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import torch
import torch.nn as nn
from .eva_vit import create_eva_vit_g, _cfg
from .processor import ImageTrainProcessor, ImageEvalProcessor
class EVAVisionTower(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()
else:
self.cfg_only = _cfg()
def load_model(self):
self.image_processor = ImageTrainProcessor()
self.image_eval_processor = ImageEvalProcessor()
self.vision_tower = create_eva_vit_g(
img_size=224, drop_path_rate=0, use_checkpoint=False, precision="fp16"
)
# self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs, select_feature='patch'):
image_features = image_forward_outs[self.select_layer]
if select_feature == 'patch':
image_features = image_features[:, 1:]
elif 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, select_feature='patch'):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower.get_intermediate_layers(image.to(device=self.device, dtype=self.dtype).unsqueeze(0),)
image_feature = self.feature_select(image_forward_out, select_feature).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower.get_intermediate_layers(images.to(device=self.device, dtype=self.dtype))
image_features = self.feature_select(image_forward_outs, select_feature).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.cls_token.dtype
@property
def device(self):
return self.vision_tower.cls_token.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.vision_tower.num_features
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2 |