python - 如何将 Pytorch Dataloader 转换为 numpy 数组以使用 matplotlib 显示图像数据？
问题描述
我是 Pytorch 的新手。在我开始训练我的 CNN 之前，我一直在尝试学习如何查看我的输入图像。我很难将图像更改为可与 matplotlib 一起使用的形式。
到目前为止，我已经尝试过：
from multiprocessing import freeze_support
import torch
from torch import nn
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader, Sampler
from torchvision import datasets
from torchvision.transforms import transforms
from torch.optim import Adam
import matplotlib.pyplot as plt
import numpy as np
import PIL
num_classes = 5
batch_size = 100
num_of_workers = 5
DATA_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()
但是，这会引发错误：
[[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]]
[[0.2352941 0.06274509 0.15686274 ... 0.09411764 0.3019608
0.19215685]
[0.22745097 0.07843137 0.12549019 ... 0.07843137 0.10588235
0.3019608 ]
[0.20392156 0.13333333 0.1607843 ... 0.16862744 0.2117647
0.22745097]
...
[0.18039215 0.16862744 0.1490196 ... 0.45882353 0.36078432
0.16470587]
[0.1607843 0.10588235 0.14117646 ... 0.2117647 0.18039215
0.10980392]
[0.18039215 0.3019608 0.2117647 ... 0.11372548 0.06274509
0.04705882]]]
...
[[[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]]
[[0.9254902 0.90588236 0.88235295 ... 0.60784316 0.6
0.5921569 ]
[0.88235295 0.85490197 0.8235294 ... 0.5411765 0.5372549
0.53333336]
[0.8235294 0.7882353 0.75686276 ... 0.47058824 0.47058824
0.47058824]
...
[0.50980395 0.5176471 0.5137255 ... 0.58431375 0.5647059
0.53333336]
[0.5137255 0.53333336 0.5254902 ... 0.58431375 0.5686275
0.53333336]
[0.5176471 0.53333336 0.5294118 ... 0.5764706 0.56078434
0.5294118 ]]
[[0.95686275 0.9372549 0.90588236 ... 0.18823528 0.19999999
0.20784312]
[0.9098039 0.8784314 0.8352941 ... 0.1607843 0.17254901
0.18039215]
[0.84313726 0.7921569 0.7490196 ... 0.1372549 0.14509803
0.15294117]
...
[0.03921568 0.05490196 0.05098039 ... 0.11764705 0.09411764
0.02745098]
[0.04705882 0.07843137 0.06666666 ... 0.12156862 0.10196078
0.03529412]
[0.05098039 0.0745098 0.07843137 ... 0.12549019 0.10196078
0.04705882]]]
[[[0.30588236 0.28627452 0.24313724 ... 0.2901961 0.26666668
0.21568626]
[0.8156863 0.6666667 0.5921569 ... 0.18039215 0.23921567
0.21568626]
[0.9019608 0.83137256 0.85490197 ... 0.21960783 0.36862746
0.23921567]
...
[0.7058824 0.83137256 0.85490197 ... 0.2627451 0.24313724
0.20784312]
[0.7137255 0.84313726 0.84705883 ... 0.26666668 0.29803923
0.21568626]
[0.7254902 0.8235294 0.8392157 ... 0.2509804 0.27058825
0.2352941 ]]
[[0.24705881 0.22745097 0.19215685 ... 0.2784314 0.25490198
0.19607842]
[0.59607846 0.37254903 0.29803923 ... 0.16470587 0.22745097
0.20392156]
[0.5921569 0.4509804 0.49803922 ... 0.20784312 0.3764706
0.2352941 ]
...
[0.42352942 0.4627451 0.42352942 ... 0.23921567 0.23137254
0.19999999]
[0.45882353 0.5176471 0.35686275 ... 0.23921567 0.26666668
0.19607842]
[0.41568628 0.44313726 0.34901962 ... 0.21960783 0.23921567
0.21568626]]
[[0.23137254 0.20784312 0.1490196 ... 0.30588236 0.28627452
0.19607842]
[0.61960787 0.3764706 0.26666668 ... 0.16470587 0.24313724
0.21568626]
[0.57254905 0.43137255 0.48235294 ... 0.2235294 0.40392157
0.25882354]
...
[0.4 0.42352942 0.37254903 ... 0.25490198 0.24705881
0.21568626]
[0.43137255 0.4509804 0.29411766 ... 0.25882354 0.28235295
0.20392156]
[0.38431373 0.3529412 0.25490198 ... 0.2352941 0.25490198
0.23137254]]]
[[[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 ]]
[[0.06666666 0.12156862 0.17647058 ... 0.59607846 0.5529412
0.6039216 ]
[0.07058823 0.10588235 0.11764705 ... 0.56078434 0.5254902
0.5372549 ]
[0.03921568 0.0745098 0.09803921 ... 0.48235294 0.4392157
0.4117647 ]
...
[0.2117647 0.14509803 0.2784314 ... 0.43137255 0.3529412
0.34117648]
[0.2235294 0.11372548 0.2509804 ... 0.4509804 0.39607844
0.2509804 ]
[0.25490198 0.12156862 0.24705881 ... 0.38039216 0.36078432
0.3254902 ]]
[[0.05490196 0.09803921 0.12549019 ... 0.46666667 0.38039216
0.45490196]
[0.06274509 0.09803921 0.10196078 ... 0.44705883 0.41568628
0.3882353 ]
[0.03921568 0.06666666 0.0862745 ... 0.3764706 0.33333334
0.28235295]
...
[0.12156862 0.14509803 0.16862744 ... 0.15686274 0.0745098
0.09411764]
[0.10588235 0.11372548 0.16862744 ... 0.25882354 0.18431371
0.05490196]
[0.12156862 0.11372548 0.17254901 ... 0.2352941 0.17254901
0.14117646]]]]
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
我试图打印出数组以获取尺寸，但我不知道该怎么做。这非常令人困惑。
这是我的直接问题：如何在使用 DataLoader 对象中的张量进行训练之前查看输入图像？
解决方案
首先，dataloader输出 4 维张量 - [batch, channel, height, width]。Matplotlib 和其他图像处理库通常需要[height, width, channel]. 您使用转置是正确的，只是方式不正确。
您的图像中会有很多图像，images因此首先您需要选择一个（或编写一个 for 循环来保存所有图像）。这很简单images[i]，我通常使用i=0.
然后，您的转置应该将现在的[channel, height, width]张量转换为[height, width, channel]一个。为此，请使用np.transpose(image.numpy(), (1, 2, 0)), 非常像你的。
把它们放在一起，你应该有
plt.imshow(np.transpose(images[0].numpy(), (1, 2, 0)))
有时您需要根据用例调用.detach()（从计算图中分离这部分）和.cpu()（将数据从 GPU 传输到 CPU），这将是
plt.imshow(np.transpose(images[0].cpu().detach().numpy(), (1, 2, 0)))
推荐阅读
- reactjs - Typescript 在枚举中报告未使用的变量
- javascript - 在jquery li中选择连续元素
- c - main 中引用的未解析的外部符号
- c# - 提示错误消息框后如何清除文本框？
- python - 搜索特定字符串的方法
- java - 测试需要模拟成员方法并使用 PowerMockito 抑制单例构造函数的静态方法
- javascript - 我想通过 post 将对象列表从 javascript 发送到控制器方法
- python - X 和 Y 中缺少数据：如何从实际估计模型中获取 X 和 Y？
- javascript - 重定向到带有 Autorization 标头的页面
- c - libwebsockets 停止处理以读取传入的数据包