测试pytorch 级联grad

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function

import alog
import torch
import torch.nn as nn


class Model1(nn.Module):
    def __init__(self):
        super(Model1, self).__init__()

        self.input_size = 10
        self.output_size = 10
        self.w1 = nn.Linear(self.input_size, self.output_size)

    def print_grad(self):
        print("Model1 w1 has grad ", self.w1.weight.requires_grad)
        print("w1 grad", self.w1.weight.grad)

    def forward(self, x):
        y = self.w1(x)
        return y


class Model2(nn.Module):
    def __init__(self):
        super(Model2, self).__init__()

        self.input_size = 10
        self.output_size = 10
        self.w1 = nn.Linear(self.input_size, self.output_size)

    def print_grad(self):
        print("Model2 w1 has grad ", self.w1.weight.requires_grad)
        print("w1 grad", self.w1.weight.grad)

    def forward(self, x):
        y = self.w1(x)
        return y


class Model3(nn.Module):
    def __init__(self):
        super(Model3, self).__init__()

        self.input_size = 10
        self.output_size = 10
        self.w1 = nn.Linear(self.input_size, self.output_size)

    def print_grad(self):
        print("Model3 w1 has grad ", self.w1.weight.requires_grad)
        print("w1 grad", self.w1.weight.grad)

    def forward(self, x):
        y = self.w1(x)
        return y


class TotalModel(nn.Module):
    def __init__(self):
        super(TotalModel, self).__init__()
        self.model1 = Model1()
        self.model2 = Model2()
        self.model3 = Model3()

    def print_grad(self):
        self.model1.print_grad()
        self.model2.print_grad()
        self.model3.print_grad()

    def forward(self, x):
        y = self.model1(x)
        y = self.model2(y)
        y = self.model3(y)
        return y


model = TotalModel()
x = torch.randn(3, 10)
y = model(x)

criterion = nn.MSELoss(size_average=True)
target = torch.randn(3, 10)
loss = criterion(y, target)
loss.backward()
model.print_grad()
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Model1 w1 has grad  True
w1 grad tensor([[ 0.0237, -0.0069, -0.0120,  0.0015,  0.0038,  0.0112, -0.0029,
          0.0047, -0.0267, -0.0093],
        [ 0.0483,  0.0260, -0.0237,  0.0168, -0.0000,  0.0211,  0.0017,
         -0.0104, -0.0395, -0.0108],
        [ 0.0204,  0.0269,  0.0060,  0.0180, -0.0555, -0.0240, -0.0061,
          0.0076,  0.0231,  0.0060],
        [-0.1250, -0.0052,  0.0274, -0.0343,  0.1048,  0.0142,  0.0295,
         -0.0484,  0.0499,  0.0243],
        [-0.0997, -0.0209,  0.0131, -0.0361,  0.1149,  0.0294,  0.0256,
         -0.0409,  0.0154,  0.0121],
        [ 0.0215,  0.0297, -0.0167,  0.0114,  0.0181,  0.0218,  0.0084,
         -0.0219, -0.0248, -0.0041],
        [ 0.0130, -0.0943, -0.0195, -0.0341,  0.0569,  0.0333, -0.0120,
          0.0335, -0.0725, -0.0287],
        [-0.1711, -0.0050,  0.0365, -0.0467,  0.1465,  0.0216,  0.0414,
         -0.0686,  0.0669,  0.0332],
        [-0.2682,  0.0491,  0.0646, -0.0515,  0.1978,  0.0183,  0.0720,
         -0.1280,  0.1395,  0.0666],
        [-0.0565,  0.1078,  0.0438,  0.0324, -0.0715, -0.0586,  0.0162,
         -0.0397,  0.1266,  0.0469]])
Model2 w1 has grad  True
w1 grad tensor([[ 0.0295, -0.0207, -0.0064, -0.0087,  0.0577, -0.0405, -0.0094,
         -0.0207,  0.0490,  0.0002],
        [ 0.0410, -0.0451,  0.0368,  0.1388, -0.1122,  0.1371, -0.0073,
         -0.0063, -0.1194, -0.0762],
        [-0.0020,  0.0121, -0.0659, -0.0638,  0.0530, -0.0535,  0.0165,
          0.0036,  0.0281,  0.0592],
        [ 0.0900, -0.0744,  0.0270,  0.0621,  0.0738, -0.0211, -0.0330,
         -0.0549,  0.0600, -0.0556],
        [ 0.0399, -0.0417,  0.0400,  0.1044, -0.0627,  0.0865, -0.0136,
         -0.0141, -0.0640, -0.0663],
        [ 0.1057, -0.0722, -0.0631, -0.0201,  0.1694, -0.1066, -0.0162,
         -0.0615,  0.1168,  0.0197],
        [ 0.0734, -0.0625,  0.0181,  0.0755,  0.0223,  0.0211, -0.0215,
         -0.0382,  0.0061, -0.0514],
        [ 0.1538, -0.1126, -0.1070,  0.0758,  0.0873,  0.0058, -0.0012,
         -0.0620, -0.0092,  0.0021],
        [ 0.0539, -0.0356, -0.0625,  0.0038,  0.0500, -0.0171,  0.0058,
         -0.0205,  0.0064,  0.0226],
        [-0.0055, -0.0043,  0.0645,  0.0392, -0.0307,  0.0266, -0.0171,
         -0.0037, -0.0019, -0.0486]])
Model3 w1 has grad  True
w1 grad tensor([[ 0.0018, -0.0045,  0.0175, -0.0154,  0.0253, -0.0375,  0.0574,
          0.0155, -0.0139,  0.0022],
        [ 0.1069,  0.0265,  0.0383,  0.0559, -0.1969,  0.1059,  0.0688,
          0.0279,  0.1663, -0.1112],
        [-0.0022, -0.0031, -0.0035,  0.0035, -0.0044,  0.0095, -0.0111,
         -0.0026,  0.0031,  0.0026],
        [-0.0862, -0.0261, -0.0486, -0.0220,  0.1177, -0.0284, -0.1166,
         -0.0371, -0.1056,  0.0883],
        [-0.0623,  0.0647, -0.1075,  0.0172,  0.0316,  0.0507, -0.3152,
         -0.0999, -0.0735,  0.0283],
        [-0.0432,  0.0199, -0.0191, -0.0365,  0.1060, -0.0805, -0.0350,
         -0.0185, -0.0962,  0.0354],
        [-0.0444,  0.0063, -0.0257, -0.0223,  0.0812, -0.0437, -0.0589,
         -0.0223, -0.0753,  0.0397],
        [-0.0821,  0.0236, -0.0624, -0.0313,  0.1331, -0.0579, -0.1573,
         -0.0555, -0.1328,  0.0674],
        [ 0.0241,  0.0421, -0.0186,  0.0224, -0.0595,  0.0435, -0.0706,
         -0.0218,  0.0335, -0.0399],
        [ 0.0387,  0.0043,  0.0331,  0.0014, -0.0384, -0.0070,  0.0892,
          0.0273,  0.0408, -0.0358]])
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