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

1

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.

2

The issue was not just using model = model.eval() . I was able to solve this by calling model.eval()

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