def __getitem__(self, idx):
sample_info = self.annotation_db[idx]
current_sample = Sample()
text_processor_argument = {"text": sample_info["question_str"]}
processed_question = self.text_processor(text_processor_argument)
current_sample.text = processed_question["text"]
if "input_ids" in processed_question:
current_sample.update(processed_question)
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]
if hasattr(self, "transformer_bbox_processor"):
features["image_info_0"] = self.transformer_bbox_processor(
features["image_info_0"]
)
current_sample.update(features)
# 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 __getitem__(self, idx):
sample_info = self.annotation_db[idx]
sample_info = self.preprocess_sample_info(sample_info)
current_sample = Sample()
# breaking change from VQA2Dataset: load question_id
current_sample.question_id = torch.tensor(sample_info["question_id"],
dtype=torch.int)
if isinstance(sample_info["image_id"], int):
current_sample.image_id = str(sample_info["image_id"])
else:
current_sample.image_id = sample_info["image_id"]
if self._use_features is True:
features = self.features_db[idx]
current_sample.update(features)
current_sample = self.add_sample_details(sample_info, current_sample)
current_sample = self.add_answer_info(sample_info, current_sample)
# only the 'max_features' key is needed
# pop other keys to minimize data loading overhead
if hasattr(current_sample, "image_info_0"):
for k in list(current_sample.image_info_0):
if k != "max_features":
current_sample.image_info_0.pop(k)
if hasattr(current_sample, "image_info_1"):
for k in list(current_sample.image_info_1):
if k != "max_features":
current_sample.image_info_1.pop(k)
return current_sample
def load_item(self, idx):
sample_info = self.annotation_db[idx]
current_sample = Sample()
if "question_tokens" in sample_info:
text_processor_argument = {
"tokens": sample_info["question_tokens"],
"text": sample_info["question_str"],
}
else:
text_processor_argument = {"text": sample_info["question"]}
processed_question = self.text_processor(text_processor_argument)
current_sample.text = processed_question["text"]
if "input_ids" in processed_question:
current_sample.update(processed_question)
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 "question_tokens" in sample_info:
current_sample.text_len = torch.tensor(len(
sample_info["question_tokens"]),
dtype=torch.int)
if self._use_features:
features = self.features_db[idx]
if hasattr(self, "transformer_bbox_processor"):
features["image_info_0"] = self.transformer_bbox_processor(
features["image_info_0"])
current_sample.update(features)
else:
image_path = sample_info["image_name"] + ".jpg"
current_sample.image = self.image_db.from_path(
image_path)["images"][0]
# 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
