72e0907d7784f62157906818686da273ab6aa7ed,niftynet/contrib/segmentation_bf_aug/segmentation_application_bfaug.py,SegmentationApplicationBFAug,initialise_dataset_loader,#SegmentationApplicationBFAug#Any#Any#Any#,58

Before Change

        self.segmentation_param = task_param

        // read each line of csv files into an instance of Subject
        if self.is_training:
            file_lists = []
            if self.action_param.validation_every_n > 0:
                file_lists.append(data_partitioner.train_files)
                file_lists.append(data_partitioner.validation_files)
            else:
                file_lists.append(data_partitioner.train_files)

            self.readers = []
            for file_list in file_lists:
                reader = ImageReader(SUPPORTED_INPUT)
                reader.initialise(data_param, task_param, file_list)
                self.readers.append(reader)

        else:  // in the inference process use image input only
            inference_reader = ImageReader(["image"])
            file_list = data_partitioner.inference_files
            inference_reader.initialise(data_param, task_param, file_list)
            self.readers = [inference_reader]

        foreground_masking_layer = None
        if self.net_param.normalise_foreground_only:
            foreground_masking_layer = BinaryMaskingLayer(
                type_str=self.net_param.foreground_type,

After Change

            reader_phase = self.action_param.dataset_to_infer
        except AttributeError:
            reader_phase = None
        file_lists = data_partitioner.get_file_lists_by(
            phase=reader_phase, action=self.action)
        self.readers = [
            ImageReader(reader_names).initialise(
                data_param, task_param, file_list) for file_list in file_lists]

        foreground_masking_layer = None
        if self.net_param.normalise_foreground_only:

Instances