The final pipeline of the given task involves two main parts: video frame prediction and mask segmentation.

In the first part, a previously trained model is loaded using the --model1_path argument. This model is used to predict the 22nd frame of a test dataset, which is provided using the --data_root argument.

Once the 22nd frame is predicted, the second part of the pipeline involves mask segmentation. A saved model, which has been trained specifically for this task, is loaded using the --model2_path argument. The predicted 22nd frame is then passed through this model to generate a set of predicted masks.

Finally, the predicted masks are saved as a .npy file in the ./results/ folder. To reproduce the results, you need to run the command:

python main.py --model1_path "Path to the checkpoint of Model 1" --model2_path "Path to the checkpoint of Model 2" --data_root "Path to the test dataset folder"

For example:

python main.py --model1_path '/scratch/sxp8182/SimVP-Simpler-yet-Better-Video-Prediction/results_5/Debug/checkpoint.pth' --model2_path '/scratch/sxp8182/model2504_new.pt' --data_root '/scratch/sxp8182/hidden'

The results can be found in the ./results/numpy_y_pred_masks.npy file.

Overall, this pipeline demonstrates a powerful application of deep learning in video processing tasks, such as predicting future frames and segmenting objects within those frames.