-
Notifications
You must be signed in to change notification settings - Fork 21
/
Copy pathmetric_enc.sh
84 lines (63 loc) · 2.67 KB
/
metric_enc.sh
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
74
75
76
77
78
79
80
81
82
83
84
# dataset_path
# 702
# -i data/nnUNet_raw/Dataset702_AbdomenMR/imagesTs
# -g data/nnUNet_raw/Dataset702_AbdomenMR/labelsTs
# 703
# -i data/nnUNet_raw/Dataset703_NeurIPSCell/imagesVal
# -g data/nnUNet_raw/Dataset703_NeurIPSCell/labelsVal-instance-mask
# 704
# -i data/nnUNet_raw/Dataset704_Endovis17/imagesTs
# -g data/nnUNet_raw/Dataset704_Endovis17/labelsTs
# dataset_id="702"
# exp_name="pretrained_weight_Enc"
# output_path="infer_result/${dataset_id}/${exp_name}"
# mkdir -p $output_path
# save_path="evaluate_result/${dataset_id}/pretrained_weight"
# CUDA_VISIBLE_DEVICES=2 nnUNetv2_predict -i data/nnUNet_raw/Dataset702_AbdomenMR/imagesTs \
# -o $output_path -d ${dataset_id} \
# -f ${dataset_id} -tr nnUNetTrainerUxLSTMEnc \
# --disable_tta -c 2d
# python evaluation/abdomen_DSC_Eval.py \
# --gt_path /home/ubuntu/public_c/crl/cchenzong/U_xlstm/data/nnUNet_raw/Dataset702_AbdomenMR/labelsTs \
# --seg_path $output_path \
# --save_path $save_path
# python evaluation/abdomen_NSD_Eval.py \
# --gt_path /home/ubuntu/public_c/crl/cchenzong/U_xlstm/data/nnUNet_raw/Dataset702_AbdomenMR/labelsTs \
# --seg_path $output_path \
# --save_path $save_path
# dataset_id="703"
# exp_name="pretrained_weight_Enc"
# output_path="infer_result/${dataset_id}/${exp_name}"
# mkdir -p $output_path
# save_path="evaluate_result/703/pretrained_weight"
# nnUNetv2_predict -i data/nnUNet_raw/Dataset703_NeurIPSCell/imagesVal/ \
# -o $output_path -d ${dataset_id} \
# -f ${dataset_id} -tr nnUNetTrainerUxLSTMEnc \
# --disable_tta -c 2d
# python evaluation/compute_cell_metric.py \
# -g data/nnUNet_raw/Dataset703_NeurIPSCell/labelsVal-instance-mask \
# -s $output_path \
# -o $save_path
dataset_id="704"
exp_name="pretrained_weight_Enc"
output_path="infer_result/${dataset_id}/${exp_name}"
mkdir -p $output_path
save_path="evaluate_result/${dataset_id}/pretrained_weight"
nnUNetv2_predict -i data/nnUNet_raw/Dataset704_Endovis17/imagesTs \
-o $output_path -d ${dataset_id} \
-f ${dataset_id} -tr nnUNetTrainerUxLSTMEnc \
--disable_tta -c 2d
python evaluation/endoscopy_DSC_Eval.py \
--gt_path data/nnUNet_raw/Dataset704_Endovis17/labelsTs \
--seg_path $output_path \
--save_path $save_path
python evaluation/endoscopy_NSD_Eval.py \
--gt_path data/nnUNet_raw/Dataset704_Endovis17/labelsTs \
--seg_path $output_path \
--save_path $save_path
# If your experiment name is set to "all" and you wish to use these trained weights for prediction,
# modify the -f parameter to match the {exp_name} used during training. This could be 'all' or a specific integer identifier.
# nnUNetv2_predict -i data/nnUNet_raw/Dataset704_Endovis17/imagesTs \
# -o $output_path -d ${dataset_id} \
# -f all -tr nnUNetTrainerUxLSTMEnc \
# --disable_tta -c 2d