nnUNet_calvingfront_detection
/
nnunet
/experiment_planning
/alternative_experiment_planning
/target_spacing
/experiment_planner_baseline_3DUNet_v21_noResampling.py
# 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() |