nn.Module中定義引數：不需要加cuda，可以求導，反向傳播

class BiFPN(nn.Module):
  def __init__(self,fpn_sizes):

  self.w1 = nn.Parameter(torch.rand(1))

  print("no---------------------------------------------------",self.w1.data,self.w1.grad)

下面這個例子說明中間變數可能沒有梯度，但是最終變數有梯度：

cy1 cd都有梯度

import torch
 
xP=torch.Tensor([[ 3233.8557,3239.0657,3243.4355,3234.4507,3241.7087,3243.7292,3234.6826,3237.6609,3249.7937,3244.8623,3239.5349,3241.4626,3251.3457,3247.4263,3236.4924,3251.5735,3246.4731,3242.4692,3239.4958,3247.7283,3251.7134,3249.0237,3247.5637],[ 1619.9011,1619.7140,1620.4883,1620.0642,1620.2191,1619.9796,1617.6597,1621.1522,1621.0869,1620.9725,1620.7130,1620.6071,1620.7437,1621.4825,1620.5107,1621.1519,1620.8462,1620.5944,1619.8038,1621.3364,1620.7399,1621.1178,1618.7080],[ 1619.9330,1619.8542,1620.5176,1620.1167,1620.1577,1620.0579,1617.7155,1621.1718,1621.1338,1620.9572,1620.6288,1620.6621,1620.7074,1621.5305,1620.5656,1621.2281,1620.8346,1620.6021,1619.8228,1621.3936,1620.7616,1621.1954,1618.7983],[ 1922.6078,1922.5680,1923.1331,1922.6604,1922.9589,1922.8818,1920.4602,1923.8107,1924.0142,1923.6907,1923.4465,1923.2820,1923.5728,1924.4071,1922.8853,1924.1107,1923.5465,1923.5121,1922.4673,1924.1871,1923.6248,1923.9086,1921.9496],[ 1922.5948,1922.5311,1923.2850,1922.6613,1922.9734,1922.9271,1920.5950,1923.8757,1924.0422,1923.7318,1923.4889,1923.3296,1923.5752,1924.4948,1922.9866,1924.1642,1923.6427,1923.6067,1922.5214,1924.2761,1923.6636,1923.9481,1921.9005]])
 
yP=torch.Tensor([[ 2577.7729,2590.9868,2600.9712,2579.0195,2596.3684,2602.2771,2584.0305,2584.7749,2615.4897,2603.3164,2589.8406,2595.3486,2621.9116,2608.2820,2582.9534,2619.2073,2607.1233,2597.7888,2591.5735,2608.9060,2620.8992,2613.3511,2614.2195],[ 673.7830,693.8904,709.2661,675.4254,702.4049,711.2085,683.1571,684.6160,731.3878,712.7546,692.3011,701.0069,740.6815,720.4229,681.8199,736.9869,718.5508,704.3666,695.0511,721.5912,739.6672,728.0584,729.3143],[ 673.8367,693.9529,709.3196,675.5266,702.3820,711.2159,683.2151,684.6421,731.5291,712.6366,692.1913,701.0057,740.6229,720.4082,681.8656,737.0168,718.4943,704.2719,695.0775,721.5616,739.7233,728.1235,729.3387],[ 872.9419,891.7061,905.8004,874.6565,899.2053,907.5082,881.5528,883.0028,926.3083,908.9742,890.0403,897.8606,934.6913,916.0902,880.4689,931.3562,914.4233,901.2154,892.5759,916.9590,933.9291,923.0745,924.4461],[ 872.9661,891.7683,905.8128,874.6301,899.2887,907.5155,881.6916,883.0234,926.3242,908.9561,890.0731,897.9221,934.7324,916.0806,880.4300,931.3933,914.5662,901.2715,892.5501,916.9894,933.9813,923.0823,924.3654]])
 
 
shape=[4000,6000]
cx,cy1=torch.rand(1,requires_grad=True),torch.rand(1,requires_grad=True)
 
cd=torch.rand(1,requires_grad=True)
ox,oy=cx,cy1
print('cx:{},cy:{}'.format(id(cx),id(cy1)))
print('ox:{},oy:{}'.format(id(ox),id(oy)))
cx,cy=cx*shape[1],cy1*shape[0]
print('cx:{},id(cy)))
print('ox:{},id(oy)))
distance=torch.sqrt(torch.pow((xP-cx),2)+torch.pow((yP-cy),2))
mean=torch.mean(distance,1)
starsFC=cd*torch.pow((distance-mean[...,None]),2)
loss=torch.sum(torch.mean(starsFC,1).squeeze(),0)
loss.backward()
print(loss)
print(cx)
print(cy1)
print("cx",cx.grad)
print("cy",cy1.grad)
print("cd",cd.grad)
print(ox.grad)
print(oy.grad)
print('cx:{},id(oy)))

以上這篇pytorch 自定義引數不更新方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。