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from .vqa_dataset import VQADataset
TEMPLATE = {
"description": "Template used by Alpaca-LoRA.",
# "prompt_choice": "Below is a multiple choice question about an image, along with answer options. Please choose the correct answer from these options.\n\n### Image:\n{image}\n\n### Question:\n{question}\n\n### Options:\n{options}\n\n### Answer:\n",
# "prompt_qa": "Below is a question about an image. Write a response to answer the question.\n\n### Image:\n{image}\n\n### Question:\n{question}\n\n### Answer:\n",
"prompt_choice": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Image:\n{image}\n\n### Instruction:\n{question}\n\n### Input:\n{options}\n\n### Response:\n",
"prompt_qa": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Image:\n{image}\n\n### Instruction:\n{question}\n\n### Response:\n",
"prompt_dial": "\n\n### Instruction:\n{question}\n\n### Response:\n",
"response_split": "### Response:",
}
class DialPrompter:
def __call__(self, question, options=None):
if options:
options = ", ".join(options)
res = TEMPLATE["prompt_choice"].format(image="<image>", question=question, options=options)
else:
res = TEMPLATE["prompt_dial"].format(question=question)
return res
def get_response(self, output: str) -> str:
return output.split(TEMPLATE["response_split"])[-1].strip()
class DialDataset(VQADataset):
def __init__(self, *args, **kwargs):
super(DialDataset, self).__init__(*args, **kwargs)
self.prompter = DialPrompter()
def _add_instance_ids(self, key="id"):
for idx, ann in enumerate(self.annotation):
ann[key] = str(idx)
def process_text(self, anns):
# TODO remove this
begin_string = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Image:\n{image}".format(
image="<image>"
)
num_convs = len(anns["conversations"]) // 2
conv_list = []
for conv_id in range(num_convs):
question = anns["conversations"][conv_id]["value"]
# remove '<image>' tag and '\n'
question = question.replace("<image>", "").replace("\n", "")
answer = anns["conversations"][conv_id + 1]["value"]
instruction = self.prompter(question)
if conv_id == 0:
single_conv = dict(instruction=begin_string + instruction, answer=answer)
else:
single_conv = dict(instruction=instruction, answer=answer)
conv_list.append(single_conv)
return conv_list
def __getitem__(self, index):
ann = self.annotation[index]
image = self.process_image(ann)
text_list = self.process_text(ann)
res_list = []
for text in text_list:
single_res = self.tokenize(text)
single_res["instruction"] = text["instruction"]
single_res["answer"] = text["answer"]
res_list.append(single_res)
input_ids = []
attention_mask = []
labels = []
instruction = []
answer = []
for res in res_list:
input_ids.extend(res["input_ids"])
attention_mask.extend(res["attention_mask"])
labels.extend(res["labels"])
instruction.extend(res["instruction"])
answer.extend(res["answer"])
res = dict(
input_ids=input_ids, attention_mask=attention_mask, labels=labels, instruction=instruction, answer=answer
)
res.update(image=image)
return res
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