01f1bcb376dfd967603c785a255f927dea2712b6,demos/BRATS17/brats_segmentation.py,BRATSApp,initialise_dataset_loader,#BRATSApp#,44

Before Change

                        self.initialise_grid_aggregator)
        }

    def initialise_dataset_loader(self, data_param=None, task_param=None):
        self.data_param = data_param
        self.segmentation_param = task_param

        // read each line of csv files into an instance of Subject
        if self.is_training:
            self.reader = ImageReader(SUPPORTED_INPUT)
        else:  // in the inference process use image input only
            self.reader = ImageReader(["image"])
        self.reader.initialise(data_param, task_param)

        if self.net_param.normalise_foreground_only:
            foreground_masking_layer = BinaryMaskingLayer(
                type_str=self.net_param.foreground_type,
                multimod_fusion=self.net_param.multimod_foreground_type,
                threshold=0.0)
        else:
            foreground_masking_layer = None
        mean_var_normaliser = MeanVarNormalisationLayer(
            image_name="image", binary_masking_func=foreground_masking_layer)

        label_normaliser = DiscreteLabelNormalisationLayer(
            image_name="label",
            modalities=vars(task_param).get("label"),
            model_filename=self.net_param.histogram_ref_file)

        normalisation_layers = []
        normalisation_layers.append(mean_var_normaliser)
        if task_param.label_normalisation:
            normalisation_layers.append(label_normaliser)

        volume_padding_layer = []
        if self.net_param.volume_padding_size:
            volume_padding_layer.append(PadLayer(
                image_name=SUPPORTED_INPUT,
                border=self.net_param.volume_padding_size))
        self.reader.add_preprocessing_layers(
            normalisation_layers + volume_padding_layer)

    def initialise_uniform_sampler(self):
        self.sampler = [UniformSampler(

After Change

        self.segmentation_param = None

    def initialise_dataset_loader(
            self, data_param=None, task_param=None, data_partitioner=None):
        self.data_param = data_param
        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.all_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,
                multimod_fusion=self.net_param.multimod_foreground_type,
                threshold=0.0)

        mean_var_normaliser = MeanVarNormalisationLayer(
            image_name="image", binary_masking_func=foreground_masking_layer)

        label_normaliser = DiscreteLabelNormalisationLayer(
            image_name="label",
            modalities=vars(task_param).get("label"),
            model_filename=self.net_param.histogram_ref_file)

        normalisation_layers = []
        normalisation_layers.append(mean_var_normaliser)
        if task_param.label_normalisation:
            normalisation_layers.append(label_normaliser)

        volume_padding_layer = []
        if self.net_param.volume_padding_size:
            volume_padding_layer.append(PadLayer(
                image_name=SUPPORTED_INPUT,
                border=self.net_param.volume_padding_size))
        for reader in self.readers:
            reader.add_preprocessing_layers(
                normalisation_layers + volume_padding_layer)

    def initialise_sampler(self):
        if self.is_training:

Instances