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class CLIPTempScore

Bases: BaseMetric

Initialize the CLIPTempScore evaluator.

Parameters:

Name Type Description Default
model_name str

The name of the CLIP encoder model. Defaults to openai/clip-vit-base-patch32.

 'openai/clip-vit-base-patch32'
logit_scale bool

Whether to calcualte the cosine similarity as logits. Defaults to False.

 False
Source code in aigve/metrics/text_video_alignment/similarity_based/clipscore/cliptemp.py 
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@METRICS.register_module()
class CLIPTempScore(BaseMetric):
    """ Initialize the ``CLIPTempScore`` evaluator.

    Args:
        model_name (str): The name of the CLIP encoder model. Defaults to ``openai/clip-vit-base-patch32``.
        logit_scale (bool): Whether to calcualte the cosine similarity as logits. Defaults to False.

    """
    def __init__(self,
                 model_name: str = "openai/clip-vit-base-patch32",
                 logit_scale: bool = False,
                #  train_index: int = 4
                 ) -> None:
        super().__init__()
        self.model_name = model_name
        self.logit_scale = logit_scale

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = CLIPModel.from_pretrained(self.model_name).to(self.device)
        self.model.eval()

    def process(self, data_batch: Sequence, data_samples: Sequence) -> None:
        """CLIPTempScore 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_videos = data_samples
        # bsz = len(input_videos)


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

        # Generate embeddings for each frame and concatenate the features
        clip_temp_score_sum, clip_temp_score_cnt = 0, 0
        logit_scale = self.model.logit_scale.exp() if self.logit_scale else 1
        with torch.no_grad():  
            for input_frames in input_videos: # Too many frames in a video, must split before CLIP embedding, limited by the memory
                input_frames = input_frames.to(self.device)
                frame_feature = self.model.get_image_features(input_frames)
                frame_feature = frame_feature / torch.norm(frame_feature, dim=-1, keepdim=True)
                # print(frame_feature.shape)

                clip_temp_score_list = []
                for i in range(frame_feature.shape[0]-1):
                    clip_temp_score = logit_scale * frame_feature[i].unsqueeze(0) @ frame_feature[i+1].unsqueeze(0).T
                    clip_temp_score = clip_temp_score.item()
                    # print(clip_temp_score)
                    clip_temp_score_list.append(clip_temp_score)
                clip_temp_cur_avg_score = sum(clip_temp_score_list)/len(clip_temp_score_list)
                clip_temp_score_sum += clip_temp_cur_avg_score
                clip_temp_score_cnt += 1
                print('current clip temp similarity score', clip_temp_cur_avg_score)

        clip_temp_score_avg = clip_temp_score_sum/clip_temp_score_cnt

        result['clip_temp_score'] = clip_temp_score_avg

        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()

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

        clip_temp_mean = np.mean(clip_score_np) 

        print("Test results: clip temporal consistency score={:.4f}"
              .format(clip_temp_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/similarity_based/clipscore/cliptemp.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()

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

    clip_temp_mean = np.mean(clip_score_np) 

    print("Test results: clip temporal consistency score={:.4f}"
          .format(clip_temp_mean))

    return result

process(data_batch, data_samples)

CLIPTempScore 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/similarity_based/clipscore/cliptemp.py 
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def process(self, data_batch: Sequence, data_samples: Sequence) -> None:
    """CLIPTempScore 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_videos = data_samples
    # bsz = len(input_videos)


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

    # Generate embeddings for each frame and concatenate the features
    clip_temp_score_sum, clip_temp_score_cnt = 0, 0
    logit_scale = self.model.logit_scale.exp() if self.logit_scale else 1
    with torch.no_grad():  
        for input_frames in input_videos: # Too many frames in a video, must split before CLIP embedding, limited by the memory
            input_frames = input_frames.to(self.device)
            frame_feature = self.model.get_image_features(input_frames)
            frame_feature = frame_feature / torch.norm(frame_feature, dim=-1, keepdim=True)
            # print(frame_feature.shape)

            clip_temp_score_list = []
            for i in range(frame_feature.shape[0]-1):
                clip_temp_score = logit_scale * frame_feature[i].unsqueeze(0) @ frame_feature[i+1].unsqueeze(0).T
                clip_temp_score = clip_temp_score.item()
                # print(clip_temp_score)
                clip_temp_score_list.append(clip_temp_score)
            clip_temp_cur_avg_score = sum(clip_temp_score_list)/len(clip_temp_score_list)
            clip_temp_score_sum += clip_temp_cur_avg_score
            clip_temp_score_cnt += 1
            print('current clip temp similarity score', clip_temp_cur_avg_score)

    clip_temp_score_avg = clip_temp_score_sum/clip_temp_score_cnt

    result['clip_temp_score'] = clip_temp_score_avg

    self.results.append(result)