如何使用plt.imshow和torchvision.utils.make_grid在PyTorch中生成和显示图像网格？

Question

如何使用plt.imshow和torchvision.utils.make_grid在PyTorch中生成和显示图像网格？

pythonmatplotlibpytorchimshowtorchvision

26

我试图理解torchvision如何与matplotlib交互以生成图像网格。生成图像并迭代显示很容易：

import torch
import torchvision
import matplotlib.pyplot as plt

w = torch.randn(10,3,640,640)
for i in range (0,10):
    z = w[i]
    plt.imshow(z.permute(1,2,0))
    plt.show()

然而，将这些图像以网格形式展示似乎并不是那么简单。

Translated text:

然而，将这些图像以网格形式展示似乎并不是那么简单。

w = torch.randn(10,3,640,640)
grid = torchvision.utils.make_grid(w, nrow=5)
plt.imshow(grid)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-61-1601915e10f3> in <module>()
      1 w = torch.randn(10,3,640,640)
      2 grid = torchvision.utils.make_grid(w, nrow=5)
----> 3 plt.imshow(grid)

/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py in imshow(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)
   3203                         filternorm=filternorm, filterrad=filterrad,
   3204                         imlim=imlim, resample=resample, url=url, data=data,
-> 3205                         **kwargs)
   3206     finally:
   3207         ax._hold = washold

/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
   1853                         "the Matplotlib list!)" % (label_namer, func.__name__),
   1854                         RuntimeWarning, stacklevel=2)
-> 1855             return func(ax, *args, **kwargs)
   1856 
   1857         inner.__doc__ = _add_data_doc(inner.__doc__,

/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)
   5485                               resample=resample, **kwargs)
   5486 
-> 5487         im.set_data(X)
   5488         im.set_alpha(alpha)
   5489         if im.get_clip_path() is None:

/anaconda3/lib/python3.6/site-packages/matplotlib/image.py in set_data(self, A)
    651         if not (self._A.ndim == 2
    652                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):
--> 653             raise TypeError("Invalid dimensions for image data")
    654 
    655         if self._A.ndim == 3:

TypeError: Invalid dimensions for image data

尽管PyTorch文档表明w是正确的形状，但Python却表示它不是。因此我尝试对张量的索引进行排列：

尽管 PyTorch 的文档表明 w 是正确的形状，但 Python 却说不是。所以我尝试对张量的索引进行排列：

    w = torch.randn(10,3,640,640)
    grid = torchvision.utils.make_grid(w.permute(0,2,3,1), nrow=5)
    plt.imshow(grid)
---------------------------------------------------------------------------
    RuntimeError                              Traceback (most recent call last)
    <ipython-input-62-6f2dc6313e29> in <module>()
          1 w = torch.randn(10,3,640,640)
    ----> 2 grid = torchvision.utils.make_grid(w.permute(0,2,3,1), nrow=5)
          3 plt.imshow(grid)

    /anaconda3/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/utils.py in make_grid(tensor, nrow, padding, normalize, range, scale_each, pad_value)
         83             grid.narrow(1, y * height + padding, height - padding)\
         84                 .narrow(2, x * width + padding, width - padding)\
    ---> 85                 .copy_(tensor[k])
         86             k = k + 1
         87     return grid

    RuntimeError: The expanded size of the tensor (3) must match the existing size (640) at non-singleton dimension 0

这里发生了什么？我怎样才能将一堆随机生成的图像放入网格中并展示它们？

- Jon Middleton

2个回答

4

您需要先转换为numpy格式

import numpy as np

def show(img):
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest')

w = torch.randn(10,3,640,640)
grid = torchvision.utils.make_grid(w, nrow=10, padding=100)
show(grid)

- iacolippo

嗨@iacolippo，这里的(1, 2, 0)是什么意思？你能解释一下吗？ - Md. Musfiqur Rahaman

2

只需转置图像尺寸以将颜色通道放在最后 - 即从 (color, width, height) 转换为 (width, height, color)。 - iacolippo

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- kmario23 · Accepted Answer

你的代码中有一个小错误。torchvision.utils.make_grid()返回包含图像网格的张量。但是，通道维度必须移动到末尾，因为这是matplotlib所识别的。以下是可以正常工作的代码：

In [107]: import torchvision

# sample input (10 RGB images containing just Gaussian Noise)
In [108]: batch_tensor = torch.randn(*(10, 3, 256, 256))   # (N, C, H, W)

# make grid (2 rows and 5 columns) to display our 10 images
In [109]: grid_img = torchvision.utils.make_grid(batch_tensor, nrow=5)

# check shape
In [110]: grid_img.shape
Out[110]: torch.Size([3, 518, 1292])

# reshape and plot (because matplotlib needs channel as the last dimension)
In [111]: plt.imshow(grid_img.permute(1, 2, 0))
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Out[111]: <matplotlib.image.AxesImage at 0x7f62081ef080>

显示输出如下：