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import torch
import torch.nn as nn
from transformers import CLIPVisionModel
class DFN5B_CLIP_ViT_H_14_378(nn.Module):
def __init__(self, vision_tower):
super().__init__()
self.is_loaded = False
self.is_resize_pos = False
self.vision_tower_name = vision_tower
self.select_layer = -1
self.select_feature = 'patch'
self.load_model()
def load_model(self):
# self.vision_tower = CLIPVisionModel.from_pretrained('/root/lwt/tech/mcmd-72b/acc_finetune/DFN5B-bfloat16')#self.vision_tower_name
self.vision_tower = CLIPVisionModel.from_pretrained('/root/LWT/Models/DFN5B-CLIP-ViT-H-14-378')#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
def forward(self, images):
if not self.is_loaded:
self.load_model()
if type(images) is list: # not batch infurence speed!
image_features = []
for image in images:
image_forward_out = self.vision_tower(
image.to(device=self.device,
dtype=image.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=images.dtype),
output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def device(self):
return self.vision_tower.device
@property
def dtype(self):
return self.vision_tower.dtype
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