transforms.PILToTensor vs transforms.ToTensor

vision
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I have recently started using pytorch and have written a few working models. Now i have 2 models that did not work at all and in inference only output something that looked like noise. I have now fixxed the problem by changing my data transform to Tensor from transform.PILToTensor to transforms.ToTensor and now i wanted to know what the actual difference between these two functions is as i do not see the difference in the documentation.

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PILToTensor transforms a PIL.Image to a PyTorch tensor of the same dtype while ToTensor normalizes the tensor to [0, 1] and will return a FloatTensor.
Usually normalized/standardized tensors are preferred while training a model as the convergence often benefits from it.

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