I wanted to test the effect of dropout at test time (I am using PyTorch 0.4). Simply:
dropout was set to p=0.5 before training (Fine-Tuning). I am using ResNet-152 (pre-trained with ImageNet). Using Cifar-100, I trained the model for 15 epochs; then, at test time, I tried to use different p values.
P = [0, 0.2, 0.5, 0.7, 1]
for p in P: 
    model.dropout.p = p
    test(test_loader) # prints accuracy

Printed output:
40.5% 40.5% 40.5% 40.5% 40.5%
As shown in Dropout: A Simple Way to Prevent Neural Networks from Overfitting 7 , the p values should affect the output at test time. 
dropout .jpeg1120×417 53.2 KB

Seems dropout has no effect at the testing. Or, am I missing something here?