test_nn.py 文件源码

def _test_InstanceNorm(self, cls, input):
        b, c = input.size(0), input.size(1)
        input_var = Variable(input)

        IN = cls(c, eps=0)

        output = IN(input_var)
        out_reshaped = output.transpose(1, 0).contiguous().view(c, -1)

        mean = out_reshaped.mean(1)
        var = out_reshaped.var(1, unbiased=False)

        self.assertAlmostEqual(torch.abs(mean.data).mean(), 0, delta=1e-5)
        self.assertAlmostEqual(torch.abs(var.data).mean(), 1, delta=1e-5)

        # If momentum==1 running_mean/var should be
        # equal to mean/var of the input
        IN = cls(c, momentum=1, eps=0)

        output = IN(input_var)

        input_reshaped = input_var.transpose(1, 0).contiguous().view(c, -1)
        mean = input_reshaped.mean(1)

        input_reshaped = input_var.transpose(1, 0).contiguous().view(c, b, -1)
        var = input_reshaped.var(2, unbiased=True)[:, :]

        self.assertAlmostEqual(torch.abs(mean.data - IN.running_mean).mean(), 0, delta=1e-5)
        self.assertAlmostEqual(torch.abs(var.data.mean(1) - IN.running_var).mean(), 0, delta=1e-5)