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A10G
Running
on
A10G
| 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 | |
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.image_tower.embeddings.class_embedding.dtype ############# | |
| def device(self): | |
| return self.image_tower.embeddings.class_embedding.device ############## | |
| def config(self): | |
| if self.is_loaded: | |
| return self.image_tower.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| 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 | |
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.video_tower.embeddings.class_embedding.dtype ############# | |
| # return torch.randn(1).cuda().dtype | |
| def device(self): | |
| return self.video_tower.embeddings.class_embedding.device ############## | |
| # return torch.randn(1).cuda().device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.video_tower.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 | |