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- # 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.
-
-
- from copy import deepcopy
-
- import numpy as np
- from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \
- ExperimentPlanner3D_v21
- from nnunet.experiment_planning.common_utils import get_pool_and_conv_props
- from nnunet.paths import *
- from nnunet.network_architecture.generic_modular_residual_UNet import FabiansUNet
-
-
- class ExperimentPlanner3DFabiansResUNet_v21(ExperimentPlanner3D_v21):
- def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
- super(ExperimentPlanner3DFabiansResUNet_v21, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
- self.data_identifier = "nnUNetData_plans_v2.1"# "nnUNetData_FabiansResUNet_v2.1"
- self.plans_fname = join(self.preprocessed_output_folder,
- "nnUNetPlans_FabiansResUNet_v2.1_plans_3D.pkl")
-
- def get_properties_for_stage(self, current_spacing, original_spacing, original_shape, num_cases,
- num_modalities, num_classes):
- """
- We use FabiansUNet instead of Generic_UNet
- """
- new_median_shape = np.round(original_spacing / current_spacing * original_shape).astype(int)
- dataset_num_voxels = np.prod(new_median_shape) * num_cases
-
- # the next line is what we had before as a default. The patch size had the same aspect ratio as the median shape of a patient. We swapped t
- # input_patch_size = new_median_shape
-
- # compute how many voxels are one mm
- input_patch_size = 1 / np.array(current_spacing)
-
- # normalize voxels per mm
- input_patch_size /= input_patch_size.mean()
-
- # create an isotropic patch of size 512x512x512mm
- input_patch_size *= 1 / min(input_patch_size) * 512 # to get a starting value
- input_patch_size = np.round(input_patch_size).astype(int)
-
- # clip it to the median shape of the dataset because patches larger then that make not much sense
- input_patch_size = [min(i, j) for i, j in zip(input_patch_size, new_median_shape)]
-
- network_num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, new_shp, \
- shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing, input_patch_size,
- self.unet_featuremap_min_edge_length,
- self.unet_max_numpool)
- pool_op_kernel_sizes = [[1, 1, 1]] + pool_op_kernel_sizes
- blocks_per_stage_encoder = FabiansUNet.default_blocks_per_stage_encoder[:len(pool_op_kernel_sizes)]
- blocks_per_stage_decoder = FabiansUNet.default_blocks_per_stage_decoder[:len(pool_op_kernel_sizes) - 1]
-
- ref = FabiansUNet.use_this_for_3D_configuration
- here = FabiansUNet.compute_approx_vram_consumption(input_patch_size, self.unet_base_num_features,
- self.unet_max_num_filters, num_modalities, num_classes,
- pool_op_kernel_sizes, blocks_per_stage_encoder,
- blocks_per_stage_decoder, 2, self.unet_min_batch_size,)
- while here > ref:
- axis_to_be_reduced = np.argsort(new_shp / new_median_shape)[-1]
-
- tmp = deepcopy(new_shp)
- tmp[axis_to_be_reduced] -= shape_must_be_divisible_by[axis_to_be_reduced]
- _, _, _, _, shape_must_be_divisible_by_new = \
- get_pool_and_conv_props(current_spacing, tmp,
- self.unet_featuremap_min_edge_length,
- self.unet_max_numpool,
- )
- new_shp[axis_to_be_reduced] -= shape_must_be_divisible_by_new[axis_to_be_reduced]
-
- # we have to recompute numpool now:
- network_num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, new_shp, \
- shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing, new_shp,
- self.unet_featuremap_min_edge_length,
- self.unet_max_numpool,
- )
- pool_op_kernel_sizes = [[1, 1, 1]] + pool_op_kernel_sizes
- blocks_per_stage_encoder = FabiansUNet.default_blocks_per_stage_encoder[:len(pool_op_kernel_sizes)]
- blocks_per_stage_decoder = FabiansUNet.default_blocks_per_stage_decoder[:len(pool_op_kernel_sizes) - 1]
- here = FabiansUNet.compute_approx_vram_consumption(new_shp, self.unet_base_num_features,
- self.unet_max_num_filters, num_modalities, num_classes,
- pool_op_kernel_sizes, blocks_per_stage_encoder,
- blocks_per_stage_decoder, 2, self.unet_min_batch_size)
- input_patch_size = new_shp
-
- batch_size = FabiansUNet.default_min_batch_size
- batch_size = int(np.floor(max(ref / here, 1) * batch_size))
-
- # check if batch size is too large
- max_batch_size = np.round(self.batch_size_covers_max_percent_of_dataset * dataset_num_voxels /
- np.prod(input_patch_size, dtype=np.int64)).astype(int)
- max_batch_size = max(max_batch_size, self.unet_min_batch_size)
- batch_size = max(1, min(batch_size, max_batch_size))
-
- do_dummy_2D_data_aug = (max(input_patch_size) / input_patch_size[
- 0]) > self.anisotropy_threshold
-
- plan = {
- 'batch_size': batch_size,
- 'num_pool_per_axis': network_num_pool_per_axis,
- 'patch_size': input_patch_size,
- 'median_patient_size_in_voxels': new_median_shape,
- 'current_spacing': current_spacing,
- 'original_spacing': original_spacing,
- 'do_dummy_2D_data_aug': do_dummy_2D_data_aug,
- 'pool_op_kernel_sizes': pool_op_kernel_sizes,
- 'conv_kernel_sizes': conv_kernel_sizes,
- 'num_blocks_encoder': blocks_per_stage_encoder,
- 'num_blocks_decoder': blocks_per_stage_decoder
- }
- return plan
-
- def run_preprocessing(self, num_threads):
- """
- On all datasets except 3d fullres on spleen the preprocessed data would look identical to
- ExperimentPlanner3D_v21 (I tested decathlon data only). Therefore we just reuse the preprocessed data of
- that other planner
- :param num_threads:
- :return:
- """
- pass
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