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import warnings
warnings.filterwarnings("ignore")
from models.vit import VisionTransformer, interpolate_pos_embed
from models.med import BertConfig, 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
import pdb
class CapModel(nn.Module):
def __init__(self,
med_config = 'SMILE/BLIP/configs/med_config.json',
image_size = 224,
vit = 'base',
vit_grad_ckpt = False,
vit_ckpt_layer = 0,
prompt = 'a picture of ',
):
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
self.vocab_emb = None
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
# # First-token Shifting: Change the first token 'word' to '##word'
# for i in range(text.input_ids.size(0)):
# text.input_ids[i, self.prompt_length] = self.tokenizer.convert_tokens_to_ids('##' + self.tokenizer.convert_ids_to_tokens(text.input_ids[i,self.prompt_length].item()))
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,
)
# # mle
# mle_loss = decoder_output.loss
label = text.input_ids[:, self.prompt_length:].contiguous()
bs = text.input_ids.size(0)
N = label.size(1)
vs = self.text_decoder.config.vocab_size
logits = decoder_output.logits[:, self.prompt_length-1:-1]
# smile
mask = torch.zeros(bs, vs).to(logits.device).scatter_(1, label, True)
mask[:, 0] = 0
mask = mask.unsqueeze(1).expand(-1, N, -1).clone()
mask[:, 0, :] = 1 # mle on first token
selected_logits = logits.masked_fill(mask == 0, -1e9)
smile_loss = F.cross_entropy(selected_logits.view(-1, vs), label.view(-1), ignore_index=0, reduction='mean')
# # reverse smile
# reverse_mask = torch.ones(bs, vs).to(logits.device).scatter_(1, label, False)
# reverse_mask = reverse_mask.unsqueeze(1).expand(-1, N, -1).clone()
# reverse_mask.scatter_(2, label.unsqueeze(-1), 1)
# reverse_mask[:, 0, :] = 1 # mle on first token
# reverse_selected_logits = logits.masked_fill(reverse_mask == 0, -1e9)
# reverse_smile_loss = F.cross_entropy(reverse_selected_logits.view(-1, vs), label.view(-1), ignore_index=0, reduction='mean')
# # random sample (efficient implementation)
# sample_num = 10
# rand_indices = torch.randint(vs, (bs, N, sample_num)).to(label.device)
# rand_indices_with_label = torch.cat((rand_indices, label.unsqueeze(2)), dim=2) # (bs, N, sample_num + 1)
# batch_indices = torch.arange(bs)[:, None, None].expand(bs, N, sample_num + 1)
# seq_indices = torch.arange(N)[None, :, None].expand(bs, N, sample_num + 1)
# random_mask = torch.zeros(bs, N, vs).to(label.device)
# random_mask[batch_indices, seq_indices, rand_indices_with_label] = 1
# random_mask[:, :, 0] = 0
# random_selected_logits = logits.masked_fill(mask == 0, -1e9)
# random_smile_loss = F.cross_entropy(random_selected_logits.view(-1, vs), label.view(-1), ignore_index=0, reduction='mean')
loss = smile_loss
# loss = 0.5 * reverse_smile_loss + 0.5 * mle_loss
return loss
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)
prompt = [self.prompt] * image.size(0)
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device)
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}
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):])
# caption = self.tokenizer.decode(output[4:], skip_special_tokens=True)
# captions.append(caption)
return captions
def caption_model(pretrained='',**kwargs):
model = CapModel(**kwargs)
if pretrained:
model,msg = load_checkpoint(model,pretrained)
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 |