Add training pipeline for fine-tuning models, support MONAI Label active learning
Browse files- README.md +5 -1
- configs/metadata.json +3 -2
- configs/multi_gpu_train.json +36 -0
- configs/train.json +321 -0
- docs/README.md +5 -1
README.md
CHANGED
@@ -6,7 +6,7 @@ library_name: monai
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license: unknown
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---
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# Description
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-
A pre-trained model for inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase).
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A tutorial and release of model for kidney cortex, medulla and collecting system segmentation.
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@@ -68,7 +68,11 @@ Add scripts component: To run the workflow with customized components, PYTHONPA
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export PYTHONPATH=$PYTHONPATH:"'<path to the bundle root dir>/scripts'"
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```
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Execute inference:
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license: unknown
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---
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# Description
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+
A pre-trained model for training and inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase). Training pipeline is provided to support model fine-tuning with bundle and MONAI Label active learning.
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A tutorial and release of model for kidney cortex, medulla and collecting system segmentation.
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export PYTHONPATH=$PYTHONPATH:"'<path to the bundle root dir>/scripts'"
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```
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Execute Training:
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```
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python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
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```
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Execute inference:
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configs/metadata.json
CHANGED
@@ -1,12 +1,13 @@
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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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-
"version": "0.1.
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"changelog": {
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"0.1.2": "fixed the dimension in convolution according to MONAI 1.0 update",
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"0.1.1": "fixed the model state dict name",
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"0.1.0": "complete the model package"
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},
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-
"monai_version": "0.
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"pytorch_version": "1.10.0",
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"numpy_version": "1.21.2",
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"optional_packages_version": {
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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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+
"version": "0.1.3",
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"changelog": {
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"0.1.3": "Add training pipeline for fine-tuning models, support MONAI Label active learning",
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"0.1.2": "fixed the dimension in convolution according to MONAI 1.0 update",
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"0.1.1": "fixed the model state dict name",
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"0.1.0": "complete the model package"
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},
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+
"monai_version": "1.0.0",
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"pytorch_version": "1.10.0",
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"numpy_version": "1.21.2",
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"optional_packages_version": {
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configs/multi_gpu_train.json
ADDED
@@ -0,0 +1,36 @@
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{
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"device": "$torch.device(f'cuda:{dist.get_rank()}')",
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"network": {
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"_target_": "torch.nn.parallel.DistributedDataParallel",
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"module": "$@network_def.to(@device)",
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"device_ids": [
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"@device"
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]
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},
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"train#sampler": {
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"_target_": "DistributedSampler",
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"dataset": "@train#dataset",
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"even_divisible": true,
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"shuffle": true
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},
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"train#dataloader#sampler": "@train#sampler",
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"train#dataloader#shuffle": false,
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"train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
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"validate#sampler": {
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"_target_": "DistributedSampler",
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"dataset": "@validate#dataset",
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"even_divisible": false,
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"shuffle": false
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},
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"validate#dataloader#sampler": "@validate#sampler",
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"validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
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"training": [
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"$import torch.distributed as dist",
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"$dist.init_process_group(backend='nccl')",
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"$torch.cuda.set_device(@device)",
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"$monai.utils.set_determinism(seed=123)",
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"$setattr(torch.backends.cudnn, 'benchmark', True)",
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"$@train#trainer.run()",
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"$dist.destroy_process_group()"
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]
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}
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configs/train.json
ADDED
@@ -0,0 +1,321 @@
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{
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"imports": [
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3 |
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"$import glob",
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4 |
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"$import os",
|
5 |
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"$import ignite"
|
6 |
+
],
|
7 |
+
"bundle_root": "/models/renalStructures_UNEST_segmentation",
|
8 |
+
"ckpt_dir": "$@bundle_root + '/models'",
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9 |
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"output_dir": "$@bundle_root + '/eval'",
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10 |
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"dataset_dir": "$@bundle_root + './dataset'",
|
11 |
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"images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
|
12 |
+
"labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
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13 |
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"val_interval": 5,
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+
"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
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15 |
+
"network_def": {
|
16 |
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"_target_": "scripts.networks.unest.UNesT",
|
17 |
+
"in_channels": 1,
|
18 |
+
"out_channels": 4
|
19 |
+
},
|
20 |
+
"network": "$@network_def.to(@device)",
|
21 |
+
"loss": {
|
22 |
+
"_target_": "DiceCELoss",
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23 |
+
"to_onehot_y": true,
|
24 |
+
"softmax": true,
|
25 |
+
"squared_pred": true,
|
26 |
+
"batch": true
|
27 |
+
},
|
28 |
+
"optimizer": {
|
29 |
+
"_target_": "torch.optim.Adam",
|
30 |
+
"params": "$@network.parameters()",
|
31 |
+
"lr": 0.0002
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32 |
+
},
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33 |
+
"train": {
|
34 |
+
"deterministic_transforms": [
|
35 |
+
{
|
36 |
+
"_target_": "LoadImaged",
|
37 |
+
"keys": [
|
38 |
+
"image",
|
39 |
+
"label"
|
40 |
+
]
|
41 |
+
},
|
42 |
+
{
|
43 |
+
"_target_": "EnsureChannelFirstd",
|
44 |
+
"keys": [
|
45 |
+
"image",
|
46 |
+
"label"
|
47 |
+
]
|
48 |
+
},
|
49 |
+
{
|
50 |
+
"_target_": "Orientationd",
|
51 |
+
"keys": [
|
52 |
+
"image",
|
53 |
+
"label"
|
54 |
+
],
|
55 |
+
"axcodes": "RAS"
|
56 |
+
},
|
57 |
+
{
|
58 |
+
"_target_": "Spacingd",
|
59 |
+
"keys": [
|
60 |
+
"image",
|
61 |
+
"label"
|
62 |
+
],
|
63 |
+
"pixdim": [
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64 |
+
1.0,
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65 |
+
1.0,
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66 |
+
1.0
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67 |
+
],
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68 |
+
"mode": [
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69 |
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"bilinear",
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70 |
+
"nearest"
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71 |
+
]
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72 |
+
},
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73 |
+
{
|
74 |
+
"_target_": "ScaleIntensityRanged",
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75 |
+
"keys": "image",
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76 |
+
"a_min": -175,
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77 |
+
"a_max": 250,
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78 |
+
"b_min": 0.0,
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79 |
+
"b_max": 1.0,
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80 |
+
"clip": true
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81 |
+
},
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82 |
+
{
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83 |
+
"_target_": "EnsureTyped",
|
84 |
+
"keys": [
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85 |
+
"image",
|
86 |
+
"label"
|
87 |
+
]
|
88 |
+
}
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89 |
+
],
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90 |
+
"random_transforms": [
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91 |
+
{
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92 |
+
"_target_": "RandCropByPosNegLabeld",
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93 |
+
"keys": [
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94 |
+
"image",
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95 |
+
"label"
|
96 |
+
],
|
97 |
+
"label_key": "label",
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98 |
+
"spatial_size": [
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99 |
+
96,
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100 |
+
96,
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101 |
+
96
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102 |
+
],
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103 |
+
"pos": 1,
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104 |
+
"neg": 1,
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105 |
+
"num_samples": 4,
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106 |
+
"image_key": "image",
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107 |
+
"image_threshold": 0
|
108 |
+
},
|
109 |
+
{
|
110 |
+
"_target_": "RandFlipd",
|
111 |
+
"keys": [
|
112 |
+
"image",
|
113 |
+
"label"
|
114 |
+
],
|
115 |
+
"spatial_axis": [
|
116 |
+
0
|
117 |
+
],
|
118 |
+
"prob": 0.1
|
119 |
+
},
|
120 |
+
{
|
121 |
+
"_target_": "RandFlipd",
|
122 |
+
"keys": [
|
123 |
+
"image",
|
124 |
+
"label"
|
125 |
+
],
|
126 |
+
"spatial_axis": [
|
127 |
+
1
|
128 |
+
],
|
129 |
+
"prob": 0.1
|
130 |
+
},
|
131 |
+
{
|
132 |
+
"_target_": "RandFlipd",
|
133 |
+
"keys": [
|
134 |
+
"image",
|
135 |
+
"label"
|
136 |
+
],
|
137 |
+
"spatial_axis": [
|
138 |
+
2
|
139 |
+
],
|
140 |
+
"prob": 0.1
|
141 |
+
},
|
142 |
+
{
|
143 |
+
"_target_": "RandRotate90d",
|
144 |
+
"keys": [
|
145 |
+
"image",
|
146 |
+
"label"
|
147 |
+
],
|
148 |
+
"max_k": 3,
|
149 |
+
"prob": 0.1
|
150 |
+
},
|
151 |
+
{
|
152 |
+
"_target_": "RandShiftIntensityd",
|
153 |
+
"keys": "image",
|
154 |
+
"offsets": 0.1,
|
155 |
+
"prob": 0.5
|
156 |
+
}
|
157 |
+
],
|
158 |
+
"preprocessing": {
|
159 |
+
"_target_": "Compose",
|
160 |
+
"transforms": "$@train#deterministic_transforms + @train#random_transforms"
|
161 |
+
},
|
162 |
+
"dataset": {
|
163 |
+
"_target_": "CacheDataset",
|
164 |
+
"data": "$[{'image': i, 'label': l} for i, l in zip(@images[:-9], @labels[:-9])]",
|
165 |
+
"transform": "@train#preprocessing",
|
166 |
+
"cache_rate": 1.0,
|
167 |
+
"num_workers": 4
|
168 |
+
},
|
169 |
+
"dataloader": {
|
170 |
+
"_target_": "DataLoader",
|
171 |
+
"dataset": "@train#dataset",
|
172 |
+
"batch_size": 2,
|
173 |
+
"shuffle": true,
|
174 |
+
"num_workers": 4
|
175 |
+
},
|
176 |
+
"inferer": {
|
177 |
+
"_target_": "SimpleInferer"
|
178 |
+
},
|
179 |
+
"postprocessing": {
|
180 |
+
"_target_": "Compose",
|
181 |
+
"transforms": [
|
182 |
+
{
|
183 |
+
"_target_": "Activationsd",
|
184 |
+
"keys": "pred",
|
185 |
+
"softmax": true
|
186 |
+
},
|
187 |
+
{
|
188 |
+
"_target_": "AsDiscreted",
|
189 |
+
"keys": [
|
190 |
+
"pred",
|
191 |
+
"label"
|
192 |
+
],
|
193 |
+
"argmax": [
|
194 |
+
true,
|
195 |
+
false
|
196 |
+
],
|
197 |
+
"to_onehot": 4
|
198 |
+
}
|
199 |
+
]
|
200 |
+
},
|
201 |
+
"handlers": [
|
202 |
+
{
|
203 |
+
"_target_": "ValidationHandler",
|
204 |
+
"validator": "@validate#evaluator",
|
205 |
+
"epoch_level": true,
|
206 |
+
"interval": "@val_interval"
|
207 |
+
},
|
208 |
+
{
|
209 |
+
"_target_": "StatsHandler",
|
210 |
+
"tag_name": "train_loss",
|
211 |
+
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
|
212 |
+
},
|
213 |
+
{
|
214 |
+
"_target_": "TensorBoardStatsHandler",
|
215 |
+
"log_dir": "@output_dir",
|
216 |
+
"tag_name": "train_loss",
|
217 |
+
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
|
218 |
+
}
|
219 |
+
],
|
220 |
+
"key_metric": {
|
221 |
+
"train_accuracy": {
|
222 |
+
"_target_": "ignite.metrics.Accuracy",
|
223 |
+
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
|
224 |
+
}
|
225 |
+
},
|
226 |
+
"trainer": {
|
227 |
+
"_target_": "SupervisedTrainer",
|
228 |
+
"max_epochs": 1000,
|
229 |
+
"device": "@device",
|
230 |
+
"train_data_loader": "@train#dataloader",
|
231 |
+
"network": "@network",
|
232 |
+
"loss_function": "@loss",
|
233 |
+
"optimizer": "@optimizer",
|
234 |
+
"inferer": "@train#inferer",
|
235 |
+
"postprocessing": "@train#postprocessing",
|
236 |
+
"key_train_metric": "@train#key_metric",
|
237 |
+
"train_handlers": "@train#handlers",
|
238 |
+
"amp": true
|
239 |
+
}
|
240 |
+
},
|
241 |
+
"validate": {
|
242 |
+
"preprocessing": {
|
243 |
+
"_target_": "Compose",
|
244 |
+
"transforms": "%train#deterministic_transforms"
|
245 |
+
},
|
246 |
+
"dataset": {
|
247 |
+
"_target_": "CacheDataset",
|
248 |
+
"data": "$[{'image': i, 'label': l} for i, l in zip(@images[-9:], @labels[-9:])]",
|
249 |
+
"transform": "@validate#preprocessing",
|
250 |
+
"cache_rate": 1.0
|
251 |
+
},
|
252 |
+
"dataloader": {
|
253 |
+
"_target_": "DataLoader",
|
254 |
+
"dataset": "@validate#dataset",
|
255 |
+
"batch_size": 1,
|
256 |
+
"shuffle": false,
|
257 |
+
"num_workers": 4
|
258 |
+
},
|
259 |
+
"inferer": {
|
260 |
+
"_target_": "SlidingWindowInferer",
|
261 |
+
"roi_size": [
|
262 |
+
96,
|
263 |
+
96,
|
264 |
+
96
|
265 |
+
],
|
266 |
+
"sw_batch_size": 4,
|
267 |
+
"overlap": 0.5
|
268 |
+
},
|
269 |
+
"postprocessing": "%train#postprocessing",
|
270 |
+
"handlers": [
|
271 |
+
{
|
272 |
+
"_target_": "StatsHandler",
|
273 |
+
"iteration_log": false
|
274 |
+
},
|
275 |
+
{
|
276 |
+
"_target_": "TensorBoardStatsHandler",
|
277 |
+
"log_dir": "@output_dir",
|
278 |
+
"iteration_log": false
|
279 |
+
},
|
280 |
+
{
|
281 |
+
"_target_": "CheckpointSaver",
|
282 |
+
"save_dir": "@ckpt_dir",
|
283 |
+
"save_dict": {
|
284 |
+
"model": "@network"
|
285 |
+
},
|
286 |
+
"save_key_metric": true,
|
287 |
+
"key_metric_filename": "model.pt"
|
288 |
+
}
|
289 |
+
],
|
290 |
+
"key_metric": {
|
291 |
+
"val_mean_dice": {
|
292 |
+
"_target_": "MeanDice",
|
293 |
+
"include_background": false,
|
294 |
+
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
|
295 |
+
}
|
296 |
+
},
|
297 |
+
"additional_metrics": {
|
298 |
+
"val_accuracy": {
|
299 |
+
"_target_": "ignite.metrics.Accuracy",
|
300 |
+
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
|
301 |
+
}
|
302 |
+
},
|
303 |
+
"evaluator": {
|
304 |
+
"_target_": "SupervisedEvaluator",
|
305 |
+
"device": "@device",
|
306 |
+
"val_data_loader": "@validate#dataloader",
|
307 |
+
"network": "@network",
|
308 |
+
"inferer": "@validate#inferer",
|
309 |
+
"postprocessing": "@validate#postprocessing",
|
310 |
+
"key_val_metric": "@validate#key_metric",
|
311 |
+
"additional_metrics": "@validate#additional_metrics",
|
312 |
+
"val_handlers": "@validate#handlers",
|
313 |
+
"amp": true
|
314 |
+
}
|
315 |
+
},
|
316 |
+
"training": [
|
317 |
+
"$monai.utils.set_determinism(seed=123)",
|
318 |
+
"$setattr(torch.backends.cudnn, 'benchmark', True)",
|
319 |
+
"$@train#trainer.run()"
|
320 |
+
]
|
321 |
+
}
|
docs/README.md
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
# Description
|
2 |
-
A pre-trained model for inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase).
|
3 |
|
4 |
A tutorial and release of model for kidney cortex, medulla and collecting system segmentation.
|
5 |
|
@@ -61,7 +61,11 @@ Add scripts component: To run the workflow with customized components, PYTHONPA
|
|
61 |
export PYTHONPATH=$PYTHONPATH:"'<path to the bundle root dir>/scripts'"
|
62 |
|
63 |
```
|
|
|
64 |
|
|
|
|
|
|
|
65 |
|
66 |
Execute inference:
|
67 |
|
|
|
1 |
# Description
|
2 |
+
A pre-trained model for training and inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase). Training pipeline is provided to support model fine-tuning with bundle and MONAI Label active learning.
|
3 |
|
4 |
A tutorial and release of model for kidney cortex, medulla and collecting system segmentation.
|
5 |
|
|
|
61 |
export PYTHONPATH=$PYTHONPATH:"'<path to the bundle root dir>/scripts'"
|
62 |
|
63 |
```
|
64 |
+
Execute Training:
|
65 |
|
66 |
+
```
|
67 |
+
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
|
68 |
+
```
|
69 |
|
70 |
Execute inference:
|
71 |
|