Spaces:
Runtime error
Runtime error
File size: 1,876 Bytes
54199b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import torch
from PIL import Image
from src.open_clip import create_model_and_transforms, get_tokenizer
import warnings
import argparse
warnings.filterwarnings("ignore", category=UserWarning)
# Create an argument parser
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', type=str, required=True, help='Path to the input image')
parser.add_argument('--prompt', type=str, required=True, help='Text prompt')
parser.add_argument('--checkpoint', type=str, default='../HPSv2.pt', help='Path to the model checkpoint')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model, preprocess_train, preprocess_val = create_model_and_transforms(
'ViT-H-14',
'laion2B-s32B-b79K',
precision='amp',
device=device,
jit=False,
force_quick_gelu=False,
force_custom_text=False,
force_patch_dropout=False,
force_image_size=None,
pretrained_image=False,
image_mean=None,
image_std=None,
light_augmentation=True,
aug_cfg={},
output_dict=True,
with_score_predictor=False,
with_region_predictor=False
)
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['state_dict'])
tokenizer = get_tokenizer('ViT-H-14')
model.eval()
# Load your image and prompt
with torch.no_grad():
# Process the image
image = preprocess_val(Image.open(args.image_path)).unsqueeze(0).to(device=device, non_blocking=True)
# Process the prompt
text = tokenizer([args.prompt]).to(device=device, non_blocking=True)
# Calculate the HPS
with torch.cuda.amp.autocast():
outputs = model(image, text)
image_features, text_features = outputs["image_features"], outputs["text_features"]
logits_per_image = image_features @ text_features.T
hps_score = torch.diagonal(logits_per_image).cpu().numpy()
print('HPSv2 score:', hps_score[0])
|