import torch from einops import rearrange from typing import Optional, Tuple, Union from torch import nn from transformers import CLIPModel as HFCLIPModel, CLIPVisionConfig from transformers.modeling_outputs import BaseModelOutputWithPooling from transformers.models.clip.modeling_clip import CLIP_VISION_INPUTS_DOCSTRING from transformers.utils import replace_return_docstrings, add_start_docstrings_to_model_forward # class VT_CLIP(nn.Module): # output_dict: torch.jit.Final[bool] # # def __init__( # self, # embed_dim: int, # vision_cfg: CLIPVisionCfg, # text_cfg: CLIPTextCfg, # quick_gelu: bool = False, # cast_dtype: Optional[torch.dtype] = None, # output_dict: bool = False, # ): # super().__init__() # self.output_dict = output_dict # self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) # # text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) # self.transformer = text.transformer # self.context_length = text.context_length # self.vocab_size = text.vocab_size # self.token_embedding = text.token_embedding # self.positional_embedding = text.positional_embedding # self.ln_final = text.ln_final # self.text_projection = text.text_projection # self.register_buffer('attn_mask', text.attn_mask, persistent=False) # # self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) # # # # def unlock_time_attn(self): # for name, param in self.named_parameters(): # if 'time' in name: # param.requires_grad = True # # def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): # # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 # self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) # # def lock_text_tower(self, unlocked_layers=0, freeze_layer_norm=False): # for param in self.transformer.parameters(): # param.requires_grad = False # for param in self.token_embedding.parameters(): # param.requires_grad = False # for param in self.ln_final.parameters(): # param.requires_grad = False # self.positional_embedding.requires_grad = False # self.text_projection.requires_grad = False # # if unlocked_layers != 0: # groups = [ # [ # self.token_embedding, # self.positional_embedding, # ], # *self.transformer.resblocks[:-1], # [ # self.transformer.resblocks[-1], # self.ln_final, # ], # self.text_projection, # ] # # def _unlock(x): # if isinstance(x, Sequence): # for g in x: # _unlock(g) # else: # if isinstance(x, torch.nn.Parameter): # x.requires_grad = True # else: # for p in x.parameters(): # p.requires_grad = True # # _unlock(groups[-unlocked_layers:]) # # @torch.jit.ignore # def set_grad_checkpointing(self, enable=True): # self.visual.set_grad_checkpointing(enable) # self.transformer.grad_checkpointing = enable # # def encode_image(self, image, normalize: bool = False): # features = self.visual(image) # return F.normalize(features, dim=-1) if normalize else features # # def encode_text(self, text, normalize: bool = False): # cast_dtype = self.transformer.get_cast_dtype() # # x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] # # x = x + self.positional_embedding.to(cast_dtype) # x = x.permute(1, 0, 2) # NLD -> LND # x = self.transformer(x, attn_mask=self.attn_mask) # x = x.permute(1, 0, 2) # LND -> NLD # x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] # # take features from the eot embedding (eot_token is the highest number in each sequence) # x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection # return F.normalize(x, dim=-1) if normalize else x # # def forward( # self, # image: Optional[torch.Tensor] = None, # text: Optional[torch.Tensor] = None, # ): # image_features = self.encode_image(image, normalize=True) if image is not None else None # text_features = self.encode_text(text, normalize=True) if text is not None else None # if self.output_dict: # return { # "image_features": image_features, # "text_features": text_features, # "logit_scale": self.logit_scale.exp() # } # return image_features, text_features, self.logit_scale.exp() from model.process_clip import get_global_value class CLIPModel(HFCLIPModel): def __init__(self, config, num_frames, add_time_attn): super(CLIPModel, self).__init__(config) if add_time_attn: config.vision_config.num_frames = num_frames self.vision_model.forward = self.vision_model_forward @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig) def vision_model_forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.vision_model.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.vision_model.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.vision_model.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") if len(pixel_values.shape) == 7: b_new, pair_new, T, bs_new, channel_new, h_new, w_new = pixel_values.shape # print(pixel_values.shape) B = b_new * pair_new * bs_new pixel_values = pixel_values.reshape(B*T, channel_new, h_new, w_new) elif len(pixel_values.shape) == 5: B, _, T, _, _ = pixel_values.shape # print(pixel_values.shape) pixel_values = rearrange(pixel_values, 'b c t h w -> (b t) c h w') else: # print(pixel_values.shape) B, _, _, _ = pixel_values.shape T = 1 hidden_states = self.vision_model.embeddings(pixel_values) # # if self.temporal_embedding is not None and get_global_value()['NUM_FRAMES'] != 1: # n = hidden_states.shape[1] # hidden_states = rearrange(hidden_states, '(b t) n d -> (b n) t d', t=T) # hidden_states = hidden_states + self.temporal_embedding[:, :T, :] # hidden_states = rearrange(hidden_states, '(b n) t d -> (b t) n d', n=n) hidden_states = self.vision_model.patch_dropout(hidden_states, B, T) # print(hidden_states.shape) hidden_states = self.vision_model.pre_layrnorm(hidden_states) encoder_outputs = self.vision_model.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.vision_model.post_layernorm(pooled_output) pooled_output = pooled_output.reshape(B, T, -1).mean(1) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def encode_image(self, image, normalize: bool = False): vision_outputs = self.vision_model( pixel_values=image, return_dict=True, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) return image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) if normalize else image_embeds def encode_text(self, input_ids, attention_mask, normalize: bool = False): text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, return_dict=True, ) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) return text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) if normalize else text_embeds def forward( self, image=None, input_ids=None, attention_mask=None ): image_features = self.encode_image(image, normalize=True) if image is not None else None text_features = self.encode_text(input_ids, attention_mask, normalize=True) if input_ids is not None else None # if self.output_dict: return { "image_features": image_features, "text_features": text_features, "logit_scale": self.logit_scale.exp() } # return image_features, text_features, self.logit_scale.exp()