3,28,28)# 将图像们排列成网格,grid_img 就是将 6 张图片拼接后的网格图像# torchvision.utils.make_grid() 函数生成的网格图像,它的形状是 (C, H, W)grid_img = torchvision.utils.make_grid(images)print("grid_img.shape",grid_img.shape)# grid_img.shape torch.Size([3, 32, 182])# 显示...
z=torch.randn(64,input_size)fake_images=G(z)fake_images=fake_images.view(fake_images.size(0),1,28,28)# 可视化生成的图像 grid=torchvision.utils.make_grid(fake_images,nrow=8,normalize=True)plt.imshow(grid.permute(1,2,0).detach().numpy())plt.title('Generated Images')plt.show() 总结 ...
plt.show()# get some random training imagesdataiter =iter(train_loader) images, labels = dataiter.__next__()# show imagesimshow(torchvision.utils.make_grid(images))# nn.Conv2d 是 PyTorch 用于定义二维卷积层的类# 三个参数分别为 in_channels、out_channels 和 kernel_size# in_channels (输入...
next() imshow = torchvision.utils.make_grid(images) imshow = imshow.numpy().transpose((1, 2, 0)) imshow = imshow / 2 + 0.5 imshow = np.clip(imshow, 0, 1) plt.imshow(imshow) print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4))) outputs = net...
train_data=datasets.MNIST(root="model-fuxian/data set/MNIST/MNIST/raw/MNIST",train=True,transform=transforms.ToTensor(),download=False)train_loader=DataLoader(dataset=train_data,batch_size=64,shuffle=True)fornum,(image,label)inenumerate(train_loader):image_batch=torchvision.utils.make_grid(image,pa...
# 显示 labels_try 的5张图片,即valid里第一个batch的5张图片 out = torchvision.utils.make_grid(inputs_try) imshow(out, title=[dset_classes[x] for x in labels_try]) 4.创建VGG Model !wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json ...
可以利用torchvision.utils.make_grid来将一个batch的样本图像进行拼接,然后利用matplotlib将一个batch的图像进行显示: def show_batch(data, nrow): batch_img = torchvision.utils.make_grid(data, nrow) batch_img = np.array(batch_img) img = batch_img.transpose((1, 2, 0)) # Pytorch与Numpy存储数据...
dataiter=iter(test_loader)images,labels=dataiter.next()# 打印图像imshow(torchvision.utils.make_grid(images))# 打印标签print('GroundTruth: ',' '.join(f'{classes[labels[j]]}'forjinrange(4)))# 打印预测结果 outputs=model(images.to(device))_,predicted=torch.max(outputs,1)print('Predicted: ...
sample = sample.view(64,1,28,28)# 可视化生成的图像grid = torchvision.utils.make_grid(sample, nrow=8, normalize=True) plt.imshow(grid.permute(1,2,0).numpy(), cmap='gray') plt.title('Generated Images') plt.show() 总结 通过本教程,你学会了如何使用Python和PyTorch库实现一个简单的变分自编...
from torchvision.utils import make_grid import matplotlib.pyplot as plt %matplotlib inline 错误是 Error loading preloads: Could not find renderer 我试过但无法在互联网上找到它的解决方案。我该如何解决这个问题? 就我而言,我安装了Jupyter notebook扩展。所以,我做了ctrl+shift+P和Reload window;解决!