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99ec8a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | from utils.omega_parser import EPUCNNParams
from utils.omega_parser import DataParams, DataPreprocessing
def model_cfg_to_epucnn(model_cfg: EPUCNNParams):
return {
"num_classes": model_cfg.num_classes,
"subnetwork_name": model_cfg.subnetwork_config.architecture,
"num_subnetworks": model_cfg.num_subnetworks,
"epu_activation": model_cfg.epu_activation,
"subnet_activation": model_cfg.subnetwork_config.pred_activation,
"subnet_cfg": {
"input_channels": model_cfg.subnetwork_config.input_channels,
"base_channels": model_cfg.subnetwork_config.base_channels,
"fc_units": model_cfg.subnetwork_config.fc_hidden_units,
}
}
def data_cfg_to_dataparser(dataset_path: str,
images_extension: str,
data_mode: str,
preprocessing_cfg: DataPreprocessing,
group_by: str = None,):
group_by_key = None
group_by_value = None
if group_by is not None:
if preprocessing_cfg.data_parser in ["filename", "folder"]:
group_by_key = group_by # eg "normal"
elif preprocessing_cfg.data_parser in ["medmnist"]:
group_by_value = preprocessing_cfg.label_mapping.get(group_by) # eg "0" from item ("normal": "0")
return {
"dataset_folder": dataset_path,
"mode": data_mode,
"image_ext": images_extension,
"label_mapping": preprocessing_cfg.label_mapping,
"csv_file": preprocessing_cfg.medmnist_csv_file,
"group_by_key": group_by_key,
"group_by_value": group_by_value
}
# def data_cfg_to_dataparser(data_cfg: DataParams, data_mode: str = 'train'):
# return {
# "dataset_folder": data_cfg.dataset_path,
# "mode": data_mode,
# "image_ext": data_cfg.images_extension,
# "kwargs": {
# "label_mapping": data_cfg.data_preprocessing.label_mapping,
# "csv_file": data_cfg.data_preprocessing.medmnist_csv_file
# }
# }
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