chainer-pix2pixHD

Chainer implementation of pix2pixHD https://github.com/NVIDIA/pix2pixHD. This version does not (yet) implement instance labels or VGG feature loss.

Example

Setup

• pip install -r requirements.txt
• conda install -c menpo opencv3=3.1.0
• pip install -U git+https://github.com/mcordts/cityscapesScripts.git (in order to use the cityscapes dataset properly)

Usage

• To reproduce the results on cityscapes at 512x1024 resolution, first pretrain the global generator at 256x512:
  • python tools/train.py -g <gpu> -G GlobalGenerator -o out/pretrained_global/ --config configs/cityscapes_256.json
• Then tune the full model at 512x1024 using the results of the pretraining;
  • python tools/train.py -g <gpu> -G MultiScaleGenerator -o out/total/ --global_generator_model out/pretrained_global/generator_model_200 --fix_global_num_epochs 10 --config configs/cityscapes_512.json

Note that you will require a GPU with at least 16Gb of VRAM to train for image size 512x1024. The results shown here were trained on a Tesla P100.

Multi-GPU training

• You can use ChainerMN for multi-GPU training. Please install ChainerMN in order to use it. The following command illustrates usage:
• mpiexec -n 4 python tools/train.py -g 0 1 2 3 ...
• where we have specified 4 workers and the individual GPU ids
• note that this is the equivalent of having a batchsize of 4. The argument -b will specify the batchsize of each worker, so the effective batchsize is multiplied by the number of workers.