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import torch
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import torch.nn as nn
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from transformers import CLIPImageProcessor
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try:
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from imagebind.models import imagebind_model
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from imagebind.models.imagebind_model import ModalityType
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from imagebind.data import load_and_transform_audio_data
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except ImportError:
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pass
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class ImageBindWrapper(nn.Module):
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def __init__(self, vision_tower, select_layer, select_feature="patch", delay_load=False):
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super().__init__()
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self.is_loaded = False
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self.vision_tower_name = vision_tower
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self.select_layer = select_layer
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self.select_feature = select_feature
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if not delay_load:
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self.load_model()
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def load_model(self):
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self.image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
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self.vision_tower = imagebind_model.imagebind_huge(pretrained=True)
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for p in self.vision_tower.parameters():
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p.requires_grad = False
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self.vision_tower.eval()
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self.is_loaded = True
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def train(self, mode=True):
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self.training = mode
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if self.is_loaded:
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self.vision_tower.eval()
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@torch.no_grad()
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def forward(self, x):
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if type(x) == dict:
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if x["audios"] is not None:
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inputs = {ModalityType.AUDIO: load_and_transform_audio_data(x["audios"], device=self.device).half()}
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embeddings = self.vision_tower(inputs)
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audio_embedding = embeddings[ModalityType.AUDIO]
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return audio_embedding.unsqueeze(1)
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else:
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inputs = {ModalityType.VISION: x.to(dtype=self.dtype)}
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embeddings = self.vision_tower(inputs)
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vision_embedding = embeddings[ModalityType.VISION]
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if vision_embedding.ndim == 2:
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return vision_embedding.unsqueeze(1)
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if vision_embedding.shape[1] == 257:
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return vision_embedding[:, 1:]
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raise ValueError(f"Unexpected shape: {vision_embedding.shape}")
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@property
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def dummy_feature(self):
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return torch.zeros(1, 1024, device=self.device, dtype=self.dtype)
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@property
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def dtype(self):
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return self.vision_tower.modality_preprocessors.vision.cls_token.dtype
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@property
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def device(self):
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return self.vision_tower.modality_preprocessors.vision.cls_token.device
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@property
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def hidden_size(self):
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return 1024
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