bd333dd43d69b26015eb3f201afe1772ba701a41,niftynet/contrib/dataset_sampler/sampler_uniform_v2.py,UniformSampler,layer_op,#UniformSampler#Any#,83

Before Change

        
        while True:
            image_id, data, _ = self.reader(idx=idx, shuffle=True)
            if not data:
                break
            image_shapes = dict((name, data[name].shape)
                                for name in self.window.names)
            static_window_shapes = self.window.match_image_shapes(image_shapes)

            // find random coordinates based on window and image shapes
            coordinates = self._spatial_coordinates_generator(
                subject_id=image_id,
                data=data,
                img_sizes=image_shapes,
                win_sizes=static_window_shapes,
                n_samples=self.window.n_samples)

            for window_id in range(self.window.n_samples):
                output_dict = {}
                for  name in list(data):
                    image_data_key = name
                    coordinates_key = LOCATION_FORMAT.format(name)

                    coord = coordinates[name][window_id]
                    x_start, y_start, z_start, x_end, y_end, z_end = coord[1:]
                    try:
                        image_window = data[name][
                            x_start:x_end, y_start:y_end, z_start:z_end, ...]
                        output_dict[image_data_key] = image_window
                        output_dict[coordinates_key] = coord
                    except ValueError:
                        tf.logging.fatal(
                            "dimensionality miss match in input volumes, "
                            "please specify spatial_window_size with a "
                            "3D tuple and make sure each element is "
                            "smaller than the image length in each dim. "
                            "Current coords %s", coord)
                        raise
                yield output_dict

    def _spatial_coordinates_generator(self,
                                       subject_id,
                                       data,

After Change

            output_dict[coordinates_key] = location_array

            // fill output window array
            image_array = []
            for window_id in range(self.window.n_samples):
                x_start, y_start, z_start, x_end, y_end, z_end = \
                    location_array[window_id, 1:]
                try:
                    image_window = data[name][
                        x_start:x_end, y_start:y_end, z_start:z_end, ...]
                    image_array.append(image_window[np.newaxis, ...])
                except ValueError:
                    tf.logging.fatal(
                        "dimensionality miss match in input volumes, "
                        "please specify spatial_window_size with a "
                        "3D tuple and make sure each element is "
                        "smaller than the image length in each dim. "
                        "Current coords %s", location_array[window_id])
                    raise
            if len(image_array) > 1:
                output_dict[image_data_key] = \
                    np.concatenate(image_array, axis=0).astype(np.float32)
            else:
                output_dict[image_data_key] = image_array[0].astype(np.float32)
        // the output image shape should be
        // [enqueue_batch_size, x, y, z, time, modality]
        // where enqueue_batch_size = windows_per_image
        return output_dict

    def _init_dataset(self):
        

Instances