Customized dataset

To make the code run with your new dataset, prepare these components:

Data: images and corresponding labels stored in <root_data>
File list: train.txt, val.txt, test.txt are files in which each line contains a pair of image path and label path: <image_path>\t<label_path>. The absolute path of image and label are <root_data>/<image_path> and <root_data>/<label_path> respectively.
Data loader: define class encodings in the label.

The first two components can be prepared on your own. To create a data loader, please follow these steps:

Create a new sub-class of BaseDataset in base.py. For example, we will create MyDataset in magnet/dataset/mydataset.py:

from .base import BaseDataset

class MyDataset(BaseDataset):
    def __init__(self, opt):
        super().__init__(opt)

        self.label2color = {
            <class_id>: <rgb_color>
            ...
        }
        self.ignore_label = <ignore_class_id>

Define custom information:

Inside the __init__ function, define the mappings between <class_id> and <rgb_color> to help transfer color label to segmentation map.

If your dataset contains ignore class that is not considered in the loss function, please specify it in <ignore_class_id>.

If you want to read the label as a grayscale image, please change the reading mode in __init__ function:

self.label_reading_mode = cv2.IMREAD_GRAYSCALE

and also overwrite the function image2class to process the label correctly:

def image2class(self, label):
    ```
    label: h x w x 1 (grayscale) or h x w x 3 (rgb)
    return: h x w (classmap)
    ```
    # Process here
    return label

Define dataset name in NAME2DATASET in magnet/dataset/__init__.py. That name will be used for training/ evaluation