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---
imports:
- "$import glob"
- "$import os"
- "$import scripts.monai_utils"
workflow_type: inference
input_channels: 1
output_classes: 4
output_channels: 4
# arch_ckpt_path: "$@bundle_root + '/models/dynunet_FT.pt'"
# arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))"
bundle_root: "."
output_dir: "$@bundle_root + '/eval/dynunet_FT'"
dataset_dir: "/processed/Public/CT_TotalSegmentator/TS_split/test/"
data_list_file_path: "$@bundle_root + '/configs/TS_test.json'"
datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='validation')"
device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
spatial_size:
- 96
- 96
- 96
spatial_dims: "$len(@spatial_size)"
labels:
background: 0
liver: 1
spleen: 2
pancreas: 3
network_def:
_target_: monai.networks.nets.DynUNet
spatial_dims: "@spatial_dims"
in_channels: "@input_channels"
out_channels: "@output_channels"
kernel_size:
- 3
- 3
- 3
- 3
- 3
- 3
strides:
- 1
- 2
- 2
- 2
- 2
-
- 2
- 2
- 1
upsample_kernel_size:
- 2
- 2
- 2
- 2
-
- 2
- 2
- 1
norm_name: "instance"
deep_supervision: false
res_block: true
network: "$@network_def.to(@device)"
image_key: image
preprocessing:
_target_: Compose
transforms:
- _target_: LoadImaged
keys: "@image_key"
reader: ITKReader
- _target_: EnsureChannelFirstd
keys: "@image_key"
- _target_: Orientationd
keys: image
axcodes: RAS
- _target_: Spacingd
keys:
- "@image_key"
pixdim:
- 1.5
- 1.5
- 3.0
mode:
- bilinear
- _target_: ScaleIntensityRanged
keys: "@image_key"
a_min: -250
a_max: 400
b_min: 0
b_max: 1
clip: true
- _target_: CropForegroundd
keys:
- "@image_key"
source_key: "@image_key"
mode:
- "minimum"
- _target_: EnsureTyped
keys: image
- _target_: CastToTyped
keys: "@image_key"
dtype: "$torch.float32"
dataset:
_target_: Dataset
data: "@datalist"
transform: "@preprocessing"
dataloader:
_target_: DataLoader
dataset: "@dataset"
batch_size: 1
shuffle: false
num_workers: 4
inferer:
_target_: SlidingWindowInferer
roi_size:
- 96
- 96
- 96
sw_batch_size: 4
overlap: 0.75
postprocessing:
_target_: Compose
transforms:
- _target_: Activationsd
keys: pred
softmax: true
- _target_: Invertd
keys: pred
transform: "@preprocessing"
orig_keys: image
meta_key_postfix: meta_dict
nearest_interp: false
to_tensor: true
- _target_: AsDiscreted
keys: pred
argmax: true
- _target_: SaveImaged
keys: pred
meta_keys: pred_meta_dict
output_dir: "@output_dir"
separate_folder: false
output_dtype: "$torch.int16"
handlers:
- _target_: CheckpointLoader
load_path: "$@bundle_root + '/models/dynunet_FT.pt'"
load_dict:
model: "@network"
- _target_: StatsHandler
iteration_log: false
evaluator:
_target_: SupervisedEvaluator
device: "@device"
val_data_loader: "@dataloader"
network: "@network"
inferer: "@inferer"
postprocessing: "@postprocessing"
val_handlers: "@handlers"
amp: true
initialize:
- "$setattr(torch.backends.cudnn, 'benchmark', True)"
run:
- "$@evaluator.run()"
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