101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
class QwenVLMMBenchPromptConstructor:
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"""MMBench prompt constructor for Qwen-VL.
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The output is a dict following the input format of Qwen-VL tokenizer.
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"""
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def __init__(self) -> None:
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pass
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def __call__(self, inputs: dict) -> list:
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data_samples = inputs['data_samples']
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assert len(data_samples) == 1
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data_sample = data_samples[0]
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question = data_sample.get('question')
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options = data_sample.get('options')
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context = data_sample.get('context')
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if context is not None:
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prompt = context + ' ' + question + ' ' + options
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else:
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prompt = question + ' ' + options
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format_input = [
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{
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'image': 'This_is_path_to_an_image.'
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}, # Just placeholder for Image Tokens
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{
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'text': prompt
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},
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]
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return format_input
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class QwenVLChatPromptConstructor:
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"""Prompt constructorfor Qwen-VL-Chat."""
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def __init__(self, prompt='') -> None:
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self.prompt = prompt
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def __call__(self, inputs: dict) -> list:
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assert len(inputs['data_samples']) == 1
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format_input = [
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{
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'image': 'This_is_path_to_an_image.'
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}, # Just placeholder for Image Tokens
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{
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'text': self.prompt
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},
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]
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return format_input
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class QwenVLChatVQAPromptConstructor:
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"""VQA prompt constructor for Qwen-VL-Chat."""
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def __init__(self, prompt='') -> None:
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self.prompt = prompt
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def __call__(self, inputs: dict) -> list:
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data_samples = inputs['data_samples']
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assert len(data_samples) == 1
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data_sample = data_samples[0]
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question = data_sample.get('question')
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format_input = [
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{
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'image': 'This_is_path_to_an_image.'
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}, # Just placeholder for Image Tokens
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{
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'text': question + self.prompt
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},
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]
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return format_input
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class QwenVLChatScienceQAPromptConstructor:
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"""ScienceQA prompt constructor for Qwen-VL-Chat."""
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choice_mapping = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F'}
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def __init__(self, prompt='') -> None:
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self.prompt = prompt
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def __call__(self, inputs: dict) -> list:
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data_samples = inputs['data_samples']
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assert len(data_samples) == 1
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data_sample = data_samples[0]
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question = data_sample.get('question')
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choices = data_sample.get('choices')
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choices = [
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f'({self.choice_mapping[i]}) ' + item
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for i, item in enumerate(choices)
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]
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choices = 'Choices: ' + ' '.join(choices) + '\n'
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contexts = 'Context: ' + data_sample.get('hint')
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format_input = [
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{
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'image': 'This_is_path_to_an_image.'
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}, # Just placeholder for Image Tokens
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{
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'text': contexts + question + choices + self.prompt
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},
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]
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return format_input
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