Seunghun Lee, Sunghyun Cho, Sunghoon Im, "DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation", CVPR (2021)
Pytorch 1.8.0
CUDA 11.1
python 3.8.10
numpy 1.21.0
scipy 1.7.1
tensorboardX
prettytable
Download MNIST-M, Cityscapes, GTA5
├── data
├── MNIST
├── USPS
├── mnist_m
├── mnist_m_train
├── *.png
├── mnist_m_test
├── *.png
├── mnist_m_train_labels.txt
├── mnist_m_test_labels.txt
├── Cityscapes
├── GT
├── train
├── val
├── test
├── Images
├── train
├── val
├── test
├── GTA5
├── GT
├── 01_labels
├── 02_labels
├── ...
├── Images
├── 01_images
├── 02_images
├── ...
├── data_list
├── Cityscapes
├── train_imgs.txt
├── val_imgs.txt
├── train_labels.txt
├── val_labels.txt
├── GTA5
├── train_imgs.txt
├── train_labels.txt
You must input the task(clf or seg), datasets(M, MM, U, G, C), and experiment name.
python train.py -T [task] -D [datasets] --ex [experiment_name]
example) python train.py -T clf -D M MM --ex M2MM
Input the same experiment_name that you trained and specific iteration.
python test.py -T [task] -D [datasets] --ex [experiment_name (that you trained)] --load_step [specific iteration]
example) python test.py -T clf -D M MM --ex M2MM --load_step 100000
You can see all the results of each experiment on tensorboard.
CUDA_VISIBLE_DEVICES=-1 tensorboard --logdir tensorboard --bind_all