = None, device = "cpu", dataset = None, multi_task = False):
# encoding = tokenizer.encode_plus(question, text)
# print(text, question, candidate, correct_label)
# if candidate: max_len = max([len(tokenizing(opt)) for opt in candidate])
encoding = tokenizer(input_text, max_length=512, return_tensors="pt", padding=True, return_attention_mask = True)
# encoding = tokenizer(input_text, max_length=512, pad_to_max_length = True, return_attention_mask = True, return_tensors="pt")
input_ids, token_type_ids = [], []
input_ids = encoding["input_ids"].to(device)
if baseline == "roberta":
token_type_ids = None
else:
token_type_ids = encoding["token_type_ids"].to(device)
for k in range(len(token_type_ids)):
key = list(input_ids[k]).index(102)
for i in range(key):
token_type_ids[k][i] = 1
labels = []
for correct_label in correct_labels:
if q_type == 'FR':
#TODO check this for stepgame
if dataset not in [ "spartqa", "stepgame"]:
_correct_label = []
for label in correct_label:
_correct_label += [candidate.index(label.lower())]
if dataset == "stepgame":
_correct_label = correct_label[0]
#TODO check this
else: _correct_label = correct_label[0]
label = torch.tensor(_correct_label, device = device).long()
elif q_type == 'YN' and (other == "noDK" or dataset in[ "sprlqa", "babi", "boolq"]):
if correct_label == ['Yes']: label = torch.tensor(0, device = device).long()
elif correct_label == ['No']: label = torch.tensor(1, device = device).long()
elif q_type == 'YN': #spartqa,
if correct_label == ['Yes']: label = torch.tensor(0, device = device).long()
elif correct_label == ['No']: label = torch.tensor(1, device = device).long()
else: label = torch.tensor(2, device = device).long()
labels += [label]
if labels == [] : labels = None
else: labels = torch.stack(labels)
outputs = model(input_ids, labels=labels, token_type_ids = token_type_ids, attention_mask = encoding["attention_mask"].to(device), task = q_type, multi_task = multi_task) #,
loss, logits = outputs[:2]
outs = []
for logit in logits:
out = []
if q_type == 'FR':
max_arg = torch.argmax(logit)
# max_arg = torch.argmax(logits[0])
if dataset not in ["stepgame"]:
out = [candidate[max_arg.item()]]
else:
out = [max_arg.item()]
elif q_type == 'YN' :
max_arg = torch.argmax(logit)
# max_arg = torch.argmax(logits[0])
if max_arg.item() == 0: out = ['Yes']
elif max_arg.item() == 1: out = ['No']
else: out = ['DK'] # if only two class then this never happen
outs += [out]
return loss, outs #, out_logit
def spatial_relation_extraction(model, input_text, labels, device = "cuda:0"):
encoding = tokenizer.batch_encode_plus(input_, max_length=512)
input_ids, token_type_ids, attention_mask = encoding["input_ids"], encoding["token_type_ids"], encoding['attention_mask']
labels = torch.stack(labels)
outputs = model(input_ids, token_type_ids = token_type_ids, attention_mask = attention_mask, labels = labels)
if labels:
loss, logits = outputs[:2]
else:
loss = None
logits = outputs[:1]
outs = []
for logit in logits:
out_logit = [torch.argmax(log) for log in logit]
if torch.count_nonzero(out_logit[1:]) > 0:
out_logit[0] = 0
else:
out_logit[0] = 1
outs += [out_logit]
return loss, outs
def Masked_LM(model, text, question, answer, other, device, file):
# print(question, answer)
# input_ids = tokenizer(text, return_tensors="pt")["input_ids"].to(device)
input_ids = tokenizer.encode_plus(question, text, return_tensors="pt")["input_ids"].to(device)
masked = []
for ind, i in enumerate(input_ids[0]):
if i == 103: masked.append(ind)
token_answer = [tokenizer.convert_tokens_to_ids(i) for i in tokenizing(answer)]
x = [-100] * len(input_ids[0])
for ind,i in enumerate(masked):
x[i] = token_answer[ind]
label_ids = torch.tensor([x], device = device)
# print("input_ids2",input_ids)
# print("label_ids",label_ids)
# segments_ids = torch.tensor([[0]* (len(input_ids))], device = device)
outputs = model(input_ids, labels = label_ids) #, token_type_ids=segments_ids)
# print(outputs)
loss, predictions = outputs[:2]
ground_truth = [label_ids[0][i].item() for i in masked]
truth_token = tokenizer.convert_ids_to_tokens(ground_truth)
print("truth token: ", truth_token, file = file)
predicted_index = [torch.argmax(predictions[0, predict]).item() for predict in masked ]
predicted_token = tokenizer.convert_ids_to_tokens(predicted_index)
# print(predicted_index,predicted_token, file = file)
print("pred_token: ", predicted_token, file = file)
return loss, predicted_index, ground_truth
def Masked_LM_random(model, text, seed_num, other, device, file):
input_ids = tokenizer(text, return_tensors="pt")["input_ids"].to(device)
# print("input_ids1",input_ids)
# masked tokens
masked = []
# forbiden = [".", ",", "!", "'","?", 'the', 'of']
random.seed(seed_num)
masked = random.sample(range(1, len(input_ids[0])-1), int(len(input_ids[0])* 0.12))
# print("masks: ", masked[0], story_tokenized[masked[0]], file = file)
#unchanged tokens, just predict
not_changed = []
temp , not_changed_num= 0, int(len(input_ids[0])* 0.015)
while True:
x = random.choice(range(1, len(input_ids[0])-1))
if x not in masked: not_changed.append(x); temp+=1
if temp >= not_changed_num: break
#changed tokens
changed = []
temp , changed_num= 0, int(len(input_ids[0])* 0.015)
while True:
x = random.choice(range(1, len(input_ids[0])-1))
if x not in masked and x not in not_changed: changed.append(x); temp+=1
if temp >= changed_num: break
# print("nums",masked, not_changed, changed)
x = [-100] * len(input_ids[0])
for i in masked:
x[i] = input_ids[0][i].item()
input_ids[0][i] = tokenizer.convert_tokens_to_ids('[MASK]') #103 #[masked]
for i in not_changed:
x[i] = input_ids[0][i].item()
for i in changed:
changed_word =random.choice(range(0,30522))
x[i] = input_ids[0][i].item()
input_ids[0][i]= changed_word
label_ids = torch.tensor([x], device = device)
# print("input_ids2",input_ids)
# print("label_ids",label_ids)
segments_ids = torch.tensor([[0]* (len(input_ids))], device = device)
outputs = model(input_ids, labels = label_ids, token_type_ids=segments_ids) #, token_type_ids=segments_ids)
# print(outputs)
loss, predictions = outputs[:2]
predicted = []
ground_truth = [label_ids[0][i].item() for i in masked] + [label_ids[0][i].item() for i in not_changed] + [label_ids[0][i].item() for i in changed]
truth_token = tokenizer.convert_ids_to_tokens(ground_truth)
print("truth token: ", truth_token, file = file)
predicted_index = [torch.argmax(predictions[0, predict]).item() for predict in masked ]
predicted_token = tokenizer.convert_ids_to_tokens(predicted_index)
# print(predicted_index,predicted_token, file = file)
predicted += predicted_index
predicted_index = [torch.argmax(predictions[0, predict]).item() for predict in not_changed ]
predicted_token = tokenizer.convert_ids_to_tokens(predicted_index)
# print(predicted_index,predicted_t, file = fileoken)
predicted += predicted_index
predicted_index = [torch.argmax(predictions[0, predict]).item() for predict in changed ]
predicted_token = tokenizer.convert_ids_to_tokens(predicted_index)
# print(predicted_index,predicte, file = filed_token)
predicted += predicted_index
pred_token = tokenizer.convert_ids_to_tokens(predicted)
print("pred_token: ", pred_token, file = file)
return loss, predicted, ground_truth
# with just triplet classifier
def spatial_classification(model, sentence, triplet, label, device):
# print('triplet',triplet, label)
encoding = tokenizer.encode(sentence, add_special_tokens=True)
token_type_ids = [0]*len(encoding)
triplets_tokens = []
# print('encoded sentence', encoding)
if triplet['trajector'] != [-1,-1] :
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
for x in range(triplet['trajector'][0],triplet['trajector'][1]+1):
token_type_ids[x] = 1
else: triplets_tokens += [102]
if triplet['landmark'] != [-1,-1]:
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
for x in range(triplet['landmark'][0],triplet['landmark'][1]+1):
token_type_ids[x] = 1
else: triplets_tokens += [102]
if triplet['spatial_indicator'] != [-1,-1]:
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
for x in range(triplet['spatial_indicator'][0],triplet['spatial_indicator'][1]+1):
token_type_ids[x] = 1
# print('triple tokens: ', triplets_tokens)
token_type_ids = [0]*len(triplets_tokens) + token_type_ids
encoding =[ encoding[0]]+ triplets_tokens + encoding[1:]
token_type_ids = torch.tensor([token_type_ids], device = device)
inputs = torch.tensor(encoding, device = device).unsqueeze(0)
labels = torch.tensor([label], device = device).float()
# print(inputs.shape, labels.shape)
loss, logits = model(inputs, token_type_ids = token_type_ids, labels=labels)
# print(outputs)
# loss = outputs.loss
# logits = outputs.logits
# print('logits', logits)
predicted_index = 1 if logits[0] > 0.5 else 0
# predicted_index = torch.argmax(logits).item()
# print('predict', predicted_index)
return loss, predicted_index#, predicted_index
#triplet classifier and relation tyoe extraction
def spatial_type_classification(model, sentence, triplets, triplet_labels = None, type_labels = None, device= 'cuda:0', asked_class_label = None, other = None, asked = False):
if other == 'sptypeQA':
encoding = sentence[0]
else:
tokenized_text = tokenizing(sentence)
encoding = tokenizer.encode(sentence, add_special_tokens=True)
all_token_type_ids, triplets_input = [], []
for triplet in triplets:
token_type_ids = [0]*len(encoding)
triplets_tokens, triplets_tokens_pass, _triplets_tokens_pass = [], [], []
if triplet['trajector'] not in [['',''], [-1,-1]] :
if other == 'sptypeQA':
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1].tolist()+[102]
triplets_tokens_pass += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1].tolist()#+[1010]
elif other == 'triplet':
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
triplets_tokens_pass += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
else:
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
for x in range(triplet['trajector'][0],triplet['trajector'][1]+1):
token_type_ids[x] = 1
if triplet['spatial_indicator'] not in [['',''], [-1,-1]]:
if other == 'sptypeQA' :
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1].tolist()+[102]
triplets_tokens_pass += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1].tolist()+[102]#+[1010]
elif other == 'triplet':
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
triplets_tokens_pass += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
else:
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
for x in range(triplet['spatial_indicator'][0],triplet['spatial_indicator'][1]+1):
token_type_ids[x] = 1
# print('&&', tokenized_text[triplet['spatial_indicator'][0]-1:triplet['spatial_indicator'][1]]) # shouldn't consider [cls] so subtract 1
spatial_indicator = ''
if other != 'sptypeQA' and other != 'triplet':
for z in tokenized_text[triplet['spatial_indicator'][0]-1:triplet['spatial_indicator'][1]]:
spatial_indicator += z +' '
if triplet['landmark'] not in [['',''], [-1,-1]]:
if other == 'sptypeQA' :
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1].tolist()+[102]
triplets_tokens_pass += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1].tolist()#+[1010]
elif other == 'triplet':
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
_triplets_tokens_pass += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
else:
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
for x in range(triplet['landmark'][0],triplet['landmark'][1]+1):
token_type_ids[x] = 1
token_type_ids = [0]*len(triplets_tokens) + token_type_ids + [0]*(26-len(triplets_tokens)) #add PAD to the batch for 6 for larger land and traj and 4 for spatial_ndicator
all_token_type_ids += [token_type_ids]
if _triplets_tokens_pass: triplets_tokens_pass = _triplets_tokens_pass
if other == 'sptypeQA':
_encoding =[encoding[0].tolist()]+ triplets_tokens + encoding[1:].tolist()+ [0]*(26-len(triplets_tokens))
else: _encoding = [encoding[0]]+ triplets_tokens + encoding[1:]+ [0]*(26-len(triplets_tokens))
triplets_input += [_encoding]
token_type_ids = torch.tensor(all_token_type_ids, device = device)
inputs = torch.tensor(triplets_input, device = device)
if triplet_labels != None:
labels = [torch.tensor(triplet_labels, device = device).float(), torch.tensor(type_labels, device = device).long()]
# labels = [torch.tensor([label], device = device).float(), torch.tensor([type_label], device = device).long(), torch.tensor([asked_class_label if asked_class_label!= None else 0], device = device).float() ]
# print(inputs.shape, labels.shape)
loss, logits1, logits2 = model(inputs, token_type_ids = token_type_ids, labels=labels)
# loss, logits1, logits2, logits3 = model(inputs, token_type_ids = token_type_ids, labels=labels, asked_compute = asked_class_label)
# print(logits1)
else:
type_labels = None
loss = None
# _,logits1, logits2, logits3 = model(inputs, token_type_ids = token_type_ids, asked_compute = True)
_,logits1, logits2 = model(inputs, token_type_ids = token_type_ids)
predicted_index1 = [1 if x > 0.65 else 0 for x in logits1[0]]
# print('logits2', logits2)
predicted_index2 = [torch.argmax(x).item() for x in logits2[0]]
# if asked_class_label != None:
# predicted_index3 = 1 if logits3[0] > 0.65 else 0
# else: predicted_index3 = None
# predicted_index = torch.argmax(logits).item()
if triplet_labels == None and (other == 'sptypeQA' or other == ' triplet'):
if predicted_index1 == 1:
if other == 'sptypeQA':
return triplets_tokens_pass
#if we want to pass the triplet in the form of text
elif other == 'triplet':
#all_rel_types = ['NaN', 'LEFT','RIGHT','BELOW','ABOVE','NEAR', 'FAR', 'TPP', 'NTPP', 'NTPPI', 'EC']
triplet_sent = tokenizer.decode(triplets_tokens_pass)
triplet_sent = triplet_sent[:-5].replace('[SEP]',',')+'.'
###For replacing spatial indicator
#end = triplet_sent.rfind('[SEP]',0,len(triplet_sent)-5)
#triplet_sent = triplet_sent[:end].replace('[SEP]',',') + '. ' #+all_rel_types[predicted_index2]+'.'
# print('%%', triplet_sent)
return triplet_sent
#else
# triplet_sent = triplet_sent.replace('[SEP]',',')
# triplet_sent += ' '+all_rel_types[predicted_index2]+'.'
# triplet_sent = triplet_sent[:-1]+'.'
# print('In BERT, triplet tokens: ',triplet_sent)
else: return None
elif triplet_labels == None:
# if logits3:
return _, predicted_index1, predicted_index2#, predicted_index3
# else:
# return _, predicted_index1, predicted_index2
else:
# if logits3:
return loss, predicted_index1, predicted_index2#, predicted_index3 #, predicted_index
# else:
# return loss, predicted_index1, predicted_index2 #, predicted_index
def spatial_type_classification_before_batch(model, sentence, triplets, triplet_label = None, type_labels = None, device= 'cuda:0', asked_class_label = None, other = None, asked = False):
# print(sentence,'\n',triplet)
#tokenized sentence
if other == 'sptypeQA':
encoding = sentence[0]
else:
tokenized_text = tokenizing(sentence)
encoding = tokenizer.encode(sentence, add_special_tokens=True)
token_type_ids = [0]*len(encoding)
# if other == 'sptypeQA': triplets_tokens, triplets_tokens_pass = [], []
# else:
triplets_tokens, triplets_tokens_pass, _triplets_tokens_pass = [], [], []
# print('encoded sentence', encoding)
if triplet['trajector'] != ['','']:
if other == 'sptypeQA':
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1].tolist()+[102]
triplets_tokens_pass += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1].tolist()#+[1010]
elif other == 'triplet':
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
triplets_tokens_pass += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
else:
triplets_tokens += encoding[triplet['trajector'][0]:triplet['trajector'][1]+1]+[102]
for x in range(triplet['trajector'][0],triplet['trajector'][1]+1):
token_type_ids[x] = 1
if triplet['spatial_indicator'] != ['','']:
if other == 'sptypeQA' :
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1].tolist()+[102]
triplets_tokens_pass += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1].tolist()+[102]#+[1010]
elif other == 'triplet':
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
triplets_tokens_pass += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
else:
triplets_tokens += encoding[triplet['spatial_indicator'][0]:triplet['spatial_indicator'][1]+1]+[102]
for x in range(triplet['spatial_indicator'][0],triplet['spatial_indicator'][1]+1):
token_type_ids[x] = 1
# print('&&', tokenized_text[triplet['spatial_indicator'][0]-1:triplet['spatial_indicator'][1]]) # shouldn't consider [cls] so subtract 1
spatial_indicator = ''
if other != 'sptypeQA' and other != 'triplet':
for z in tokenized_text[triplet['spatial_indicator'][0]-1:triplet['spatial_indicator'][1]]:
spatial_indicator += z +' '
if triplet['landmark'] != ['','']:
if other == 'sptypeQA' :
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1].tolist()+[102]
triplets_tokens_pass += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1].tolist()#+[1010]
elif other == 'triplet':
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
_triplets_tokens_pass += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
else:
triplets_tokens += encoding[triplet['landmark'][0]:triplet['landmark'][1]+1]+[102]
for x in range(triplet['landmark'][0],triplet['landmark'][1]+1):
token_type_ids[x] = 1
if _triplets_tokens_pass: triplets_tokens_pass += _triplets_tokens_pass
# print(spatial_indicator, 'type rel', type_label, all_rels_type)
# print()
token_type_ids = [0]*len(triplets_tokens) + token_type_ids
if other == 'sptypeQA':
encoding =[ encoding[0].tolist()]+ triplets_tokens + encoding[1:].tolist()
else: encoding = [encoding[0]]+ triplets_tokens + encoding[1:]
token_type_ids = torch.tensor([token_type_ids], device = device)
inputs = torch.tensor(encoding, device = device).unsqueeze(0)
logits3 = None
if label != None:
labels = [torch.tensor([label], device = device).float(), torch.tensor([type_label], device = device).long()]
# labels = [torch.tensor([label], device = device).float(), torch.tensor([type_label], device = device).long(), torch.tensor([asked_class_label if asked_class_label!= None else 0], device = device).float() ]
# print(inputs.shape, labels.shape)
loss, logits1, logits2 = model(inputs, token_type_ids = token_type_ids, labels=labels)
# loss, logits1, logits2, logits3 = model(inputs, token_type_ids = token_type_ids, labels=labels, asked_compute = asked_class_label)
# print(logits1)
else:
type_label = None
loss = None
# _,logits1, logits2, logits3 = model(inputs, token_type_ids = token_type_ids, asked_compute = True)
_,logits1, logits2 = model(inputs, token_type_ids = token_type_ids)
predicted_index1 = 1 if logits1[0] > 0.65 else 0
# print('logits2', logits2)
predicted_index2 = torch.argmax(logits2[0]).item()
# if asked_class_label != None:
# predicted_index3 = 1 if logits3[0] > 0.65 else 0
# else: predicted_index3 = None
# predicted_index = torch.argmax(logits).item()
if label == None and (other == 'sptypeQA' or other == ' triplet'):
if predicted_index1 == 1:
if other == 'sptypeQA':
return triplets_tokens_pass
#if we want to pass the triplet in the form of text
elif other == 'triplet':
#all_rel_types = ['NaN', 'LEFT','RIGHT','BELOW','ABOVE','NEAR', 'FAR', 'TPP', 'NTPP', 'NTPPI', 'EC']
triplet_sent = tokenizer.decode(triplets_tokens_pass)
triplet_sent = triplet_sent[:-5].replace('[SEP]',',')+'.'
###For replacing spatial indicator
#end = triplet_sent.rfind('[SEP]',0,len(triplet_sent)-5)
#triplet_sent = triplet_sent[:end].replace('[SEP]',',') + '. ' #+all_rel_types[predicted_index2]+'.'
# print('%%', triplet_sent)
return triplet_sent
#else
# triplet_sent = triplet_sent.replace('[SEP]',',')
# triplet_sent += ' '+all_rel_types[predicted_index2]+'.'
# triplet_sent = triplet_sent[:-1]+'.'
# print('In BERT, triplet tokens: ',triplet_sent)
else: return None
elif label == None:
# if logits3:
return _, predicted_index1, predicted_index2#, predicted_index3
# else:
# return _, predicted_index1, predicted_index2
else:
# if logits3:
return loss, predicted_index1, predicted_index2#, predicted_index3 #, predicted_index
# else:
# return loss, predicted_index1, predicted_index2 #, predicted_index
def token_classification(model, text, traj=None, land=None, indicator=None, other=None, device = 'cuda:0', file = ''):
loss, truth = '', ''
if other != 'sptypeQA':
inputs = tokenizer(text, return_tensors="pt")["input_ids"].to(device)
else:
inputs = text
if traj or land or indicator:
labels = make_token_label(text, traj, land, indicator, other).to(device)
outputs = model(inputs, labels=labels)
loss = outputs.loss
truth = [element.item() for element in labels[0].flatten()]
else: outputs = model(inputs)
logits = outputs.logits
predicted_index = [torch.argmax(predict).item() for predict in logits[0] ]
return loss, predicted_index, truth
def make_token_label(text, traj, land, indicator,other):
# print(text, traj, land, indicator)
encoding = tokenizerFast(text, return_offsets_mapping= True)
token_starts = [item[0] for item in encoding['offset_mapping']]
token_ends = [item[1] for item in encoding['offset_mapping']]
# print(token_starts, token_ends)
# print()
# labels_id = ['O', 'B_traj', 'I_traj', 'B_land', 'I_land', 'B_spatial', 'I_spatial']
labels_id = ['O', 'B_entity', 'I_entity', 'B_indicator', 'I_indicator']
# text_token = tokenized_text #tokenizing(text)
labels = torch.tensor([0] * len(encoding['input_ids']))
#trajector
for ind, t in enumerate(traj):
# if t['start']!= '' and t['end']!= '':
# print(t)
#skip WRONG ANNOTATION
if t['start'] not in token_starts and t['end'] not in token_ends: continue
B_token = token_starts[1:-1].index(t['start'])+1
E_token = token_ends[1:-1].index(t['end'])+1
# print(B_token, E_token)
labels[B_token] = labels_id.index('B_entity')
for i in range(B_token+1, E_token+1):
labels[i] = labels_id.index('I_entity')
#landmark
for l in land:
# if l['start']!= '' and l['end']!= '':
# print(l)
#skip WRONG ANNOTATION
if l['start'] not in token_starts and l['end'] not in token_ends: continue
B_token = token_starts[1:-1].index(l['start'])+1
E_token = token_ends[1:-1].index(l['end'])+1
# print(B_token, E_token)
labels[B_token] = labels_id.index('B_entity')
for i in range(B_token+1, E_token+1):
labels[i] = labels_id.index('I_entity')
#spatial
for ind in indicator:
# if ind['start']!= '' and ind['end']!= '':
# print(ind)
#skip WRONG ANNOTATION or it is empty
if ind['start'] not in token_starts and ind['end'] not in token_ends: continue
B_token = token_starts[1:-1].index(ind['start'])+1
E_token = token_ends[1:-1].index(ind['end'])+1
# print(B_token, E_token)
labels[B_token] = labels_id.index('B_indicator')
for i in range(B_token+1, E_token+1):
labels[i] = labels_id.index('I_indicator')
# print('labels:', labels)
labels = labels.unsqueeze(0)
return labels
def extract_entity_token(text, traj, land, indicator, _tuple=False):
# print(text, traj, land, indicator)
encoding = tokenizerFast(text, return_offsets_mapping= True)
token_starts = [item[0] for item in encoding['offset_mapping']]
token_ends = [item[1] for item in encoding['offset_mapping']]
# print(token_starts, token_ends)
if _tuple:
token_index = {'trajector': [-1,-1], 'landmark': [-1,-1], 'spatial_indicator':[-1,-1], 'rel_type': ''}
# spatial_indicator = []
else:
token_index = {'trajector': [-1,-1], 'landmark': [-1,-1], 'spatial_indicator':[-1,-1]}
#trajector
if (traj['start']!= '' and traj['start']!= -1) and (traj['end']!= '' and traj['end']!= -1):
# print(t)
#start
token_index['trajector'][0]= token_starts[1:-1].index(traj['start'])+1
#end
token_index['trajector'][1]= token_ends[1:-1].index(traj['end'])+1
#landmark
if (land['start']!= '' and land['start']!= -1) and (land['end']!= '' and land['end']!= -1):
# print(l)
#start
token_index['landmark'][0]= token_starts[1:-1].index(land['start'])+1
#end
token_index['landmark'][1]= token_ends[1:-1].index(land['end'])+1
#spatial
if (indicator['start']!= '' and indicator['start']!= -1) and (indicator['end']!= '' and indicator['end']!= -1):
# if _tuple:
# spatial_indicator = [token_starts[1:-1].index(indicator['start'])+1, token_ends[1:-1].index(indicator['end'])+1]
# # print(ind)
# else:
token_index['spatial_indicator'][0] = token_starts[1:-1].index(indicator['start'])+1
token_index['spatial_indicator'][1] = token_ends[1:-1].index(indicator['end'])+1
# print('token_index:', token_index)
# if _tuple:
# return token_index, spatial_indicator
# else:
return token_index
# def boolean_classification_end2end(model, question, text, q_type, candidate ,correct_label, other, device):
def boolean_classification_end2end(model, questions, text, q_type, candidates ,correct_labels, other, device, story_annot =None, qs_annot=None, seperate = False):
# encoding = tokenizer.encode_plus(question, text)
# print(text, question, candidate, correct_label)
if seperate:
#seperate each sentence
sentences = [h+'.' for h in text.split('. ')]
sentences[-1] = sentences[-1][:-1]
text_tokenized = tokenizer(sentences, return_tensors="pt", padding=True)["input_ids"].to(device)
else:
text_tokenized = tokenizer(text, return_tensors="pt", padding=True)["input_ids"].to(device)
# print(text,'\n', text_tokenized)
# print('&&', questions)
qs_tokenized = tokenizer(questions, return_tensors="pt", padding=True)["input_ids"].to(device)
# print(qs_tokenized)
# print('## text+qs_tokenized: ',text_tokenized)
# qs_tokenized = tokenizer(question, return_tensors="pt")["input_ids"].to(device)
# print('### qs_tokenized: ',qs_tokenized)
# inputs = tokenizer(text, return_tensors="pt")["input_ids"].to(device)
# if candidate: max_len = max([len(tokenizing(opt)) for opt in candidate])
input_ids, token_type_ids = [], []
labels =[]
for _ind,correct_label in enumerate(correct_labels):
if q_type == 'CO':
label = torch.tensor([[0]]*2, device = device).long()
# candid_tokenized = tokenizer(candidate[:2], return_tensors="pt", padding=True)["input_ids"].to(device)
for opt in candidates[_ind][:2]:
# tokenized_opt = tokenizing(opt)
# num_tok = len(tokenized_opt)
# encoded_options = tokenizer.encode(tokenized_opt + ['[PAD]']*(max_len - num_tok))#[1:]
input_ids += [encoded_options + encoding["input_ids"][1:]]
if correct_label == [0] or correct_label == [2]: label[0][0] = 1
if correct_label == [1] or correct_label == [2]: label[1][0] = 1
elif q_type == 'FB':
label = torch.tensor([[0]]*len(candidates[_ind]), device = device).long()
candid_tokenized = tokenizer(candidates[_ind], return_tensors="pt", padding=True)["input_ids"].to(device)
for opt in candidates[_ind]:
# tokenized_opt = tokenizing(opt)
# # num_tok = len(tokenized_opt)
# encoded_options = tokenizer.encode(tokenized_opt)#[1:]
input_ids += [encoded_options + encoding["input_ids"][1:]]
if 'A' in correct_label: label[0][0] = 1
if 'B' in correct_label: label[1][0] = 1
if 'C' in correct_label: label[2][0] = 1
elif q_type == 'FR':
# _input = []
label = torch.tensor([0]*7, device = device).long()
for ind, opt in enumerate(candidates[_ind][:7]):
# _input += [text_tokenized]
if ind in correct_label:label[ind] = 1
# text_tokenized = torch.stack(_input)
# print('label', label)
# elif q_type == 'YN' and other == "DK": #and candidate != ['babi']:
# if correct_label == ['Yes']: label = torch.tensor([1,0,0], device = device).long()
# elif correct_label == ['No']: label = torch.tensor([0,1,0], device = device).long()
# else: label = torch.tensor([0,0,1], device = device).long()
# input_ids = [encoding["input_ids"]]
# elif q_type == 'YN' and other == "noDK":
# if correct_label == ['Yes']: label = torch.tensor([1,0], device = device).long()
# elif correct_label == ['No']: label = torch.tensor([0,1], device = device).long()
# input_ids = [encoding["input_ids"]]
# elif q_type == 'YN' and candidate == ['boolq']:
# if correct_label == ['Yes']: label = torch.tensor([1,0], device = device).long()
# elif correct_label == ['No']: label = torch.tensor([0,1], device = device).long()
# # else: label = torch.tensor([0,0,1], device = device).long()
# input_ids = [encoding["input_ids"]]
elif q_type == 'YN': #and candidate != ['babi']:
if correct_label == ['Yes']: label = torch.tensor([1,0,0], device = device).long()
elif correct_label == ['No']: label = torch.tensor([0,1,0], device = device).long()
else: label = torch.tensor([0,0,1], device = device).long()
# input_ids = text_tokenized
# else : label = torch.tensor([0,0], device = device).long()
# elif q_type == 'YN' and candidate == ['babi']:
# label = torch.tensor([1,0], device = device).long() if correct_label == ['Yes'] else torch.tensor([0,1], device = device).long()
# input_ids = [encoding["input_ids"]]
labels += [label]
labels = torch.stack(labels).to(device)
# print('$', correct_labels)
# print('$$', labels, type(labels))
# input_ids = torch.tensor(input_ids, device = device)
# print('input shape, label shape',text_tokenized.shape, qs_tokenized.shape, labels.shape)
if other == 'supervised':
_outputs = model(text_tokenized, qs_tokenized, story_annotations = story_annot, questions_annotations = qs_annot, labels=labels)
else:
_outputs = model(text_tokenized, qs_tokenized, labels=labels)
# print('&&&&&&&&&& outputs', outputs)
losses, outs = [], []
for outputs in _outputs:
loss, logits = outputs[:2]
# print('$$', loss)
losses += [loss]
# print("loss, logits ", loss, logits)
out_logit = [torch.argmax(log) for log in logits]
out = [0]
if q_type == 'FR':
out = [ind for ind,o in enumerate(out_logit) if o.item() == 1]
if 2 in out and 3 in out:
if logits[2][1] >= logits[3][1]:
out.remove(3)
else:
out.remove(2)
if 0 in out and 1 in out:
if logits[0][1] >= logits[1][1]:
out.remove(1)
else:
out.remove(0)
if 4 in out and 5 in out:
if logits[4][1] >= logits[5][1]:
out.remove(5)
else:
out.remove(4)
if out == []: out = [7]
elif q_type == 'FB':
blocks = ['A', 'B', 'C']
out = [blocks[ind] for ind,o in enumerate(out_logit) if o.item() == 1]
# out = [blocks[ind] for ind,o in enumerate(out_logit) if o.item() == 1]
# if 'C' in out and 'C' not in candidate: out.remove('C')
elif q_type == 'CO':
out = [ind for ind,o in enumerate(out_logit) if o.item() == 1]
if 0 in out and 1 in out:
out = [2]
elif out == []: out = [3]
elif q_type == 'YN' and other == 'multiple_class':