在利用torch.max函數(shù)和F.Ssoftmax函數(shù)時(shí)，對(duì)應(yīng)該設(shè)置什么維度，總是有點(diǎn)懵，遂總結(jié)一下：
首先看看二維tensor的函數(shù)的例子：
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import torchimport torch.nn.functional as F input = torch.randn(3,4)print(input)tensor([[-0.5526, -0.0194, 2.1469, -0.2567], [-0.3337, -0.9229, 0.0376, -0.0801], [ 1.4721, 0.1181, -2.6214, 1.7721]]) b = F.softmax(input,dim=0) # 按列SoftMax,列和為1print(b)tensor([[0.1018, 0.3918, 0.8851, 0.1021], [0.1268, 0.1587, 0.1074, 0.1218], [0.7714, 0.4495, 0.0075, 0.7762]]) c = F.softmax(input,dim=1) # 按行SoftMax,行和為1print(c)tensor([[0.0529, 0.0901, 0.7860, 0.0710], [0.2329, 0.1292, 0.3377, 0.3002], [0.3810, 0.0984, 0.0064, 0.5143]]) d = torch.max(input,dim=0) # 按列取max,print(d)torch.return_types.max(values=tensor([1.4721, 0.1181, 2.1469, 1.7721]),indices=tensor([2, 2, 0, 2])) e = torch.max(input,dim=1) # 按行取max，print(e)torch.return_types.max(values=tensor([2.1469, 0.0376, 1.7721]),indices=tensor([2, 2, 3])) |
下面看看三維tensor解釋例子：
函數(shù)softmax輸出的是所給矩陣的概率分布；
b輸出的是在dim=0維上的概率分布，b[0][5][6]+b[1][5][6]+b[2][5][6]=1
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a=torch.rand(3,16,20)b=F.softmax(a,dim=0)c=F.softmax(a,dim=1)d=F.softmax(a,dim=2) In [1]: import torch as tIn [2]: import torch.nn.functional as FIn [4]: a=t.Tensor(3,4,5)In [5]: b=F.softmax(a,dim=0)In [6]: c=F.softmax(a,dim=1)In [7]: d=F.softmax(a,dim=2) In [8]: aOut[8]: tensor([[[-0.1581, 0.0000, 0.0000, 0.0000, -0.0344], [ 0.0000, -0.0344, 0.0000, -0.0344, 0.0000], [-0.0344, 0.0000, -0.0344, 0.0000, -0.0344], [ 0.0000, -0.0344, 0.0000, -0.0344, 0.0000]], [[-0.0344, 0.0000, -0.0344, 0.0000, -0.0344], [ 0.0000, -0.0344, 0.0000, -0.0344, 0.0000], [-0.0344, 0.0000, -0.0344, 0.0000, -0.0344], [ 0.0000, -0.0344, 0.0000, -0.0344, 0.0000]], [[-0.0344, 0.0000, -0.0344, 0.0000, -0.0344], [ 0.0000, -0.0344, 0.0000, -0.0344, 0.0000], [-0.0344, 0.0000, -0.0344, 0.0000, -0.0344], [ 0.0000, -0.0344, 0.0000, -0.0344, 0.0000]]]) In [9]: bOut[9]: tensor([[[0.3064, 0.3333, 0.3410, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333]], [[0.3468, 0.3333, 0.3295, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333]], [[0.3468, 0.3333, 0.3295, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333], [0.3333, 0.3333, 0.3333, 0.3333, 0.3333]]]) In [10]: b.sum()Out[10]: tensor(20.0000) In [11]: b[0][0][0]+b[1][0][0]+b[2][0][0]Out[11]: tensor(1.0000) In [12]: c.sum()Out[12]: tensor(15.) In [13]: cOut[13]: tensor([[[0.2235, 0.2543, 0.2521, 0.2543, 0.2457], [0.2618, 0.2457, 0.2521, 0.2457, 0.2543], [0.2529, 0.2543, 0.2436, 0.2543, 0.2457], [0.2618, 0.2457, 0.2521, 0.2457, 0.2543]], [[0.2457, 0.2543, 0.2457, 0.2543, 0.2457], [0.2543, 0.2457, 0.2543, 0.2457, 0.2543], [0.2457, 0.2543, 0.2457, 0.2543, 0.2457], [0.2543, 0.2457, 0.2543, 0.2457, 0.2543]], [[0.2457, 0.2543, 0.2457, 0.2543, 0.2457], [0.2543, 0.2457, 0.2543, 0.2457, 0.2543], [0.2457, 0.2543, 0.2457, 0.2543, 0.2457], [0.2543, 0.2457, 0.2543, 0.2457, 0.2543]]]) In [14]: n=t.rand(3,4) In [15]: nOut[15]: tensor([[0.2769, 0.3475, 0.8914, 0.6845], [0.9251, 0.3976, 0.8690, 0.4510], [0.8249, 0.1157, 0.3075, 0.3799]]) In [16]: m=t.argmax(n,dim=0) In [17]: mOut[17]: tensor([1, 1, 0, 0]) In [18]: p=t.argmax(n,dim=1) In [19]: pOut[19]: tensor([2, 0, 0]) In [20]: d.sum()Out[20]: tensor(12.0000) In [22]: dOut[22]: tensor([[[0.1771, 0.2075, 0.2075, 0.2075, 0.2005], [0.2027, 0.1959, 0.2027, 0.1959, 0.2027], [0.1972, 0.2041, 0.1972, 0.2041, 0.1972], [0.2027, 0.1959, 0.2027, 0.1959, 0.2027]], [[0.1972, 0.2041, 0.1972, 0.2041, 0.1972], [0.2027, 0.1959, 0.2027, 0.1959, 0.2027], [0.1972, 0.2041, 0.1972, 0.2041, 0.1972], [0.2027, 0.1959, 0.2027, 0.1959, 0.2027]], [[0.1972, 0.2041, 0.1972, 0.2041, 0.1972], [0.2027, 0.1959, 0.2027, 0.1959, 0.2027], [0.1972, 0.2041, 0.1972, 0.2041, 0.1972], [0.2027, 0.1959, 0.2027, 0.1959, 0.2027]]]) In [23]: d[0][0].sum()Out[23]: tensor(1.) |
補(bǔ)充知識(shí)：多分類問題torch.nn.Softmax的使用
為什么談?wù)撨@個(gè)問題呢？是因?yàn)槲以诠ぷ鞯倪^程中遇到了語(yǔ)義分割預(yù)測(cè)輸出特征圖個(gè)數(shù)為16，也就是所謂的16分類問題。
因?yàn)槊總€(gè)通道的像素的值的大小代表了像素屬于該通道的類的大小，為了在一張圖上用不同的顏色顯示出來(lái)，我不得不學(xué)習(xí)了torch.nn.Softmax的使用。
首先看一個(gè)簡(jiǎn)答的例子，倘若輸出為(3, 4, 4)，也就是3張4x4的特征圖。
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import torchimg = torch.rand((3,4,4))print(img) |
輸出為：
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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]]]) |
我們可以看到共三張?zhí)卣鲌D，每張?zhí)卣鲌D上對(duì)應(yīng)的值越大，說明屬于該特征圖對(duì)應(yīng)類的概率越大。
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import torch.nn as nnsogtmax = nn.Softmax(dim=0)img = sogtmax(img)print(img) |
輸出為：
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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，上面的代碼對(duì)每張?zhí)卣鲌D對(duì)應(yīng)位置的像素值進(jìn)行Softmax函數(shù)處理， 圖中標(biāo)紅位置加和=1，同理，標(biāo)藍(lán)位置加和=1。
我們看到Softmax函數(shù)會(huì)對(duì)原特征圖每個(gè)像素的值在對(duì)應(yīng)維度(這里dim=0，也就是第一維)上進(jìn)行計(jì)算，將其處理到0～1之間，并且大小固定不變。
print(torch.max(img,0))
輸出為：
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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]])) |
可以看到這里3x4x4變成了1x4x4，而且對(duì)應(yīng)位置上的值為像素對(duì)應(yīng)每個(gè)通道上的最大值，并且indices是對(duì)應(yīng)的分類。
清楚理解了上面的流程，那么我們就容易處理了。
看具體案例，這里輸出output的大小為：16x416x416.
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output = torch.tensor(output) sm = nn.Softmax(dim=0)output = sm(output) mask = torch.max(output,0).indices.numpy() # 因?yàn)橐D(zhuǎn)化為RGB彩色圖，所以增加一維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) |
最后保存得到的圖為：
以上這篇淺談pytorch中torch.max和F.softmax函數(shù)的維度解釋就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/Jasminexjf/article/details/90402990
