def load_item(self, idx):
sample_info = self.imdb[idx]
current_sample = Sample()
current_sample.dataset_name = self.dataset
if self.dataset == 'train_vqa':
text_processor_argument = {
"tokens": sample_info["question_tokens"]
}
processed_question = self.text_processor(text_processor_argument)
current_sample.text_len = torch.tensor(len(
sample_info["question_tokens"]),
dtype=torch.int)
current_sample.text = processed_question["text"]
current_sample.question_text = sample_info["question_str"]
current_sample.text_sq = current_sample.text
current_sample.text_oq = current_sample.text
current_sample.reasoning_question = sample_info["question_str"]
current_sample.reasoning_answer = sample_info["answers"][0]
current_sample.sub_question = sample_info["question_str"]
current_sample.other_question = sample_info["question_str"]
elif self.dataset == 'train_introspect' or self.dataset == 'test':
text_processor_argument = {
"text": sample_info["main_question_str"]
}
processed_question = self.text_processor(text_processor_argument)
current_sample.text = processed_question["text"]
if "sub_question_str" in sample_info:
text_processor_argument_sq = {
"text": sample_info["sub_question_str"]
}
processed_question_sq = self.text_processor(
text_processor_argument_sq)
current_sample.text_sq = processed_question_sq["text"]
if "other_question_str" in sample_info:
text_processor_argument_oq = {
"text": sample_info["other_question_str"]
}
processed_question_oq = self.text_processor(
text_processor_argument_oq)
current_sample.text_oq = processed_question_oq["text"]
current_sample.question_text = sample_info["main_question_str"]
current_sample.reasoning_question = sample_info[
"main_question_str"]
current_sample.reasoning_answer = sample_info["main_answer_str"][0]
current_sample.sub_question = sample_info["sub_question_str"]
current_sample.other_question = sample_info["other_question_str"]
current_sample.text_len = torch.tensor(len(
sample_info["main_question_tokens"]),
dtype=torch.int)
else:
text_processor_argument = {"text": sample_info["question_str"]}
processed_question = self.text_processor(text_processor_argument)
current_sample.text = processed_question["text"]
if "sub_question_str" in sample_info:
text_processor_argument_sq = {
"text": sample_info["sub_question_str"]
}
processed_question_sq = self.text_processor(
text_processor_argument_sq)
current_sample.text_sq = processed_question_sq["text"]
if "other_question_str" in sample_info:
text_processor_argument_oq = {
"text": sample_info["other_question_str"]
}
processed_question_oq = self.text_processor(
text_processor_argument_oq)
current_sample.text_oq = processed_question_oq["text"]
else:
current_sample.text_oq = current_sample.text_sq
current_sample.question_text = sample_info["question_str"]
current_sample.reasoning_question = sample_info["question_str"]
current_sample.reasoning_answer = sample_info["answers"][0]
current_sample.sub_question = sample_info["sub_question_str"]
current_sample.other_question = sample_info["sub_question_str"]
current_sample.text_len = torch.tensor(len(
sample_info["question_tokens"]),
dtype=torch.int)
current_sample.question_id = torch.tensor(sample_info["question_id"],
dtype=torch.int)
if isinstance(sample_info["image_id"], int):
current_sample.image_id = torch.tensor(sample_info["image_id"],
dtype=torch.int)
else:
current_sample.image_id = sample_info["image_id"]
if self._use_features is True:
features = self.features_db[idx]
current_sample.update(features)
# Add details for OCR like OCR bbox, vectors, tokens here
current_sample = self.add_ocr_details(sample_info, current_sample)
# Depending on whether we are using soft copy this can add
# dynamic answer space
current_sample = self.add_answer_info(sample_info, current_sample)
return current_sample
def load_item(self, idx):
sample_info = self.imdb[idx]
current_sample = Sample()
if "question_tokens" in sample_info:
text_processor_argument = {
"tokens": sample_info["question_tokens"]
}
else:
#text_processor_argument = {"text": sample_info["question"]}
text_processor_argument = {
"text": sample_info["main_question_str"]
}
if "sub_question_str" in sample_info:
text_processor_argument_sq = {
"text": sample_info["sub_question_str"]
}
if "other_question_str" in sample_info:
text_processor_argument_oq = {
"text": sample_info["other_question_str"]
}
processed_question = self.text_processor(text_processor_argument)
processed_question_sq = self.text_processor(text_processor_argument_sq)
processed_question_oq = self.text_processor(text_processor_argument_oq)
current_sample.text = processed_question["text"]
current_sample.text_sq = processed_question_sq["text"]
current_sample.text_oq = processed_question_oq["text"]
current_sample.question_text = sample_info["main_question_str"]
current_sample.reasoning_question = sample_info["main_question_str"]
current_sample.reasoning_answer = sample_info["main_answer_str"][0]
#current_sample.image_url = sample_info["img_path"]
current_sample.image_url = sample_info["image_path"]
current_sample.sub_question = sample_info["sub_question_str"]
current_sample.other_question = sample_info["other_question_str"]
current_sample.question_id = torch.tensor(sample_info["question_id"],
dtype=torch.int)
if isinstance(sample_info["image_id"], int):
current_sample.image_id = torch.tensor(sample_info["image_id"],
dtype=torch.int)
else:
current_sample.image_id = sample_info["image_id"]
current_sample.text_len = torch.tensor(
#len(sample_info["question_tokens"]), dtype=torch.int
len(sample_info["main_question_tokens"]),
dtype=torch.int)
if self._use_features is True:
features = self.features_db[idx]
current_sample.update(features)
# Add details for OCR like OCR bbox, vectors, tokens here
current_sample = self.add_ocr_details(sample_info, current_sample)
# Depending on whether we are using soft copy this can add
# dynamic answer space
current_sample = self.add_answer_info(sample_info, current_sample)
#print("current sample : {}".format(current_sample))
#pdb.set_trace()
#print("Current sample : {}".format(current_sample))
return current_sample
