Quick Start¶
In this section, we will use DANN as an example, and show you how to reproduce the benchmark results.
Before going deeper, please make sure you have installed all the dependency.
Step1: Find it¶
Our code for domain adaptation is in the directory examples/domain_adaptation.
DANN is designed for close set domain adaptation tasks. You can find the training code in
examples/domain_adaptation/image_classification. This directory contains implementations for other algorithms such as CDAN, MDD.
For now, you only need to care about two files: dann.py and dann.sh.
Step2: Run it¶
dann.py is an executable python file. And you can find all the necessary running scripts to
reproduce the benchmarks with specified hyper-parameters in dann.sh.
For instance, running the following command will start training on Office-31 dataset.
CUDA_VISIBLE_DEVICES=0 python dann.py data/office31 -d Office31 -s A -t W -a resnet50 --epochs 20 --seed 1 --log logs/dann/Office31_A2W
Note that -s specifies the source domain, -t specifies the target domain,
and --log specifies where to store results.
After running the above command, it will download Office-31 datasets from the Internet if it’s the
first time you run the code. Directory that stores datasets will be named as
examples/domain_adaptation/image_classification/data/<dataset name>.
Step3: Analysis it¶
If everything works fine, you will see results in following format:
Epoch: [1][ 900/1000] Time 0.60 ( 0.69) Data 0.22 ( 0.31) Loss 0.74 ( 0.85) Cls Acc 96.9 (95.1) Domain Acc 64.1 (62.6)
Trans-Learn provides detailed statistics to monitor during training.
Here your algorithm is running in epoch 2 (index starts from 0), has been trained for 900 iterations. The loss comes down to 0.74, on source domain your classifier achieves 96.9% accuracy and your domain discriminator has an accuracy of 64.1%. Different algorithms may show you different statistics.
You can also watch these results in a log file.
They are located in directory logs/dann/Office31_A2W/log.txt.
During training, latest and best model checkpoints will be saved in directory logs/dann/Office31_A2W/checkpoints.
Resuming from checkpoint is also supported.
After training, you can test your algorithm’s performance by passing in --phase test.
CUDA_VISIBLE_DEVICES=0 python dann.py data/office31 -d Office31 -s A -t W -a resnet50 --epochs 20 --seed 1 --log logs/dann/Office31_A2W --phase test
In next section, we will introduce how to visualize the results in details.
