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import argparse
import csv
import os
from PIL import Image
from utils import load_model
import jax.numpy as jnp
from jax import jit
from tqdm import tqdm
def main(args):
root = args.image_path
files = list(os.listdir(root))
for f in files:
assert(f[-4:] == ".jpg")
for model_name in ["koclip-base", "koclip-large"]:
model, processor = load_model(f"koclip/{model_name}")
with tqdm(total=len(files)) as pbar:
for counter in range(0, len(files), args.batch_size):
images = []
image_ids = []
for idx in range(counter, min(len(files), counter + args.batch_size)):
file_ = files[idx]
image = Image.open(os.path.join(root, file_)).convert('RGB')
images.append(image)
image_ids.append(file_)
pbar.update(args.batch_size)
try:
inputs = processor(text=[""], images=images, return_tensors="jax", padding=True)
except:
print(image_ids)
break
inputs['pixel_values'] = jnp.transpose(inputs['pixel_values'], axes=[0, 2, 3, 1])
features = model(**inputs).image_embeds
with open(os.path.join(args.out_path, f"{model_name}.tsv"), "a+") as f:
writer = csv.writer(f, delimiter="\t")
for image_id, feature in zip(image_ids, features):
writer.writerow([image_id, ",".join(map(lambda x: str(x), feature))])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", default=16)
parser.add_argument("--image_path", default="images")
parser.add_argument("--out_path", default="features")
args = parser.parse_args()
main(args)
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