chore: upgrade moel

model.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4e25f635133a806a543cc19b1fae70ae4ade6a30d2770a58fae8a69834b5428e
+oid sha256:abe326cb527103d52edd6d5fba82f8dfa34d1012d1f005b298c652434d283d39
 size 235

src/sscd.py

@@ -1,42 +0,0 @@
-from torchvision import transforms
-import torch
-from PIL import Image
-import torch.nn.functional as F
-from matplotlib import pyplot as plt
-
-##### Global variable
-normalize = transforms.Normalize(
-    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],
-)
-preproccess = transforms.Compose([
-    transforms.Resize(288),
-    transforms.ToTensor(),
-    normalize,
-])
-
-model = torch.jit.load("./msscddiscmixup.torchscript.pt")
-
-
-def visualize(path):
-    image = Image.open(path)
-    plt.figure(figsize=(10, 10))
-    plt.axis('off')
-    plt.imshow(image)
-
-
-def extract_feature(img_path):
-    img = Image.open(img_path).convert('RGB')
-    batch = preproccess(img).unsqueeze(0)
-    return model(batch)[0, :]
-
-
-def simi(img1, img2):
-    visualize(img1)
-    visualize(img2)
-    vec1 = extract_feature(img1)
-    vec2 = extract_feature(img2)
-    # subtract the mean and then L2 norm
-    cos_sim = F.cosine_similarity(vec1, vec2, dim=0)
-    # 余弦相似度得分大于0.75，匹配准确度90%
-    print('similarity:', cos_sim)
-