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demo.py
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demo.py
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'''
LinCIR
Copyright (c) 2023-present NAVER Corp.
CC BY-NC-4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
'''
import os
import time
from argparse import ArgumentParser
import numpy as np
import torch
import gradio as gr
from clip_retrieval.clip_client import ClipClient
from encode_with_pseudo_tokens import encode_with_pseudo_tokens_HF
from models import build_text_encoder, Phi, PIC2WORD
import transformers
from huggingface_hub import hf_hub_url, cached_download
def parse_args():
parser = ArgumentParser()
parser.add_argument("--lincir_ckpt_path", default=None, type=str,
help="The output directory where the model predictions and checkpoints will be written")
parser.add_argument("--pic2word_ckpt_path", default=None, type=str)
parser.add_argument("--cache_dir", default="./hf_models", type=str,
help="Path to model cache folder")
parser.add_argument("--clip_model_name", default="large", type=str,
help="CLIP model to use, e.g 'large', 'huge', 'giga'")
parser.add_argument("--mixed_precision", default="fp16", type=str)
parser.add_argument("--test_fps", action="store_true")
args = parser.parse_args()
return args
def load_models(args):
if torch.cuda.is_available():
device = 'cuda:0'
dtype = torch.float16
else:
device = 'cpu'
dtype = torch.float32
clip_vision_model, clip_preprocess, clip_text_model, tokenizer = build_text_encoder(args)
tokenizer.add_special_tokens({'additional_special_tokens':["[$]"]}) # 49408
# ours
phi = Phi(input_dim=clip_text_model.config.projection_dim,
hidden_dim=clip_text_model.config.projection_dim * 4,
output_dim=clip_text_model.config.hidden_size, dropout=0.0)
phi.eval()
# searle
phi_searle, _ = torch.hub.load(repo_or_dir='miccunifi/SEARLE', model='searle', source='github',
backbone='ViT-L/14')
phi_searle.eval()
# pic2word
phi_pic2word = PIC2WORD(embed_dim=clip_text_model.config.projection_dim,
output_dim=clip_text_model.config.hidden_size)
phi_pic2word.eval()
clip_vision_model.to(device, dtype=dtype)
clip_text_model.to(device, dtype=dtype)
if not args.test_fps:
# download and load sd
if not os.path.exists('./pretrained_models/lincir_large.pt'):
model_file_url = hf_hub_url(repo_id='navervision/zeroshot-cir-models', filename='lincir_large.pt')
cached_download(model_file_url, cache_dir='./pretrained_models', force_filename='lincir_large.pt')
state_dict = torch.load('./pretrained_models/lincir_large.pt', map_location=device)
phi.load_state_dict(state_dict['Phi'])
if not os.path.exists('./pretrained_models/pic2word_large.pt'):
model_file_url = hf_hub_url(repo_id='navervision/zeroshot-cir-models', filename='pic2word_large.pt')
cached_download(model_file_url, cache_dir='./pretrained_models', force_filename='pic2word_large.pt')
sd = torch.load('./pretrained_models/pic2word_large.pt', map_location=device)['state_dict_img2text']
sd = {k[len('module.'):]: v for k, v in sd.items()}
phi_pic2word.load_state_dict(sd)
phi.to(device, dtype=dtype)
phi_searle.to(device, dtype=dtype)
phi_pic2word.to(device, dtype=dtype)
decoder = None
return {'clip_vision_model': clip_vision_model,
'clip_preprocess': clip_preprocess,
'clip_text_model': clip_text_model,
'tokenizer': tokenizer,
'phi': phi,
'phi_searle': phi_searle,
'phi_pic2word': phi_pic2word,
'decoder': decoder,
'device': device,
'dtype': dtype,
'clip_model_name': args.clip_model_name,
}
def predict(images, input_text, model_name):
start_time = time.time()
input_images = model_dict['clip_preprocess'](images, return_tensors='pt')['pixel_values'].to(model_dict['device'])
input_text = input_text.replace('$', '[$]')
input_tokens = model_dict['tokenizer'](text=input_text, return_tensors='pt', padding='max_length', truncation=True)['input_ids'].to(model_dict['device'])
input_tokens = torch.where(input_tokens == 49408,
torch.ones_like(input_tokens) * 259,
input_tokens)
image_features = model_dict['clip_vision_model'](pixel_values=input_images.to(model_dict['dtype'])).image_embeds
clip_image_time = time.time() - start_time
start_time = time.time()
if model_name == 'lincir':
estimated_token_embeddings = model_dict['phi'](image_features)
elif model_name == 'searle':
estimated_token_embeddings = model_dict['phi_searle'](image_features)
else: # model_name == 'pic2word'
estimated_token_embeddings = model_dict['phi_pic2word'](image_features)
phi_time = time.time() - start_time
start_time = time.time()
text_embeddings, text_last_hidden_states = encode_with_pseudo_tokens_HF(model_dict['clip_text_model'], input_tokens, estimated_token_embeddings, return_last_states=True)
clip_text_time = time.time() - start_time
start_time = time.time()
results = client.query(embedding_input=text_embeddings[0].tolist())
retrieval_time = time.time() - start_time
output = ''
for idx, result in enumerate(results):
image_url = result['url']
output += f'\n'
time_output = {'CLIP visual extractor': clip_image_time,
'CLIP textual extractor': clip_text_time,
'Phi projection': phi_time,
'CLIP retrieval': retrieval_time,
}
setup_output = {'device': model_dict['device'],
'dtype': model_dict['dtype'],
'Phi': model_name,
'CLIP': model_dict['clip_model_name'],
}
return {'time': time_output, 'setup': setup_output}, output
def test_fps(batch_size=1):
dummy_images = torch.rand([batch_size, 3, 224, 224])
todo_list = ['phi', 'phi_pic2word']
input_tokens = model_dict['tokenizer'](text=['a photo of $1 with flowers'] * batch_size, return_tensors='pt', padding='max_length', truncation=True)['input_ids'].to(model_dict['device'])
input_tokens = torch.where(input_tokens == 49409,
torch.ones_like(input_tokens) * 259,
input_tokens)
for model_name in todo_list:
time_array = []
n_repeat = 100
for _ in range(n_repeat):
start_time = time.time()
image_features = model_dict['clip_vision_model'](pixel_values=dummy_images.to(model_dict['clip_vision_model'].device, dtype=model_dict['clip_vision_model'].dtype)).image_embeds
token_embeddings = model_dict[model_name](image_features)
text_embeddings = encode_with_pseudo_tokens_HF(model_dict['clip_text_model'], input_tokens, token_embeddings)
end_time = time.time()
if _ > 5:
time_array.append(end_time - start_time)
print(f"{model_name}: {np.mean(time_array):.4f}")
if __name__ == '__main__':
args = parse_args()
global model_dict, client
model_dict = load_models(args)
if args.test_fps:
# check FPS of all models.
test_fps(1)
exit()
client = ClipClient(url="https://knn.laion.ai/knn-service",
indice_name="laion5B-H-14" if args.clip_model_name == "huge" else "laion5B-L-14",
)
title = 'Zeroshot CIR demo'
md_title = f'''# {title}
[LinCIR](https://arxiv.org/abs/2312.01998): Language-only Training of Zero-shot Composed Image Retrieval
[SEARLE](https://arxiv.org/abs/2303.15247): Zero-shot Composed Image Retrieval with Textual Inversion
[Pic2Word](https://arxiv.org/abs/2302.03084): Mapping Pictures to Words for Zero-shot Composed Image Retrieval
K-NN index for the retrieval results are entirely trained using the entire Laion-5B imageset. This is made possible thanks to the great work of [rom1504](https://github.com/rom1504/clip-retrieval).
'''
with gr.Blocks(title=title) as demo:
gr.Markdown(md_title)
with gr.Row():
with gr.Column():
with gr.Row():
image_source = gr.Image(type='pil', label='image1')
model_name = gr.Radio(['lincir', 'searle', 'pic2word'], label='Phi model', value='lincir')
text_input = gr.Textbox(value='', label='Input text guidance. Special token is $')
submit_button = gr.Button('Submit')
gr.Examples([["example1.jpg", "$, pencil sketch", 'lincir']], inputs=[image_source, text_input, model_name])
with gr.Column():
json_output = gr.JSON(label='Processing time')
md_output = gr.Markdown(label='Output')
submit_button.click(predict, inputs=[image_source, text_input, model_name], outputs=[json_output, md_output])
demo.queue()
demo.launch(server_name='0.0.0.0',
server_port=8000)