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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")
import os
from urllib.parse import urlparse
import torch
from hydra.utils import get_original_cwd
from timm.models.hub import download_cached_file
from torch import nn
from transformers import BertTokenizer
from src.model.med import BertConfig, BertLMHeadModel, BertModel
from src.model.vit import VisionTransformer, interpolate_pos_embed
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():
try:
bert_pth = os.path.join(get_original_cwd(), "bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained(bert_pth)
except:
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,
)
else:
raise NotImplementedError
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(f"checkpoint {url_or_filename} is invalid")
state_dict = checkpoint["model"]
state_dict = remove_module(state_dict)
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
def remove_module(state_dict):
new_state_dict = {}
for key in state_dict.keys():
if key.startswith("module."):
new_state_dict[key[7:]] = state_dict[key]
else:
new_state_dict[key] = state_dict[key]
return new_state_dict
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