daylight-gan

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Real-time daylight prediction using Rhinoceros3D, Grasshopper, Ladybug Tools and pyTorch

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# DaylightGAN
Real-time daylight prediction pipeline, using Rhinoceros 3D, Grasshopper 3D, Ladybug Tools and pyTorch

## Downloading the Daylight Autonomy (DA) dataset

If you wish to train your own model on the DA dataset, you can download the files from this location: https://drive.google.com/open?id=1_ND3DuOmRmeSiFpVpRmvooqeJtneHxwn. Extract the files in cycleGAN/datasets folder.

## Pretrained model

You can download the Daylight Autonomy (DA) pretrained model from this location: https://drive.google.com/open?id=1_ND3DuOmRmeSiFpVpRmvooqeJtneHxwn. Extract the files into cycleGAN/checkpoints folder.

## Training your own model on the DA dataset

After extracting the dataset you can train your own model by running:

```python
python train.py --dataroot ./datasets/da --name p2p_512 --checkpoints_dir ./checkpoints/da --model pix2pix --batch_size 4 --norm instance --init_type kaiming --dataset aligned --direction AtoB --load_size 512 --crop_size 512 --no_flip --display_winsize 512 --gan_mode lsgan --lr_policy linear --netG unet_512 --netD n_layers --n_layers_D 4
```
This will train a pix2pix model on 512x512 input heightmaps, using a batch size of 4. Feel free to reduce either the batch size or the input image resolution if your GPU memory isn't enough.

## Training your own model on a custom dataset

To train your own model you need to generate 3d geometry models using a parametric design of your choice (you can use the ParametricModel.gh file as a starting point). You will also need to run annual daylight simulations for each model, using the process detailed in the xxx.ipynb file. Finally, you can generate your input and output images using the PostProcess.gh file.

After you have all your images, you can use a paired image dataset using the xxx.ipynb file. Make sure to save the folders in cycleGAN/datasets under the name of your choosing, e.g. cycleGAN/datasets/yourNameHere.

To train a 512x512 pix2pix model you can run the following command:

```python
python train.py --dataroot ./datasets/yourNameHere --name p2p_512 --checkpoints_dir ./checkpoints/yourNameHere --model pix2pix --batch_size 4 --norm instance --init_type kaiming --dataset aligned --direction AtoB --load_size 512 --crop_size 512 --no_flip --display_winsize 512 --gan_mode lsgan --lr_policy linear --netG unet_512 --netD n_layers --n_layers_D 4
```

Again feel free to use a lower resolution or a smaller batch size to fit the model on your GPU.

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