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| import os | |
| import warnings | |
| from PIL import Image | |
| from torchvision import transforms | |
| from torchvision.transforms import InterpolationMode | |
| warnings.filterwarnings("ignore") | |
| from models.vit import VisionTransformer, interpolate_pos_embed | |
| from models.med import BertConfig, BertModel, BertLMHeadModel | |
| from transformers import BertTokenizer, CLIPConfig | |
| from models.modeling_clip import CLIPModel, CLIPVisionModel, CLIPVisionConfig | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| 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='[DEC]', | |
| ): | |
| super().__init__() | |
| self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, 0) | |
| 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=False) | |
| captions.append(caption[len(self.prompt):]) | |
| return captions | |
| def init_tokenizer(): | |
| tokenizer = BertTokenizer.from_pretrained('resources/bert-large-chinese', do_lower_case=True) | |
| 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', 'large_v2'], "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 | |
| ) | |
| elif vit == 'large_v2': | |
| vision_width = 1024 | |
| clip_config = CLIPConfig.from_pretrained('resources/clip_vit_large_patch14') | |
| visual_encoder = CLIPVisionModel(clip_config) | |
| return visual_encoder, vision_width | |
| def load_image(image, image_size, device): | |
| raw_image = Image.open(str(image)).convert('RGB') | |
| w, h = raw_image.size | |
| transform = transforms.Compose([ | |
| transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| return image | |