m3 commited on
Commit
ea7f2de
·
1 Parent(s): bfa11e6

chore: upgrade moel

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Files changed (2) hide show
  1. model.onnx +1 -1
  2. src/sscd.py +0 -42
model.onnx CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:4e25f635133a806a543cc19b1fae70ae4ade6a30d2770a58fae8a69834b5428e
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  size 235
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:abe326cb527103d52edd6d5fba82f8dfa34d1012d1f005b298c652434d283d39
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  size 235
src/sscd.py DELETED
@@ -1,42 +0,0 @@
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- from torchvision import transforms
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- import torch
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- from PIL import Image
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- import torch.nn.functional as F
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- from matplotlib import pyplot as plt
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-
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- ##### Global variable
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- normalize = transforms.Normalize(
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- mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],
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- )
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- preproccess = transforms.Compose([
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- transforms.Resize(288),
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- transforms.ToTensor(),
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- normalize,
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- ])
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-
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- model = torch.jit.load("./msscddiscmixup.torchscript.pt")
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-
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-
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- def visualize(path):
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- image = Image.open(path)
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- plt.figure(figsize=(10, 10))
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- plt.axis('off')
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- plt.imshow(image)
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-
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-
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- def extract_feature(img_path):
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- img = Image.open(img_path).convert('RGB')
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- batch = preproccess(img).unsqueeze(0)
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- return model(batch)[0, :]
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-
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-
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- def simi(img1, img2):
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- visualize(img1)
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- visualize(img2)
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- vec1 = extract_feature(img1)
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- vec2 = extract_feature(img2)
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- # subtract the mean and then L2 norm
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- cos_sim = F.cosine_similarity(vec1, vec2, dim=0)
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- # 余弦相似度得分大于0.75,匹配准确度90%
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- print('similarity:', cos_sim)
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-