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test_config_unet3D.yaml
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test_config_unet3D.yaml
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# path to the checkpoint file containing the model
model_path: /mnt/lustre/shenrui/project/edgeDL/saved_weights/pelvis/xxx.pytorch
# path to the folder of the predictions
save_path: /mnt/lustre/shenrui/data/pelvis_predict
prediction_channel: null
# model configuration
model:
# model class
name: UNet3D
# number of input channels to the model
in_channels: 1
# number of output channels
out_channels: 6
# determines the order of operators in a single layer (crg - Conv3d+ReLU+GroupNorm)
layer_order: crg
# feature maps scale factor
f_maps: 32
# basic module
basic_module: DoubleConv
# number of groups in the groupnorm
num_groups: 8
# apply element-wise nn.Sigmoid after the final 1x1 convolution, otherwise apply nn.Softmax
final_sigmoid: false
# specify the test datasets
loaders:
# test patch size given to the network (adapt to fit in your GPU mem)
test_patch: [64, 256, 256]
# test stride between patches (make sure the the patches overlap in order to get smoother prediction maps)
test_stride: [50, 100, 100]
# clip value within the range
clip_val: [-1000, 2000]
# how many subprocesses to use for data loading
num_workers: 8
# paths to the datasets
test_path:
- '/mnt/lustre/shenrui/data/pelvis_resampled/dataset_test.txt'
transformer:
test:
raw:
- name: ClipNormalize
- name: ToTensor
expand_dims: true