域自适应，从cityscape->foggycitycscape torch代码

A Pytorch Implementation for Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer ## Introduction Please follow [faster-rcnn](https://github.com/jwyang/faster-rcnn.pytorch/tree/pytorch-1.0) and [HTCN](https://github.com/chaoqichen/HTCN) respository to setup the environment. In this project, we use Pytorch 1.6.1 and CUDA version is 10.2 ## Additional Information - We use Sacred([link](https://sacred.readthedocs.io/en/stable/)) to run/manage our experiments. It can be installed with: ``` pip install sacred ``` - To monitor the training, we used MongoObserver of Sacred with Omniboard. However, it is disabled by default. If you want to plug in your own monitoring. It can be done in **class LoggerForSacred** of **experiments/exp_utils.py** which will be called at every 100 iteration. By default, log are printed to stdout. - All models are expected to be saved in *all_saves* folder in repo's root. This can be changed in the code. ## Datasets ### Datasets Preparation * **Cityscape and FoggyCityscape:** Download the [Cityscape](https://www.cityscapes-dataset.com/) dataset, see dataset preparation code in [DA-Faster RCNN](https://github.com/yuhuayc/da-faster-rcnn/tree/master/prepare_data). * **PASCAL_VOC 07+12:** Please follow the [instruction](https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models) to prepare VOC dataset. * **Clipart - Watercolor - Comic:** Please follow the [instruction](https://github.com/naoto0804/cross-domain-detection/tree/master/datasets) to download ### Datasets Format All codes are written to fit for the **format of PASCAL_VOC**. If you want to use this code on your own dataset, please arrange the dataset in the format of PASCAL, make dataset class in ```lib/datasets/```, and add it to ```lib/datasets/factory.py```, ```lib/datasets/config_dataset.py```. Then, add the dataset option to ```lib/model/utils/parser_func.py```. For RainCityscape train and evaluation list you can find them here: [train](https://drive.google.com/file/d/1K9TILq7zmecvuiDNGeHnYaulXhcJaX2N/view?usp=sharing), [val](https://drive.google.com/file/d/1lTOjhGxcAsKS1HtAU-vE3eaTmqwoHF36/view?usp=sharing) ## Models ### Pre-trained Models In our experiments, we used two pre-trained models on ImageNet, i.e., VGG16 and ResNet101. Please download these two models from: * **VGG16:** [Dropbox](https://www.dropbox.com/s/s3brpk0bdq60nyb/vgg16_caffe.pth?dl=0) [VT Server](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/vgg16_caffe.pth) * **ResNet50:** Download the model (the one for BGR) from [link](https://github.com/ruotianluo/pytorch-resnet) Download them and write the path in **__C.VGG_PATH** and **__C.RESNET50_PATH** at ```lib/model/utils/config.py```. ## Train Example **Don't forget to install sacred**, you cannot run our code without it. To run the first step of **Cityscape** -> **FoggyCityscape** on VGG16 with our hyper-parameters : ``` CUDA_VISIBLE_DEVICES=$GPU_ID \ python experiments/exp_traineval_htcn.py ``` If you want to do your own hyper-parameters, etc..., **exp_traineval_htcn.py** is fairly easy to understand to modify. We provided an example to modify this at the end. To train DTM for the next incremental step, you will have to change 'load_name' inside **exp_train_dtm.py** to the model obtained from previous step: ``` CUDA_VISIBLE_DEVICES=$GPU_ID \ python experiments/exp_train_dtm.py ``` The next incremental domain adaptation, you will have to point 'dtm_load_p' to the DTM model you just trained and 'load_name' the model obtained from the first domain adaptation inside **exp_traineval_htcn_inc_ida_dtm.py**: ``` CUDA_VISIBLE_DEVICES=$GPU_ID \ python experiments/exp_traineval_htcn_inc_ida_dtm.py ``` Example of how to edit hyper-parameters etc... in **exp_traineval_htcn.py**. Original: ``` if __name__ == "__main__": ex.run(config_updates={'cfg_file': 'cfgs/vgg16.yml', 'lr': 0.001, 'lr_decay_step': [5], 'max_epochs': 7, 'net': 'vgg16', 'pretrained': True, 'dataset_source': 'cs', 'dataset_target': 'cs_fg', 'val_datasets': ['cs_fg']}, options={"--name": 'htcn_cs_2_cs_fg_vgg16'}) ``` Edit for **PascalVOC** -> **Clipart** in **exp_traineval_htcn.py**: ``` if __name__ == "__main__": ex.run(config_updates={'cfg_file': 'cfgs/resnet50.yml', 'lr': 0.001, 'lr_decay_step': [5], 'max_epochs': 7, 'net': 'res50', 'pretrained': True, 'dataset_source': 'voc_0712', 'dataset_target': 'clipart', 'val_datasets': ['clipart']}, options={"--name": 'NAME_OF_YOUR_EXPERIMENT'}) ``` ## Test Similar to training, modify **exp_eval.py** to evaluate a model. Edit 'val_datasets' for the dataset you want to test, it can be multiple datasets (it will return overall accuracy of all datasets on each class), 'model_pth' the path to the model: ``` CUDA_VISIBLE_DEVICES=$GPU_ID \ python experiments/exp_eval.py ``` ### Trained Model - **VGG16 Model** of **Cityscape** -> **FoggyCityscape** -> **RainCityscape**(Overall mAP = 39.1): [Link](https://drive.google.com/file/d/1jx5oiUYAPWCxMnQn6sBlWDaDr2EmbxII/view?usp=sharing) - **ResNet50 Model** of **Pascal** -> **Clipart** -> **Watercolor**(Overall mAP = 39.8): [Link](https://drive.google.com/file/d/1nDp1bEPaDB-I5nBBjGc3TfPd5etKxVaJ/view?usp=sharing) - **ResNet50 Model** of **Pascal** -> **Clipart** -> **Watercolor** -> **Comic**(Overall mAP = 38.1): [Link](https://drive.google.com/file/d/1R4FwIoD-mOJ8SS_awZfBgDlY6AZvN3ox/view?usp=sharing)