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from extras.BLIP.models.med import BertConfig
from extras.BLIP.models.nlvr_encoder import BertModel
from extras.BLIP.models.vit import interpolate_pos_embed
from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url
from timm.models.hub import download_cached_file
import torch
from torch import nn
import torch.nn.functional as F
from transformers import BertTokenizer
import numpy as np
import os
class BLIP_NLVR(nn.Module):
def __init__(self,
med_config = 'configs/med_config.json',
image_size = 480,
vit = 'base',
vit_grad_ckpt = False,
vit_ckpt_layer = 0,
):
"""
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, drop_path_rate=0.1)
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)
self.cls_head = nn.Sequential(
nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size),
nn.ReLU(),
nn.Linear(self.text_encoder.config.hidden_size, 2)
)
def forward(self, image, text, targets, train=True):
image_embeds = self.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0))
text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device)
text.input_ids[:,0] = self.tokenizer.enc_token_id
output = self.text_encoder(text.input_ids,
attention_mask = text.attention_mask,
encoder_hidden_states = [image0_embeds,image1_embeds],
encoder_attention_mask = [image_atts[:image0_embeds.size(0)],
image_atts[image0_embeds.size(0):]],
return_dict = True,
)
hidden_state = output.last_hidden_state[:,0,:]
prediction = self.cls_head(hidden_state)
if train:
loss = F.cross_entropy(prediction, targets)
return loss
else:
return prediction
def blip_nlvr(pretrained='',**kwargs):
model = BLIP_NLVR(**kwargs)
if pretrained:
model,msg = load_checkpoint(model,pretrained)
print("missing keys:")
print(msg.missing_keys)
return model
def load_checkpoint(model,url_or_filename):
if is_url(url_or_filename):
cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
checkpoint = torch.load(cached_file, map_location='cpu')
elif os.path.isfile(url_or_filename):
checkpoint = torch.load(url_or_filename, map_location='cpu')
else:
raise RuntimeError('checkpoint url or path is invalid')
state_dict = checkpoint['model']
state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
for key in list(state_dict.keys()):
if 'crossattention.self.' in key:
new_key0 = key.replace('self','self0')
new_key1 = key.replace('self','self1')
state_dict[new_key0] = state_dict[key]
state_dict[new_key1] = state_dict[key]
elif 'crossattention.output.dense.' in key:
new_key0 = key.replace('dense','dense0')
new_key1 = key.replace('dense','dense1')
state_dict[new_key0] = state_dict[key]
state_dict[new_key1] = state_dict[key]
msg = model.load_state_dict(state_dict,strict=False)
print('load checkpoint from %s'%url_or_filename)
return model,msg
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