update

class CaformerBackbone(torch.nn.Module):

    def __init__(self, input_resolution: int = 384, heads: int = 32, out_dims: int = 1024, **kwargs):

    def __init__(self, input_resolution: int = 384, heads: int = 8, out_dims: int = 768, **kwargs):

        torch.nn.Module.__init__(self)

        self.input_resolution = input_resolution

        self.caformer = CAFormerBuilder(**kwargs)()

class CCIPBatchMetrics(nn.Module):

    def __init__(self):

        nn.Module.__init__(self)

        super().__init__()

        self.logit_scale = nn.Parameter(torch.ones([])*np.log(1/0.07))

        #self.sim = nn.CosineSimilarity(dim=-1)

        return logits_per_image

class CCIPFeature(torch.nn.Module):

class CCIPFeature(nn.Module):

    def __init__(self, name: str = "clip/ViT-B/32"):

        torch.nn.Module.__init__(self)

        super().__init__()

        self.backbone, self.preprocess = get_backbone(name)

    def forward(self, x):

        return x

class CCIP(torch.nn.Module):

class CCIP(nn.Module):

    def __init__(self, name: str = "clip/ViT-B/32"):

        torch.nn.Module.__init__(self)

        super().__init__()

        self.feature = CCIPFeature(name)

        self.metrics = CCIPBatchMetrics()