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| from modelsn.med import BertConfig | |
| from modelsn.nlvr_encoder import BertModel | |
| from modelsn.vit import interpolate_pos_embed | |
| from modelsn.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 | |
| 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 | |