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Make a folder for seperating blip files, add seed.

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Kohya S
2022-12-20 08:18:24 +09:00
parent e53adbdbcc
commit dadea12ad2
5 changed files with 18 additions and 5 deletions
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finetune/blip/blip.py Normal file
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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 models.vit import VisionTransformer, interpolate_pos_embed
# from models.med import BertConfig, BertModel, BertLMHeadModel
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
class BLIP_Base(nn.Module):
def __init__(self,
med_config = '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 = '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