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