| ''' |
| * 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 |
| import torch.nn.functional as F |
|
|
| import os |
| from urllib.parse import urlparse |
| from timm.models.hub import download_cached_file |
|
|
| 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: |
| |
| 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: |
| |
| 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 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 |
|
|
