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---
imports:
- "$from scipy import ndimage"
arch_ckpt_path: models
amp: true
data_file_base_dir: /workspace/data/msd/Task07_Pancreas
data_list_file_path: configs/dataset_0.json
determ: true
input_channels: 1
learning_rate: 0.025
learning_rate_arch: 0.001
learning_rate_milestones:
- 0.4
- 0.8
num_images_per_batch: 1
num_epochs: 1430
num_epochs_per_validation: 100
num_epochs_warmup: 715
num_patches_per_image: 1
num_sw_batch_size: 6
output_classes: 3
overlap_ratio: 0.625
patch_size:
- 96
- 96
- 96
patch_size_valid:
- 96
- 96
- 96
ram_cost_factor: 0.8
image_key: image
label_key: label
transform_train:
_target_: Compose
transforms:
- _target_: LoadImaged
keys:
- "@image_key"
- "@label_key"
- _target_: EnsureChannelFirstd
keys:
- "@image_key"
- "@label_key"
- _target_: Orientationd
keys:
- "@image_key"
- "@label_key"
axcodes: RAS
- _target_: Spacingd
keys:
- "@image_key"
- "@label_key"
pixdim:
- 1
- 1
- 1
mode:
- bilinear
- nearest
align_corners:
- true
- true
- _target_: CastToTyped
keys: "@image_key"
dtype: "$torch.float32"
- _target_: ScaleIntensityRanged
keys: "@image_key"
a_min: -87
a_max: 199
b_min: 0
b_max: 1
clip: true
- _target_: CastToTyped
keys:
- "@image_key"
- "@label_key"
dtype:
- "$np.float16"
- "$np.uint8"
- _target_: CopyItemsd
keys: "@label_key"
times: 1
names:
- label4crop
- _target_: Lambdad
keys: label4crop
func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype), iterations=48).astype(float) for _k in range(s)]), axis=0)"
overwrite: true
- _target_: EnsureTyped
keys:
- "@image_key"
- "@label_key"
- _target_: CastToTyped
keys: "@image_key"
dtype: "$torch.float32"
- _target_: SpatialPadd
keys:
- "@image_key"
- "@label_key"
- label4crop
spatial_size: "@patch_size"
mode:
- reflect
- constant
- constant
- _target_: RandCropByLabelClassesd
keys:
- "@image_key"
- "@label_key"
label_key: label4crop
num_classes: "@output_classes"
ratios: "$[1,] * @output_classes"
spatial_size: "@patch_size"
num_samples: "@num_patches_per_image"
- _target_: Lambdad
keys: label4crop
func: "$lambda x: 0"
- _target_: RandRotated
keys:
- "@image_key"
- "@label_key"
range_x: 0.3
range_y: 0.3
range_z: 0.3
mode:
- bilinear
- nearest
prob: 0.2
- _target_: RandZoomd
keys:
- "@image_key"
- "@label_key"
min_zoom: 0.8
max_zoom: 1.2
mode:
- trilinear
- nearest
align_corners:
- null
- null
prob: 0.16
- _target_: RandGaussianSmoothd
keys: "@image_key"
sigma_x:
- 0.5
- 1.15
sigma_y:
- 0.5
- 1.15
sigma_z:
- 0.5
- 1.15
prob: 0.15
- _target_: RandScaleIntensityd
keys: "@image_key"
factors: 0.3
prob: 0.5
- _target_: RandShiftIntensityd
keys: "@image_key"
offsets: 0.1
prob: 0.5
- _target_: RandGaussianNoised
keys: "@image_key"
std: 0.01
prob: 0.15
- _target_: RandFlipd
keys:
- "@image_key"
- "@label_key"
spatial_axis: 0
prob: 0.5
- _target_: RandFlipd
keys:
- "@image_key"
- "@label_key"
spatial_axis: 1
prob: 0.5
- _target_: RandFlipd
keys:
- "@image_key"
- "@label_key"
spatial_axis: 2
prob: 0.5
- _target_: CastToTyped
keys:
- "@image_key"
- "@label_key"
dtype:
- "$torch.float32"
- "$torch.uint8"
- _target_: ToTensord
keys:
- "@image_key"
- "@label_key"
transform_validation:
_target_: Compose
transforms:
- _target_: LoadImaged
keys:
- "@image_key"
- "@label_key"
- _target_: EnsureChannelFirstd
keys:
- "@image_key"
- "@label_key"
- _target_: Orientationd
keys:
- "@image_key"
- "@label_key"
axcodes: RAS
- _target_: Spacingd
keys:
- "@image_key"
- "@label_key"
pixdim:
- 1
- 1
- 1
mode:
- bilinear
- nearest
align_corners:
- true
- true
- _target_: CastToTyped
keys: "@image_key"
dtype: "$torch.float32"
- _target_: ScaleIntensityRanged
keys: "@image_key"
a_min: -87
a_max: 199
b_min: 0
b_max: 1
clip: true
- _target_: CastToTyped
keys:
- "@image_key"
- "@label_key"
dtype:
- "$np.float16"
- "$np.uint8"
- _target_: CastToTyped
keys:
- "@image_key"
- "@label_key"
dtype:
- "$torch.float32"
- "$torch.uint8"
- _target_: ToTensord
keys:
- "@image_key"
- "@label_key"
loss:
_target_: DiceCELoss
include_background: false
to_onehot_y: true
softmax: true
squared_pred: true
batch: true
smooth_nr: 0.00001
smooth_dr: 0.00001
dints_space:
_target_: monai.networks.nets.TopologySearch
channel_mul: 0.5
num_blocks: 12
num_depths: 4
use_downsample: true
device: "$torch.device('cuda')"
network:
_target_: monai.networks.nets.DiNTS
dints_space: "@dints_space"
in_channels: "@input_channels"
num_classes: "@output_classes"
use_downsample: true
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