1. Duration

Sunday, February 19th, 2023 - Saturday, February 25th, 2023

2. Learning Records

2.1 Fixed the Crash Error

The jupyter_core and jupyter_client are alright. But I found out that the Linux server runs out of memory when the macro- and micro-expression spotting experiments are in process simultaneously.

2.2 Extracted Features

Extracted micro-expression features on the SAMM Long Videos dataset.

2.3 Wrote my Paper

Wrote the Related Work part.

2.4 Fixed the Count Mismatch Problem

Due to the problematic naming in the original cropping function, not only the images' names were wrong, but also the cropped image count mismatched the original image count. I fixed the naming problem last week and fixed the mismatch count problem this week.

In addition, I tried to extract features from the video 016_7 but the machine crashed twice because of the limited RAM. Damn! Each extraction experiment cost me more than an hour, which means I wasted about three hours that afternoon.

2.5 Added New Features

Allowed assigning specific GPU to do the training. Besides, Added adjustments to restrict TensorFlow to only allocate specific memory on the GPU.

2.6 Building

Built the test dataset cropping function, training and test function. And I combined all processes in one .ipynb file.

Moreover, I built the function for features extraction and pre-processing which could save the .pkl file after finishing extracting and pre-processing one video.

2.7 Refactoring

Added a parameter debug_preds to each .ipynb file. When debug_preds is True, the training would not be processed and the program would load the .pkl prediction files and execute the spotting and evaluation process.

2.8 Fixing the Model

It was when I was writing my paper and reading the vision transformer for small scall datasets that I found out the Shifted Patch Tokenization has a process called pos_embedding which I forgot to add in my SL models.

I added the pos_embedding on both the TensorFlow and PyTorch models.

2.9 Fixing the Cropping

I found that the dlib face detector could not detect the face landmark on every image of the test video. This weird situation let me reconsider whether my cropping function has some problem. Consequently, I changed the cropping scale.

The good news was everything worked fine and the bad news was I needed to crop all images again which was time-consuming.

3. Feelings

No special feelings.

Coding is more intriguing than paper work.