#Tf image resize 3d how to#
#Tf image resize 3d full#

First, you will use high-level Keras preprocessing utilities (such as tf._dataset_from_directory) and layers (such as tf.) to read a directory of images on disk.

#Tf image resize 3d how to#

Torchvision version: 0.12.This tutorial shows how to load and preprocess an image dataset in three ways: If I fix locally the issue with with non-contiguous output from read_image the results are the following: Num threads: 6 Similar benchmark results are for downsampling 500 -> 224 1 threads:.

auto output = output_.contiguous(channels_last_memory_format),.auto t = at::rand().contiguous(at::MemoryFormat::ChannelsLast) Īs output is contiguous channels first but input is channels last, there are 2 places where algorithm takes time: Thus input to torch.nn.functional.interpolate is 4D channels last tensor, but output constructed from input suggested format here is channels first contiguous. In Resize we call unsqueeze(dim=0) to make it 4D. Tensor returned by read_image has memory format equivalent to channels last but has 3D. Why “read_image tensor -> Resize (32->224)” is much slower than “CL contig tensor -> Resize (32->224)” for 1 thread ? “CF contig tensor -> Resize (32->224)” and “CL contig tensor -> Resize (32->224)” measure upsampling resize on channels first (CF) and channels last (CL) contiguous 4D tensors, (1, 3, 32, 32).This tensor is 3D (3, 32, 32) and could be seen as channels first. “read_image tensor + contiguous -> Resize (32->224)” measures upsampling resize on the tensor returned by read_image and on which we applied contiguous op.This tensor is 3D (3, 32, 32) and could be seen as channels last.

“read_image tensor -> Resize (32->224)” measures upsampling resize on the tensor returned by read_image.

Upsampling and downsampling are slow if using the tensor coming from read_image:ġ threads:.

Python platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.23ĬuDNN version: Probably one of the following:

#Tf image resize 3d full#

Please refer to my post on the Pytorch Forum here for the full analysis. I can’t really understand why this happens, since both calls to Resize are on tensors of type torch.FloatTensor. However, applying transforms.Resize on the tensor generated by io.read_image + transforms.ConvertImageDtype is much slower than applying the same resize operation on the output of PIL read + transforms.ToTensor. To add onto point 2, the two sets of functions I mention return the same type of tensor: torch.float. While io.read_image + transforms.ConvertImageDtype itself is significantly faster than using PIL, combining it with the transforms.Resize operation - specifically when upsampling - makes the operation much slower than the PIL alternative.io.read_image + transforms.ConvertImageDtype do not actually return the same tensor values as PIL + transforms.ToTensor, even though they are supposed to provide the same functionality.However, I have found that there are two issues: Recent releases of Torchvision and the documentations that support it seem to suggest that we can use io.read_image + transforms.ConvertImageDtype instead of the traditional _fn + transforms.ToTensor.