Yu Zeng zengyu714

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

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Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Pry Cheat Sheet

Youtube Tutorial 2013

Command Line

pry -r ./config/app_init_file.rb - load your app into a pry session (look at the file loaded by config.ru)
pry -r ./config/environment.rb - load your rails into a pry session

Debugger

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	# Working example for my blog post at:
	# https://danijar.github.io/structuring-your-tensorflow-models
	import functools
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data


	def doublewrap(function):
	"""
	A decorator decorator, allowing to use the decorator to be used without