''' * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li ''' import warnings warnings.filterwarnings("ignore") from models.vit import VisionTransformer, interpolate_pos_embed from models.med import BertConfig, BertModel, BertLMHeadModel from transformers import BertTokenizer import torch from torch import nn from pathlib import Path import torch.nn.functional as F import os from urllib.parse import urlparse from timm.models.hub import download_cached_file class BLIP_Base(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 224, 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__() dirpath = str(Path(__file__).parent.parent) med_config = dirpath + '/' + med_config 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) def forward(self, image, caption, mode, device): assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" text = self.tokenizer(caption, return_tensors="pt").to(device) if mode=='image': # return image features image_embeds = self.visual_encoder(image) return image_embeds elif mode=='text': # return text features text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') return text_output.last_hidden_state elif mode=='multimodal': # return multimodel features image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(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 = image_embeds, encoder_attention_mask = image_atts, return_dict = True, ) return output.last_hidden_state class BLIP_Decoder(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 384, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, prompt = 'a picture of ', ): """ 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_decoder = BertLMHeadModel(config=med_config) self.prompt = prompt self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 def forward(self, image, caption): 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='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) text.input_ids[:,0] = self.tokenizer.bos_token_id decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) decoder_targets[:,:self.prompt_length] = -100 decoder_output = self.text_decoder(text.input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, labels = decoder_targets, return_dict = True, ) loss_lm = decoder_output.loss return loss_lm def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): image_embeds = self.visual_encoder(image) if not sample: image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} prompt = [self.prompt] * image.size(0) input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) input_ids[:,0] = self.tokenizer.bos_token_id input_ids = input_ids[:, :-1] if sample: #nucleus sampling outputs = self.text_decoder.generate(input_ids=input_ids, max_length=max_length, min_length=min_length, do_sample=True, top_p=top_p, num_return_sequences=1, eos_token_id=self.tokenizer.sep_token_id, pad_token_id=self.tokenizer.pad_token_id, repetition_penalty=1.1, **model_kwargs) else: #beam search outputs = self.text_decoder.generate(input_ids=input_ids, max_length=max_length, min_length=min_length, num_beams=num_beams, eos_token_id=self.tokenizer.sep_token_id, pad_token_id=self.tokenizer.pad_token_id, repetition_penalty=repetition_penalty, **model_kwargs) captions = [] for output in outputs: caption = self.tokenizer.decode(output, skip_special_tokens=True) captions.append(caption[len(self.prompt):]) return captions def blip_decoder(pretrained='',**kwargs): model = BLIP_Decoder(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) assert(len(msg.missing_keys)==0) return model def blip_feature_extractor(pretrained='',**kwargs): model = BLIP_Base(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) assert(len(msg.missing_keys)==0) return model def init_tokenizer(): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') tokenizer.add_special_tokens({'bos_token':'[DEC]'}) tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] return tokenizer def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): assert vit in ['base', 'large'], "vit parameter must be base or large" if vit=='base': vision_width = 768 visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, drop_path_rate=0 or drop_path_rate ) elif vit=='large': vision_width = 1024 visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, drop_path_rate=0.1 or drop_path_rate ) return visual_encoder, vision_width def is_url(url_or_filename): parsed = urlparse(url_or_filename) return parsed.scheme in ("http", "https") 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) if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], model.visual_encoder_m) for key in model.state_dict().keys(): if key in state_dict.keys(): if state_dict[key].shape!=model.state_dict()[key].shape: del state_dict[key] msg = model.load_state_dict(state_dict,strict=False) print('load checkpoint from %s'%url_or_filename) return model,msg