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import torch
import torch.nn as nn
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, 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')
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)
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
@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
class CLIPVisionTowerS2(CLIPVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__(vision_tower, args, delay_load)
self.s2_scales = getattr(args, 's2_scales', '336,672,1008')
self.s2_scales = list(map(int, self.s2_scales.split(',')))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
try:
from s2wrapper import forward as multiscale_forward
except ImportError:
raise ImportError('Package s2wrapper not found! Please install by running: \npip install git+https://github.com/bfshi/scaling_on_scales.git')
self.multiscale_forward = multiscale_forward
# change resize/crop size in preprocessing to the largest image size in s2_scale
if not delay_load or getattr(args, 'unfreeze_mm_vision_tower', False):
self.image_processor.size['shortest_edge'] = self.s2_image_size
self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
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)
self.vision_tower.requires_grad_(False)
self.image_processor.size['shortest_edge'] = self.s2_image_size
self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
self.is_loaded = True
@torch.no_grad()
def forward_feature(self, images):
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
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = self.multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
image_features.append(image_feature)
else:
image_features = self.multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)
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