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class TIFAScore

Bases: BaseMetric

Source code in aigve/metrics/text_video_alignment/gpt_based/TIFA/tifa_eval.py 
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@METRICS.register_module()
class TIFAScore(BaseMetric):
    '''
    '''
    def __init__(self, 
                 openai_key,
                 llm_model: str = 'gpt-3.5-turbo',
                 unifiedqa_model_name: str = 'allenai/unifiedqa-v2-t5-large-1363200',
                 vqa_model_name: str = 'mplug-large'):
        super().__init__()

        self.openai_key = openai_key
        self.llm_model = llm_model
        self.unifiedqa_model_name = unifiedqa_model_name
        self.unifiedqa_model = UnifiedQAModel(self.unifiedqa_model_name)
        self.vqa_model_name = vqa_model_name
        self.vqa_model = VQAModel(self.vqa_model_name)

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.openai_setup()

    def openai_setup(self):
        print('set up openai client')
        openai.api_key = self.openai_key
        assert openai.api_key is not None
        test_prompt_string = 'hello, how are you doing?'
        print('test prompt: ', test_prompt_string)
        response = openai_completion(
            test_prompt_string,
            model=self.llm_model,
        )
        print('test response: ', response)


    def process(self, data_batch: Sequence, data_samples: Sequence) -> None:
        """TIFAScore process
        Process one batch of data samples and predictions. The processed
        results should be stored in ``self.results``, which will be used to
        compute the metrics when all batches have been processed.

        Args:
            data_batch (Sequence): A batch of data from the dataloader.
            data_samples (Sequence): A batch of data samples that
                contain annotations and predictions.
        """

        result = dict()

        input_prompts, input_videos = data_samples
        bsz = len(input_prompts)

        # Ensure prompt_input is a tensor
        if isinstance(input_prompts, tuple):
            input_prompts = list(input_prompts)

        if isinstance(input_videos, tuple):
            input_videos = list(input_videos)

        average_tifa_score_list = []
        for input_prompt, input_video in zip(input_prompts, input_videos):
            tifa_score = []
            # Generate questions with GPT-3.5-turbo
            gpt3_questions = get_question_and_answers(input_prompt)
            # print(gpt3_questions)
            # Filter questions with UnifiedQA
            filtered_questions = filter_question_and_answers(self.unifiedqa_model, gpt3_questions)
            for index, frame_path in enumerate(input_video):
                # calucluate TIFA score
                result = tifa_score_single(self.vqa_model, filtered_questions, frame_path)
                # print(result)
                tifa_score.append(result['tifa_score'])
            average_tifa_score = sum(tifa_score)/len(tifa_score)
            average_tifa_score_list.append(average_tifa_score)

        result['tifa_score'] = sum(average_tifa_score_list)/len(average_tifa_score_list)

        self.results.append(result)


    def compute_metrics(self, results: list) -> Dict[str, float]:
        """Compute the metrics from processed results.

        Args:
            results (list): The processed results of each batch.

        Returns:
            Dict[str, float]: The computed metrics. The keys are the names of
            the metrics, and the values are corresponding results.
        """
        logger: MMLogger = MMLogger.get_current_instance()

        tifa_score_np = np.zeros(len(results))
        for i, result in enumerate(results):
            tifa_score_np[i] = result['tifa_score']

        tifa_score_np_mean = np.mean(tifa_score_np) 

        print("Test results: tifa score={:.4f}"
              .format(tifa_score_np_mean))

        return result

compute_metrics(results)

Compute the metrics from processed results.

Parameters:

Name Type Description Default
results list

The processed results of each batch.

 required

Returns:

Type Description
Dict[str, float]

Dict[str, float]: The computed metrics. The keys are the names of
Dict[str, float]

the metrics, and the values are corresponding results.
Source code in aigve/metrics/text_video_alignment/gpt_based/TIFA/tifa_eval.py 
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def compute_metrics(self, results: list) -> Dict[str, float]:
    """Compute the metrics from processed results.

    Args:
        results (list): The processed results of each batch.

    Returns:
        Dict[str, float]: The computed metrics. The keys are the names of
        the metrics, and the values are corresponding results.
    """
    logger: MMLogger = MMLogger.get_current_instance()

    tifa_score_np = np.zeros(len(results))
    for i, result in enumerate(results):
        tifa_score_np[i] = result['tifa_score']

    tifa_score_np_mean = np.mean(tifa_score_np) 

    print("Test results: tifa score={:.4f}"
          .format(tifa_score_np_mean))

    return result

process(data_batch, data_samples)

TIFAScore process Process one batch of data samples and predictions. The processed results should be stored in self.results, which will be used to compute the metrics when all batches have been processed.

Parameters:

Name Type Description Default
data_batch Sequence

A batch of data from the dataloader.

 required
data_samples Sequence

A batch of data samples that contain annotations and predictions.

 required
Source code in aigve/metrics/text_video_alignment/gpt_based/TIFA/tifa_eval.py 
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def process(self, data_batch: Sequence, data_samples: Sequence) -> None:
    """TIFAScore process
    Process one batch of data samples and predictions. The processed
    results should be stored in ``self.results``, which will be used to
    compute the metrics when all batches have been processed.

    Args:
        data_batch (Sequence): A batch of data from the dataloader.
        data_samples (Sequence): A batch of data samples that
            contain annotations and predictions.
    """

    result = dict()

    input_prompts, input_videos = data_samples
    bsz = len(input_prompts)

    # Ensure prompt_input is a tensor
    if isinstance(input_prompts, tuple):
        input_prompts = list(input_prompts)

    if isinstance(input_videos, tuple):
        input_videos = list(input_videos)

    average_tifa_score_list = []
    for input_prompt, input_video in zip(input_prompts, input_videos):
        tifa_score = []
        # Generate questions with GPT-3.5-turbo
        gpt3_questions = get_question_and_answers(input_prompt)
        # print(gpt3_questions)
        # Filter questions with UnifiedQA
        filtered_questions = filter_question_and_answers(self.unifiedqa_model, gpt3_questions)
        for index, frame_path in enumerate(input_video):
            # calucluate TIFA score
            result = tifa_score_single(self.vqa_model, filtered_questions, frame_path)
            # print(result)
            tifa_score.append(result['tifa_score'])
        average_tifa_score = sum(tifa_score)/len(tifa_score)
        average_tifa_score_list.append(average_tifa_score)

    result['tifa_score'] = sum(average_tifa_score_list)/len(average_tifa_score_list)

    self.results.append(result)