Overall structure of the DFCPS:
Model renderings:
- Create a conda environment:
$ conda env create -f semiseg.yaml
$ conda activate semiseg
- Install apex 0.1 (requires CUDA)
$ cd . /furnace/apex
$ python setup.py install --cpp_ext --cuda_ext
The implementation of our model code references TorchSemiSeg, which you can also refer to for further details by checking out this model.
-
Download the data. Support the import of medical image datasets, including CT scans, MRI images, etc., this time using the Kvasir-SEG dataset for segmentation, if necessary, you can replace the dataset.
-
Download the pre-trained ResNet models from OneDrive link.
DATA/
|-- Kvasir
|-- pytorch-weight
| |-- resnet50_v1c.pth
| |-- resnet101_v1c.pth
$ cd . /exp.voc/voc8.res50v3+.CPS+DFCPS
$ bash script.sh
- The tensorboard file is saved in
log/tb/directory. - In
script.sh, you need to specify some variables, such as the path to your data dir, the path to your snapshot dir that stores checkpoints, etc.
To try other data partitions beside 1/8, you just need to change two variables in config.py:
C.labeled_ratio = 8
C.nepochs = 34
Please note that, for fair comparison, we control the total iterations during training in each experiment similar (almost the same), including the supervised baseline and semi-supervised methods. Therefore, the nepochs for different partitions are different.
Yifei Chen