mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-14 22:13:41 +03:00
76 lines
3.1 KiB
Python
76 lines
3.1 KiB
Python
from models.med import BertConfig, BertModel
|
|
from transformers import BertTokenizer
|
|
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
|
|
from models.blip import create_vit, init_tokenizer, load_checkpoint
|
|
|
|
class BLIP_ITM(nn.Module):
|
|
def __init__(self,
|
|
med_config = 'configs/med_config.json',
|
|
image_size = 384,
|
|
vit = 'base',
|
|
vit_grad_ckpt = False,
|
|
vit_ckpt_layer = 0,
|
|
embed_dim = 256,
|
|
):
|
|
"""
|
|
Args:
|
|
med_config (str): path for the mixture of encoder-decoder model's configuration file
|
|
image_size (int): input image size
|
|
vit (str): model size of vision transformer
|
|
"""
|
|
super().__init__()
|
|
|
|
self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
|
|
self.tokenizer = init_tokenizer()
|
|
med_config = BertConfig.from_json_file(med_config)
|
|
med_config.encoder_width = vision_width
|
|
self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
|
|
|
|
text_width = self.text_encoder.config.hidden_size
|
|
|
|
self.vision_proj = nn.Linear(vision_width, embed_dim)
|
|
self.text_proj = nn.Linear(text_width, embed_dim)
|
|
|
|
self.itm_head = nn.Linear(text_width, 2)
|
|
|
|
|
|
def forward(self, image, caption, match_head='itm'):
|
|
|
|
image_embeds = self.visual_encoder(image)
|
|
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
|
|
|
|
text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35,
|
|
return_tensors="pt").to(image.device)
|
|
|
|
|
|
if match_head=='itm':
|
|
output = self.text_encoder(text.input_ids,
|
|
attention_mask = text.attention_mask,
|
|
encoder_hidden_states = image_embeds,
|
|
encoder_attention_mask = image_atts,
|
|
return_dict = True,
|
|
)
|
|
itm_output = self.itm_head(output.last_hidden_state[:,0,:])
|
|
return itm_output
|
|
|
|
elif match_head=='itc':
|
|
text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
|
|
return_dict = True, mode = 'text')
|
|
image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)
|
|
text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)
|
|
|
|
sim = image_feat @ text_feat.t()
|
|
return sim
|
|
|
|
|
|
def blip_itm(pretrained='',**kwargs):
|
|
model = BLIP_ITM(**kwargs)
|
|
if pretrained:
|
|
model,msg = load_checkpoint(model,pretrained)
|
|
assert(len(msg.missing_keys)==0)
|
|
return model
|
|
|