dfdad808d0979d6e45419720fa0d73b4cedcbb96,niftynet/application/segmentation_application.py,SegmentationApplication,initialise_dataset_loader,#SegmentationApplication#Any#Any#,59

Before Change

        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_reader(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)
        if self.net_param.histogram_ref_file:
            histogram_normaliser = HistogramNormalisationLayer(
                image_name="image",
                modalities=vars(task_param).get("image"),
                model_filename=self.net_param.histogram_ref_file,
                binary_masking_func=foreground_masking_layer,
                norm_type=self.net_param.norm_type,
                cutoff=self.net_param.cutoff,
                name="hist_norm_layer")
        else:
            histogram_normaliser = None

        if self.net_param.histogram_ref_file:
            label_normaliser = DiscreteLabelNormalisationLayer(
                image_name="label",
                modalities=vars(task_param).get("label"),
                model_filename=self.net_param.histogram_ref_file)
        else:
            label_normaliser = None

        normalisation_layers = []
        if self.net_param.normalisation:
            normalisation_layers.append(histogram_normaliser)
        if self.net_param.whitening:
            normalisation_layers.append(mean_var_normaliser)
        if task_param.label_normalisation:
            normalisation_layers.append(label_normaliser)

        augmentation_layers = []
        if self.is_training:
            if self.action_param.random_flipping_axes != -1:
                augmentation_layers.append(RandomFlipLayer(
                    flip_axes=self.action_param.random_flipping_axes))
            if self.action_param.scaling_percentage:
                augmentation_layers.append(RandomSpatialScalingLayer(
                    min_percentage=self.action_param.scaling_percentage[0],
                    max_percentage=self.action_param.scaling_percentage[1]))
            if self.action_param.rotation_angle or \
                    self.action_param.rotation_angle_x or \
                    self.action_param.rotation_angle_y or \
                    self.action_param.rotation_angle_z:
                rotation_layer = RandomRotationLayer()
                if self.action_param.rotation_angle:
                    rotation_layer.init_uniform_angle(
                        self.action_param.rotation_angle)
                else:
                    rotation_layer.init_non_uniform_angle(
                        self.action_param.rotation_angle_x,
                        self.action_param.rotation_angle_y,
                        self.action_param.rotation_angle_z)
                augmentation_layers.append(rotation_layer)

        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(
            volume_padding_layer + normalisation_layers + augmentation_layers)

    def initialise_uniform_sampler(self):

After Change

        // read each line of csv files into an instance of Subject
        if self.is_training:
            self.readers = [ImageReader(SUPPORTED_INPUT, phase="train"),
                            ImageReader(SUPPORTED_INPUT, phase="validation")]
        else:  // in the inference process use image input only
            self.readers = [ImageReader(["image"], phase="test")]
        for reader in self.readers:
            reader.initialise_reader(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)
        if self.net_param.histogram_ref_file:
            histogram_normaliser = HistogramNormalisationLayer(
                image_name="image",
                modalities=vars(task_param).get("image"),
                model_filename=self.net_param.histogram_ref_file,
                binary_masking_func=foreground_masking_layer,
                norm_type=self.net_param.norm_type,
                cutoff=self.net_param.cutoff,
                name="hist_norm_layer")
        else:
            histogram_normaliser = None

        if self.net_param.histogram_ref_file:
            label_normaliser = DiscreteLabelNormalisationLayer(
                image_name="label",
                modalities=vars(task_param).get("label"),
                model_filename=self.net_param.histogram_ref_file)
        else:
            label_normaliser = None

        normalisation_layers = []
        if self.net_param.normalisation:
            normalisation_layers.append(histogram_normaliser)
        if self.net_param.whitening:
            normalisation_layers.append(mean_var_normaliser)
        if task_param.label_normalisation:
            normalisation_layers.append(label_normaliser)

        augmentation_layers = []
        if self.is_training:
            if self.action_param.random_flipping_axes != -1:
                augmentation_layers.append(RandomFlipLayer(
                    flip_axes=self.action_param.random_flipping_axes))
            if self.action_param.scaling_percentage:
                augmentation_layers.append(RandomSpatialScalingLayer(
                    min_percentage=self.action_param.scaling_percentage[0],
                    max_percentage=self.action_param.scaling_percentage[1]))
            if self.action_param.rotation_angle or \
                    self.action_param.rotation_angle_x or \
                    self.action_param.rotation_angle_y or \
                    self.action_param.rotation_angle_z:
                rotation_layer = RandomRotationLayer()
                if self.action_param.rotation_angle:
                    rotation_layer.init_uniform_angle(
                        self.action_param.rotation_angle)
                else:
                    rotation_layer.init_non_uniform_angle(
                        self.action_param.rotation_angle_x,
                        self.action_param.rotation_angle_y,
                        self.action_param.rotation_angle_z)
                augmentation_layers.append(rotation_layer)

        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(
            volume_padding_layer + normalisation_layers + augmentation_layers)

    def initialise_uniform_sampler(self):
        self.sampler = [[UniformSampler(
            reader=reader,
            data_param=self.data_param,

Instances