def mixup(data, targets1, targets2, targets3, alpha): indices = torch.randperm(data.size(0)) shuffled_data = data[indices] shuffled_targets1 = targets1[indices] shuffled_targets2 = targets2[indices] shuffled_targets3 = targets3[indices] lam = np.random.beta(alpha, alpha) data = data ...
import torch import pytorch_msssim # 假设x和y是两个形状相同的PyTorch张量,代表两张图片 x = torch.randn(1, 3, 256, 256) # 示例张量,形状为(batch_size, channels, height, width) y = torch.randn(1, 3, 256, 256) # 调用ssim函数 ssim_index = pytorch_msssim.ssim(x, y, window_size=11...
(344.8978 ms), ssim_torch=0.515791 (96.4440 ms) sigma=50.0 ssim_skimage=0.422011 (148.2900 ms), ssim_tf=0.422007 (345.4076 ms), ssim_torch=0.422005 (86.3799 ms) sigma=60.0 ssim_skimage=0.351139 (146.2039 ms), ssim_tf=0.351139 (343.4428 ms), ssim_torch=0.351133 (93.3445 ms) sigma=70.0 ...
sigma=2.000000 compare_ssim=0.966521 (485.862017 ms) ssim_torch=0.966520 (237.199068 ms) sigma=3.000000 compare_ssim=0.928799 (323.492050 ms) ssim_torch=0.928797 (148.905993 ms) sigma=4.000000 compare_ssim=0.882271 (290.801048 ms) ssim_torch=0.882267 (146.914005 ms) sigma=5.000000 compare_ssim=0.831310...
(344.8978 ms), ssim_torch=0.515791 (96.4440 ms) sigma=50.0 ssim_skimage=0.422011 (148.2900 ms), ssim_tf=0.422007 (345.4076 ms), ssim_torch=0.422005 (86.3799 ms) sigma=60.0 ssim_skimage=0.351139 (146.2039 ms), ssim_tf=0.351139 (343.4428 ms), ssim_torch=0.351133 (93.3445 ms) sigma=70.0 ...
importpytorch_msssimimporttorchdevice=torch.device('cuda'iftorch.cuda.is_available()else'cpu')m=pytorch_msssim.MSSSIM()img1=torch.rand(1,1,256,256)img2=torch.rand(1,1,256,256)print(pytorch_msssim.msssim(img1,img2))print(m(img1,img2)) ...
importpytorch_msssimimporttorchdevice=torch.device('cuda'iftorch.cuda.is_available()else'cpu')m=pytorch_msssim.MSSSIM()img1=torch.rand(1,1,256,256)img2=torch.rand(1,1,256,256)print(pytorch_msssim.msssim(img1,img2))print(m(img1,img2)) ...