Dassl

Dassl is a PyTorch toolbox for domain adaptation and semi-supervised learning. It has a modular design and unified interfaces, allowing fast prototyping and experimentation. With Dassl, a new method can be implemented with only a few lines of code.

Besides the efforts to facilitate algorithm development and push state of the art, Dassl is also aimed at providing a uniform benchmarking platform, which allows methods to be evaluated on a common ground using the same set of environment and parameters.

You can use Dassl as a library for the following research:

• Domain adaptation
• Domain generalization
• Semi-supervised learning

What's new

• [May 2020] v0.1.3: Added the Digit-Single dataset for benchmarking single-source DG methods. The corresponding CNN model is dassl/modeling/backbone/cnn_digitsingle.py and the dataset config file is configs/datasets/dg/digit_single.yaml. See Volpi et al. NIPS'18 for how to evaluate your method.
• [May 2020] v0.1.2: 1) Added EfficientNet models (B0-B7) (credit to https://github.com/lukemelas/EfficientNet-PyTorch). To use EfficientNet, set MODEL.BACKBONE.NAME to efficientnet_b{N} where N={0, ..., 7}. 2) dassl/modeling/models has been renamed to dassl/modeling/network, including the build_model() method changed to build_network() and the MODEL_REGISTRY to NETWORK_RESIGTRY.

Overview

Dassl has implemented the following papers:

• Single-source domain adaptation

  • Semi-supervised Domain Adaptation via Minimax Entropy (ICCV'19) [dassl/engine/da/mme.py]
  • Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (CVPR'18) [dassl/engine/da/mcd.py]
  • Self-ensembling for visual domain adaptation (ICLR'18) [dassl/engine/da/self_ensembling.py]
  • Revisiting Batch Normalization For Practical Domain Adaptation (ICLR-W'17) [dassl/engine/da/adabn.py]
  • Adversarial Discriminative Domain Adaptation (CVPR'17) [dassl/engine/da/adda.py]
  • Domain-Adversarial Training of Neural Networks (JMLR'16) [dassl/engine/da/dann.py]

• Multi-source domain adaptation

  • Domain Aadaptive Ensemble Learning [dassl/engine/da/dael.py]
  • Moment Matching for Multi-Source Domain Adaptation (ICCV'19) [dassl/engine/da/m3sda.py]

• Domain generalization

  • Deep Domain-Adversarial Image Generation for Domain Generalisation (AAAI'20) [dassl/engine/dg/ddaig.py]
  • Generalizing Across Domains via Cross-Gradient Training (ICLR'18) [dassl/engine/dg/crossgrad.py]

• Semi-supervised learning

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence [dassl/engine/ssl/fixmatch.py]
  • MixMatch: A Holistic Approach to Semi-Supervised Learning (NeurIPS'19) [dassl/engine/ssl/mixmatch.py]
  • Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results (NeurIPS'17) [dassl/engine/ssl/mean_teacher.py]
  • Semi-supervised Learning by Entropy Minimization (NeurIPS'04) [dassl/engine/ssl/entmin.py]

Dassl supports the following datasets.

• Domain adaptation

  • Office-31
  • Office-Home
  • VisDA17
  • CIFAR10-STL10
  • Digit-5
  • DomainNet
  • miniDomainNet

• Domain generalization

  • PACS
  • Office-Home
  • Digits-DG
  • Digit-Single

• Semi-supervised learning

  • CIFAR10/100
  • SVHN
  • STL10

Get started

Installation

Make sure conda is installed properly.

# Clone this repo
git clone https://github.com/KaiyangZhou/Dassl.pytorch.git
cd Dassl.pytorch/

# Create a conda environment
conda create -n dassl python=3.7

# Activate the environment
conda activate dassl

# Install dependencies
pip install -r requirements.txt

# Install torch and torchvision (select a version that suits your machine)
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

# Install this library (no need to re-build if the source code is modified)
python setup.py develop

Follow the instructions in DATASETS.md to install the datasets.

Training

The main interface is implemented in tools/train.py, which basically does three things:

1. Initialize the config with cfg = setup_cfg(args) where args contains the command-line input (see tools/train.py for the list of input arguments).
2. Instantiate a trainer with build_trainer(cfg) which loads the dataset and builds a deep neural network model.
3. Call trainer.train() for training and evaluating the model.

Below we provide an example for training a source-only baseline on the popular domain adaptation dataset, Office-31,

CUDA_VISIBLE_DEVICES=0 python tools/train.py \
--root $DATA \
--trainer SourceOnly \
--source-domains amazon \
--target-domains webcam \
--dataset-config-file configs/datasets/da/office31.yaml \
--config-file configs/trainers/da/source_only/office31.yaml \
--output-dir output/source_only_office31

$DATA denotes the location where datasets are installed. --dataset-config-file loads the common setting for the dataset (Office-31 in this case) such as image size and model architecture. --config-file loads the algorithm-specific setting such as hyper-parameters and optimization parameters.

To use multiple sources, namely the multi-source domain adaptation task, one just needs to add more sources to --source-domains. For instance, to train a source-only baseline on miniDomainNet, one can do

CUDA_VISIBLE_DEVICES=0 python tools/train.py \
--root $DATA \
--trainer SourceOnly \
--source-domains clipart painting real \
--target-domains sketch \
--dataset-config-file configs/datasets/da/mini_domainnet.yaml \
--config-file configs/trainers/da/source_only/mini_domainnet.yaml \
--output-dir output/source_only_minidn

After the training finishes, the model weights will be saved under the specified output directory, along with a log file and a tensorboard file for visualization.

For other trainers such as MCD, you can set --trainer MCD while keeping the config file unchanged, i.e. using the same training parameters as SourceOnly (in the simplest case). To modify the algorithm-specific hyper-parameters, in this case N_STEP_F (number of steps to update the feature extractor), you can append TRAINER.MCD.N_STEP_F 4 to the existing input arguments, otherwise the default value will be used. Alternatively, you can create a new .yaml config file to store your custom setting. See here for a complete list of algorithm-specific hyper-parameters.

Test

Testing can be achieved by using --eval-only, which tells the script to run trainer.test(). You also need to provide the trained model and specify which model file (i.e. saved at which epoch) to use. For example, to use model.pth.tar-20 saved at output/source_only_office31/model, you can do

CUDA_VISIBLE_DEVICES=0 python tools/train.py \
--root $DATA \
--trainer SourceOnly \
--source-domains amazon \
--target-domains webcam \
--dataset-config-file configs/datasets/da/office31.yaml \
--config-file configs/trainers/da/source_only/office31.yaml \
--output-dir output/source_only_office31_test \
--eval-only \
--model-dir output/source_only_office31 \
--load-epoch 20

Note that --model-dir takes as input the directory path which was specified in --output-dir in the training stage.

Write a new trainer

A good practice is to go through dassl/engine/trainer.py to get familar with the base trainer classes, which provide generic functions and training loops. To write a trainer class for domain adaptation or semi-supervised learning, the new class can subclass TrainerXU. For domain generalization, the new class can subclass TrainerX. In particular, TrainerXU and TrainerX mainly differ in whether using a data loader for unlabeled data. With the base classes, a new trainer may only need to implement the forward_backward() method, which performs loss computation and model update. See dassl/enigne/da/source_only.py for example.

Some tips:

• Write a new trainer, which can inherit the mother class, and put it in the corresponding folder, e.g., dassl/engine/ for da methods.
• Import the class to dassl/engine/da/__init__.py file.
• Define some parameters in 'dassl/config/defaults.py' for your new method.

Add a new backbone/head/network

backbone corresponds to a convolutional neural network model which performs feature extraction. head (which is an optional module) is mounted on top of backbone for further processing, which can be, for example, a MLP. backbone and head are basic building blocks for constructing a SimpleNet() (see dassl/engine/trainer.py) which serves as the primary model for a task. network contains custom neural network models, such as an image generator.

To add a new module, namely a backbone/head/network, you need to first register the module using the corresponding registry, i.e. BACKBONE_REGISTRY for backbone, HEAD_REGISTRY for head and NETWORK_RESIGTRY for network. Note that for a new backbone, we require the model to subclass Backbone as defined in dassl/modeling/backbone/backbone.py and specify the self._out_features attribute.

We provide an example below for how to add a new backbone.

from dassl.modeling import Backbone, BACKBONE_REGISTRY

class MyBackbone(Backbone):

    def __init__(self):
        super().__init__()
        # Create layers
        self.conv = ...

        self._out_features = 2048

    def forward(self, x):
        # Extract and return features

@BACKBONE_REGISTRY.register()
def my_backbone(**kwargs):
    return MyBackbone()

Then, you can set MODEL.BACKBONE.NAME to my_backbone to use your own architecture. For more details, please refer to the source code in dassl/modeling.

Citation

Please cite the following paper if you find Dassl useful to your research.

@article{zhou2020domain,
  title={Domain Adaptive Ensemble Learning},
  author={Zhou, Kaiyang and Yang, Yongxin and Qiao, Yu and Xiang, Tao},
  journal={arXiv preprint arXiv:2003.07325},
  year={2020}
}