File size: 2,542 Bytes
1f90840
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
_meta_: {}
bundle_root: /Users/sakshirathi/Downloads/work_dir/segresnet_0
ckpt_path: $@bundle_root + '/model'
mlflow_tracking_uri: $@ckpt_path + '/mlruns/'
mlflow_experiment_name: Auto3DSeg
data_file_base_dir: /Users/sakshirathi/Documents/ShamLab
data_list_file_path: /Users/sakshirathi/Downloads/work_dir/dataset_local.json
modality: ct
fold: 0
input_channels: 1
output_classes: 2
class_names: null
class_index: null
debug: false
ckpt_save: true
cache_rate: null
roi_size: [384, 384, 60]
auto_scale_allowed: true
auto_scale_batch: true
auto_scale_roi: false
auto_scale_filters: false
quick: false
channels_last: true
validate_final_original_res: true
calc_val_loss: false
amp: true
log_output_file: null
cache_class_indices: null
early_stopping_fraction: 0.001
determ: false
orientation_ras: true
crop_foreground: true
learning_rate: 0.0002
batch_size: 1
num_images_per_batch: 1
num_epochs: 1250
num_warmup_epochs: 3
sigmoid: false
resample: true
resample_resolution: [0.48766356436698155, 0.4876635832539761, 2.748479210553717]
crop_mode: ratio
normalize_mode: range
intensity_bounds: [39.63595217750186, 97.59593563988095]
num_epochs_per_validation: null
num_epochs_per_saving: 1
num_workers: 4
num_steps_per_image: null
num_crops_per_image: 2
loss: {_target_: DiceCELoss, include_background: true, squared_pred: true, smooth_nr: 0,
  smooth_dr: 1.0e-05, softmax: $not @sigmoid, sigmoid: $@sigmoid, to_onehot_y: $not
    @sigmoid}
optimizer: {_target_: torch.optim.AdamW, lr: '@learning_rate', weight_decay: 1.0e-05}
network:
  _target_: SegResNetDS
  init_filters: 32
  blocks_down: [1, 2, 2, 4, 4]
  norm: INSTANCE_NVFUSER
  in_channels: '@input_channels'
  out_channels: '@output_classes'
  dsdepth: 4
finetune: {enabled: false, ckpt_name: $@bundle_root + '/model/model.pt'}
validate: {enabled: false, ckpt_name: $@bundle_root + '/model/model.pt', output_path: $@bundle_root
    + '/prediction_validation', save_mask: false, invert: true}
infer: {enabled: false, ckpt_name: $@bundle_root + '/model/model.pt', output_path: $@bundle_root
    + '/prediction_' + @infer#data_list_key, data_list_key: testing}
anisotropic_scales: true
spacing_median: [0.48766356436698155, 0.4876635832539761, 4.770811902267695]
spacing_lower: [0.42813486948609353, 0.428134856247896, 2.499999978382533]
spacing_upper: [0.5859375, 0.5859375004856939, 5.012642938162783]
image_size_mm_median: [249.68374495589455, 249.68375462603575, 168.30083390623668]
image_size_mm_90: [265.61599121093747, 265.6159922216141, 190.12765338720757]
image_size: [544, 544, 69]