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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def load_demo_image(image_size, device):
img_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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
def rename_key(key):
if "visual_encoder" in key:
key = re.sub("visual_encoder*", "vision_model.encoder", key)
if "blocks" in key:
key = re.sub(r"blocks", "layers", key)
if "attn" in key:
key = re.sub(r"attn", "self_attn", key)
if "norm1" in key:
key = re.sub(r"norm1", "layer_norm1", key)
if "norm2" in key:
key = re.sub(r"norm2", "layer_norm2", key)
if "encoder.norm" in key:
key = re.sub(r"encoder.norm", "post_layernorm", key)
if "encoder.patch_embed.proj" in key:
key = re.sub(r"encoder.patch_embed.proj", "embeddings.patch_embedding", key)
if "encoder.pos_embed" in key:
key = re.sub(r"encoder.pos_embed", "embeddings.position_embedding", key)
if "encoder.cls_token" in key:
key = re.sub(r"encoder.cls_token", "embeddings.class_embedding", key)
if "self_attn" in key:
key = re.sub(r"self_attn.proj", "self_attn.projection", key)
return key
@torch.no_grad()
def convert_blip_checkpoint(pytorch_dump_folder_path, config_path=None):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = BlipConfig.from_pretrained(config_path)
else:
config = BlipConfig(projection_dim=512, text_config={}, vision_config={})
hf_model = BlipForConditionalGeneration(config).eval()
model_url = "model_base_capfilt_large.pth"
# pt_model = blip_decoder(pretrained=model_url, image_size=384, vit="base")
# pt_model = pt_model.eval()
# modified_state_dict = pt_model.state_dict()
# for key in modified_state_dict.copy():
# value = modified_state_dict.pop(key)
# renamed_key = rename_key(key)
# modified_state_dict[renamed_key] = value
#
# hf_model.load_state_dict(modified_state_dict)
#
image_size = 384
image = load_demo_image(image_size=image_size, device="cpu")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
# input_ids = tokenizer(["a picture of"]).input_ids
#
# out = hf_model.generate(image, input_ids)
#
# assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
#
# out = hf_model.generate(image)
#
# assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
#
# if pytorch_dump_folder_path is not None:
# hf_model.save_pretrained(pytorch_dump_folder_path)
#
# # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
# model_url = (
# # "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
# # )
vqa_model = blip_vqa(pretrained=model_url, image_size=image_size, vit="base")
vqa_model.eval()
modified_state_dict = vqa_model.state_dict()
for key in modified_state_dict.copy():
value = modified_state_dict.pop(key)
renamed_key = rename_key(key)
modified_state_dict[renamed_key] = value
hf_vqa_model = BlipForQuestionAnswering(config)
offset_keys = [i for i in modified_state_dict.keys() if i not in hf_vqa_model.state_dict().keys()]
print(len([i for i in hf_vqa_model.state_dict().keys() if i in modified_state_dict.keys()]))
for key in offset_keys:
modified_state_dict.pop(key)
hf_vqa_model.load_state_dict(modified_state_dict)
question = ["How many dogs are in this image?"]
question_input_ids = tokenizer(question, return_tensors="pt").input_ids
answer = hf_vqa_model.generate(question_input_ids, image)
print(tokenizer.decode(answer[0]))
# assert tokenizer.decode(answer[0]) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa")
# model_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"
#
# itm_model = blip_itm(pretrained=model_url, image_size=image_size, vit="base")
# itm_model.eval()
#
# modified_state_dict = itm_model.state_dict()
# for key in modified_state_dict.copy():
# value = modified_state_dict.pop(key)
# renamed_key = rename_key(key)
# modified_state_dict[renamed_key] = value
#
# hf_itm_model = BlipForImageTextRetrieval(config)
#
# question = ["A picture of a woman with a dog sitting in a beach"]
# question_input_ids = tokenizer(
# question,
# return_tensors="pt",
# padding="max_length",
# truncation=True,
# max_length=35,
# ).input_ids
#
# hf_itm_model.load_state_dict(modified_state_dict)
# hf_itm_model.eval()
#
# out_itm = hf_itm_model(question_input_ids, image, use_itm_head=True)
# out = hf_itm_model(question_input_ids, image, use_itm_head=False)
#
# assert out[0].item() == 0.2110687494277954
# assert torch.nn.functional.softmax(out_itm[0], dim=1)[:, 1].item() == 0.45698845386505127
#
# if pytorch_dump_folder_path is not None:
# hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
args = parser.parse_args()
convert_blip_checkpoint(args.pytorch_dump_folder_path, args.config_path)
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