Grade Two Science Topics May 3 2020 nbsp 0183 32 When align corners True the grid positions depend on the pixel size relative to the input image size and so the locations sampled by grid sample will differ for the same input given at different resolutions that is after being upsampled or downsampled
May 18 2022 nbsp 0183 32 In the code below I ve created a simple image and then applied grid sample to it using scaled integer row col coordinates The goal is to reproduce the input image exactly and I achieve this using align corners Tru amp hellip We notice that PyTorch API changes a lot after v1 0 so that our official implementation is NOT fully compatible with PyTorch 1 0 or later version In this repository we upgrade the interface to make our code smooth again
Grade Two Science Topics
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Aug 10 2022 nbsp 0183 32 I am debugging a neural network which has a torch nn functional grid sample operator inside Using the Pycharm IDE I can watch the values during debugging My grid is a 1 15 2 tensor here are Aug 8 2018 nbsp 0183 32 When align corners True pixels are regarded as a grid of points Points at the corners are aligned When align corners False pixels are regarded as 1x1 areas Area boundaries rather than their centers are aligned
Creates grids of coordinates specified by the 1D inputs in attr tensors This is helpful when you want to visualize data over some range of inputs See below for a plotting example Jun 17 2020 nbsp 0183 32 According to the doc when align corners is True the input grid coordinates should be the position of the pixel center And when align corners is False it should be the index of the pixel e g if True I need to input 0 5 0 5 and normalized it by 0 5 0 5 size 2 1 to get the upper left pixel Otherwise I need to input 0 0
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Jan 6 2020 nbsp 0183 32 Please look at the documentation of grid sample Your input tensor has a shape of 1x32x296x400 that is you have a single example in the batch with 32 channels and spatial dimensions of 296x400 pixels Apr 17 2025 nbsp 0183 32 When align corners False this line maps normalized coordinate in 1 1 to 0 5 size 0 5 Then it is possible to have coordinates larger than size 1 after this mapping Let s say we have a coordinate mapped to x y with x gt size 1
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