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import torch
from torch import nn
from transformers import AutoConfig
from .image.configuration_image import LanguageBindImageConfig
from .image.modeling_image import LanguageBindImage
from .image.tokenization_image import LanguageBindImageTokenizer
from .image.processing_image import LanguageBindImageProcessor
from .video.configuration_video import LanguageBindVideoConfig
from .video.modeling_video import LanguageBindVideo
from .video.tokenization_video import LanguageBindVideoTokenizer
from .video.processing_video import LanguageBindVideoProcessor
from .depth.configuration_depth import LanguageBindDepthConfig
from .depth.modeling_depth import LanguageBindDepth
from .depth.tokenization_depth import LanguageBindDepthTokenizer
from .depth.processing_depth import LanguageBindDepthProcessor
from .audio.configuration_audio import LanguageBindAudioConfig
from .audio.modeling_audio import LanguageBindAudio
from .audio.tokenization_audio import LanguageBindAudioTokenizer
from .audio.processing_audio import LanguageBindAudioProcessor
from .thermal.configuration_thermal import LanguageBindThermalConfig
from .thermal.modeling_thermal import LanguageBindThermal
from .thermal.tokenization_thermal import LanguageBindThermalTokenizer
from .thermal.processing_thermal import LanguageBindThermalProcessor
config_dict = {
'thermal': LanguageBindThermalConfig,
'image': LanguageBindImageConfig,
'video': LanguageBindVideoConfig,
'depth': LanguageBindDepthConfig,
'audio': LanguageBindAudioConfig
}
model_dict = {
'thermal': LanguageBindThermal,
'image': LanguageBindImage,
'video': LanguageBindVideo,
'depth': LanguageBindDepth,
'audio': LanguageBindAudio
}
transform_dict = {
'video': LanguageBindVideoProcessor,
'audio': LanguageBindAudioProcessor,
'depth': LanguageBindDepthProcessor,
'thermal': LanguageBindThermalProcessor,
'image': LanguageBindImageProcessor,
}
class LanguageBind(nn.Module):
def __init__(self, clip_type=('thermal', 'image', 'video', 'depth', 'audio'), use_temp=True, cache_dir='./cache_dir'):
super(LanguageBind, self).__init__()
self.use_temp = use_temp
self.modality_encoder = {}
self.modality_proj = {}
self.modality_scale = {}
self.modality_config = {}
for c in clip_type:
pretrained_ckpt = f'LanguageBind/LanguageBind_{c.capitalize()}'
model = model_dict[c].from_pretrained(pretrained_ckpt, cache_dir=cache_dir)
self.modality_encoder[c] = model.vision_model
self.modality_proj[c] = model.visual_projection
self.modality_scale[c] = model.logit_scale
self.modality_config[c] = model.config
self.modality_encoder['language'] = model.text_model
self.modality_proj['language'] = model.text_projection
self.modality_encoder = nn.ModuleDict(self.modality_encoder)
self.modality_proj = nn.ModuleDict(self.modality_proj)
def forward(self, inputs):
outputs = {}
for key, value in inputs.items():
value = self.modality_encoder[key](**value)[1]
value = self.modality_proj[key](value)
value = value / value.norm(p=2, dim=-1, keepdim=True)
if self.use_temp:
if key != 'language':
value = value * self.modality_scale[key].exp()
outputs[key] = value
return outputs
def to_device(x, device):
out_dict = {k: v.to(device) for k, v in x.items()}
return out_dict
class LanguageBindImageTower(nn.Module):
def __init__(self, image_tower, args, delay_load=False, cache_dir='./cache_dir'):
super().__init__()
self.is_loaded = False
self.image_tower_name = image_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
self.cache_dir = cache_dir
if not delay_load:
self.load_model()
else:
self.cfg_only = LanguageBindImageConfig.from_pretrained(self.image_tower_name, cache_dir=self.cache_dir)
############################################################
def load_model(self):
model = LanguageBindImage.from_pretrained(self.image_tower_name, cache_dir=self.cache_dir)
self.image_tower = model.vision_model
self.image_tower.requires_grad_(False)
self.image_processor = LanguageBindImageProcessor(model.config)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
if self.select_feature == 'patch':
image_features = image_features[:, 1:]
elif self.select_feature == 'cls_patch':
image_features = image_features
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.image_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
# print('images', images.shape)
image_forward_outs = self.image_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
# print('image_forward_outs', len(image_forward_outs), image_forward_outs[0].shape)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
# print('image_features', image_features.shape)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.image_tower.embeddings.class_embedding.dtype #############
@property
def device(self):
return self.image_tower.embeddings.class_embedding.device ##############
@property
def config(self):
if self.is_loaded:
return self.image_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
class temp_model(nn.Module):
def __init__(self):
super(temp_model, self).__init__()
def forward(self, **kwargs):
return torch.randn(25, 1, 256, 1024)
class LanguageBindVideoTower(nn.Module):
def __init__(self, video_tower, args, delay_load=False, cache_dir='./cache_dir'):
super().__init__()
self.is_loaded = False
self.video_tower_name = video_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
self.cache_dir = cache_dir
if not delay_load:
self.load_model()
else:
self.cfg_only = LanguageBindVideoConfig.from_pretrained(self.video_tower_name, cache_dir=self.cache_dir)
############################################################
def load_model(self):
model = LanguageBindVideo.from_pretrained(self.video_tower_name, cache_dir=self.cache_dir)
self.video_processor = LanguageBindVideoProcessor(model.config)
# model = LanguageBindImage.from_pretrained('LanguageBind/LanguageBind_Image', cache_dir=self.cache_dir)
self.video_tower = model.vision_model
self.video_tower.requires_grad_(False)
self.is_loaded = True
# def feature_select(self, image_forward_outs):
# image_features = image_forward_outs.hidden_states[self.select_layer]
# if self.select_feature == 'patch':
# image_features = image_features[:, 1:]
# elif self.select_feature == 'cls_patch':
# image_features = image_features
# else:
# raise ValueError(f'Unexpected select feature: {self.select_feature}')
# return image_features
def feature_select(self, video_forward_outs):
# print('len(video_forward_outs.hidden_states)', len(video_forward_outs.hidden_states))
video_features = video_forward_outs.hidden_states[self.select_layer] # b t n c
b, t, n, c = video_features.shape
# print('video_features', video_features.shape)
if self.select_feature == 'patch':
# video_features = video_features[:, 1:]
video_features = video_features[:, :, 1:]
video_features = video_features.reshape(b, -1, c)
elif self.select_feature == 'cls_patch':
# video_features = video_features
video_features = video_features.reshape(b, -1, c)
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return video_features
@torch.no_grad()
def forward(self, videos):
if type(videos) is list:
video_features = []
for video in videos:
video_forward_out = self.video_tower(video.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
video_feature = self.feature_select(video_forward_out).to(video.dtype)
video_features.append(video_feature)
else:
# print(11111111111, videos.shape)
video_forward_outs = self.video_tower(videos.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
video_features = self.feature_select(video_forward_outs).to(videos.dtype)
return video_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.video_tower.embeddings.class_embedding.dtype #############
# return torch.randn(1).cuda().dtype
@property
def device(self):
return self.video_tower.embeddings.class_embedding.device ##############
# return torch.randn(1).cuda().device
@property
def config(self):
if self.is_loaded:
return self.video_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
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