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import torch
from torch import nn
from transformers import CLIPVisionModel, CLIPImageProcessor
class VisualToGPTMapping(nn.Module):
def __init__(self, visual_emb_dim, gpt_emb_dim, num_gpt_embs, num_heads):
super(VisualToGPTMapping, self).__init__()
self.transformer_layer = TransformerEncoderLayer(d_model=visual_emb_dim, nhead=num_heads, batch_first=True, norm_first=False)
self.linear = Linear(visual_emb_dim, gpt_emb_dim)
self.n_embeddings = num_gpt_embs
self.embedding_dim = gpt_emb_dim
def forward(self, visual_embs):
out = self.transformer_layer(visual_embs)
out = self.linear(out).view(-1, self.n_embeddings, self.embedding_dim)
return out
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = -2
self.select_feature = 'patch'
if not delay_load:
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self):
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
self.vision_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
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_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:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
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.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size |