如何将Pytorch数据加载器转换为numpy数组以使用matplotlib显示图像数据? [英] How do I turn a Pytorch Dataloader into a numpy array to display image data with matplotlib?

问题描述

我是Pytorch的新手.在开始使用CNN进行训练之前，我一直在尝试学习如何查看输入的图像.我很难将图像更改为可以与matplotlib一起使用的形式.

I am new to Pytorch. I have been trying to learn how to view my input images before I begin training on my CNN. I am having a very hard time changing the images into a form that can be used with matplotlib.

到目前为止，我已经尝试过:

So far I have tried this:

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，但是我不知道该怎么做.这很令人困惑.

I tried to print out the arrays to get the dimensions but I do not know what to make of this. It is very confusing.

这是我的直接问题:在使用DataLoader对象中的张量进行训练之前，如何查看输入图像?

Here is my direct question: How do I view the input images before training using the tensors in my DataLoader object?

推荐答案

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].您正确使用移调是正确的，只是使用方式不正确.

First of all, dataloader output 4 dimensional tensor - [batch, channel, height, width]. Matplotlib and other image processing libraries often requires [height, width, channel]. You are right about using the transpose, just not in the right way.

images中将有很多图像，因此首先需要选择一个(或编写一个for循环以保存所有图像).这将是简单的images[i]，通常我使用i=0.

There will be a lot of images in your images so first you need to pick one (or write a for loop to save all of them). This will be simply images[i], typically I use i=0.

然后，转置应该将现在的[channel, height, width]张量转换为[height, width, channel]的张量.为此，请像您一样使用np.transpose(image.numpy(), (1, 2, 0)).

Then, your transpose should convert a now [channel, height, width] tensor to a [height, width, channel] one. To do this, use np.transpose(image.numpy(), (1, 2, 0)), very much like yours.

把它们放在一起，你应该有

Putting them together, you should have

plt.imshow(np.transpose(images[0].numpy(), (1, 2, 0)))

根据使用情况，有时需要调用.detach()(将这部分与计算图分开)和.cpu()(将数据从GPU传输到CPU)，具体取决于

Sometimes you need to call .detach() (detach this part from the computational graph) and .cpu() (transfer data from GPU to CPU) depending on the use case, that will be

plt.imshow(np.transpose(images[0].cpu().detach().numpy(), (1, 2, 0)))

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