File size: 3,269 Bytes
0a2e9f2 | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import sys
import os
sys.path.append(os.path.dirname(__file__) + os.sep + '../')
import clip
from config import *
class CLIPSimilarity(nn.Module):
def __init__(self, model_filename):
super(CLIPSimilarity, self).__init__()
self.model_filename = model_filename
self.model, self.p = clip.load(model_filename, device='cpu')
self.model = self.model.eval()
def forward(self, text, image, batch_size=None):
'''
text: [X]
for example, ['str_1', 'str_2', ..., 'str_X']
image: [Y, c, w, h]
return: [X, Y]
'''
device = image.device
img_input = F.interpolate(image, size=224)
image_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).to(device)
image_std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).to(device)
img_input = (img_input.clamp(min=-1.0, max=1.0) + 1.0) / 2.0
img_input -= image_mean[:, None, None]
img_input /= image_std[:, None, None]
text_input = clip.tokenize(text, truncate=True).to(device)
with torch.no_grad():
text_features = self.model.encode_text(text_input).float()
image_features = self.model.encode_image(img_input).float()
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
logit_scale = 1.0 # Default value is models.logit_scale.exp()
if batch_size:
text_features = rearrange(text_features, '(b x) d -> b x d', b=batch_size)
image_features = rearrange(image_features, '(b y) d -> b d y', b=batch_size)
else:
image_features = image_features.t()
logits_per_text = logit_scale * torch.matmul(text_features, image_features)
similarity = logits_per_text
return similarity
class BaseDataset(Dataset):
def __init__(self, img_list, txt_list):
self.img_list = img_list
self.txt_list = txt_list
assert len(img_list) == len(txt_list), 'len_img must equal len_txt!'
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
def __len__(self):
return len(self.img_list)
def __getitem__(self, i):
img_path = self.img_list[i]
caption = self.txt_list[i]
img = Image.open(img_path).convert('RGB')
image = self.transform(img)
sample = {"label_imgs": image, 'input_text': caption}
return sample
def compute_clip_score(model_pth, img_list, txt_list, batch_size=32, cuda_device=0):
device = torch.device(f'cuda:{cuda_device}')
model = CLIPSimilarity(model_pth).to(device)
data = BaseDataset(img_list, txt_list)
loader = DataLoader(data, batch_size=batch_size, shuffle=False, num_workers=32)
clip_sim_total = 0
num = 0
for sample in tqdm(loader):
imgs = sample['label_imgs'].to(device)
caps = sample['input_text']
clip_sim = model(caps, imgs, batch_size=len(caps))
clip_sim_total += clip_sim.mean().item()
num += 1
return clip_sim_total/num
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