# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from nnunet.experiment_planning.alternative_experiment_planning.experiment_planner_baseline_3DUNet_v21_16GB import \ ExperimentPlanner3D_v21_16GB from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \ ExperimentPlanner3D_v21 from nnunet.paths import * class ExperimentPlanner3D_v21_noResampling(ExperimentPlanner3D_v21): def __init__(self, folder_with_cropped_data, preprocessed_output_folder): super(ExperimentPlanner3D_v21_noResampling, self).__init__(folder_with_cropped_data, preprocessed_output_folder) self.data_identifier = "nnUNetData_noRes_plans_v2.1" self.plans_fname = join(self.preprocessed_output_folder, "nnUNetPlansv2.1_noRes_plans_3D.pkl") self.preprocessor_name = "PreprocessorFor3D_NoResampling" def plan_experiment(self): """ DIFFERENCE TO ExperimentPlanner3D_v21: no 3d lowres :return: """ use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm() print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization) spacings = self.dataset_properties['all_spacings'] sizes = self.dataset_properties['all_sizes'] all_classes = self.dataset_properties['all_classes'] modalities = self.dataset_properties['modalities'] num_modalities = len(list(modalities.keys())) target_spacing = self.get_target_spacing() new_shapes = [np.array(i) / target_spacing * np.array(j) for i, j in zip(spacings, sizes)] max_spacing_axis = np.argmax(target_spacing) remaining_axes = [i for i in list(range(3)) if i != max_spacing_axis] self.transpose_forward = [max_spacing_axis] + remaining_axes self.transpose_backward = [np.argwhere(np.array(self.transpose_forward) == i)[0][0] for i in range(3)] # we base our calculations on the median shape of the datasets median_shape = np.median(np.vstack(new_shapes), 0) print("the median shape of the dataset is ", median_shape) max_shape = np.max(np.vstack(new_shapes), 0) print("the max shape in the dataset is ", max_shape) min_shape = np.min(np.vstack(new_shapes), 0) print("the min shape in the dataset is ", min_shape) print("we don't want feature maps smaller than ", self.unet_featuremap_min_edge_length, " in the bottleneck") # how many stages will the image pyramid have? self.plans_per_stage = list() target_spacing_transposed = np.array(target_spacing)[self.transpose_forward] median_shape_transposed = np.array(median_shape)[self.transpose_forward] print("the transposed median shape of the dataset is ", median_shape_transposed) print("generating configuration for 3d_fullres") self.plans_per_stage.append(self.get_properties_for_stage(target_spacing_transposed, target_spacing_transposed, median_shape_transposed, len(self.list_of_cropped_npz_files), num_modalities, len(all_classes) + 1)) # thanks Zakiyi (https://github.com/MIC-DKFZ/nnUNet/issues/61) for spotting this bug :-) # if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \ # architecture_input_voxels < HOW_MUCH_OF_A_PATIENT_MUST_THE_NETWORK_SEE_AT_STAGE0: architecture_input_voxels_here = np.prod(self.plans_per_stage[-1]['patch_size'], dtype=np.int64) if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \ architecture_input_voxels_here < self.how_much_of_a_patient_must_the_network_see_at_stage0: more = False else: more = True if more: pass self.plans_per_stage = self.plans_per_stage[::-1] self.plans_per_stage = {i: self.plans_per_stage[i] for i in range(len(self.plans_per_stage))} # convert to dict print(self.plans_per_stage) print("transpose forward", self.transpose_forward) print("transpose backward", self.transpose_backward) normalization_schemes = self.determine_normalization_scheme() only_keep_largest_connected_component, min_size_per_class, min_region_size_per_class = None, None, None # removed training data based postprocessing. This is deprecated # these are independent of the stage plans = {'num_stages': len(list(self.plans_per_stage.keys())), 'num_modalities': num_modalities, 'modalities': modalities, 'normalization_schemes': normalization_schemes, 'dataset_properties': self.dataset_properties, 'list_of_npz_files': self.list_of_cropped_npz_files, 'original_spacings': spacings, 'original_sizes': sizes, 'preprocessed_data_folder': self.preprocessed_output_folder, 'num_classes': len(all_classes), 'all_classes': all_classes, 'base_num_features': self.unet_base_num_features, 'use_mask_for_norm': use_nonzero_mask_for_normalization, 'keep_only_largest_region': only_keep_largest_connected_component, 'min_region_size_per_class': min_region_size_per_class, 'min_size_per_class': min_size_per_class, 'transpose_forward': self.transpose_forward, 'transpose_backward': self.transpose_backward, 'data_identifier': self.data_identifier, 'plans_per_stage': self.plans_per_stage, 'preprocessor_name': self.preprocessor_name, 'conv_per_stage': self.conv_per_stage, } self.plans = plans self.save_my_plans() class ExperimentPlanner3D_v21_noResampling_16GB(ExperimentPlanner3D_v21_16GB): def __init__(self, folder_with_cropped_data, preprocessed_output_folder): super(ExperimentPlanner3D_v21_noResampling_16GB, self).__init__(folder_with_cropped_data, preprocessed_output_folder) self.data_identifier = "nnUNetData_noRes_plans_16GB_v2.1" self.plans_fname = join(self.preprocessed_output_folder, "nnUNetPlansv2.1_noRes_16GB_plans_3D.pkl") self.preprocessor_name = "PreprocessorFor3D_NoResampling" def plan_experiment(self): """ DIFFERENCE TO ExperimentPlanner3D_v21: no 3d lowres :return: """ use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm() print("Are we using the nonzero mask for normalizaion?", use_nonzero_mask_for_normalization) spacings = self.dataset_properties['all_spacings'] sizes = self.dataset_properties['all_sizes'] all_classes = self.dataset_properties['all_classes'] modalities = self.dataset_properties['modalities'] num_modalities = len(list(modalities.keys())) target_spacing = self.get_target_spacing() new_shapes = [np.array(i) / target_spacing * np.array(j) for i, j in zip(spacings, sizes)] max_spacing_axis = np.argmax(target_spacing) remaining_axes = [i for i in list(range(3)) if i != max_spacing_axis] self.transpose_forward = [max_spacing_axis] + remaining_axes self.transpose_backward = [np.argwhere(np.array(self.transpose_forward) == i)[0][0] for i in range(3)] # we base our calculations on the median shape of the datasets median_shape = np.median(np.vstack(new_shapes), 0) print("the median shape of the dataset is ", median_shape) max_shape = np.max(np.vstack(new_shapes), 0) print("the max shape in the dataset is ", max_shape) min_shape = np.min(np.vstack(new_shapes), 0) print("the min shape in the dataset is ", min_shape) print("we don't want feature maps smaller than ", self.unet_featuremap_min_edge_length, " in the bottleneck") # how many stages will the image pyramid have? self.plans_per_stage = list() target_spacing_transposed = np.array(target_spacing)[self.transpose_forward] median_shape_transposed = np.array(median_shape)[self.transpose_forward] print("the transposed median shape of the dataset is ", median_shape_transposed) print("generating configuration for 3d_fullres") self.plans_per_stage.append(self.get_properties_for_stage(target_spacing_transposed, target_spacing_transposed, median_shape_transposed, len(self.list_of_cropped_npz_files), num_modalities, len(all_classes) + 1)) # thanks Zakiyi (https://github.com/MIC-DKFZ/nnUNet/issues/61) for spotting this bug :-) # if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \ # architecture_input_voxels < HOW_MUCH_OF_A_PATIENT_MUST_THE_NETWORK_SEE_AT_STAGE0: architecture_input_voxels_here = np.prod(self.plans_per_stage[-1]['patch_size'], dtype=np.int64) if np.prod(self.plans_per_stage[-1]['median_patient_size_in_voxels'], dtype=np.int64) / \ architecture_input_voxels_here < self.how_much_of_a_patient_must_the_network_see_at_stage0: more = False else: more = True if more: pass self.plans_per_stage = self.plans_per_stage[::-1] self.plans_per_stage = {i: self.plans_per_stage[i] for i in range(len(self.plans_per_stage))} # convert to dict print(self.plans_per_stage) print("transpose forward", self.transpose_forward) print("transpose backward", self.transpose_backward) normalization_schemes = self.determine_normalization_scheme() only_keep_largest_connected_component, min_size_per_class, min_region_size_per_class = None, None, None # removed training data based postprocessing. This is deprecated # these are independent of the stage plans = {'num_stages': len(list(self.plans_per_stage.keys())), 'num_modalities': num_modalities, 'modalities': modalities, 'normalization_schemes': normalization_schemes, 'dataset_properties': self.dataset_properties, 'list_of_npz_files': self.list_of_cropped_npz_files, 'original_spacings': spacings, 'original_sizes': sizes, 'preprocessed_data_folder': self.preprocessed_output_folder, 'num_classes': len(all_classes), 'all_classes': all_classes, 'base_num_features': self.unet_base_num_features, 'use_mask_for_norm': use_nonzero_mask_for_normalization, 'keep_only_largest_region': only_keep_largest_connected_component, 'min_region_size_per_class': min_region_size_per_class, 'min_size_per_class': min_size_per_class, 'transpose_forward': self.transpose_forward, 'transpose_backward': self.transpose_backward, 'data_identifier': self.data_identifier, 'plans_per_stage': self.plans_per_stage, 'preprocessor_name': self.preprocessor_name, 'conv_per_stage': self.conv_per_stage, } self.plans = plans self.save_my_plans()