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import torch
import torch.nn as nn
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, freeze_vision_tower=False, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
self.freeze_vision_tower = freeze_vision_tower
if not delay_load:
self.load_model()
elif getattr(args, "unfreeze_mm_vision_tower", False):
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self, device_map=None):
if self.is_loaded:
print(
"{} is already loaded, `load_model` called again, skipping.".format(
self.vision_tower_name
)
)
return
self.image_processor = CLIPImageProcessor.from_pretrained(
self.vision_tower_name
)
self.vision_tower = CLIPVisionModel.from_pretrained(
self.vision_tower_name, device_map=device_map
)
if self.freeze_vision_tower:
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
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
if self.freeze_vision_tower:
with torch.no_grad():
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_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:
if self.freeze_vision_tower:
with torch.no_grad():
image_forward_out = self.vision_tower(
images.to(device=self.device, dtype=self.dtype),
output_hidden_states=True,
)
image_features = self.feature_select(image_forward_out).to(
images.dtype
)
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
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
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