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'''
* 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 .blip_vit import VisionTransformer, interpolate_pos_embed
from .blip_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
import numpy as np
from pathlib import Path
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
# BLIP
class BLIP_Base(nn.Module):
def __init__(self,
med_config = Path(LOCAL_PATH, 'blip_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__()
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):
assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
text = self.tokenizer(caption, return_tensors="pt").to(image.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(image.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 = Path(LOCAL_PATH, 'blip_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
# BLIP VQA
class BLIP_VQA(nn.Module):
def __init__(self,
med_config = Path(LOCAL_PATH, 'blip_configs/med_config.json'),
image_size = 480,
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__()
self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)
self.tokenizer = init_tokenizer()
encoder_config = BertConfig.from_json_file(med_config)
encoder_config.encoder_width = vision_width
self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False)
decoder_config = BertConfig.from_json_file(med_config)
self.text_decoder = BertLMHeadModel(config=decoder_config)
def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128):
image_embeds = self.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
question = self.tokenizer(question, padding='longest', truncation=True, max_length=35,
return_tensors="pt").to(image.device)
question.input_ids[:,0] = self.tokenizer.enc_token_id
if train:
'''
n: number of answers for each question
weights: weight for each answer
'''
answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device)
answer.input_ids[:,0] = self.tokenizer.bos_token_id
answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100)
question_output = self.text_encoder(question.input_ids,
attention_mask = question.attention_mask,
encoder_hidden_states = image_embeds,
encoder_attention_mask = image_atts,
return_dict = True)
question_states = []
question_atts = []
for b, n in enumerate(n):
question_states += [question_output.last_hidden_state[b]]*n
question_atts += [question.attention_mask[b]]*n
question_states = torch.stack(question_states,0)
question_atts = torch.stack(question_atts,0)
answer_output = self.text_decoder(answer.input_ids,
attention_mask = answer.attention_mask,
encoder_hidden_states = question_states,
encoder_attention_mask = question_atts,
labels = answer_targets,
return_dict = True,
reduction = 'none',
)
loss = weights * answer_output.loss
loss = loss.sum()/image.size(0)
return loss
else:
question_output = self.text_encoder(question.input_ids,
attention_mask = question.attention_mask,
encoder_hidden_states = image_embeds,
encoder_attention_mask = image_atts,
return_dict = True)
if inference=='generate':
num_beams = 3
question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0)
question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device)
model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts}
bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device)
outputs = self.text_decoder.generate(input_ids=bos_ids,
max_length=10,
min_length=1,
num_beams=num_beams,
eos_token_id=self.tokenizer.sep_token_id,
pad_token_id=self.tokenizer.pad_token_id,
**model_kwargs)
answers = []
for output in outputs:
answer = self.tokenizer.decode(output, skip_special_tokens=True)
answers.append(answer)
return answers
elif inference=='rank':
max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask,
answer.input_ids, answer.attention_mask, k_test)
return max_ids
def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k):
num_ques = question_states.size(0)
start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token
start_output = self.text_decoder(start_ids,
encoder_hidden_states = question_states,
encoder_attention_mask = question_atts,
return_dict = True,
reduction = 'none')
logits = start_output.logits[:,0,:] # first token's logit
# topk_probs: top-k probability
# topk_ids: [num_question, k]
answer_first_token = answer_ids[:,1]
prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token)
topk_probs, topk_ids = prob_first_token.topk(k,dim=1)
# answer input: [num_question*k, answer_len]
input_ids = []
input_atts = []
for b, topk_id in enumerate(topk_ids):
input_ids.append(answer_ids.index_select(dim=0, index=topk_id))
input_atts.append(answer_atts.index_select(dim=0, index=topk_id))
input_ids = torch.cat(input_ids,dim=0)
input_atts = torch.cat(input_atts,dim=0)
targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100)
# repeat encoder's output for top-k answers
question_states = tile(question_states, 0, k)
question_atts = tile(question_atts, 0, k)
output = self.text_decoder(input_ids,
attention_mask = input_atts,
encoder_hidden_states = question_states,
encoder_attention_mask = question_atts,
labels = targets_ids,
return_dict = True,
reduction = 'none')
log_probs_sum = -output.loss
log_probs_sum = log_probs_sum.view(num_ques,k)
max_topk_ids = log_probs_sum.argmax(dim=1)
max_ids = topk_ids[max_topk_ids>=0,max_topk_ids]
return max_ids
def blip_vqa(pretrained='',**kwargs):
model = BLIP_VQA(**kwargs)
if pretrained:
model,msg = load_checkpoint(model,pretrained)
# assert(len(msg.missing_keys)==0)
return model
def tile(x, dim, n_tile):
init_dim = x.size(dim)
repeat_idx = [1] * x.dim()
repeat_idx[dim] = n_tile
x = x.repeat(*(repeat_idx))
order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
return torch.index_select(x, dim, order_index.to(x.device))
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