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import torch
import torch.nn as nn
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
class CLIPVisionEncoder(nn.Module):
def __init__(self, encoder_name="openai/clip-vit-large-patch14", delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_encoder_name = encoder_name
# self.select_layer = args.mm_vision_select_layer
# self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
self.select_layer = -1
self.select_feature = "patch"
if not delay_load:
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_encoder_name)
def load_model(self):
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_encoder_name)
self.vision_encoder = CLIPVisionModel.from_pretrained(self.vision_encoder_name)
self.vision_encoder.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[:, :]
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_encoder(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_encoder(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
# print("image feature shape", image_features.shape)
# print(type(image_forward_outs))
# print(type(image_forward_outs.shape))
# image_features = 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_encoder.dtype
@property
def device(self):
return self.vision_encoder.device
@property
def config(self):
if self.is_loaded:
return self.vision_encoder.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 |