if __name__ == "__main__": image_shape = (255, 255) data_root = './data/ILSVRC/ILSVRC2015' transform = transforms.Compose([transforms.ToTensor()]) test_set = ILSVRC(data_root, image_shape=image_shape, data_percentage=0.5, train=False, transform=transform) model = ConvVAE() model.load_state_dict(torch.load("./trained_models/ConvVAE.pt")) model.eval() model2 = ConvVAE() model2.load_state_dict( torch.load("./trained_models/ConvVAE_firstConv_GMN_batch.pt")) model2.eval() index = random.randint(0, len(test_set)) print(index) images, _, ground_truth, count, resized_template = test_set[index] templates = torch.reshape( resized_template, (1, resized_template.shape[-3], resized_template.shape[-2], resized_template.shape[-1])) decoded, _, _ = model(templates) im1 = transforms.ToPILImage()(templates[0]).convert("RGB")
transforms.ToTensor() ]) test_set = datasets.CIFAR10(root=dataset_root, train=False, download=True, transform=transform) model_r = ConvVAE(channels=1) model_g = ConvVAE(channels=1) model_b = ConvVAE(channels=1) model_r.load_state_dict(torch.load("./trained_models/ConvVAE_r.pt")) model_g.load_state_dict(torch.load("./trained_models/ConvVAE_g.pt")) model_b.load_state_dict(torch.load("./trained_models/ConvVAE_b.pt")) model_r.eval() model_g.eval() model_b.eval() index = random.randint(0, len(test_set)) print(index) image, class_index = test_set[4942] image = torch.reshape( image, (1, image.shape[-3], image.shape[-2], image.shape[-1])) decoded_r, _, _ = model_r(image[:, 0, :, :].unsqueeze_(0)) decoded_g, _, _ = model_g(image[:, 1, :, :].unsqueeze_(0)) decoded_b, _, _ = model_b(image[:, 2, :, :].unsqueeze_(0)) decoded = torch.cat((decoded_r, decoded_g, decoded_b), 1) im1 = transforms.ToPILImage()(image[0]).convert("RGB")