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# This obtains the list of known identities from the known folder known_regex = "/content/data/11-785-s23-hw2p2-verification/known/*/*" known_paths = [i.split('/')[-2] for i in sorted(glob.glob(known_regex))] # Obtain a list of images from unknown folders unknown_dev_regex = "/content/data/11-785-s23-hw2p2-verification/unknown_dev/*" unknown_test_regex = "/content/data/11-785-s23-hw2p2-verification/unknown_test/*" # We load the images from known and unknown folders unknown_dev_images = [Image.open(p) for p in tqdm(sorted(glob.glob(unknown_dev_regex)))] unknown_test_images = [Image.open(p) for p in tqdm(sorted(glob.glob(unknown_test_regex)))] known_images = [Image.open(p) for p in tqdm(sorted(glob.glob(known_regex)))] # Why do you need only ToTensor() here? transforms = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), normalize]) unknown_dev_images = torch.stack([transforms(x) for x in unknown_dev_images]) unknown_test_images = torch.stack([transforms(x) for x in unknown_test_images]) known_images = torch.stack([transforms(y) for y in known_images ]) #Print your shapes here to understand what we have done # You can use other similarity metrics like Euclidean Distance if you wish similarity_metric = torch.nn.CosineSimilarity(dim= 1, eps= 1e-6) # similarity_metric = torch.nn.PairwiseDistance()
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