![]() ![]() TL DR :torchvision's Resize behaves differently if the input is a PIL.Image or a torch tensor from read_image. path untardata(URLs.PETS) files getimagefiles(path/'images') def labelfunc(f): return f0.isupper() dls omnamefunc(path, files, labelfunc, itemtfmsResize( (256, 192))) A learner it is just a wrapper of Dataloaders and the model. The solution was not to use the new Tensor API and just use PIL as the image reader. This transform can accept or Tensors, in short, the resizing does not produce the same image, one is way softer than the other. Initially I thought that it was fastai's fault, but all the problem came from the new interaction between the tochvision.io.images.read_image and the. ![]() So, I have to make the reading and preprocessing of images as close as possible as fastai Transform pipeline, to get accurate model outputs.Īfter converting the transforms to ansforms I noticed that my model performance dropped significantly. ![]() (I am waiting to finally be able to install only the fastai vision part, without the NLP dependencies, this is coming soon, probably in fastai 2.3, at least it is in Jeremy's roadmap). Python Program from PIL import Image read the image im Image.open('sample-image.png') rotate image angle 45 out im.rotate(angle) out.save('rotate-output.png') Input Image sample-image. In our deployment env we are not including fastai as requirements and rely only on pure pytorch to process the data and make the inference. 1: Rotate given image by 45 degrees In the following example, we will rotate the image by 45 degrees in counter clockwise direction. It is a simple image classifier trained with fastai. Yesterday I was refactoring some code to put on our production code base. ![]()
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