我是Pytorch的新手。我一直在尝试学习如何在开始训练我的CNN之前查看我的输入图像。我很难将图像转换为可以与matplotlib一起使用的形式。
到目前为止，我尝试了以下方法：
from multiprocessing import freeze_supportimport torchfrom torch import nnimport torchvisionfrom torch.autograd import Variablefrom torch.utils.data import DataLoader, Samplerfrom torchvision import datasetsfrom torchvision.transforms import transformsfrom torch.optim import Adamimport matplotlib.pyplot as pltimport numpy as npimport PILnum_classes = 5batch_size = 100num_of_workers = 5DATA_PATH_TRAIN = 'C:\\Users\Aeryes\PycharmProjects\simplecnn\images\\train'DATA_PATH_TEST = 'C:\\Users\Aeryes\PycharmProjects\simplecnn\images\\test'trans = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.Resize(32), transforms.CenterCrop(32), transforms.ToPImage(), transforms.Normalize((0.5, 0.5, 0.5),(0.5, 0.5, 0.5)) ])train_dataset = datasets.ImageFolder(root=DATA_PATH_TRAIN, transform=trans)train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_of_workers)def imshow(img): img = img / 2 + 0.5 # unnormalize npimg = img.numpy() print(npimg) plt.imshow(np.transpose(npimg, (1, 2, 0, 1)))def main(): # get some random training images dataiter = iter(train_loader) images, labels = dataiter.next() # show images imshow(images) # print labels print(' '.join('%5s' % classes[labels[j]] for j in range(4)))if __name__ == "__main__": main()然而，这会抛出错误：
[[0.27058825 0.18431371 0.31764707 ... 0.18823528 0.3882353 0.27450982] [0.23137254 0.11372548 0.24313724 ... 0.16862744 0.14117646 0.40784314] [0.25490198 0.19607842 0.30588236 ... 0.27450982 0.25882354 0.34509805] ... [0.2784314 0.21960783 0.2352941 ... 0.5803922 0.46666667 0.25882354] [0.26666668 0.16862744 0.23137254 ... 0.2901961 0.29803923 0.2509804 ] [0.30980393 0.39607844 0.28627452 ... 0.1490196 0.10588235 0.19607842]] ... [[[0.8980392 0.8784314 0.8509804 ... 0.627451 0.627451 0.627451 ] [0.8509804 0.8235294 0.7921569 ... 0.54901963 0.5568628 0.56078434] [0.7921569 0.7529412 0.7176471 ... 0.47058824 0.48235294 0.49411765] ... [0.3764706 0.38431373 0.3764706 ... 0.4509804 0.43137255 0.39607844] [0.38431373 0.39607844 0.3882353 ... 0.4509804 0.43137255 0.39607844] [0.3882353 0.4 0.39607844 ... 0.44313726 0.42352942 0.39215687]] ... [[[0.06274509 0.09019607 0.11372548 ... 0.5803922 0.5176471 0.59607846] [0.09411764 0.14509803 0.1372549 ... 0.5294118 0.49803922 0.5058824 ] [0.04705882 0.09411764 0.10196078 ... 0.45882353 0.42352942 0.38431373] ... [0.15294117 0.12941176 0.1607843 ... 0.85882354 0.8509804 0.80784315] [0.14509803 0.10588235 0.1607843 ... 0.8666667 0.85882354 0.8 ] [0.1490196 0.10588235 0.16470587 ... 0.827451 0.8156863 0.7921569 ]]]Traceback (most recent call last): File "image_loader.py", line 51, in <module> main() File "image_loader.py", line 46, in main imshow(images) File "image_loader.py", line 38, in imshow plt.imshow(np.transpose(npimg, (1, 2, 0, 1))) File "C:\Users\Aeryes\AppData\Local\Programs\Python\Python36\lib\site-packages\numpy\core\fromnumeric.py", line 598, in transpose return _wrapfunc(a, 'transpose', axes) File "C:\Users\Aeryes\AppData\Local\Programs\Python\Python36\lib\site-packages\numpy\core\fromnumeric.py", line 51, in _wrapfunc return getattr(obj, method)(*args, **kwds)ValueError: repeated axis in transpose我尝试打印出数组以获取维度，但我不知道该如何处理这些数据。这很 confusing。
我的直接问题是：如何在训练之前使用我的DataLoader对象中的张量查看输入图像？
回答：
首先，dataloader输出的是4维张量 – [batch, channel, height, width]。matplotlib和其他图像处理库通常需要[height, width, channel]。你关于使用转置的想法是对的，只是方式不对。
你的images中会有很多图像，所以首先你需要选择一个（或者编写一个for循环来保存所有图像）。这将简单地是images[i]，通常我使用i=0。
然后，你的转置应该将现在是[channel, height, width]的张量转换为[height, width, channel]的张量。为此，使用np.transpose(image.numpy(), (1, 2, 0))，与你的方法非常相似。
将它们组合在一起，你应该有
plt.imshow(np.transpose(images[0].numpy(), (1, 2, 0)))有时候你需要调用.detach()（从计算图中分离这部分）和.cpu()（将数据从GPU传输到CPU），这取决于使用情况，将是
plt.imshow(np.transpose(images[0].cpu().detach().numpy(), (1, 2, 0)))