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class CLIPSimScore

Bases: BaseMetric

Initialize the CLIPSimScore evaluator.

Parameters:

Name Type Description Default
processor_name str

The name of the CLIP processor, which wraps a CLIP feature extractor and a CLIP tokenizer into this single procesor. Defaults to openai/clip-vit-base-patch32.

 'openai/clip-vit-base-patch32'
model_name str

The name of the CLIP 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/clipsim.py 
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@METRICS.register_module()
class CLIPSimScore(BaseMetric):
    """ Initialize the ``CLIPSimScore`` evaluator.

    Args:
        processor_name (str): The name of the CLIP processor, which wraps a CLIP feature extractor and a CLIP tokenizer into this single procesor. 
                                Defaults to ``openai/clip-vit-base-patch32``.
        model_name (str): The name of the CLIP 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,
                 processor_name: str = "openai/clip-vit-base-patch32",
                 model_name: str = "openai/clip-vit-base-patch32",
                 logit_scale: bool = False,
                #  train_index: int = 4
                 ) -> None:
        super().__init__()
        self.processor_name = processor_name
        self.model_name = model_name
        self.logit_scale = logit_scale

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

    def process(self, data_batch: Sequence, data_samples: Sequence) -> None:
        """CLIPSimScore 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)

        # Initialize an empty list to store each similarity score
        clip_score_sum, clip_score_cnt = 0, 0
        logit_scale = self.model.logit_scale.exp() if self.logit_scale else 1
        with torch.no_grad():
            for input_prompt, input_frames in zip(input_prompts, input_videos):
                input_prompt = input_prompt.to(self.device)
                text_feature = self.model.get_text_features(input_prompt) # [bsz, hid_dim]
                text_feature = text_feature / torch.norm(text_feature, dim=-1, keepdim=True)

                input_frames = input_frames.to(self.device)  # Add batch dimension and move the frame to the device
                frame_feature = self.model.get_image_features(input_frames)
                frame_feature = frame_feature / torch.norm(frame_feature, dim=-1, keepdim=True)

                clip_score = logit_scale * (frame_feature @ text_feature.T).mean().item()
                print('current clip similarity score', clip_score)
                clip_score_sum += clip_score
                clip_score_cnt += 1

        # Calculate the average CLIP score across all frames
        clip_score_videos_avg = clip_score_sum/clip_score_cnt

        result['clip_sim_score'] = clip_score_videos_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_sim_score']

        clip_sim_mean = np.mean(clip_score_np) 

        print("Test results: clip similarity score={:.4f}"
              .format(clip_sim_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/clipsim.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_sim_score']

    clip_sim_mean = np.mean(clip_score_np) 

    print("Test results: clip similarity score={:.4f}"
          .format(clip_sim_mean))

    return result

process(data_batch, data_samples)

CLIPSimScore 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/clipsim.py 
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def process(self, data_batch: Sequence, data_samples: Sequence) -> None:
    """CLIPSimScore 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)

    # Initialize an empty list to store each similarity score
    clip_score_sum, clip_score_cnt = 0, 0
    logit_scale = self.model.logit_scale.exp() if self.logit_scale else 1
    with torch.no_grad():
        for input_prompt, input_frames in zip(input_prompts, input_videos):
            input_prompt = input_prompt.to(self.device)
            text_feature = self.model.get_text_features(input_prompt) # [bsz, hid_dim]
            text_feature = text_feature / torch.norm(text_feature, dim=-1, keepdim=True)

            input_frames = input_frames.to(self.device)  # Add batch dimension and move the frame to the device
            frame_feature = self.model.get_image_features(input_frames)
            frame_feature = frame_feature / torch.norm(frame_feature, dim=-1, keepdim=True)

            clip_score = logit_scale * (frame_feature @ text_feature.T).mean().item()
            print('current clip similarity score', clip_score)
            clip_score_sum += clip_score
            clip_score_cnt += 1

    # Calculate the average CLIP score across all frames
    clip_score_videos_avg = clip_score_sum/clip_score_cnt

    result['clip_sim_score'] = clip_score_videos_avg

    self.results.append(result)