pytorch 自定义参数不更新方式

 class BiFPN(nn.Module): def __init__(self, fpn_sizes): self.w1 = nn.Parameter(torch.rand(1)) print("no---------------------------------------------------",self.w1.data, self.w1.grad) 

 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:{},cy:{}'.format(id(cx),id(cy))) print('ox:{},oy:{}'.format(id(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:{},cy:{}'.format(id(cx),id(cy))) print('ox:{},oy:{}'.format(id(ox),id(oy)))