b752af4b1c6f2b76173a840741e5bc61f7ab7c9b,niftynet/application/segmentation_application.py,SegmentationApplication,initialise_dataset_loader,#SegmentationApplication#Any#Any#,89

Before Change

        augmentation_layers = []
        if self.is_training:
            rand_flip_layer = RandomFlipLayer(
                flip_axes=self.action_param.flip_axes)
            rand_scaling_layer = RandomSpatialScalingLayer(
                min_percentage=self.action_param.scaling_percentage[0],
                max_percentage=self.action_param.scaling_percentage[1])
            rand_rotate_layer = RandomRotationLayer(
                min_angle=self.action_param.rotation_angle[0],
                max_angle=self.action_param.rotation_angle[1])

            if self.action_param.random_flip:
                augmentation_layers.append(rand_flip_layer)
            if self.action_param.spatial_scaling:
                augmentation_layers.append(rand_scaling_layer)
            if self.action_param.rotation:
                augmentation_layers.append(rand_rotate_layer)

        self.reader.add_preprocessing_layers(
            normalisation_layers + augmentation_layers)

    def initialise_sampler(self, is_training):

After Change

        augmentation_layers = []
        if self.is_training:
            if self.action_param.random_flipping_axes > 0:
                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:
                augmentation_layers.append(RandomRotationLayer(
                    min_angle=self.action_param.rotation_angle[0],
                    max_angle=self.action_param.rotation_angle[1]))

        volume_padding_layer = []
        if self.net_param.volume_padding_size:
            volume_padding_layer.append(PadLayer(
                field=SUPPORTED_INPUT,

Instances