A sureshot empirical way to test this would be to make the code completely reproducible,  only changing the way the ReLU module is used. In order to make code reproducible, use the below lines of code.

torch.manual_seed(5) ## Any arbitarary number
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

Do note that the only change in the model should be the use of ReLU layers. You can even try changing the forward function as shown below to prevent something funny from happening.
def forward(self, x,same_relu = True):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.conv2(x)
        if(same_relu):
          x = self.relu(x)
        else:
          x = self.relu_2(x)
        return x