class nnUNetTrainerV2_3ConvPerStage(nnUNetTrainerV2):
def initialize_network(self):
self.base_num_features = 24 # otherwise we run out of VRAM
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), 3, 2, conv_op, norm_op,
norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
# self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
# self.print_to_log_file(self.net_conv_kernel_sizes)
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
net_numpool, self.conv_per_stage, 2, conv_op, norm_op,
norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
self.network.inference_apply_nonlin = softmax_helper
if torch.cuda.is_available():
self.network.cuda()
class nnUNetTrainerV2BraTSRegions_BN(nnUNetTrainerV2):
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.BatchNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.BatchNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes),
self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2),
self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = torch.nn.Softmax(1)
def initialize_network(self):
"""
changed deep supervision to False
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = FRN3D
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
raise NotImplementedError
norm_op = nn.BatchNorm2d
norm_op_kwargs = {'eps': 1e-6}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = Identity
net_nonlin_kwargs = {}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
class nnUNetTrainerV2_Mish(nnUNetTrainerV2):
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = Mish
net_nonlin_kwargs = {}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(0), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
"""
changed deep supervision to False
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network_optimizer_and_scheduler(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
#self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
#self.print_to_log_file(self.net_conv_kernel_sizes)
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, net_numpool,
2, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x, InitWeights_He(1e-2),
self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, amsgrad=True)
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2, patience=self.lr_scheduler_patience,
verbose=True, threshold=self.lr_scheduler_eps, threshold_mode="abs")
#if torch.cuda.is_available(): #edit PW
# self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
"""
- momentum 0.99
- SGD instead of Adam
- self.lr_scheduler = None because we do poly_lr
- deep supervision = True
- i am sure I forgot something here
Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
class nnUNetTrainerV2_NoNormalization(nnUNetTrainerV2):
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = Identity
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = Identity
norm_op_kwargs = {}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network_optimizer_and_scheduler(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
# self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
# self.print_to_log_file(self.net_conv_kernel_sizes)
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
net_numpool, 2, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
self.optimizer = torch.optim.Adam(self.network.parameters(),
self.initial_lr,
weight_decay=self.weight_decay,
amsgrad=True)
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(
self.optimizer,
mode='min',
factor=0.2,
patience=self.lr_scheduler_patience,
verbose=True,
threshold=self.lr_scheduler_eps,
threshold_mode="abs")
self.network.cuda()
if torch.cuda.device_count() > 1:
torch.distributed.init_process_group(
backend='nccl',
init_method='tcp://localhost:23456',
rank=0,
world_size=1)
self.network = torch.nn.parallel.DistributedDataParallel(
self.network)
# self.network, self.optimizer = amp.initialize(self.network, self.optimizer, opt_level="O1")
# self.network = DistributedDataParallel(self.network)
print("Let's use", torch.cuda.device_count(), "GPUs!")
self.network.inference_apply_nonlin = softmax_helper
class nnUNetTrainerV2_MMS(nnUNetTrainerV2_insaneDA):
def setup_DA_params(self):
super().setup_DA_params()
self.data_aug_params["p_rot"] = 0.7
self.data_aug_params["p_eldef"] = 0.1
self.data_aug_params["p_scale"] = 0.3
self.data_aug_params["independent_scale_factor_for_each_axis"] = True
self.data_aug_params["p_independent_scale_per_axis"] = 0.3
self.data_aug_params["do_additive_brightness"] = True
self.data_aug_params["additive_brightness_mu"] = 0
self.data_aug_params["additive_brightness_sigma"] = 0.2
self.data_aug_params["additive_brightness_p_per_sample"] = 0.3
self.data_aug_params["additive_brightness_p_per_channel"] = 1
self.data_aug_params["elastic_deform_alpha"] = (0., 300.)
self.data_aug_params["elastic_deform_sigma"] = (9., 15.)
self.data_aug_params['gamma_range'] = (0.5, 1.6)
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.BatchNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.BatchNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
"""def run_training(self):
def initialize_network_optimizer_and_scheduler(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
#self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
#self.print_to_log_file(self.net_conv_kernel_sizes)
print('Has entered into the initialize_network_optimizer_and_scheduler！')
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
torch.cuda.set_device(0)
self.do_supervision=False
self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, net_numpool,
2, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, self.do_supervision, False, lambda x: x, InitWeights_He(1e-2),
self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
# print('self.network is:',self.network)
# Here!!! Modify it to be parallel model
# self.network=nn.DataParallel(self.network,device_ids=[0,1,2,3])
g=tw.draw_model(self.network,[8,1,128,128,128])
g.save('/cache/WenshuaiZhao/ProjectFiles/NNUnet/nnunet/model_structure.png')
print('draw model has been done!')
self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, amsgrad=True)
# Here!!! optimizer is also need to be parallel
# self.optimizer=nn.DataParallel(self.optimizer,device_ids=[0,1,2,3])
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2, patience=self.lr_scheduler_patience,
verbose=True, threshold=self.lr_scheduler_eps, threshold_mode="abs")
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
class nnUNetTrainerV2_Mish(nnUNetTrainerV2):
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = Mish
net_nonlin_kwargs = {}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(0), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def _maybe_init_amp(self):
"""
In O1 mish will result in super super high memory usage. I believe that may be because amp decides to be save
and use fp32 for all activation functions. By using O2 we reduce memory comsumption by a lot
:return:
"""
# we use fp16 for training only, not inference
if self.fp16:
if not self.amp_initialized:
if amp is not None:
self.network, self.optimizer = amp.initialize(
self.network, self.optimizer, opt_level="O2")
self.amp_initialized = True
else:
self.print_to_log_file(
"WARNING: FP16 training was requested but nvidia apex is not installed. "
"Install it from https://github.com/NVIDIA/apex")
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_numpool, net_pool_kernel_sizes, net_conv_kernel_sizes, input_patch_size, \
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 mor ethan 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)
estimated_gpu_ram_consumption = Generic_UNet.compute_approx_vram_consumption(
input_patch_size,
network_numpool,
30,
self.unet_max_num_filters,
num_modalities,
num_classes,
net_pool_kernel_sizes,
conv_per_stage=self.conv_per_stage)
batch_size = int(
np.floor(Generic_UNet.use_this_for_batch_size_computation_2D /
estimated_gpu_ram_consumption *
Generic_UNet.DEFAULT_BATCH_SIZE_2D))
if batch_size < self.unet_min_batch_size:
raise RuntimeError(
"This framework is not made to process patches this large. We will add patch-based "
"2D networks later. Sorry for the inconvenience")
# 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 = min(batch_size, max_batch_size)
plan = {
'batch_size': batch_size,
'num_pool_per_axis': network_numpool,
'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': net_pool_kernel_sizes,
'conv_kernel_sizes': net_conv_kernel_sizes,
'do_dummy_2D_data_aug': False
}
return plan
class nnUNetTrainerV2_lReLU_biasInSegOutput(nnUNetTrainerV2):
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(self.num_input_channels,
self.base_num_features,
self.num_classes,
len(self.net_num_pool_op_kernel_sizes),
self.conv_per_stage,
2,
conv_op,
norm_op,
norm_op_kwargs,
dropout_op,
dropout_op_kwargs,
net_nonlin,
net_nonlin_kwargs,
True,
False,
lambda x: x,
InitWeights_He(0),
self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes,
False,
True,
True,
seg_output_use_bias=True)
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.ReLU
net_nonlin_kwargs = {'inplace': True}
self.network = Generic_UNet(self.num_input_channels,
self.base_num_features,
self.num_classes,
len(self.net_num_pool_op_kernel_sizes),
self.conv_per_stage,
2,
conv_op,
norm_op,
norm_op_kwargs,
dropout_op,
dropout_op_kwargs,
net_nonlin,
net_nonlin_kwargs,
True,
False,
lambda x: x,
InitWeights_He(0),
self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes,
False,
True,
True,
basic_block=ConvDropoutNonlinNorm)
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def get_properties_for_stage(current_spacing, original_spacing,
original_shape, num_cases, num_modalities,
num_classes, transpose_forward):
spacing_transposed = current_spacing[transpose_forward]
new_median_shape = np.round(original_spacing / current_spacing *
original_shape).astype(int)
new_median_shape_transposed = new_median_shape[transpose_forward]
dataset_num_voxels = np.prod(new_median_shape) * num_cases
input_patch_size = new_median_shape_transposed[1:]
network_numpool, net_pool_kernel_sizes, net_conv_kernel_sizes, input_patch_size, \
shape_must_be_divisible_by = get_pool_and_conv_props(spacing_transposed[1:], input_patch_size,
FEATUREMAP_MIN_EDGE_LENGTH_BOTTLENECK,
Generic_UNet.MAX_NUMPOOL_2D)
estimated_gpu_ram_consumption = Generic_UNet.compute_approx_vram_consumption(
input_patch_size, network_numpool,
Generic_UNet.BASE_NUM_FEATURES_2D, Generic_UNet.MAX_FILTERS_2D,
num_modalities, num_classes, net_pool_kernel_sizes)
batch_size = int(
np.floor(Generic_UNet.use_this_for_batch_size_computation_2D /
estimated_gpu_ram_consumption *
Generic_UNet.DEFAULT_BATCH_SIZE_2D))
if batch_size < dataset_min_batch_size_cap:
raise RuntimeError(
"This framework is not made to process patches this large. We will add patch-based "
"2D networks later. Sorry for the inconvenience")
# check if batch size is too large (more than 5 % of dataset)
max_batch_size = np.round(
batch_size_covers_max_percent_of_dataset * dataset_num_voxels /
np.prod(input_patch_size)).astype(int)
batch_size = min(batch_size, max_batch_size)
plan = {
'batch_size': batch_size,
'num_pool_per_axis': network_numpool,
'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': net_pool_kernel_sizes,
'conv_kernel_sizes': net_conv_kernel_sizes,
'do_dummy_2D_data_aug': False
}
return plan
class nnUNetTrainer(NetworkTrainer):
def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
unpack_data=True, deterministic=True, fp16=False):
"""
:param deterministic:
:param fold: can be either [0 ... 5) for cross-validation, 'all' to train on all available training data or
None if you wish to load some checkpoint and do inference only
:param plans_file: the pkl file generated by preprocessing. This file will determine all design choices
:param subfolder_with_preprocessed_data: must be a subfolder of dataset_directory (just the name of the folder,
not the entire path). This is where the preprocessed data lies that will be used for network training. We made
this explicitly available so that differently preprocessed data can coexist and the user can choose what to use.
Can be None if you are doing inference only.
:param output_folder: where to store parameters, plot progress and to the validation
:param dataset_directory: the parent directory in which the preprocessed Task data is stored. This is required
because the split information is stored in this directory. For running prediction only this input is not
required and may be set to None
:param batch_dice: compute dice loss for each sample and average over all samples in the batch or pretend the
batch is a pseudo volume?
:param stage: The plans file may contain several stages (used for lowres / highres / pyramid). Stage must be
specified for training:
if stage 1 exists then stage 1 is the high resolution stage, otherwise it's 0
:param unpack_data: if False, npz preprocessed data will not be unpacked to npy. This consumes less space but
is considerably slower! Running unpack_data=False with 2d should never be done!
IMPORTANT: If you inherit from nnUNetTrainer and the init args change then you need to redefine self.init_args
in your init accordingly. Otherwise checkpoints won't load properly!
"""
super(nnUNetTrainer, self).__init__(deterministic, fp16)
self.unpack_data = unpack_data
self.init_args = (plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
deterministic, fp16)
# set through arguments from init
self.stage = stage
self.experiment_name = self.__class__.__name__
self.plans_file = plans_file
self.output_folder = output_folder
self.dataset_directory = dataset_directory
self.output_folder_base = self.output_folder
self.fold = fold
self.plans = None
# if we are running inference only then the self.dataset_directory is set (due to checkpoint loading) but it
# irrelevant
if self.dataset_directory is not None and isdir(self.dataset_directory):
self.gt_niftis_folder = join(self.dataset_directory, "gt_segmentations")
else:
self.gt_niftis_folder = None
self.folder_with_preprocessed_data = None
# set in self.initialize()
self.dl_tr = self.dl_val = None
self.num_input_channels = self.num_classes = self.net_pool_per_axis = self.patch_size = self.batch_size = \
self.threeD = self.base_num_features = self.intensity_properties = self.normalization_schemes = \
self.net_num_pool_op_kernel_sizes = self.net_conv_kernel_sizes = None # loaded automatically from plans_file
self.basic_generator_patch_size = self.data_aug_params = self.transpose_forward = self.transpose_backward = None
self.batch_dice = batch_dice
self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
self.online_eval_foreground_dc = []
self.online_eval_tp = []
self.online_eval_fp = []
self.online_eval_fn = []
self.classes = self.do_dummy_2D_aug = self.use_mask_for_norm = self.only_keep_largest_connected_component = \
self.min_region_size_per_class = self.min_size_per_class = None
self.inference_pad_border_mode = "constant"
self.inference_pad_kwargs = {'constant_values': 0}
self.update_fold(fold)
self.pad_all_sides = None
self.lr_scheduler_eps = 1e-3
self.lr_scheduler_patience = 30
self.initial_lr = 3e-4
self.weight_decay = 3e-5
self.oversample_foreground_percent = 0.33
self.conv_per_stage = None
self.regions_class_order = None
def update_fold(self, fold):
"""
used to swap between folds for inference (ensemble of models from cross-validation)
DO NOT USE DURING TRAINING AS THIS WILL NOT UPDATE THE DATASET SPLIT AND THE DATA AUGMENTATION GENERATORS
:param fold:
:return:
"""
if fold is not None:
if isinstance(fold, str):
assert fold == "all", "if self.fold is a string then it must be \'all\'"
if self.output_folder.endswith("%s" % str(self.fold)):
self.output_folder = self.output_folder_base
self.output_folder = join(self.output_folder, "%s" % str(fold))
else:
if self.output_folder.endswith("fold_%s" % str(self.fold)):
self.output_folder = self.output_folder_base
self.output_folder = join(self.output_folder, "fold_%s" % str(fold))
self.fold = fold
def setup_DA_params(self):
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params['patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
def initialize(self, training=True, force_load_plans=False):
"""
For prediction of test cases just set training=False, this will prevent loading of training data and
training batchgenerator initialization
:param training:
:return:
"""
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
if training:
self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] +
"_stage%d" % self.stage)
self.dl_tr, self.dl_val = self.get_basic_generators()
if self.unpack_data:
self.print_to_log_file("unpacking dataset")
unpack_dataset(self.folder_with_preprocessed_data)
self.print_to_log_file("done")
else:
self.print_to_log_file(
"INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
"will wait all winter for your model to finish!")
self.tr_gen, self.val_gen = get_default_augmentation(self.dl_tr, self.dl_val,
self.data_aug_params[
'patch_size_for_spatialtransform'],
self.data_aug_params)
self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())),
also_print_to_console=False)
self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())),
also_print_to_console=False)
else:
pass
self.initialize_network()
self.initialize_optimizer_and_scheduler()
# assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
self.was_initialized = True
def initialize_network(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
# self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
# self.print_to_log_file(self.net_conv_kernel_sizes)
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, net_numpool,
self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x, InitWeights_He(1e-2),
self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
self.network.inference_apply_nonlin = softmax_helper
if torch.cuda.is_available():
self.network.cuda()
def initialize_optimizer_and_scheduler(self):
assert self.network is not None, "self.initialize_network must be called first"
self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay,
amsgrad=True)
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2,
patience=self.lr_scheduler_patience,
verbose=True, threshold=self.lr_scheduler_eps,
threshold_mode="abs")
def plot_network_architecture(self):
try:
from batchgenerators.utilities.file_and_folder_operations import join
import hiddenlayer as hl
if torch.cuda.is_available():
g = hl.build_graph(self.network, torch.rand((1, self.num_input_channels, *self.patch_size)).cuda(),
transforms=None)
else:
g = hl.build_graph(self.network, torch.rand((1, self.num_input_channels, *self.patch_size)),
transforms=None)
g.save(join(self.output_folder, "network_architecture.pdf"))
del g
except Exception as e:
self.print_to_log_file("Unable to plot network architecture:")
self.print_to_log_file(e)
self.print_to_log_file("\nprinting the network instead:\n")
self.print_to_log_file(self.network)
self.print_to_log_file("\n")
finally:
if torch.cuda.is_available():
torch.cuda.empty_cache()
def run_training(self):
dct = OrderedDict()
for k in self.__dir__():
if not k.startswith("__"):
if not callable(getattr(self, k)):
dct[k] = str(getattr(self, k))
del dct['plans']
del dct['intensity_properties']
del dct['dataset']
del dct['dataset_tr']
del dct['dataset_val']
save_json(dct, join(self.output_folder, "debug.json"))
import shutil
shutil.copy(self.plans_file, join(self.output_folder_base, "plans.pkl"))
super(nnUNetTrainer, self).run_training()
def load_plans_file(self):
"""
This is what actually configures the entire experiment. The plans file is generated by experiment planning
:return:
"""
self.plans = load_pickle(self.plans_file)
def process_plans(self, plans):
if self.stage is None:
assert len(list(plans['plans_per_stage'].keys())) == 1, \
"If self.stage is None then there can be only one stage in the plans file. That seems to not be the " \
"case. Please specify which stage of the cascade must be trained"
self.stage = list(plans['plans_per_stage'].keys())[0]
self.plans = plans
stage_plans = self.plans['plans_per_stage'][self.stage]
self.batch_size = stage_plans['batch_size']
self.net_pool_per_axis = stage_plans['num_pool_per_axis']
self.patch_size = np.array(stage_plans['patch_size']).astype(int)
self.do_dummy_2D_aug = stage_plans['do_dummy_2D_data_aug']
if 'pool_op_kernel_sizes' not in stage_plans.keys():
assert 'num_pool_per_axis' in stage_plans.keys()
self.print_to_log_file("WARNING! old plans file with missing pool_op_kernel_sizes. Attempting to fix it...")
self.net_num_pool_op_kernel_sizes = []
for i in range(max(self.net_pool_per_axis)):
curr = []
for j in self.net_pool_per_axis:
if (max(self.net_pool_per_axis) - j) <= i:
curr.append(2)
else:
curr.append(1)
self.net_num_pool_op_kernel_sizes.append(curr)
else:
self.net_num_pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
if 'conv_kernel_sizes' not in stage_plans.keys():
self.print_to_log_file("WARNING! old plans file with missing conv_kernel_sizes. Attempting to fix it...")
self.net_conv_kernel_sizes = [[3] * len(self.net_pool_per_axis)] * (max(self.net_pool_per_axis) + 1)
else:
self.net_conv_kernel_sizes = stage_plans['conv_kernel_sizes']
self.pad_all_sides = None # self.patch_size
self.intensity_properties = plans['dataset_properties']['intensityproperties']
self.normalization_schemes = plans['normalization_schemes']
self.base_num_features = plans['base_num_features']
self.num_input_channels = plans['num_modalities']
self.num_classes = plans['num_classes'] + 1 # background is no longer in num_classes
self.classes = plans['all_classes']
self.use_mask_for_norm = plans['use_mask_for_norm']
self.only_keep_largest_connected_component = plans['keep_only_largest_region']
self.min_region_size_per_class = plans['min_region_size_per_class']
self.min_size_per_class = None # DONT USE THIS. plans['min_size_per_class']
if plans.get('transpose_forward') is None or plans.get('transpose_backward') is None:
print("WARNING! You seem to have data that was preprocessed with a previous version of nnU-Net. "
"You should rerun preprocessing. We will proceed and assume that both transpose_foward "
"and transpose_backward are [0, 1, 2]. If that is not correct then weird things will happen!")
plans['transpose_forward'] = [0, 1, 2]
plans['transpose_backward'] = [0, 1, 2]
self.transpose_forward = plans['transpose_forward']
self.transpose_backward = plans['transpose_backward']
if len(self.patch_size) == 2:
self.threeD = False
elif len(self.patch_size) == 3:
self.threeD = True
else:
raise RuntimeError("invalid patch size in plans file: %s" % str(self.patch_size))
if "conv_per_stage" in plans.keys(): # this ha sbeen added to the plans only recently
self.conv_per_stage = plans['conv_per_stage']
else:
self.conv_per_stage = 2
def load_dataset(self):
self.dataset = load_dataset(self.folder_with_preprocessed_data)
def get_basic_generators(self):
self.load_dataset()
self.do_split()
if self.threeD:
print("3d!")
dl_tr = DataLoader3D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
False, oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
dl_val = DataLoader3D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size, False,
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
else:
print("2d!")
dl_tr = DataLoader2D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
dl_val = DataLoader2D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size,
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
return dl_tr, dl_val
def preprocess_patient(self, input_files):
"""
Used to predict new unseen data. Not used for the preprocessing of the training/test data
:param input_files:
:return:
"""
from nnunet.training.model_restore import recursive_find_python_class
preprocessor_name = self.plans.get('preprocessor_name')
if preprocessor_name is None:
if self.threeD:
preprocessor_name = "GenericPreprocessor"
else:
preprocessor_name = "PreprocessorFor2D"
print("using preprocessor", preprocessor_name)
preprocessor_class = recursive_find_python_class([join(nnunet.__path__[0], "preprocessing")],
preprocessor_name,
current_module="nnunet.preprocessing")
assert preprocessor_class is not None, "Could not find preprocessor %s in nnunet.preprocessing" % \
preprocessor_name
preprocessor = preprocessor_class(self.normalization_schemes, self.use_mask_for_norm,
self.transpose_forward, self.intensity_properties)
d, s, properties = preprocessor.preprocess_test_case(input_files,
self.plans['plans_per_stage'][self.stage][
'current_spacing'])
return d, s, properties
def preprocess_predict_nifti(self, input_files: List[str], output_file: str = None,
softmax_ouput_file: str = None, mixed_precision: bool = True) -> None:
"""
Use this to predict new data
:param input_files:
:param output_file:
:param softmax_ouput_file:
:param mixed_precision:
:return:
"""
print("preprocessing...")
d, s, properties = self.preprocess_patient(input_files)
print("predicting...")
pred = self.predict_preprocessed_data_return_seg_and_softmax(d, do_mirroring=self.data_aug_params["do_mirror"],
mirror_axes=self.data_aug_params['mirror_axes'],
use_sliding_window=True, step_size=0.5,
use_gaussian=True, pad_border_mode='constant',
pad_kwargs={'constant_values': 0},
verbose=True, all_in_gpu=False,
mixed_precision=mixed_precision)[1]
pred = pred.transpose([0] + [i + 1 for i in self.transpose_backward])
if 'segmentation_export_params' in self.plans.keys():
force_separate_z = self.plans['segmentation_export_params']['force_separate_z']
interpolation_order = self.plans['segmentation_export_params']['interpolation_order']
interpolation_order_z = self.plans['segmentation_export_params']['interpolation_order_z']
else:
force_separate_z = None
interpolation_order = 1
interpolation_order_z = 0
print("resampling to original spacing and nifti export...")
save_segmentation_nifti_from_softmax(pred, output_file, properties, interpolation_order,
self.regions_class_order, None, None, softmax_ouput_file,
None, force_separate_z=force_separate_z,
interpolation_order_z=interpolation_order_z)
print("done")
def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True,
mirror_axes: Tuple[int] = None,
use_sliding_window: bool = True, step_size: float = 0.5,
use_gaussian: bool = True, pad_border_mode: str = 'constant',
pad_kwargs: dict = None, all_in_gpu: bool = False,
verbose: bool = True, mixed_precision: bool = True) -> Tuple[np.ndarray, np.ndarray]:
"""
:param data:
:param do_mirroring:
:param mirror_axes:
:param use_sliding_window:
:param step_size:
:param use_gaussian:
:param pad_border_mode:
:param pad_kwargs:
:param all_in_gpu:
:param verbose:
:return:
"""
if pad_border_mode == 'constant' and pad_kwargs is None:
pad_kwargs = {'constant_values': 0}
if do_mirroring and mirror_axes is None:
mirror_axes = self.data_aug_params['mirror_axes']
if do_mirroring:
assert self.data_aug_params["do_mirror"], "Cannot do mirroring as test time augmentation when training " \
"was done without mirroring"
valid = list((SegmentationNetwork, nn.DataParallel))
assert isinstance(self.network, tuple(valid))
current_mode = self.network.training
self.network.eval()
ret = self.network.predict_3D(data, do_mirroring=do_mirroring, mirror_axes=mirror_axes,
use_sliding_window=use_sliding_window, step_size=step_size,
patch_size=self.patch_size, regions_class_order=self.regions_class_order,
use_gaussian=use_gaussian, pad_border_mode=pad_border_mode,
pad_kwargs=pad_kwargs, all_in_gpu=all_in_gpu, verbose=verbose,
mixed_precision=mixed_precision)
self.network.train(current_mode)
return ret
def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5,
save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True):
"""
if debug=True then the temporary files generated for postprocessing determination will be kept
"""
current_mode = self.network.training
self.network.eval()
assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)"
if self.dataset_val is None:
self.load_dataset()
self.do_split()
if segmentation_export_kwargs is None:
if 'segmentation_export_params' in self.plans.keys():
force_separate_z = self.plans['segmentation_export_params']['force_separate_z']
interpolation_order = self.plans['segmentation_export_params']['interpolation_order']
interpolation_order_z = self.plans['segmentation_export_params']['interpolation_order_z']
else:
force_separate_z = None
interpolation_order = 1
interpolation_order_z = 0
else:
force_separate_z = segmentation_export_kwargs['force_separate_z']
interpolation_order = segmentation_export_kwargs['interpolation_order']
interpolation_order_z = segmentation_export_kwargs['interpolation_order_z']
# predictions as they come from the network go here
output_folder = join(self.output_folder, validation_folder_name)
maybe_mkdir_p(output_folder)
# this is for debug purposes
my_input_args = {'do_mirroring': do_mirroring,
'use_sliding_window': use_sliding_window,
'step_size': step_size,
'save_softmax': save_softmax,
'use_gaussian': use_gaussian,
'overwrite': overwrite,
'validation_folder_name': validation_folder_name,
'debug': debug,
'all_in_gpu': all_in_gpu,
'segmentation_export_kwargs': segmentation_export_kwargs,
}
save_json(my_input_args, join(output_folder, "validation_args.json"))
if do_mirroring:
if not self.data_aug_params['do_mirror']:
raise RuntimeError("We did not train with mirroring so you cannot do inference with mirroring enabled")
mirror_axes = self.data_aug_params['mirror_axes']
else:
mirror_axes = ()
pred_gt_tuples = []
export_pool = Pool(default_num_threads)
results = []
for k in self.dataset_val.keys():
properties = load_pickle(self.dataset[k]['properties_file'])
fname = properties['list_of_data_files'][0].split("/")[-1][:-12]
if overwrite or (not isfile(join(output_folder, fname + ".nii.gz"))) or \
(save_softmax and not isfile(join(output_folder, fname + ".npz"))):
data = np.load(self.dataset[k]['data_file'])['data']
print(k, data.shape)
data[-1][data[-1] == -1] = 0
softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax(data[:-1],
do_mirroring=do_mirroring,
mirror_axes=mirror_axes,
use_sliding_window=use_sliding_window,
step_size=step_size,
use_gaussian=use_gaussian,
all_in_gpu=all_in_gpu,
mixed_precision=self.fp16)[1]
softmax_pred = softmax_pred.transpose([0] + [i + 1 for i in self.transpose_backward])
if save_softmax:
softmax_fname = join(output_folder, fname + ".npz")
else:
softmax_fname = None
"""There is a problem with python process communication that prevents us from communicating obejcts
larger than 2 GB between processes (basically when the length of the pickle string that will be sent is
communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long
enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually
patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will
then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either
filename or np.ndarray and will handle this automatically"""
if np.prod(softmax_pred.shape) > (2e9 / 4 * 0.85): # *0.85 just to be save
np.save(join(output_folder, fname + ".npy"), softmax_pred)
softmax_pred = join(output_folder, fname + ".npy")
results.append(export_pool.starmap_async(save_segmentation_nifti_from_softmax,
((softmax_pred, join(output_folder, fname + ".nii.gz"),
properties, interpolation_order, self.regions_class_order,
None, None,
softmax_fname, None, force_separate_z,
interpolation_order_z),
)
)
)
pred_gt_tuples.append([join(output_folder, fname + ".nii.gz"),
join(self.gt_niftis_folder, fname + ".nii.gz")])
_ = [i.get() for i in results]
self.print_to_log_file("finished prediction")
# evaluate raw predictions
self.print_to_log_file("evaluation of raw predictions")
task = self.dataset_directory.split("/")[-1]
job_name = self.experiment_name
_ = aggregate_scores(pred_gt_tuples, labels=list(range(self.num_classes)),
json_output_file=join(output_folder, "summary.json"),
json_name=job_name + " val tiled %s" % (str(use_sliding_window)),
json_author="Fabian",
json_task=task, num_threads=default_num_threads)
if run_postprocessing_on_folds:
# in the old nnunet we would stop here. Now we add a postprocessing. This postprocessing can remove everything
# except the largest connected component for each class. To see if this improves results, we do this for all
# classes and then rerun the evaluation. Those classes for which this resulted in an improved dice score will
# have this applied during inference as well
self.print_to_log_file("determining postprocessing")
determine_postprocessing(self.output_folder, self.gt_niftis_folder, validation_folder_name,
final_subf_name=validation_folder_name + "_postprocessed", debug=debug)
# after this the final predictions for the vlaidation set can be found in validation_folder_name_base + "_postprocessed"
# They are always in that folder, even if no postprocessing as applied!
# detemining postprocesing on a per-fold basis may be OK for this fold but what if another fold finds another
# postprocesing to be better? In this case we need to consolidate. At the time the consolidation is going to be
# done we won't know what self.gt_niftis_folder was, so now we copy all the niftis into a separate folder to
# be used later
gt_nifti_folder = join(self.output_folder_base, "gt_niftis")
maybe_mkdir_p(gt_nifti_folder)
for f in subfiles(self.gt_niftis_folder, suffix=".nii.gz"):
success = False
attempts = 0
e = None
while not success and attempts < 10:
try:
shutil.copy(f, gt_nifti_folder)
success = True
except OSError as e:
attempts += 1
sleep(1)
if not success:
print("Could not copy gt nifti file %s into folder %s" % (f, gt_nifti_folder))
if e is not None:
raise e
self.network.train(current_mode)
def run_online_evaluation(self, output, target):
with torch.no_grad():
num_classes = output.shape[1]
output_softmax = softmax_helper(output)
output_seg = output_softmax.argmax(1)
target = target[:, 0]
axes = tuple(range(1, len(target.shape)))
tp_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)
fp_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)
fn_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)
for c in range(1, num_classes):
tp_hard[:, c - 1] = sum_tensor((output_seg == c).float() * (target == c).float(), axes=axes)
fp_hard[:, c - 1] = sum_tensor((output_seg == c).float() * (target != c).float(), axes=axes)
fn_hard[:, c - 1] = sum_tensor((output_seg != c).float() * (target == c).float(), axes=axes)
tp_hard = tp_hard.sum(0, keepdim=False).detach().cpu().numpy()
fp_hard = fp_hard.sum(0, keepdim=False).detach().cpu().numpy()
fn_hard = fn_hard.sum(0, keepdim=False).detach().cpu().numpy()
self.online_eval_foreground_dc.append(list((2 * tp_hard) / (2 * tp_hard + fp_hard + fn_hard + 1e-8)))
self.online_eval_tp.append(list(tp_hard))
self.online_eval_fp.append(list(fp_hard))
self.online_eval_fn.append(list(fn_hard))
def finish_online_evaluation(self):
self.online_eval_tp = np.sum(self.online_eval_tp, 0)
self.online_eval_fp = np.sum(self.online_eval_fp, 0)
self.online_eval_fn = np.sum(self.online_eval_fn, 0)
global_dc_per_class = [i for i in [2 * i / (2 * i + j + k) for i, j, k in
zip(self.online_eval_tp, self.online_eval_fp, self.online_eval_fn)]
if not np.isnan(i)]
self.all_val_eval_metrics.append(np.mean(global_dc_per_class))
self.print_to_log_file("Average global foreground Dice:", str(global_dc_per_class))
self.print_to_log_file("(interpret this as an estimate for the Dice of the different classes. This is not "
"exact.)")
self.online_eval_foreground_dc = []
self.online_eval_tp = []
self.online_eval_fp = []
self.online_eval_fn = []
def save_checkpoint(self, fname, save_optimizer=True):
super(nnUNetTrainer, self).save_checkpoint(fname, save_optimizer)
info = OrderedDict()
info['init'] = self.init_args
info['name'] = self.__class__.__name__
info['class'] = str(self.__class__)
info['plans'] = self.plans
write_pickle(info, fname + ".pkl")
class nnUNetTrainer(NetworkTrainer):
def __init__(self,
plans_file,
fold,
output_folder=None,
dataset_directory=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False,
lam=2,
gpu="0",
save_dir=None):
"""
:param deterministic:
:param fold: can be either [0 ... 5) for cross-validation, 'all' to train on all available training data or
None if you wish to load some checkpoint and do inference only
:param plans_file: the pkl file generated by preprocessing. This file will determine all design choices
:param subfolder_with_preprocessed_data: must be a subfolder of dataset_directory (just the name of the folder,
not the entire path). This is where the preprocessed data lies that will be used for network training. We made
this explicitly available so that differently preprocessed data can coexist and the user can choose what to use.
Can be None if you are doing inference only.
:param output_folder: where to store parameters, plot progress and to the validation
:param dataset_directory: the parent directory in which the preprocessed Task data is stored. This is required
because the split information is stored in this directory. For running prediction only this input is not
required and may be set to None
:param batch_dice: compute dice loss for each sample and average over all samples in the batch or pretend the
batch is a pseudo volume?
:param stage: The plans file may contain several stages (used for lowres / highres / pyramid). Stage must be
specified for training:
if stage 1 exists then stage 1 is the high resolution stage, otherwise it's 0
:param unpack_data: if False, npz preprocessed data will not be unpacked to npy. This consumes less space but
is considerably slower! Running unpack_data=False with 2d should never be done!
IMPORTANT: If you inherit from nnUNetTrainer and the init args change then you need to redefine self.init_args
in your init accordingly. Otherwise checkpoints won't load properly!
"""
super(nnUNetTrainer, self).__init__(deterministic, fp16, lam, gpu,
save_dir)
self.unpack_data = unpack_data
self.init_args = (plans_file, fold, output_folder, dataset_directory,
batch_dice, stage, unpack_data, deterministic, fp16)
# set through arguments from init
self.stage = stage
self.experiment_name = self.__class__.__name__
self.plans_file = plans_file
self.output_folder = output_folder
self.dataset_directory = dataset_directory
self.output_folder_base = self.output_folder
self.fold = fold
self.plans = None
# if we are running inference only then the self.dataset_directory is set (due to checkpoint loading) but it
# irrelevant
if self.dataset_directory is not None and isdir(
self.dataset_directory):
self.gt_niftis_folder = join(self.dataset_directory,
"gt_segmentations")
else:
self.gt_niftis_folder = None
self.folder_with_preprocessed_data = None
# set in self.initialize()
self.dl_tr = self.dl_val = None
self.num_input_channels = self.num_classes = self.net_pool_per_axis = self.patch_size = self.batch_size = \
self.threeD = self.base_num_features = self.intensity_properties = self.normalization_schemes = \
self.net_num_pool_op_kernel_sizes = self.net_conv_kernel_sizes = None # loaded automatically from plans_file
self.basic_generator_patch_size = self.data_aug_params = self.transpose_forward = self.transpose_backward = None
self.batch_dice = batch_dice
self.loss = DC_and_CE_loss(
{
'batch_dice': self.batch_dice,
'smooth': 1e-5,
'do_bg': False,
'square': False
}, {})
self.online_eval_foreground_dc = []
self.online_eval_tp = []
self.online_eval_fp = []
self.online_eval_fn = []
self.classes = self.do_dummy_2D_aug = self.use_mask_for_norm = self.only_keep_largest_connected_component = \
self.min_region_size_per_class = self.min_size_per_class = None
self.inference_pad_border_mode = "constant"
self.inference_pad_kwargs = {'constant_values': 0}
self.update_fold(fold)
self.pad_all_sides = None
self.lr_scheduler_eps = 1e-3
self.lr_scheduler_patience = 30
self.initial_lr = 3e-4
self.weight_decay = 3e-5
self.oversample_foreground_percent = 0.33
def update_fold(self, fold):
"""
used to swap between folds for inference (ensemble of models from cross-validation)
DO NOT USE DURING TRAINING AS THIS WILL NOT UPDATE THE DATASET SPLIT AND THE DATA AUGMENTATION GENERATORS
:param fold:
:return:
"""
if fold is not None:
if isinstance(fold, str):
assert fold == "all", "if self.fold is a string then it must be \'all\'"
if self.output_folder.endswith("%s" % str(self.fold)):
self.output_folder = self.output_folder_base
self.output_folder = join(self.output_folder, "%s" % str(fold))
else:
if self.output_folder.endswith("fold_%s" % str(self.fold)):
self.output_folder = self.output_folder_base
self.output_folder = join(self.output_folder,
"fold_%s" % str(fold))
self.fold = fold
def setup_DA_params(self):
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params[
"rotation_x"] = default_2D_augmentation_params[
"rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 *
2. * np.pi,
15. / 360 *
2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params[
'patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
def initialize(self, training=True, force_load_plans=False):
"""
For prediction of test cases just set training=False, this will prevent loading of training data and
training batchgenerator initialization
:param training:
:return:
"""
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
self.AutoAugment = AutoAugment(
self.data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_params)
self.folder_with_preprocessed_data = join(
self.dataset_directory,
self.plans['data_identifier'] + "_stage%d" % self.stage)
if training:
self.dl_tr, self.dl_val = self.get_basic_generators()
if self.unpack_data:
self.print_to_log_file("unpacking dataset")
unpack_dataset(self.folder_with_preprocessed_data)
self.print_to_log_file("done")
else:
self.print_to_log_file(
"INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
"will wait all winter for your model to finish!")
self.tr_gen, self.val_gen = get_default_augmentation(
self.dl_tr, self.dl_val,
self.data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_params)
self.print_to_log_file("TRAINING KEYS:\n %s" %
(str(self.dataset_tr.keys())),
also_print_to_console=False)
self.print_to_log_file("VALIDATION KEYS:\n %s" %
(str(self.dataset_val.keys())),
also_print_to_console=False)
else:
pass
self.initialize_network_optimizer_and_scheduler()
# assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
self.was_initialized = True
def initialize_network_optimizer_and_scheduler(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
# self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
# self.print_to_log_file(self.net_conv_kernel_sizes)
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
net_numpool, 2, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
self.optimizer = torch.optim.Adam(self.network.parameters(),
self.initial_lr,
weight_decay=self.weight_decay,
amsgrad=True)
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(
self.optimizer,
mode='min',
factor=0.2,
patience=self.lr_scheduler_patience,
verbose=True,
threshold=self.lr_scheduler_eps,
threshold_mode="abs")
gpu_ids = [int(x) for x in self.gpu.split(",")]
if len(gpu_ids) > 1:
self.network = torch.nn.DataParallel(self.network.cuda(),
device_ids=gpu_ids)
else:
self.network = self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def plot_network_architecture(self):
try:
from batchgenerators.utilities.file_and_folder_operations import join
import hiddenlayer as hl
g = hl.build_graph(self.network,
torch.rand((1, self.num_input_channels,
*self.patch_size)).cuda(),
transforms=None)
g.save(join(self.output_folder, "network_architecture.pdf"))
del g
except Exception as e:
self.print_to_log_file("Unable to plot network architecture:")
self.print_to_log_file(e)
finally:
torch.cuda.empty_cache()
def run_training(self):
dct = OrderedDict()
for k in self.__dir__():
if not k.startswith("__"):
if not callable(getattr(self, k)):
dct[k] = str(getattr(self, k))
del dct['plans']
del dct['intensity_properties']
del dct['dataset']
del dct['dataset_tr']
del dct['dataset_val']
save_json(dct, join(self.output_folder, "debug.json"))
import shutil
shutil.copy(self.plans_file, join(self.output_folder_base,
"plans.pkl"))
super(nnUNetTrainer, self).run_training()
def load_plans_file(self):
"""
This is what actually configures the entire experiment. The plans file is generated by experiment planning
:return:
"""
self.plans = load_pickle(self.plans_file)
def process_plans(self, plans):
if self.stage is None:
assert len(list(plans['plans_per_stage'].keys())) == 1, \
"If self.stage is None then there can be only one stage in the plans file. That seems to not be the " \
"case. Please specify which stage of the cascade must be trained"
self.stage = list(plans['plans_per_stage'].keys())[0]
self.plans = plans
stage_plans = self.plans['plans_per_stage'][self.stage]
self.batch_size = stage_plans['batch_size']
self.net_pool_per_axis = stage_plans['num_pool_per_axis']
self.patch_size = np.array(stage_plans['patch_size']).astype(int)
self.do_dummy_2D_aug = stage_plans['do_dummy_2D_data_aug']
self.net_num_pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
self.net_conv_kernel_sizes = stage_plans['conv_kernel_sizes']
self.pad_all_sides = None # self.patch_size
self.intensity_properties = plans['dataset_properties'][
'intensityproperties']
self.normalization_schemes = plans['normalization_schemes']
self.base_num_features = plans['base_num_features']
self.num_input_channels = plans['num_modalities']
self.num_classes = plans[
'num_classes'] + 1 # background is no longer in num_classes
self.classes = plans['all_classes']
self.use_mask_for_norm = plans['use_mask_for_norm']
self.only_keep_largest_connected_component = plans[
'keep_only_largest_region']
self.min_region_size_per_class = plans['min_region_size_per_class']
self.min_size_per_class = None # DONT USE THIS. plans['min_size_per_class']
if plans.get('transpose_forward') is None or plans.get(
'transpose_backward') is None:
print(
"WARNING! You seem to have data that was preprocessed with a previous version of nnU-Net. "
"You should rerun preprocessing. We will proceed and assume that both transpose_foward "
"and transpose_backward are [0, 1, 2]. If that is not correct then weird things will happen!"
)
plans['transpose_forward'] = [0, 1, 2]
plans['transpose_backward'] = [0, 1, 2]
self.transpose_forward = plans['transpose_forward']
self.transpose_backward = plans['transpose_backward']
if len(self.patch_size) == 2:
self.threeD = False
elif len(self.patch_size) == 3:
self.threeD = True
else:
raise RuntimeError("invalid patch size in plans file: %s" %
str(self.patch_size))
def load_dataset(self):
self.dataset = load_dataset(self.folder_with_preprocessed_data)
def get_basic_generators(self):
self.load_dataset()
self.do_split()
if self.threeD:
dl_tr = DataLoader3D(self.dataset_tr,
self.basic_generator_patch_size,
self.patch_size,
self.batch_size,
False,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
dl_val = DataLoader3D(self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
False,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
else:
dl_tr = DataLoader2D(
self.dataset_tr,
self.basic_generator_patch_size,
self.patch_size,
self.batch_size,
transpose=None, # self.plans.get('transpose_forward'),
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
dl_val = DataLoader2D(
self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
transpose=None, # self.plans.get('transpose_forward'),
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
return dl_tr, dl_val
def preprocess_patient(self, input_files):
"""
Used to predict new unseen data. Not used for the preprocessing of the training/test data
:param input_files:
:return:
"""
from nnunet.preprocessing.preprocessing import GenericPreprocessor, PreprocessorFor2D
if self.threeD:
preprocessor = GenericPreprocessor(self.normalization_schemes,
self.use_mask_for_norm,
self.transpose_forward,
self.intensity_properties)
else:
preprocessor = PreprocessorFor2D(self.normalization_schemes,
self.use_mask_for_norm,
self.transpose_forward,
self.intensity_properties)
d, s, properties = preprocessor.preprocess_test_case(
input_files,
self.plans['plans_per_stage'][self.stage]['current_spacing'])
return d, s, properties
def preprocess_predict_nifti(self,
input_files,
output_file=None,
softmax_ouput_file=None):
"""
Use this to predict new data
:param input_files:
:param output_file:
:param softmax_ouput_file:
:return:
"""
print("preprocessing...")
d, s, properties = self.preprocess_patient(input_files)
print("predicting...")
pred = self.predict_preprocessed_data_return_softmax(
d, self.data_aug_params["mirror"], 1, False, 1,
self.data_aug_params['mirror_axes'], True, True, 2,
self.patch_size, True)
pred = pred.transpose([0] + [i + 1 for i in self.transpose_backward])
print("resampling to original spacing and nifti export...")
save_segmentation_nifti_from_softmax(pred, output_file, properties, 3,
None, None, None,
softmax_ouput_file, None)
print("done")
def predict_preprocessed_data_return_softmax(self, data, do_mirroring,
num_repeats, use_train_mode,
batch_size, mirror_axes,
tiled, tile_in_z, step,
min_size, use_gaussian):
"""
Don't use this. If you need softmax output, use preprocess_predict_nifti and set softmax_output_file.
:param data:
:param do_mirroring:
:param num_repeats:
:param use_train_mode:
:param batch_size:
:param mirror_axes:
:param tiled:
:param tile_in_z:
:param step:
:param min_size:
:param use_gaussian:
:param use_temporal:
:return:
"""
assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
if isinstance(self.network, torch.nn.DataParallel):
model = self.network.module
else:
model = self.network
return model.predict_3D(data,
do_mirroring,
num_repeats,
use_train_mode,
batch_size,
mirror_axes,
tiled,
tile_in_z,
step,
min_size,
use_gaussian=use_gaussian,
pad_border_mode=self.inference_pad_border_mode,
pad_kwargs=self.inference_pad_kwargs)[2]
def validate(self,
do_mirroring=True,
use_train_mode=False,
tiled=True,
step=2,
save_softmax=True,
use_gaussian=True,
compute_global_dice=True,
override=True,
validation_folder_name='validation'):
"""
2018_12_05: I added global accumulation of TP, FP and FN for the validation in here. This is because I believe
that selecting models is easier when computing the Dice globally instead of independently for each case and
then averaging over cases. The Lung dataset in particular is very unstable because of the small size of the
Lung Lesions. My theory is that even though the global Dice is different than the acutal target metric it is
still a good enough substitute that allows us to get a lot more stable results when rerunning the same
experiment twice. FYI: computer vision community uses the global jaccard for the evaluation of Cityscapes etc,
not the per-image jaccard averaged over images.
The reason I am accumulating TP/FP/FN here and not from the nifti files (which are used by our Evaluator) is
that all predictions made here will have identical voxel spacing whereas voxel spacings in the nifti files
will be different (which we could compensate for by using the volume per voxel but that would require the
evaluator to understand spacings which is does not at this point)
:param do_mirroring:
:param use_train_mode:
:param mirror_axes:
:param tiled:
:param tile_in_z:
:param step:
:param use_nifti:
:param save_softmax:
:param use_gaussian:
:param use_temporal_models:
:return:
"""
assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)"
if self.dataset_val is None:
self.load_dataset()
self.do_split()
output_folder = join(self.output_folder, validation_folder_name)
maybe_mkdir_p(output_folder)
if do_mirroring:
mirror_axes = self.data_aug_params['mirror_axes']
else:
mirror_axes = ()
pred_gt_tuples = []
export_pool = Pool(4)
results = []
global_tp = OrderedDict()
global_fp = OrderedDict()
global_fn = OrderedDict()
for k in self.dataset_val.keys():
print(k)
properties = self.dataset[k]['properties']
fname = properties['list_of_data_files'][0].split("/")[-1][:-12]
if override or (not isfile(join(output_folder,
fname + ".nii.gz"))):
data = np.load(self.dataset[k]['data_file'])['data']
print(k, data.shape)
data[-1][data[-1] == -1] = 0
softmax_pred = self.predict_preprocessed_data_return_softmax(
data[:-1],
do_mirroring,
1,
use_train_mode,
1,
mirror_axes,
tiled,
True,
step,
self.patch_size,
use_gaussian=use_gaussian)
if compute_global_dice:
predicted_segmentation = softmax_pred.argmax(0)
gt_segmentation = data[-1]
labels = properties['classes']
labels = [int(i) for i in labels if i > 0]
for l in labels:
if l not in global_fn.keys():
global_fn[l] = 0
if l not in global_fp.keys():
global_fp[l] = 0
if l not in global_tp.keys():
global_tp[l] = 0
conf = ConfusionMatrix(
(predicted_segmentation == l).astype(int),
(gt_segmentation == l).astype(int))
conf.compute()
global_fn[l] += conf.fn
global_fp[l] += conf.fp
global_tp[l] += conf.tp
softmax_pred = softmax_pred.transpose(
[0] + [i + 1 for i in self.transpose_backward])
if save_softmax:
softmax_fname = join(output_folder, fname + ".npz")
else:
softmax_fname = None
"""There is a problem with python process communication that prevents us from communicating obejcts
larger than 2 GB between processes (basically when the length of the pickle string that will be sent is
communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long
enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually
patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will
then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either
filename or np.ndarray and will handle this automatically"""
if np.prod(softmax_pred.shape) > (2e9 / 4 *
0.9): # *0.9 just to be save
np.save(join(output_folder, fname + ".npy"), softmax_pred)
softmax_pred = join(output_folder, fname + ".npy")
results.append(
export_pool.starmap_async(
save_segmentation_nifti_from_softmax,
((softmax_pred, join(output_folder,
fname + ".nii.gz"), properties, 3,
None, None, None, softmax_fname, None), )))
# save_segmentation_nifti_from_softmax(softmax_pred, join(output_folder, fname + ".nii.gz"),
# properties, 3, None, None,
# None,
# softmax_fname,
# None)
pred_gt_tuples.append([
join(output_folder, fname + ".nii.gz"),
join(self.gt_niftis_folder, fname + ".nii.gz")
])
_ = [i.get() for i in results]
print("finished prediction, now evaluating...")
task = self.dataset_directory.split("/")[-1]
job_name = self.experiment_name
_ = aggregate_scores(
pred_gt_tuples,
labels=list(range(self.num_classes)),
json_output_file=join(output_folder, "summary.json"),
json_name=job_name + " val tiled %s" % (str(tiled)),
json_author="Fabian",
json_task=task,
num_threads=3)
if compute_global_dice:
global_dice = OrderedDict()
all_labels = list(global_fn.keys())
for l in all_labels:
global_dice[int(l)] = float(
2 * global_tp[l] /
(2 * global_tp[l] + global_fn[l] + global_fp[l]))
write_json(global_dice, join(output_folder, "global_dice.json"))
def run_online_evaluation(self, output, target):
with torch.no_grad():
num_classes = output.shape[1]
output_softmax = softmax_helper(output)
output_seg = output_softmax.argmax(1)
target = target[:, 0]
axes = tuple(range(1, len(target.shape)))
tp_hard = torch.zeros(
(target.shape[0], num_classes - 1)).to(output_seg.device.index)
fp_hard = torch.zeros(
(target.shape[0], num_classes - 1)).to(output_seg.device.index)
fn_hard = torch.zeros(
(target.shape[0], num_classes - 1)).to(output_seg.device.index)
for c in range(1, num_classes):
tp_hard[:, c - 1] = sum_tensor(
(output_seg == c).float() * (target == c).float(),
axes=axes)
fp_hard[:, c - 1] = sum_tensor(
(output_seg == c).float() * (target != c).float(),
axes=axes)
fn_hard[:, c - 1] = sum_tensor(
(output_seg != c).float() * (target == c).float(),
axes=axes)
tp_hard = tp_hard.sum(0, keepdim=False).detach().cpu().numpy()
fp_hard = fp_hard.sum(0, keepdim=False).detach().cpu().numpy()
fn_hard = fn_hard.sum(0, keepdim=False).detach().cpu().numpy()
self.online_eval_foreground_dc.append(
list((2 * tp_hard) / (2 * tp_hard + fp_hard + fn_hard + 1e-8)))
self.online_eval_tp.append(list(tp_hard))
self.online_eval_fp.append(list(fp_hard))
self.online_eval_fn.append(list(fn_hard))
def finish_online_evaluation(self):
self.online_eval_tp = np.sum(self.online_eval_tp, 0)
self.online_eval_fp = np.sum(self.online_eval_fp, 0)
self.online_eval_fn = np.sum(self.online_eval_fn, 0)
global_dc_per_class = [
i for i in [
2 * i / (2 * i + j + k)
for i, j, k in zip(self.online_eval_tp, self.online_eval_fp,
self.online_eval_fn)
] if not np.isnan(i)
]
self.all_val_eval_metrics.append(np.mean(global_dc_per_class))
self.print_to_log_file("Val glob dc per class:",
str(global_dc_per_class))
self.online_eval_foreground_dc = []
self.online_eval_tp = []
self.online_eval_fp = []
self.online_eval_fn = []
def save_checkpoint(self, fname, save_optimizer=True):
super(nnUNetTrainer, self).save_checkpoint(fname, save_optimizer)
info = OrderedDict()
info['init'] = self.init_args
info['name'] = self.__class__.__name__
info['class'] = str(self.__class__)
info['plans'] = self.plans
write_pickle(info, fname + ".pkl")
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) * num_cases
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_poolLateV2(input_patch_size,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
ref = Generic_UNet.use_this_for_batch_size_computation_3D
here = Generic_UNet.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)
while here > ref:
# find the largest axis. If patch is isotropic, pick the axis with the largest spacing
if len(np.unique(new_shp)) == 1:
axis_to_be_reduced = np.argsort(current_spacing)[-1]
else:
axis_to_be_reduced = np.argsort(new_shp)[-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_poolLateV2(tmp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
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_poolLateV2(new_shp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
here = Generic_UNet.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)
input_patch_size = new_shp
batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D # This is what works with 128**3
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 = 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,
}
return plan
def get_properties_for_stage(self, current_spacing, original_spacing,
original_shape, num_cases, num_modalities,
num_classes):
"""
Computation of input patch size starts out with the new median shape (in voxels) of a dataset. This is
opposed to prior experiments where I based it on the median size in mm. The rationale behind this is that
for some organ of interest the acquisition method will most likely be chosen such that the field of view and
voxel resolution go hand in hand to show the doctor what they need to see. This assumption may be violated
for some modalities with anisotropy (cine MRI) but we will have t live with that. In future experiments I
will try to 1) base input patch size match aspect ratio of input size in mm (instead of voxels) and 2) to
try to enforce that we see the same 'distance' in all directions (try to maintain equal size in mm of patch)
The patches created here attempt keep the aspect ratio of the new_median_shape
:param current_spacing:
:param original_spacing:
:param original_shape:
:param num_cases:
:return:
"""
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_poolLateV2(input_patch_size,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
ref = Generic_UNet.use_this_for_batch_size_computation_3D
here = Generic_UNet.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)
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_poolLateV2(tmp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
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_poolLateV2(new_shp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
here = Generic_UNet.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)
input_patch_size = new_shp
batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D # This is what works with 128**3
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,
}
return plan
def test_calculate_metric(iter_nums):
if args.net == 'unet':
# timm-efficientnet performs slightly worse.
if not args.vis_mode:
backbone_type = re.sub("^eff", "efficientnet", args.backbone_type)
net = smp.Unet(backbone_type, classes=args.num_classes, encoder_weights='imagenet')
else:
net = VanillaUNet(n_channels=3, num_classes=args.num_classes)
elif args.net == 'unet-scratch':
# net = UNet(num_classes=args.num_classes)
net = VanillaUNet(n_channels=3, num_classes=args.num_classes,
use_polyformer=args.polyformer_mode,
num_modes=args.num_modes)
elif args.net == 'nestedunet':
net = NestedUNet(num_classes=args.num_classes)
elif args.net == 'unet3plus':
net = UNet_3Plus(num_classes=args.num_classes)
elif args.net == 'pranet':
net = PraNet(num_classes=args.num_classes - 1)
elif args.net == 'attunet':
net = AttU_Net(output_ch=args.num_classes)
elif args.net == 'attr2unet':
net = R2AttU_Net(output_ch=args.num_classes)
elif args.net == 'dunet':
net = DeformableUNet(n_channels=3, n_classes=args.num_classes)
elif args.net == 'setr':
# Install mmcv first:
# pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu{Your CUDA Version}/torch{Your Pytorch Version}/index.html
# E.g.: pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.1/index.html
from mmcv.utils import Config
sys.path.append("networks/setr")
from mmseg.models import build_segmentor
task2config = { 'refuge': 'SETR_PUP_288x288_10k_refuge_context_bs_4.py',
'polyp': 'SETR_PUP_320x320_10k_polyp_context_bs_4.py' }
setr_cfg = Config.fromfile("networks/setr/configs/SETR/{}".format(task2config[args.task_name]))
net = build_segmentor(setr_cfg.model, train_cfg=setr_cfg.train_cfg, test_cfg=setr_cfg.test_cfg)
# By default, net() calls forward_train(), which receives extra parameters, and returns losses.
# net.forward_dummy() receives/returns the traditional input/output.
# Relevant file: mmseg/models/segmentors/encoder_decoder.py
net.forward = net.forward_dummy
elif args.net == 'transunet':
transunet_config = TransUNet_CONFIGS[args.backbone_type]
transunet_config.n_classes = args.num_classes
if args.backbone_type.find('R50') != -1:
# The "patch" in TransUNet means grid-like patches of the input image.
# The "patch" in our code means the whole input image after cropping/resizing (part of the augmentation)
transunet_config.patches.grid = (int(args.patch_size[0] / transunet_config.patches.size[0]),
int(args.patch_size[1] / transunet_config.patches.size[1]))
net = TransUNet(transunet_config, img_size=args.patch_size, num_classes=args.num_classes)
elif args.net.startswith('deeplab'):
use_smp_deeplab = args.net.endswith('smp')
if use_smp_deeplab:
backbone_type = re.sub("^eff", "efficientnet", args.backbone_type)
net = smp.DeepLabV3Plus(backbone_type, classes=args.num_classes, encoder_weights='imagenet')
else:
model_name = args.net + "_" + args.backbone_type
model_map = {
'deeplabv3_resnet50': deeplab.deeplabv3_resnet50,
'deeplabv3plus_resnet50': deeplab.deeplabv3plus_resnet50,
'deeplabv3_resnet101': deeplab.deeplabv3_resnet101,
'deeplabv3plus_resnet101': deeplab.deeplabv3plus_resnet101,
'deeplabv3_mobilenet': deeplab.deeplabv3_mobilenet,
'deeplabv3plus_mobilenet': deeplab.deeplabv3plus_mobilenet
}
net = model_map[model_name](num_classes=args.num_classes, output_stride=8)
elif args.net == 'nnunet':
from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.network_architecture.generic_UNet import Generic_UNet
net = Generic_UNet(
input_channels=3,
base_num_features=32,
num_classes=args.num_classes,
num_pool=7,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
norm_op=nn.InstanceNorm2d,
norm_op_kwargs={'eps': 1e-05, 'affine': True},
dropout_op_kwargs={'p': 0, 'inplace': True},
nonlin_kwargs={'negative_slope': 0.01, 'inplace': True},
final_nonlin=(lambda x: x),
weightInitializer=InitWeights_He(1e-2),
pool_op_kernel_sizes=[[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]],
conv_kernel_sizes=([[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]]),
upscale_logits=False,
convolutional_pooling=True,
convolutional_upsampling=True,
)
net.inference_apply_nonlin = (lambda x: F.softmax(x, 1))
elif args.net == 'segtran':
get_default(args, 'num_modes', default_settings, -1, [args.net, 'num_modes', args.in_fpn_layers])
set_segtran2d_config(args)
print(args)
net = Segtran2d(config)
else:
breakpoint()
net.cuda()
net.eval()
if args.robust_ref_cp_path:
refnet = copy.deepcopy(net)
print("Reference network created")
load_model(refnet, args, args.robust_ref_cp_path)
else:
refnet = None
# Currently colormap is used only for OCT task.
colormap = get_seg_colormap(args.num_classes, return_torch=True).cuda()
# prepred: pre-prediction. postpred: post-prediction.
task2mask_prepred = { 'refuge': refuge_map_mask, 'polyp': polyp_map_mask,
'oct': partial(index_to_onehot, num_classes=args.num_classes) }
task2mask_postpred = { 'refuge': refuge_inv_map_mask, 'polyp': polyp_inv_map_mask,
'oct': partial(onehot_inv_map, colormap=colormap) }
mask_prepred_mapping_func = task2mask_prepred[args.task_name]
mask_postpred_mapping_funcs = [ task2mask_postpred[args.task_name] ]
if args.do_remove_frag:
remove_frag = lambda segmap: remove_fragmentary_segs(segmap, 255)
mask_postpred_mapping_funcs.append(remove_frag)
if not args.checkpoint_dir:
if args.vis_mode is not None:
visualize_model(net, args.vis_mode, args.vis_layers, args.patch_size, db_test)
return
if args.eval_robustness:
eval_robustness(args, net, refnet, testloader, mask_prepred_mapping_func)
return
allcls_avg_metric = None
all_results = np.zeros((args.num_classes, len(iter_nums)))
for iter_idx, iter_num in enumerate(iter_nums):
if args.checkpoint_dir:
checkpoint_path = os.path.join(args.checkpoint_dir, 'iter_' + str(iter_num) + '.pth')
load_model(net, args, checkpoint_path)
if args.vis_mode is not None:
visualize_model(net, args.vis_mode, args.vis_layers, args.patch_size, db_test)
continue
if args.eval_robustness:
eval_robustness(args, net, refnet, testloader, mask_prepred_mapping_func)
continue
save_results = args.save_results and (not args.test_interp)
if save_results:
test_save_paths = []
test_save_dirs = []
test_save_dir_tmpl = "%s-%s-%s-%d" %(args.net, args.job_name, timestamp, iter_num)
for suffix in ("-soft", "-%.1f" %args.mask_thres):
test_save_dir = test_save_dir_tmpl + suffix
test_save_path = "../prediction/%s" %(test_save_dir)
if not os.path.exists(test_save_path):
os.makedirs(test_save_path)
test_save_dirs.append(test_save_dir)
test_save_paths.append(test_save_path)
else:
test_save_paths = None
test_save_dirs = None
if args.save_features_img_count > 0:
args.save_features_file_path = "%s-%s-feat-%s.pth" %(args.net, args.job_name, timestamp)
else:
args.save_features_file_path = None
allcls_avg_metric, allcls_metric_count = \
test_all_cases(net, testloader, task_name=args.task_name,
num_classes=args.num_classes,
mask_thres=args.mask_thres,
model_type=args.net,
orig_input_size=args.orig_input_size,
patch_size=args.patch_size,
stride=(args.orig_input_size[0] // 2, args.orig_input_size[1] // 2),
test_save_paths=test_save_paths,
out_origsize=args.out_origsize,
mask_prepred_mapping_func=mask_prepred_mapping_func,
mask_postpred_mapping_funcs=mask_postpred_mapping_funcs,
reload_mask=args.reload_mask,
test_interp=args.test_interp,
save_features_img_count=args.save_features_img_count,
save_features_file_path=args.save_features_file_path,
verbose=args.verbose_output)
print("Iter-%d scores on %d images:" %(iter_num, allcls_metric_count[0]))
dice_sum = 0
for cls in range(1, args.num_classes):
dice = allcls_avg_metric[cls-1]
print('class %d: dice = %.3f' %(cls, dice))
dice_sum += dice
all_results[cls, iter_idx] = dice
avg_dice = dice_sum / (args.num_classes - 1)
print("Average dice: %.3f" %avg_dice)
if args.net == 'segtran':
max_attn, avg_attn, clamp_count, call_count = \
[ segtran_shared.__dict__[v] for v in ('max_attn', 'avg_attn', 'clamp_count', 'call_count') ]
print("max_attn={:.2f}, avg_attn={:.2f}, clamp_count={}, call_count={}".format(
max_attn, avg_attn, clamp_count, call_count))
if save_results:
FNULL = open(os.devnull, 'w')
for pred_type, test_save_dir, test_save_path in zip(('soft', 'hard'), test_save_dirs, test_save_paths):
do_tar = subprocess.run(["tar", "cvf", "%s.tar" %test_save_dir, test_save_dir], cwd="../prediction",
stdout=FNULL, stderr=subprocess.STDOUT)
# print(do_tar)
print("{} tarball:\n{}.tar".format(pred_type, os.path.abspath(test_save_path)))
np.set_printoptions(precision=3, suppress=True)
print(all_results[1:])
return allcls_avg_metric
class nnUNetMultiTrainerV2(nnUNetTrainer):
"""
Info for Fabian: same as internal nnUNetTrainerV2_2
"""
def __init__(self,
plans_file,
fold,
tasks,
tags,
output_folder_dict=None,
dataset_directory_dict=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False):
"""
:param deterministic:
:param fold: can be either [0 ... 5) for cross-validation, 'all' to train on all available training data or
None if you wish to load some checkpoint and do inference only
:param plans_file: the pkl file generated by preprocessing. This file will determine all design choices
:param subfolder_with_preprocessed_data: must be a subfolder of dataset_directory (just the name of the folder,
not the entire path). This is where the preprocessed data lies that will be used for network training. We made
this explicitly available so that differently preprocessed data can coexist and the user can choose what to use.
Can be None if you are doing inference only.
:param output_folder: where to store parameters, plot progress and to the validation
:param dataset_directory: the parent directory in which the preprocessed Task data is stored. This is required
because the split information is stored in this directory. For running prediction only this input is not
required and may be set to None
:param batch_dice: compute dice loss for each sample and average over all samples in the batch or pretend the
batch is a pseudo volume?
:param stage: The plans file may contain several stages (used for lowres / highres / pyramid). Stage must be
specified for training:
if stage 1 exists then stage 1 is the high resolution stage, otherwise it's 0
:param unpack_data: if False, npz preprocessed data will not be unpacked to npy. This consumes less space but
is considerably slower! Running unpack_data=False with 2d should never be done!
IMPORTANT: If you inherit from nnUNetTrainer and the init args change then you need to redefine self.init_args
in your init accordingly. Otherwise checkpoints won't load properly!
"""
self.fp16 = fp16
self.amp_initialized = False
self.x_tags = None
if deterministic:
np.random.seed(12345)
torch.manual_seed(12345)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(12345)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
################# SET THESE IN self.initialize() ###################################
self.network: Tuple[SegmentationNetwork, nn.DataParallel] = None
self.optimizer = None
self.lr_scheduler = None
self.tr_gen = self.val_gen = None
self.was_initialized = False
################# SET THESE IN INIT ################################################
self.output_folder = None
self.fold = None
self.dataset_directory = None
################# SET THESE IN LOAD_DATASET OR DO_SPLIT ############################
self.dataset = None # these can be None for inference mode
# do not need to be used, they just appear if you are using the suggested load_dataset_and_do_split
self.dataset_tr = self.dataset_val = None
################# THESE DO NOT NECESSARILY NEED TO BE MODIFIED #####################
self.patience = 50
self.val_eval_criterion_alpha = 0.9 # alpha * old + (1-alpha) * new
# if this is too low then the moving average will be too noisy and the training may terminate early. If it is
# too high the training will take forever
self.train_loss_MA_alpha = 0.93 # alpha * old + (1-alpha) * new
# new MA must be at least this much better (smaller)
self.train_loss_MA_eps = 5e-4
self.save_every = 1
self.save_latest_only = True
self.max_num_epochs = 500
self.stage_2_start_epoch = 120
self.num_batches_per_epoch = 250
self.num_val_batches_per_epoch = 50
self.also_val_in_tr_mode = False
# the network will not terminate training if the lr is still above this threshold
self.lr_threshold = 1e-6
################# LEAVE THESE ALONE ################################################
self.val_eval_criterion_MA = None
self.train_loss_MA = None
self.best_val_eval_criterion_MA = None
self.best_MA_tr_loss_for_patience = None
self.best_epoch_based_on_MA_tr_loss = None
self.all_tr_losses = []
self.all_val_losses = []
self.all_val_losses_tr_mode = []
self.all_val_eval_metrics = [] # does not have to be used
self.epoch = 0
# self.need_updateGT = False #update p
self.log_file = None
self.deterministic = deterministic
self.use_progress_bar = True
if 'nnunet_use_progress_bar' in os.environ.keys():
self.use_progress_bar = bool(
int(os.environ['nnunet_use_progress_bar']))
#################################################################
self.unpack_data = unpack_data
self.init_args = (plans_file, fold, output_folder_dict,
dataset_directory_dict, batch_dice, stage,
unpack_data, deterministic, fp16)
# set through arguments from init
self.stage = stage
self.experiment_name = self.__class__.__name__
self.plans_file = plans_file
self.output_folder_dict = output_folder_dict
self.output_folder = output_folder_dict[tasks[0]]
self.dataset_directory_dict = dataset_directory_dict
self.output_folder_base = self.output_folder
self.fold = fold
self.tasks = tasks
self.tags = tags
self.plans = None
# if we are running inference only then the self.dataset_directory is set (due to checkpoint loading) but it
# irrelevant
self.gt_niftis_folder_dict = {}
for task in tasks:
dataset_directory = self.dataset_directory_dict[task]
if dataset_directory is not None and isdir(dataset_directory):
self.gt_niftis_folder_dict[task] = join(
dataset_directory, "gt_segmentations")
else:
self.gt_niftis_folder_dict[task] = None
self.gt_niftis_folder = self.gt_niftis_folder_dict[
self.tasks[0]] # 0 gt_nii
self.folder_with_preprocessed_data = None
# set in self.initialize()
self.dl_tr = self.dl_val = None
self.num_input_channels = self.num_classes = self.net_pool_per_axis = self.patch_size = self.batch_size = \
self.threeD = self.base_num_features = self.intensity_properties = self.normalization_schemes = \
self.net_num_pool_op_kernel_sizes = self.net_conv_kernel_sizes = None # loaded automatically from plans_file
self.basic_generator_patch_size = self.data_aug_params = self.transpose_forward = self.transpose_backward = None
self.batch_dice = batch_dice
# self.loss = None
self.online_eval_foreground_dc = []
self.online_eval_tp = []
self.online_eval_fp = []
self.online_eval_fn = []
self.classes = self.do_dummy_2D_aug = self.use_mask_for_norm = self.only_keep_largest_connected_component = \
self.min_region_size_per_class = self.min_size_per_class = None
self.inference_pad_border_mode = "constant"
self.inference_pad_kwargs = {'constant_values': 0}
self.update_fold(fold)
self.pad_all_sides = None
self.lr_scheduler_eps = 1e-3
self.lr_scheduler_patience = 30
self.initial_lr = 3e-4
self.weight_decay = 3e-5
self.oversample_foreground_percent = 0.33
self.conv_per_stage = None
self.regions_class_order = None
self.initial_lr = 1e-2
self.deep_supervision_scales = None
self.ds_loss_weights = None
self.pin_memory = True
self.loss = DC_CE_Marginal_Exclusion_loss(
{
'batch_dice': self.batch_dice,
'smooth': 1e-5,
'do_bg': False
}, {})
# self.loss = pann_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
# self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
def load_plans_file(self):
"""
This is what actually configures the entire experiment. The plans file is generated by experiment planning
:return:
"""
self.plans = load_pickle(self.plans_file[self.tasks[0]])
def initialize(self, training=True, force_load_plans=False):
"""
- replaced get_default_augmentation with get_moreDA_augmentation
- enforce to only run this code once
- loss function wrapper for deep supervision
:param training:
:param force_load_plans:
:return:
"""
if not self.was_initialized:
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = len(self.net_num_pool_op_kernel_sizes)
# print("---net_numpool:", net_numpool) #Task_100 MAB 5 class
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2**i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [
True if i < net_numpool - 1 else False
for i in range(1, net_numpool)
])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
# now wrap the loss
# self.loss = MultipleOutputLoss2withTags_pann(self.loss, self.ds_loss_weights)
self.loss = MultipleOutputLoss2withTags(self.loss,
self.ds_loss_weights)
################# END ###################
self.folder_with_preprocessed_data = {}
self.dl_tr = []
self.dl_val = []
self.tr_gens = []
self.val_gens = []
for task in self.tasks:
self.folder_with_preprocessed_data[task] = join(
self.dataset_directory_dict[task],
self.plans['data_identifier'] + "_stage%d" % self.stage)
if training:
dl_tr, dl_val = self.get_basic_generators(task)
self.dl_tr.append(dl_tr)
self.dl_val.append(dl_val)
# print('%s.dl_tr raw data size:%d'%(task, len(dl_tr)))
if self.unpack_data:
print("unpacking dataset")
unpack_dataset(
self.folder_with_preprocessed_data[task])
print("done")
else:
print(
"INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
"will wait all winter for your model to finish!")
tr_gen, val_gen = get_moreDA_augmentation( # data augmentation
dl_tr,
dl_val,
self.
data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_params,
deep_supervision_scales=self.deep_supervision_scales,
pin_memory=self.pin_memory)
self.tr_gens.append(tr_gen) #tr_gen: multithreadaug..
self.val_gens.append(val_gen)
self.print_to_log_file("TRAINING KEYS:\n %s" %
(str(self.dataset_tr.keys())),
also_print_to_console=False)
print("TRAINING KEYS:\n %s" %
(str(self.dataset_tr.keys())))
self.print_to_log_file("VALIDATION KEYS:\n %s" %
(str(self.dataset_val.keys())),
also_print_to_console=False)
else:
pass
################## dataset all in ###########################
if training:
self.tr_gen = switchable_generator(self.tr_gens)
self.val_gen = switchable_generator(self.val_gens)
self.initialize_network()
self.initialize_optimizer_and_scheduler()
assert isinstance(self.network,
(SegmentationNetwork, nn.DataParallel))
else:
self.print_to_log_file(
'self.was_initialized is True, not running self.initialize again'
)
self.was_initialized = True
def initialize_network(self):
"""
- momentum 0.99
- SGD instead of Adam
- self.lr_scheduler = None because we do poly_lr
- deep supervision = True
- i am sure I forgot something here
Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_optimizer_and_scheduler(self):
assert self.network is not None, "self.initialize_network must be called first"
self.optimizer = torch.optim.SGD(self.network.parameters(),
self.initial_lr,
weight_decay=self.weight_decay,
momentum=0.99,
nesterov=True)
self.lr_scheduler = None
def get_basic_generators(self, task):
self.load_dataset(task)
self.do_split(task)
if self.threeD:
dl_tr = DataLoader3D(self.dataset_tr,
self.basic_generator_patch_size,
self.patch_size,
self.batch_size,
self.tags[task],
False,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
dl_val = DataLoader3D(self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
self.tags[task],
False,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
else:
dl_tr = DataLoader2D(
self.dataset_tr,
self.basic_generator_patch_size,
self.patch_size,
self.batch_size,
self.tags[task],
# self.plans.get('transpose_forward'),
transpose=None,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
dl_val = DataLoader2D(
self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
self.tags[task],
# self.plans.get('transpose_forward'),
transpose=None,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
return dl_tr, dl_val
def load_dataset(self, task=None):
if task is None:
self.dataset = load_dataset(
self.folder_with_preprocessed_data[self.tasks[0]])
else:
self.dataset = load_dataset(
self.folder_with_preprocessed_data[task])
def run_online_evaluation(self, output, target):
"""
due to deep supervision the return value and the reference are now lists of tensors. We only need the full
resolution output because this is what we are interested in in the end. The others are ignored
:param output:
:param target:
:return:
"""
target = target[0]
output = output[0]
return super().run_online_evaluation(output, target)
def validate(self,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
force_separate_z: bool = None,
interpolation_order: int = 3,
interpolation_order_z=0):
"""
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
"""
ds = self.network.do_ds
self.network.do_ds = False
ret = super().validate(do_mirroring,
use_sliding_window,
step_size,
save_softmax,
use_gaussian,
overwrite,
validation_folder_name,
debug,
all_in_gpu,
force_separate_z=force_separate_z,
interpolation_order=interpolation_order,
interpolation_order_z=interpolation_order_z)
self.network.do_ds = ds
return ret
def validate_specific_data(self,
task,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = False,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
force_separate_z: bool = None,
interpolation_order: int = 3,
interpolation_order_z=0):
ds = self.network.do_ds #?
self.network.do_ds = False
###########################################
current_mode = self.network.training
self.network.eval()
assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)"
self.load_dataset(task)
self.do_split(task)
# predictions as they come from the network go here
self.output_folder = self.output_folder_dict[task]
self.gt_niftis_folder = self.gt_niftis_folder_dict[task]
output_folder = join(self.output_folder, 'fold_4',
validation_folder_name)
maybe_mkdir_p(output_folder)
# this is for debug purposes
my_input_args = {
'do_mirroring': do_mirroring,
'use_sliding_window': use_sliding_window,
'step_size': step_size,
'save_softmax': save_softmax,
'use_gaussian': use_gaussian,
'overwrite': overwrite,
'validation_folder_name': validation_folder_name,
'debug': debug,
'all_in_gpu': all_in_gpu, #? why not use
'force_separate_z': force_separate_z,
'interpolation_order': interpolation_order,
'interpolation_order_z': interpolation_order_z,
}
save_json(my_input_args, join(output_folder, "validation_args.json"))
if do_mirroring:
if not self.data_aug_params['do_mirror']:
raise RuntimeError(
"We did not train with mirroring so you cannot do inference with mirroring enabled"
)
mirror_axes = self.data_aug_params['mirror_axes']
else:
mirror_axes = ()
pred_gt_tuples = []
export_pool = Pool(default_num_threads)
results = []
if '104' in task: #kidney
temp_transpose = [0, 2, 3, 1]
else:
temp_transpose = [0, 1, 2, 3]
for k in self.dataset_val.keys():
properties = self.dataset[k]['properties']
fname = properties['list_of_data_files'][0].split("/")[-1][:-12]
if overwrite or (not isfile(join(output_folder, fname + ".nii.gz"))) or \
(save_softmax and not isfile(join(output_folder, fname + ".npz"))):
data = np.load(self.dataset[k]['data_file'])['data']
print(k, data.shape)
data[-1][data[-1] == -1] = 0
softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax(
data[:-1],
do_mirroring,
mirror_axes,
use_sliding_window,
step_size,
use_gaussian,
all_in_gpu=all_in_gpu)[1]
softmax_pred = softmax_pred.transpose(temp_transpose)
# softmax_pred = softmax_pred.transpose([0] + [i + 1 for i in self.transpose_backward])
if save_softmax:
softmax_fname = join(output_folder, fname + ".npz")
else:
softmax_fname = None
"""There is a problem with python process communication that prevents us from communicating obejcts
larger than 2 GB between processes (basically when the length of the pickle string that will be sent is
communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long
enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually
patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will
then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either
filename or np.ndarray and will handle this automatically"""
if np.prod(softmax_pred.shape) > (
2e9 / 4 * 0.85): # *0.85 just to be save
np.save(join(output_folder, fname + ".npy"), softmax_pred)
softmax_pred = join(output_folder, fname + ".npy")
# save_segmentation_nifti_from_softmax(softmax_pred, join(output_folder, fname + ".nii.gz"),
# properties, interpolation_order, None, None, None,
# softmax_fname, None, force_separate_z,
# interpolation_order_z, task)
results.append(
export_pool.starmap_async(
save_segmentation_nifti_from_softmax,
((softmax_pred, join(output_folder, fname + ".nii.gz"),
properties, interpolation_order, None, None, None,
softmax_fname, None, force_separate_z,
interpolation_order_z, task), )))
pred_gt_tuples.append([
join(output_folder, fname + ".nii.gz"),
join(self.gt_niftis_folder, fname + ".nii.gz")
])
_ = [i.get() for i in results]
self.print_to_log_file("finished prediction")
# evaluate raw predictions
self.print_to_log_file("evaluation of raw predictions")
# task = self.dataset_directory.split("/")[-1]
job_name = self.experiment_name
# x_tags = ['rightkidney','leftkidney']
x_tags = ['liver', 'spleen', 'pancreas', 'rightkidney', 'leftkidney']
if "100" in task:
y_tags = x_tags
elif "101" in task or "105" in task:
y_tags = ['liver']
elif "102" in task:
y_tags = ['spleen']
elif "103" in task:
y_tags = ['pancreas']
elif "104" in task:
y_tags = ['rightkidney', 'leftkidney']
elif "105" in task: #PrivateLiver
y_tags = ['liver']
else:
exit()
all_score = aggregate_scores_withtags(
pred_gt_tuples,
labels=list(range(self.num_classes)),
x_tags=x_tags,
y_tags=y_tags,
json_output_file=join(output_folder, "summary.json"),
json_name=job_name + " val tiled %s" % (str(use_sliding_window)),
json_author="Fabian",
json_task=task,
num_threads=default_num_threads)
# in the old nnunet we would stop here. Now we add a postprocessing. This postprocessing can remove everything
# except the largest connected component for each class. To see if this improves results, we do this for all
# classes and then rerun the evaluation. Those classes for which this resulted in an improved dice score will
# have this applied during inference as well
# after this the final predictions for the vlaidation set can be found in validation_folder_name_base + "_postprocessed"
# They are always in that folder, even if no postprocessing as applied!
# detemining postprocesing on a per-fold basis may be OK for this fold but what if another fold finds another
# postprocesing to be better? In this case we need to consolidate. At the time the consolidation is going to be
# done we won't know what self.gt_niftis_folder was, so now we copy all the niftis into a separate folder to
# be used later
self.output_folder_base = self.output_folder
gt_nifti_folder = join(self.output_folder_base, "gt_niftis")
maybe_mkdir_p(gt_nifti_folder)
for f in subfiles(self.gt_niftis_folder, suffix=".nii.gz"):
success = False
attempts = 0
e = None
while not success and attempts < 10:
try:
shutil.copy(f, gt_nifti_folder)
success = True
except OSError as e:
attempts += 1
sleep(1)
if not success:
print("Could not copy gt nifti file %s into folder %s" %
(f, gt_nifti_folder))
if e is not None:
raise e
self.network.train(current_mode)
###########################################
self.network.do_ds = ds
# return ret
def predict_preprocessed_data_return_seg_and_softmax(
self,
data: np.ndarray,
do_mirroring: bool = True,
mirror_axes: Tuple[int] = None,
use_sliding_window: bool = True,
step_size: float = 0.5,
use_gaussian: bool = True,
pad_border_mode: str = 'constant',
pad_kwargs: dict = None,
all_in_gpu: bool = True,
verbose: bool = True) -> Tuple[np.ndarray, np.ndarray]:
"""
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
"""
ds = self.network.do_ds
self.network.do_ds = False
ret = super().predict_preprocessed_data_return_seg_and_softmax(
data, do_mirroring, mirror_axes, use_sliding_window, step_size,
use_gaussian, pad_border_mode, pad_kwargs, all_in_gpu, verbose)
self.network.do_ds = ds
return ret
def run_iteration(self,
data_generator,
do_backprop=True,
run_online_evaluation=False):
"""
gradient clipping improves training stability
:param data_generator:
:param do_backprop:
:param run_online_evaluation:
:return:
"""
data_dict = next(data_generator)
# length = get_length(data_generator)
data = data_dict['data']
target = data_dict['target']
# self.x_tags = ['liver','spleen','pancreas','rightkidney','leftkidney'] #test-mk
if self.x_tags is None:
self.x_tags = [tag.lower() for tag in data_dict['tags']]
y_tags = [tag.lower() for tag in data_dict['tags']]
# print("------------------x_tags:",self.x_tags)
# print("------------------y_tags:",y_tags)
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data)
target = to_cuda(target)
self.optimizer.zero_grad()
output = self.network(data)
del data
# loss = self.loss(output, target,self.x_tags,y_tags,need_updateGT=need_updateGT)
loss = self.loss(output, target, self.x_tags, y_tags)
if run_online_evaluation:
self.run_online_evaluation(output, target)
del target
if do_backprop:
if not self.fp16 or amp is None or not torch.cuda.is_available():
loss.backward()
else:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
_ = clip_grad_norm_(self.network.parameters(), 12)
self.optimizer.step()
return loss.detach().cpu().numpy()
def do_split(self, task=None):
"""
we now allow more than 5 splits. IMPORTANT: and fold > 4 will not be a real split but just another random
80:20 split of the data. You cannot run X-fold cross-validation with this code. It will always be a 5-fold CV.
Folds > 4 will be independent from each other
:return:
"""
if task is None:
task = self.tasks[0]
if self.fold == 'all' or self.fold < 5: #fold = 4
self.dataset_directory = self.dataset_directory_dict[task]
return super().do_split()
else:
print("---------------!!!!!!!!--------------")
rnd = np.random.RandomState(seed=12345 + self.fold)
keys = np.sort(list(self.dataset.keys()))
idx_tr = rnd.choice(len(keys), int(len(keys) * 0.8), replace=False)
idx_val = [i for i in range(len(keys)) if i not in idx_tr]
self.dataset_tr = OrderedDict()
for i in idx_tr:
self.dataset_tr[keys[i]] = self.dataset[keys[i]]
self.dataset_val = OrderedDict()
for i in idx_val:
self.dataset_val[keys[i]] = self.dataset[keys[i]]
def setup_DA_params(self):
"""
- we increase roation angle from [-15, 15] to [-30, 30]
- scale range is now (0.7, 1.4), was (0.85, 1.25)
- we don't do elastic deformation anymore
:return:
"""
self.deep_supervision_scales = [[1, 1, 1]] + list(
list(i) for i in 1 / np.cumprod(
np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params[
"rotation_x"] = default_2D_augmentation_params[
"rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 *
2. * np.pi,
15. / 360 *
2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params["scale_range"] = (0.7, 1.4)
self.data_aug_params["do_elastic"] = False
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params[
'patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
self.data_aug_params["num_cached_per_thread"] = 2
def maybe_update_lr(self, epoch=None):
"""
if epoch is not None we overwrite epoch. Else we use epoch = self.epoch + 1
(maybe_update_lr is called in on_epoch_end which is called before epoch is incremented.
herefore we need to do +1 here)
:param epoch:
:return:
"""
if epoch is None:
ep = self.epoch + 1
else:
ep = epoch
self.optimizer.param_groups[0]['lr'] = poly_lr(ep, self.max_num_epochs,
self.initial_lr, 0.9)
self.print_to_log_file(
"lr:", np.round(self.optimizer.param_groups[0]['lr'], decimals=6))
def on_epoch_end(self):
"""
overwrite patient-based early stopping. Always run to 1000 epochs
:return:
"""
super().on_epoch_end()
continue_training = self.epoch < self.max_num_epochs
# it can rarely happen that the momentum of nnUNetTrainerV2 is too high for some dataset. If at epoch 100 the
# estimated validation Dice is still 0 then we reduce the momentum from 0.99 to 0.95
if self.epoch == 100:
if self.all_val_eval_metrics[-1] == 0:
self.optimizer.param_groups[0]["momentum"] = 0.95
self.network.apply(InitWeights_He(1e-2))
self.print_to_log_file(
"At epoch 100, the mean foreground Dice was 0. This can be caused by a too "
"high momentum. High momentum (0.99) is good for datasets where it works, but "
"sometimes causes issues such as this one. Momentum has now been reduced to "
"0.95 and network weights have been reinitialized")
return continue_training
def run_training(self):
"""
if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first
continued epoch with self.initial_lr
we also need to make sure deep supervision in the network is enabled for training, thus the wrapper
:return:
"""
self.maybe_update_lr(
self.epoch
) # if we dont overwrite epoch then self.epoch+1 is used which is not what we
# want at the start of the training
ds = self.network.do_ds
self.network.do_ds = True
# ret = super().run_training()
########################################################################################
dct = OrderedDict()
for k in self.__dir__():
if not k.startswith("__"):
if not callable(getattr(self, k)):
dct[k] = str(getattr(self, k))
del dct['plans']
del dct['intensity_properties']
del dct['dataset']
del dct['dataset_tr']
del dct['dataset_val']
save_json(dct, join(self.output_folder, "debug.json"))
import shutil
shutil.copy(self.plans_file[self.tasks[0]],
join(self.output_folder_base, "plans.pkl"))
for i in range(len(self.tasks)):
self.tr_gen.setPart(i)
_ = self.tr_gen.next()
_ = self.val_gen.next()
self.tr_gen.setPart(0)
if torch.cuda.is_available():
torch.cuda.empty_cache()
# self._maybe_init_amp()
self.plot_network_architecture()
if cudnn.benchmark and cudnn.deterministic:
warn(
"torch.backends.cudnn.deterministic is True indicating a deterministic training is desired. "
"But torch.backends.cudnn.benchmark is True as well and this will prevent deterministic training! "
"If you want deterministic then set benchmark=False")
maybe_mkdir_p(self.output_folder)
if not self.was_initialized:
self.initialize(True)
flag = True
while self.epoch < self.max_num_epochs:
# self.need_updateGT=True
self.print_to_log_file("\nepoch: ", self.epoch)
epoch_start_time = time()
train_losses_epoch = []
# train one epoch
self.network.train()
if self.epoch >= self.stage_2_start_epoch and flag:
self.num_batches_per_epoch = 50
flag = False
if self.use_progress_bar:
with trange(self.num_batches_per_epoch) as tbar:
for b in tbar:
tbar.set_description("Epoch {}/{}".format(
self.epoch + 1, self.max_num_epochs))
l = self.run_iteration(self.tr_gen, True)
tbar.set_postfix(loss=l)
train_losses_epoch.append(l)
if self.epoch >= self.stage_2_start_epoch:
for i in range(1, len(self.tasks)):
self.tr_gen.setPart(i)
_ = self.run_iteration(self.tr_gen, True)
self.tr_gen.setPart(0)
else:
for _ in range(self.num_batches_per_epoch):
l = self.run_iteration(self.tr_gen, True)
train_losses_epoch.append(l)
if self.epoch > self.stage_2_start_epoch:
for i in range(1, len(self.tasks)):
self.tr_gen.setPart(i)
_ = self.run_iteration(self.tr_gen, True)
self.tr_gen.setPart(0)
self.all_tr_losses.append(np.mean(train_losses_epoch))
self.print_to_log_file("train loss : %.4f" %
self.all_tr_losses[-1])
with torch.no_grad():
# validation with train=False
self.network.eval()
val_losses = []
for b in range(self.num_val_batches_per_epoch):
l = self.run_iteration(self.val_gen, False, True)
val_losses.append(l)
self.all_val_losses.append(np.mean(val_losses))
self.print_to_log_file("validation loss: %.4f" %
self.all_val_losses[-1])
if self.also_val_in_tr_mode:
self.network.train()
# validation with train=True
val_losses = []
for b in range(self.num_val_batches_per_epoch):
l = self.run_iteration(self.val_gen, False)
val_losses.append(l)
self.all_val_losses_tr_mode.append(np.mean(val_losses))
self.print_to_log_file(
"validation loss (train=True): %.4f" %
self.all_val_losses_tr_mode[-1])
self.update_train_loss_MA(
) # needed for lr scheduler and stopping of training
continue_training = self.on_epoch_end()
epoch_end_time = time()
if not continue_training:
# allows for early stopping
break
self.epoch += 1
self.print_to_log_file("This epoch took %f s\n" %
(epoch_end_time - epoch_start_time))
self.epoch -= 1 # if we don't do this we can get a problem with loading model_final_checkpoint.
self.save_checkpoint(
join(self.output_folder, "model_final_checkpoint.model"))
# now we can delete latest as it will be identical with final
if isfile(join(self.output_folder, "model_latest.model")):
os.remove(join(self.output_folder, "model_latest.model"))
if isfile(join(self.output_folder, "model_latest.model.pkl")):
os.remove(join(self.output_folder, "model_latest.model.pkl"))
#########################################################################################
self.network.do_ds = ds
class nnUNetTrainerV2_noDeepSupervision(nnUNetTrainerV2):
def __init__(self,
plans_file,
fold,
output_folder=None,
dataset_directory=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False):
super().__init__(plans_file, fold, output_folder, dataset_directory,
batch_dice, stage, unpack_data, deterministic, fp16)
self.loss = DC_and_CE_loss(
{
'batch_dice': self.batch_dice,
'smooth': 1e-5,
'do_bg': False
}, {})
def setup_DA_params(self):
"""
we leave out the creation of self.deep_supervision_scales, so it remains None
:return:
"""
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params[
"rotation_x"] = default_2D_augmentation_params[
"rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 *
2. * np.pi,
15. / 360 *
2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params["scale_range"] = (0.7, 1.4)
self.data_aug_params["do_elastic"] = False
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params[
'patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
def initialize(self, training=True, force_load_plans=False):
"""
removed deep supervision
:return:
"""
if not self.was_initialized:
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
self.folder_with_preprocessed_data = join(
self.dataset_directory,
self.plans['data_identifier'] + "_stage%d" % self.stage)
if training:
self.dl_tr, self.dl_val = self.get_basic_generators()
if self.unpack_data:
print("unpacking dataset")
unpack_dataset(self.folder_with_preprocessed_data)
print("done")
else:
print(
"INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
"will wait all winter for your model to finish!")
assert self.deep_supervision_scales is None
self.tr_gen, self.val_gen = get_moreDA_augmentation(
self.dl_tr,
self.dl_val,
self.data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_params,
deep_supervision_scales=self.deep_supervision_scales,
classes=None,
pin_memory=self.pin_memory)
self.print_to_log_file("TRAINING KEYS:\n %s" %
(str(self.dataset_tr.keys())),
also_print_to_console=False)
self.print_to_log_file("VALIDATION KEYS:\n %s" %
(str(self.dataset_val.keys())),
also_print_to_console=False)
else:
pass
self.initialize_network()
self.initialize_optimizer_and_scheduler()
assert isinstance(self.network,
(SegmentationNetwork, nn.DataParallel))
else:
self.print_to_log_file(
'self.was_initialized is True, not running self.initialize again'
)
self.was_initialized = True
def initialize_network(self):
"""
changed deep supervision to False
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, False, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def run_online_evaluation(self, output, target):
return nnUNetTrainer.run_online_evaluation(self, output, target)
def get_properties_for_stage(current_spacing, original_spacing,
original_shape, num_cases, num_modalities,
num_classes):
"""
Computation of input patch size starts out with the new median shape (in voxels) of a dataset. This is
opposed to prior experiments where I based it on the median size in mm. The rationale behind this is that
for some organ of interest the acquisition method will most likely be chosen such that the field of view and
voxel resolution go hand in hand to show the doctor what they need to see. This assumption may be violated
for some modalities with anisotropy (cine MRI) but we will have t live with that. In future experiments I
will try to 1) base input patch size match aspect ratio of input size in mm (instead of voxels) and 2) to
try to enforce that we see the same 'distance' in all directions (try to maintain equal size in mm of patch)
:param current_spacing:
:param original_spacing:
:param original_shape:
:param num_cases:
:return:
"""
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_poolLateV2(input_patch_size,
FEATUREMAP_MIN_EDGE_LENGTH_BOTTLENECK,
Generic_UNet.MAX_NUMPOOL_3D,
current_spacing)
ref = Generic_UNet.use_this_for_batch_size_computation_3D
here = Generic_UNet.compute_approx_vram_consumption(
new_shp, network_num_pool_per_axis,
Generic_UNet.BASE_NUM_FEATURES_3D,
Generic_UNet.MAX_NUM_FILTERS_3D, num_modalities, num_classes,
pool_op_kernel_sizes)
while here > ref:
argsrt = np.argsort(new_shp / new_median_shape)[::-1]
pool_fct_per_axis = np.prod(pool_op_kernel_sizes, 0)
bottleneck_size_per_axis = new_shp / pool_fct_per_axis
shape_must_be_divisible_by = [
shape_must_be_divisible_by[i]
if bottleneck_size_per_axis[i] > 4 else
shape_must_be_divisible_by[i] / 2
for i in range(len(bottleneck_size_per_axis))
]
new_shp[argsrt[0]] -= shape_must_be_divisible_by[argsrt[0]]
# 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_poolLateV2(new_shp,
FEATUREMAP_MIN_EDGE_LENGTH_BOTTLENECK,
Generic_UNet.MAX_NUMPOOL_3D,
current_spacing)
here = Generic_UNet.compute_approx_vram_consumption(
new_shp, network_num_pool_per_axis,
Generic_UNet.BASE_NUM_FEATURES_3D,
Generic_UNet.MAX_NUM_FILTERS_3D, num_modalities,
num_classes, pool_op_kernel_sizes)
print(new_shp)
input_patch_size = new_shp
batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D # This is what wirks with 128**3
batch_size = int(np.floor(max(ref / here, 1) * batch_size))
# check if batch size is too large
max_batch_size = np.round(
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, dataset_min_batch_size_cap)
batch_size = min(batch_size, max_batch_size)
do_dummy_2D_data_aug = (
max(input_patch_size) / input_patch_size[0]
) > RESAMPLING_SEPARATE_Z_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,
}
return plan
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 mor ethan 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.use_this_for_batch_size_computation_2D * Generic_UNet.DEFAULT_BATCH_SIZE_2D / 2 # for batch size 2
here = Generic_UNet.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.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
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) * 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_poolLateV2(input_patch_size,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
ref = Generic_UNet.use_this_for_batch_size_computation_3D
here = Generic_UNet.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)
while here > ref:
# here is the difference to ExperimentPlanner. In the old version we made the aspect ratio match
# between patch and new_median_shape, regardless of spacing. It could be better to enforce isotropy
# (in mm) instead
current_patch_in_mm = new_shp * current_spacing
axis_to_be_reduced = np.argsort(current_patch_in_mm)[-1]
# from here on it's the same as before
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_poolLateV2(tmp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
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_poolLateV2(new_shp,
self.unet_featuremap_min_edge_length,
self.unet_max_numpool,
current_spacing)
here = Generic_UNet.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)
input_patch_size = new_shp
batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D # This is what works with 128**3
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 = 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,
}
return plan
def get_properties_for_stage(self, current_spacing, original_spacing,
original_shape, num_cases, num_modalities,
num_classes):
"""
We need to adapt ref
"""
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)
# use_this_for_batch_size_computation_3D = 520000000 # 505789440
# typical ExperimentPlanner3D_v21 configurations use 7.5GB, but on a V100 we have 32. Allow for more space
# to be used
ref = Generic_UNet.use_this_for_batch_size_computation_3D * 32 / 8
here = Generic_UNet.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)
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,
)
here = Generic_UNet.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)
input_patch_size = new_shp
batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D # This is what wirks with 128**3
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,
}
return plan
class nnUNetTrainerV2(nnUNetTrainer):
"""
Info for Fabian: same as internal nnUNetTrainerV2_2
"""
def __init__(self,
plans_file,
fold,
output_folder=None,
dataset_directory=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False):
super().__init__(plans_file, fold, output_folder, dataset_directory,
batch_dice, stage, unpack_data, deterministic, fp16)
self.max_num_epochs = 1000
self.initial_lr = 1e-2
self.deep_supervision_scales = None
self.ds_loss_weights = None
self.pin_memory = True
def initialize(self, training=True, force_load_plans=False):
"""
- replaced get_default_augmentation with get_moreDA_augmentation
- enforce to only run this code once
- loss function wrapper for deep supervision
:param training:
:param force_load_plans:
:return:
"""
if not self.was_initialized:
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = len(self.net_num_pool_op_kernel_sizes)
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2**i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [
True if i < net_numpool - 1 else False
for i in range(1, net_numpool)
])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
# now wrap the loss
self.loss = MultipleOutputLoss2(self.loss, self.ds_loss_weights)
################# END ###################
self.folder_with_preprocessed_data = join(
self.dataset_directory,
self.plans['data_identifier'] + "_stage%d" % self.stage)
if training:
self.dl_tr, self.dl_val = self.get_basic_generators()
if self.unpack_data:
print("unpacking dataset")
unpack_dataset(self.folder_with_preprocessed_data)
print("done")
else:
print(
"INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
"will wait all winter for your model to finish!")
self.tr_gen, self.val_gen = get_moreDA_augmentation(
self.dl_tr,
self.dl_val,
self.data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_params,
deep_supervision_scales=self.deep_supervision_scales,
pin_memory=self.pin_memory)
self.print_to_log_file("TRAINING KEYS:\n %s" %
(str(self.dataset_tr.keys())),
also_print_to_console=False)
self.print_to_log_file("VALIDATION KEYS:\n %s" %
(str(self.dataset_val.keys())),
also_print_to_console=False)
else:
pass
self.initialize_network()
self.initialize_optimizer_and_scheduler()
assert isinstance(self.network,
(SegmentationNetwork, nn.DataParallel))
else:
self.print_to_log_file(
'self.was_initialized is True, not running self.initialize again'
)
self.was_initialized = True
def initialize_network(self):
"""
- momentum 0.99
- SGD instead of Adam
- self.lr_scheduler = None because we do poly_lr
- deep supervision = True
- i am sure I forgot something here
Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though
:return:
"""
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
self.network = Generic_UNet(
self.num_input_channels, self.base_num_features, self.num_classes,
len(self.net_num_pool_op_kernel_sizes), self.conv_per_stage, 2,
conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, True, False, lambda x: x,
InitWeights_He(1e-2), self.net_num_pool_op_kernel_sizes,
self.net_conv_kernel_sizes, False, True, True)
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def initialize_optimizer_and_scheduler(self):
assert self.network is not None, "self.initialize_network must be called first"
self.optimizer = torch.optim.SGD(self.network.parameters(),
self.initial_lr,
weight_decay=self.weight_decay,
momentum=0.99,
nesterov=True)
self.lr_scheduler = None
def run_online_evaluation(self, output, target):
"""
due to deep supervision the return value and the reference are now lists of tensors. We only need the full
resolution output because this is what we are interested in in the end. The others are ignored
:param output:
:param target:
:return:
"""
target = target[0]
output = output[0]
return super().run_online_evaluation(output, target)
def validate(self,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
segmentation_export_kwargs: dict = None):
"""
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
"""
ds = self.network.do_ds
self.network.do_ds = False
ret = super().validate(do_mirroring, use_sliding_window, step_size,
save_softmax, use_gaussian, overwrite,
validation_folder_name, debug, all_in_gpu,
segmentation_export_kwargs)
self.network.do_ds = ds
return ret
def predict_preprocessed_data_return_seg_and_softmax(
self,
data: np.ndarray,
do_mirroring: bool = True,
mirror_axes: Tuple[int] = None,
use_sliding_window: bool = True,
step_size: float = 0.5,
use_gaussian: bool = True,
pad_border_mode: str = 'constant',
pad_kwargs: dict = None,
all_in_gpu: bool = True,
verbose: bool = True) -> Tuple[np.ndarray, np.ndarray]:
"""
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
"""
ds = self.network.do_ds
self.network.do_ds = False
ret = super().predict_preprocessed_data_return_seg_and_softmax(
data, do_mirroring, mirror_axes, use_sliding_window, step_size,
use_gaussian, pad_border_mode, pad_kwargs, all_in_gpu, verbose)
self.network.do_ds = ds
return ret
def run_iteration(self,
data_generator,
do_backprop=True,
run_online_evaluation=False):
"""
gradient clipping improves training stability
:param data_generator:
:param do_backprop:
:param run_online_evaluation:
:return:
"""
data_dict = next(data_generator)
data = data_dict['data']
target = data_dict['target']
data = maybe_to_torch(data)
target = maybe_to_torch(target)
if torch.cuda.is_available():
data = to_cuda(data)
target = to_cuda(target)
self.optimizer.zero_grad()
output = self.network(data)
del data
loss = self.loss(output, target)
if run_online_evaluation:
self.run_online_evaluation(output, target)
del target
if do_backprop:
if not self.fp16 or amp is None or not torch.cuda.is_available():
loss.backward()
else:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
_ = clip_grad_norm_(self.network.parameters(), 12)
self.optimizer.step()
return loss.detach().cpu().numpy()
def do_split(self):
"""
we now allow more than 5 splits. IMPORTANT: and fold > 4 will not be a real split but just another random
80:20 split of the data. You cannot run X-fold cross-validation with this code. It will always be a 5-fold CV.
Folds > 4 will be independent from each other
:return:
"""
if self.fold == "all":
# if fold==all then we use all images for training and validation
tr_keys = val_keys = list(self.dataset.keys())
else:
splits_file = join(self.dataset_directory, "splits_final.pkl")
# if the split file does not exist we need to create it
if not isfile(splits_file):
self.print_to_log_file("Creating new split...")
splits = []
all_keys_sorted = np.sort(list(self.dataset.keys()))
kfold = KFold(n_splits=5, shuffle=True, random_state=12345)
for i, (train_idx,
test_idx) in enumerate(kfold.split(all_keys_sorted)):
train_keys = np.array(all_keys_sorted)[train_idx]
test_keys = np.array(all_keys_sorted)[test_idx]
splits.append(OrderedDict())
splits[-1]['train'] = train_keys
splits[-1]['val'] = test_keys
save_pickle(splits, splits_file)
splits = load_pickle(splits_file)
if self.fold < len(splits):
tr_keys = splits[self.fold]['train']
val_keys = splits[self.fold]['val']
else:
self.print_to_log_file(
"INFO: Requested fold %d but split file only has %d folds. I am now creating a "
"random 80:20 split!" % (self.fold, len(splits)))
# if we request a fold that is not in the split file, create a random 80:20 split
rnd = np.random.RandomState(seed=12345 + self.fold)
keys = np.sort(list(self.dataset.keys()))
idx_tr = rnd.choice(len(keys),
int(len(keys) * 0.8),
replace=False)
idx_val = [i for i in range(len(keys)) if i not in idx_tr]
tr_keys = [keys[i] for i in idx_tr]
val_keys = [keys[i] for i in idx_val]
tr_keys.sort()
val_keys.sort()
self.dataset_tr = OrderedDict()
for i in tr_keys:
self.dataset_tr[i] = self.dataset[i]
self.dataset_val = OrderedDict()
for i in val_keys:
self.dataset_val[i] = self.dataset[i]
def setup_DA_params(self):
"""
- we increase roation angle from [-15, 15] to [-30, 30]
- scale range is now (0.7, 1.4), was (0.85, 1.25)
- we don't do elastic deformation anymore
:return:
"""
self.deep_supervision_scales = [[1, 1, 1]] + list(
list(i) for i in 1 / np.cumprod(
np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params[
"rotation_x"] = default_2D_augmentation_params[
"rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 *
2. * np.pi,
15. / 360 *
2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params["scale_range"] = (0.7, 1.4)
self.data_aug_params["do_elastic"] = False
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params[
'patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
self.data_aug_params["num_cached_per_thread"] = 2
def maybe_update_lr(self, epoch=None):
"""
if epoch is not None we overwrite epoch. Else we use epoch = self.epoch + 1
(maybe_update_lr is called in on_epoch_end which is called before epoch is incremented.
herefore we need to do +1 here)
:param epoch:
:return:
"""
if epoch is None:
ep = self.epoch + 1
else:
ep = epoch
self.optimizer.param_groups[0]['lr'] = poly_lr(ep, self.max_num_epochs,
self.initial_lr, 0.9)
self.print_to_log_file(
"lr:", np.round(self.optimizer.param_groups[0]['lr'], decimals=6))
def on_epoch_end(self):
"""
overwrite patient-based early stopping. Always run to 1000 epochs
:return:
"""
super().on_epoch_end()
continue_training = self.epoch < self.max_num_epochs
# it can rarely happen that the momentum of nnUNetTrainerV2 is too high for some dataset. If at epoch 100 the
# estimated validation Dice is still 0 then we reduce the momentum from 0.99 to 0.95
if self.epoch == 100:
if self.all_val_eval_metrics[-1] == 0:
self.optimizer.param_groups[0]["momentum"] = 0.95
self.network.apply(InitWeights_He(1e-2))
self.print_to_log_file(
"At epoch 100, the mean foreground Dice was 0. This can be caused by a too "
"high momentum. High momentum (0.99) is good for datasets where it works, but "
"sometimes causes issues such as this one. Momentum has now been reduced to "
"0.95 and network weights have been reinitialized")
return continue_training
def run_training(self):
"""
if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first
continued epoch with self.initial_lr
we also need to make sure deep supervision in the network is enabled for training, thus the wrapper
:return:
"""
self.maybe_update_lr(
self.epoch
) # if we dont overwrite epoch then self.epoch+1 is used which is not what we
# want at the start of the training
ds = self.network.do_ds
self.network.do_ds = True
ret = super().run_training()
self.network.do_ds = ds
return ret
def get_properties_for_stage(self, current_spacing, original_spacing,
original_shape, num_cases, num_modalities,
num_classes):
"""
ExperimentPlanner configures pooling so that we pool late. Meaning that if the number of pooling per axis is
(2, 3, 3), then the first pooling operation will always pool axes 1 and 2 and not 0, irrespective of spacing.
This can cause a larger memory footprint, so it can be beneficial to revise this.
Here we are pooling based on the spacing of the data.
"""
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)
# we compute as if we were using only 30 feature maps. We can do that because fp16 training is the standard
# now. That frees up some space. The decision to go with 32 is solely due to the speedup we get (non-multiples
# of 8 are not supported in nvidia amp)
ref = ResAxialAttentionUNet.use_this_for_batch_size_computation_3D * self.unet_base_num_features / \
ResAxialAttentionUNet.BASE_NUM_FEATURES_3D
here = ResAxialAttentionUNet.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)
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,
)
here = Generic_UNet.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)
#print(here, ref)
input_patch_size = new_shp
batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D # This is what wirks with 128**3
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 = min(batch_size, max_batch_size)
do_dummy_2D_data_aug = (max(input_patch_size) / input_patch_size[0]
) > self.anisotropy_threshold
plan = {
#liuyiyao
#'batch_size': batch_size,
'batch_size': 4,
'num_pool_per_axis': network_num_pool_per_axis,
#'patch_size': input_patch_size,
#liuyiyao
'patch_size': (8, 128, 128),
'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,
}
return plan
