浅谈pytorch中torch.max和F.softmax函数的维度解释

补充知识：多分类问题torch.nn.Softmax的使用

为什么谈论这个问题呢？是因为我在工作的过程中遇到了语义分割预测输出特征图个数为16，也就是所谓的16分类问题。

因为每个通道的像素的值的大小代表了像素属于该通道的类的大小，为了在一张图上用不同的颜色显示出来，我不得不学习了torch.nn.Softmax的使用。

首先看一个简答的例子，倘若输出为(3, 4, 4)，也就是3张4x4的特征图。

import torch
img = torch.rand((3,4,4))
print(img)

输出为：

tensor([[[0.0413, 0.8728, 0.8926, 0.0693],
     [0.4072, 0.0302, 0.9248, 0.6676],
     [0.4699, 0.9197, 0.3333, 0.4809],
     [0.3877, 0.7673, 0.6132, 0.5203]],
    [[0.4940, 0.7996, 0.5513, 0.8016],
     [0.1157, 0.8323, 0.9944, 0.2127],
     [0.3055, 0.4343, 0.8123, 0.3184],
     [0.8246, 0.6731, 0.3229, 0.1730]],
    [[0.0661, 0.1905, 0.4490, 0.7484],
     [0.4013, 0.1468, 0.2145, 0.8838],
     [0.0083, 0.5029, 0.0141, 0.8998],
     [0.8673, 0.2308, 0.8808, 0.0532]]])

我们可以看到共三张特征图，每张特征图上对应的值越大，说明属于该特征图对应类的概率越大。

import torch.nn as nn
sogtmax = nn.Softmax(dim=0)
img = sogtmax(img)
print(img)

输出为：

tensor([[[0.2780, 0.4107, 0.4251, 0.1979],
     [0.3648, 0.2297, 0.3901, 0.3477],
     [0.4035, 0.4396, 0.2993, 0.2967],
     [0.2402, 0.4008, 0.3273, 0.4285]],
    [[0.4371, 0.3817, 0.3022, 0.4117],
     [0.2726, 0.5122, 0.4182, 0.2206],
     [0.3423, 0.2706, 0.4832, 0.2522],
     [0.3718, 0.3648, 0.2449, 0.3028]],
    [[0.2849, 0.2076, 0.2728, 0.3904],
     [0.3627, 0.2581, 0.1917, 0.4317],
     [0.2543, 0.2898, 0.2175, 0.4511],
     [0.3880, 0.2344, 0.4278, 0.2686]]])

可以看到，上面的代码对每张特征图对应位置的像素值进行Softmax函数处理， 图中标红位置加和=1，同理，标蓝位置加和=1。

我们看到Softmax函数会对原特征图每个像素的值在对应维度(这里dim=0，也就是第一维)上进行计算，将其处理到0～1之间，并且大小固定不变。

print(torch.max(img,0))

输出为：

torch.return_types.max(
values=tensor([[0.4371, 0.4107, 0.4251, 0.4117],
    [0.3648, 0.5122, 0.4182, 0.4317],
    [0.4035, 0.4396, 0.4832, 0.4511],
    [0.3880, 0.4008, 0.4278, 0.4285]]),
indices=tensor([[1, 0, 0, 1],
    [0, 1, 1, 2],
    [0, 0, 1, 2],
    [2, 0, 2, 0]]))

可以看到这里3x4x4变成了1x4x4，而且对应位置上的值为像素对应每个通道上的最大值，并且indices是对应的分类。

清楚理解了上面的流程，那么我们就容易处理了。

看具体案例，这里输出output的大小为：16x416x416.

output = torch.tensor(output)
 
sm = nn.Softmax(dim=0)
output = sm(output)
 
mask = torch.max(output,0).indices.numpy()
 
# 因为要转化为RGB彩色图，所以增加一维
rgb_img = np.zeros((output.shape[1], output.shape[2], 3))
for i in range(len(mask)):
  for j in range(len(mask[0])):
    if mask[i][j] == 0:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 255
      rgb_img[i][j][2] = 255
    if mask[i][j] == 1:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 180
      rgb_img[i][j][2] = 0
    if mask[i][j] == 2:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 180
      rgb_img[i][j][2] = 180
    if mask[i][j] == 3:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 180
      rgb_img[i][j][2] = 255
    if mask[i][j] == 4:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 255
      rgb_img[i][j][2] = 180
    if mask[i][j] == 5:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 255
      rgb_img[i][j][2] = 0
    if mask[i][j] == 6:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 180
    if mask[i][j] == 7:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 255
    if mask[i][j] == 8:
      rgb_img[i][j][0] = 255
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 0
    if mask[i][j] == 9:
      rgb_img[i][j][0] = 180
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 0
    if mask[i][j] == 10:
      rgb_img[i][j][0] = 180
      rgb_img[i][j][1] = 255
      rgb_img[i][j][2] = 255
    if mask[i][j] == 11:
      rgb_img[i][j][0] = 180
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 180
    if mask[i][j] == 12:
      rgb_img[i][j][0] = 180
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 255
    if mask[i][j] == 13:
      rgb_img[i][j][0] = 180
      rgb_img[i][j][1] = 255
      rgb_img[i][j][2] = 180
    if mask[i][j] == 14:
      rgb_img[i][j][0] = 0
      rgb_img[i][j][1] = 180
      rgb_img[i][j][2] = 255
    if mask[i][j] == 15:
      rgb_img[i][j][0] = 0
      rgb_img[i][j][1] = 0
      rgb_img[i][j][2] = 0
 
cv2.imwrite('output.jpg', rgb_img)
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