COMPX527_BAG (Behaviour Anomaly Group)

COMPX527-21B A2:AWS CC App Service to detect suspicious behaviour on submitted CCTV footage

Sample generation

Samples can be saved to file using predictor_src/create_json_sample.py

this takes 2-3 arguments

• location of dcsass dataset
• output file name
• optional 3rd parameter of the index of the sample to output

This script was used to generate the json string used in the demonstration of calling the API

Training on

• https://www.kaggle.com/mateohervas/dcsass-dataset (requires sign in) Has 13 classes and flags indicating a clip is of interest to the class or normal. so, 14 classes. could in theory look at it as 26 classes

Note:

for downloading the Dataset, the zip comes with a duplicate of the dataset.

Based on:

• https://github.com/PyTorchLightning/pytorch-lightning (pytorch helper module, automates a lot of 'boilerplate code'. Has some capacity to make code more portable: TODO!)
• https://github.com/YuxinZhaozyx/pytorch-VideoDataset (videodataset helper module, just uses the dataset.py and transforms.py. Expects them to be in the same dir: using only transforms.py)

most of what is in the my_prog.py is from the README.md

Some dependencies

• pytorch https://pytorch.org/ (will give you a conda command to run if you select appropriate for you system)
• pytorch lightning https://github.com/PyTorchLightning/pytorch-lightning
• CUDA toolkit https://developer.nvidia.com/cuda-downloads

Been running it in an anaconda env https://www.anaconda.com/products/individual

after installing, reload command prompt/bash (windows has a shortcut installed in the start menu for anaconda)

in my_prog.py are two vars:

• SEED = 1
• NUM_WORKERS = 8 (set to your number of CPU cores)

if you have multiple GPUs, edit the following line param gpus=x

line to edit

trainer = pl.Trainer(gpus=1)

Setup Instructions

1. Install nvidia cuda toolkit from link above

conda env create --file ./envs/video.yml

python my_prog.py /path/to/dcsass/videos/

Use - Generic

from predictor_src/scr.py

import predict
with open ("output.txt", "r") as myfile:
    data=myfile.readlines()

predict.predictor(data,"./example.ckpt")

Notes

Transforms

• VideoToTensor(frames_in_tensor=10,fps=2)
• Grayscale
• Resize(128,128)

Label Encoder

• Not well implemented: creates a sklearn labelencoder within the dataset class init then uses a helper method to create numpy arrays
• Labels in my_prog.py available as a global var list which the predict.py uses to relabel identify it's output predictions

Encoder

• Linear(in=video.xvideo.yframes_in_tensor,out=64)
• ReLU
• Linear(out=64, num_of_classes)

Decoder

• Linear(in=num_of_classes,out=64)
• ReLU
• Linear(in=64,out=video.xvideo.yframes_in_tensor)

Early Stop Callback

• generic callback, uses log_acc var. currently running tests
• Note: Doesn't produce a stable model

Forward Step

• Unpacks tuple. taking the frame representation only, ignoring any category
• Passes through the encoder and returns residual

TODO

• Optimizer description
• validation step & epoch_end desc