This week was pretty uneventful. And the progress made was a bit slow, because I got stuck in figuring out how to implement the Pixel Shuffle layer, which is my next feature after the Instance Norm layer.

Pixel Shuffle = A complex shuffling of pixels across channels of an image.

PyTorch implements it in a very sleek fashion, doing a permutation of a 6D tensor. However, in armadillo, it is not possible to represent a 6D tensor as far as I know. So, the challenge has been to understand what exactly has been happenin in 6 dimensions. While I was able to write a Forward() method based on the direct translation of the mathematical formula for the function that the layer represents, I have had a tough time figuring out how exactly the Backward() method would look like. I have asked my mentor about this as well. He was a bit pre-occupied with some tasks this week and said that he will look into this and try and provide some insights soon. Hopefully by next week, we will have some more clarity about this particular layer.

I have also re-designed my implementation of the Instance Norm layer this week. Previously, I was implementing the layer from scratch.

But, now that PR #2474 has been merged and that I am sure about the correctness of that implementation, I decided to write the Instance Norm as simply a wrapper layer for the Batch Norm layer, which is also what PyTorch does btw. A demo of the implementation can be seen from this Google Colab notebook. The idea that this approach uses is something that I discussed a couple of weeks back. For a single image, Instance Norm and Batch Norm calculates the same thing, so, we can simply transform the (N, C, H, W) input of Instance Norm into a (1, N*C, H, W) input for Batch Norm and thus re-use existing code.

Other than the Instance Norm and Pixel Shuffle, the other feature that I had planned to do during the second phase of coding was the Spatial Dropout layer. I got started with readig up about that layer this week. However, I would like to start its implementation once the Pixel Shuffle is done. Hopefully both of these new layers will be complete before the second evaluations. But, if we aren’t able to figure out the Pixel Shuffle one by next week, I might need to take an extra week from the third phase to complete the Spatial Dropout, which should be totally fine, because my plan for the third phase already took into account any pending work from the previous 2 phases. The third phase will mainly focusing on updating some sections of the mlpack-Tensorflow translator, so that we can get closer to making that a completely functional repository.

Apart from all of this, I worked on a topic that I find interesting this week for a bit to distract myself from thinking in 6D continously. I screen recorded the following video, if anyone’s interested.

I also tried getting access to GPT-3 API by filling up the form this week, but haven’t got back any reponse so far. All the cool stuff people are doing on Twitter with it really makes me want to try and play with it myself.

Continuing with the Netflix Series Dark, I had finished Season 1 last weekend, and I binged season 2 today. By this time, I have lost track of what’s the past, present or future in the series, and I am just watching it to find out what happends in the end. Definitely would need to watch the entire thing again later on to fully grasp all the timelines.

Instead of recommending a single song for this week, I would like to recommend an entire album from Juice WRLD. Yes, it is that good. 🔥

Legends Never Die by Juice WRLD

See you next week!
XOXO