ImpactSynth / MR /Config.yml
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Use SAM_Perceptual(train=True, weights=[0,1,1,0]) as training loss instead of IMPACTReg
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Trainer:
Model:
classpath: Model:UNetpp
UNetpp:
outputs_criterions:
Head:Tanh:
targets_criterions:
CT:
criterions_loader:
MAE:
schedulers:
Constant:
nb_step: 0
value: 1
is_loss: true
group: 0
start: 0
stop: None
accumulation: false
reduction: mean
SAM_Perceptual:
train: true
weights: [0, 1, 1, 0]
schedulers:
Constant:
nb_step: 0
value: 0.5
is_loss: true
group: 0
start: 0
stop: None
accumulation: false
CT;MASK:
criterions_loader:
MAE:
schedulers:
Constant:
nb_step: 0
value: 1
is_loss: false
group: 0
start: 0
stop: None
accumulation: false
reduction: mean
schedulers:
PolyLRScheduler:
initial_lr: 0.01
max_steps: 500
exponent: 0.9
current_step: 0
nb_step: 0
Patch: None
optimizer:
name: SGD
lr: 0.01
momentum: 0.99
dampening: 0
weight_decay: 3e-05
nesterov: true
maximize: false
foreach: None
differentiable: false
fused: None
nb_channel: 5
Dataset:
groups_src:
MASK:
groups_dest:
MASK:
transforms: None
patch_transforms: None
is_input: false
CT:
groups_dest:
CT:
transforms:
Clip:
min_value: -1024
max_value: 3071
save_clip_min: true
save_clip_max: true
mask: None
Statistics: {}
Normalize:
lazy: true
channels: None
min_value: -1
max_value: 1
inverse: false
patch_transforms:
Normalize:
lazy: false
channels: None
min_value: -1
max_value: 1
inverse: false
is_input: false
MR_IMPACT:
groups_dest:
MR:
transforms:
Clip:
min_value: min
max_value: percentile:99.5
save_clip_min: false
save_clip_max: false
mask: None
Statistics: {}
Normalize:
lazy: true
channels: None
min_value: -1
max_value: 1
inverse: false
patch_transforms:
Normalize:
lazy: false
channels: None
min_value: -1
max_value: 1
inverse: false
is_input: true
augmentations:
DataAugmentation_0:
data_augmentations:
Flip:
f_prob:
- 0
- 0.5
- 0.5
prob: 1
nb: 1
Patch:
patch_size:
- 1
- 320
- 320
overlap: None
mask: None
pad_value: -1
extend_slice: 4
subset: None
shuffle: true
filter: None
dataset_filenames:
- ./Dataset/:a:mha
inline_augmentations: true
use_cache: true
batch_size: 32
validation: None
train_name: FT_0
manual_seed: 32
epochs: 100
it_validation: 2500
autocast: false
gradient_checkpoints: None
gpu_checkpoints: None
ema_decay: 0
data_log:
- CT/IMAGES/5
- MR/IMAGES/5
- Head:Tanh/IMAGES/5
save_checkpoint_mode: ALL
EarlyStopping:
monitor: []
patience: 30
min_delta: 0.0
mode: min