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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
from nnunet.experiment_planning.common_utils import get_pool_and_conv_props
from nnunet.experiment_planning.experiment_planner_baseline_2DUNet import ExperimentPlanner2D
from nnunet.network_architecture.generic_UNet_MTLearly import Generic_UNet_MTLearly
from nnunet.paths import *
import numpy as np
class ExperimentPlanner2D_mtl(ExperimentPlanner2D):
def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
super(ExperimentPlanner2D_mtl, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
self.data_identifier = "nnUNetData_plans_mtl_2D"
self.plans_fname = join(self.preprocessed_output_folder,
"nnUNetPlans_mtl_plans_2D.pkl")
self.unet_base_num_features = 32
def get_properties_for_stage(self, current_spacing, original_spacing, original_shape, num_cases,
num_modalities, num_classes):
new_median_shape = np.round(original_spacing / current_spacing * original_shape).astype(int)
dataset_num_voxels = np.prod(new_median_shape, dtype=np.int64) * num_cases
input_patch_size = new_median_shape[1:]
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[1:], input_patch_size,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool)
# we pretend to use 30 feature maps. This will yield the same configuration as in V1. The larger memory
# footpring of 32 vs 30 is more than offset by the fp16 training. We make fp16 training default
# Reason for 32 vs 30 feature maps is that 32 is faster in fp16 training (because multiple of 8)
ref = Generic_UNet_MTLearly.use_this_for_batch_size_computation_2D * Generic_UNet_MTLearly.DEFAULT_BATCH_SIZE_2D / 2 # for batch size 2
here = Generic_UNet_MTLearly.compute_approx_vram_consumption(new_shp,
network_num_pool_per_axis,
30,
self.unet_max_num_filters,
num_modalities, num_classes,
pool_op_kernel_sizes,
conv_per_stage=self.conv_per_stage)
while here > ref:
axis_to_be_reduced = np.argsort(new_shp / new_median_shape[1:])[-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[1:], 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[1:], new_shp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool)
here = Generic_UNet_MTLearly.compute_approx_vram_consumption(new_shp, network_num_pool_per_axis,
self.unet_base_num_features,
self.unet_max_num_filters, num_modalities,
num_classes, pool_op_kernel_sizes,
conv_per_stage=self.conv_per_stage)
# print(new_shp)
batch_size = int(np.floor(ref / here) * 2)
input_patch_size = new_shp
if batch_size < self.unet_min_batch_size:
raise RuntimeError("This should not happen")
# check if batch size is too large (more than 5 % of dataset)
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)
batch_size = max(1, min(batch_size, max_batch_size))
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,
'pool_op_kernel_sizes': pool_op_kernel_sizes,
'conv_kernel_sizes': conv_kernel_sizes,
'do_dummy_2D_data_aug': False
}
return plan
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