I cannot reproduce the issue using your model and random input:
features.eval()
x = torch.randn(32, 1, 100, 100)
out0 = features(x).clone()
out1 = features(x).clone()

print((out0 - out1).abs().max())
> tensor(0., grad_fn=<MaxBackward1>)

Could you post an executable code snippet to reproduce this issue?