differing results when using model to infer on a batch vs individual with pytorch

Question

I have a neural network which takes input tensor of dimension (batch_size, 100, 1, 1) and produces an output tensor of dimension (batch_size, 3, 64, 64). I have differing results when using model to infer on a batch of two elements and on inferring on elements individually.

With the below code I initialize a pytorch tensor of dimension (2, 100, 1, 1). I pass this tensor through the model and I take the first element of the model output and store in variable result1. For result2 I just directly run the first element of my original input tensor through my model.

inputbatch=torch.randn(2, Z_DIM, 1, 1, device=device)
inputElement=inputbatch[0].unsqueeze(0)

result1=model(inputbatch)[0]
result2=model(inputElement)

My expectation was result1 and result2 would be same. But result1 and result2 are entirely different. Could anyone explain why the two outputs differ.

Answer 1

This is probably because your model has some random processes that are either training specific and you have not disable them (eg by using model.eval() ) or needed at the model during inference.

To test the above, use:

model = model.eval()

before obtaining result1 .