An implementation of Deep Generative Model of Radars from DeepMind in PyTorch
- dask==2022.9.1
- matplotlib==3.5.1
- numba==0.55.1
- numpy==1.21.0
- pandas==1.4.1
- properscoring==0.1
- pytorch-lightning==1.5.10
- torchvision==0.11.3
- pytorch
Execute pip install -r requirements.txt to install required packages (except for torch).
For installing torch, please check out https://pytorch.org/get-started/locally/ to figure out the version that works on your device.
- Step 1: Read your own data.
- Step 2: Store your data into
numpy.ndarraywith proper data type (e.g.int16,float64). - Step 3: Make sure your data are sorted by time.
- Step 4: Note the array's storing data type and data shape, these information will be needed in config file.
- Step 5: Save the array with the format
.dator.npy. (No headers please!)
For more information, please check out numpy's documentation.
- Data types https://numpy.org/doc/stable/user/basics.types.html
- How to save array? https://numpy.org/doc/stable/reference/generated/numpy.save.html
- Acceptable data shape: (N, H, W)
- N: number of rainfall images
- H: height of rainfall images
- W: width of rainfall images
- Step 1: Prepare data (see Data Preparation).
- Step 2: Prepare rain records
.csvfile. (optional) If rain records are not prepared, it will be calculated automatically in our program. Example of csv:
| index | nonzeros |
|---|---|
| 0 | number of nonzeros of image 0 |
| 1 | number of nonzeros of image 1 |
| ... | number of nonzeros of images |
| N-1 | number of nonzeros of image N-1 |
-
Step 3: Prepare config file. Please see configs/README.md
-
Step 4: Execute the code
- train:
python3 main.py -c /path/to/your/config -m train - validate:
python3 main.py -c /path/to/your/config -m val - test:
python3 main.py -c /path/to/your/config -m test
- train:
Execute this command to see training results.
tensorboard --logdir /path/where/records/are/stored