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Implementation of Probabilistic U-Net. #46

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Implementation of Probabilistic U-Net. #46

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b2033f1
removed files to avoid confusion
naga-karthik Jun 22, 2021
4938d21
minor modifications to config files
naga-karthik Jun 22, 2021
55a1c8c
modified for training on subvolumes and choosing GT at random
naga-karthik Jun 22, 2021
7d25bb8
added utilities - weight initializations for PU-Net
naga-karthik Jun 22, 2021
51faf40
added implementation of PU-Net
naga-karthik Jun 22, 2021
528532f
added training method
naga-karthik Jun 22, 2021
a3d0722
added ReadMe file
naga-karthik Jun 22, 2021
bdb1bf6
added mixed-precision training functionality
naga-karthik Jun 23, 2021
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60 changes: 24 additions & 36 deletions config/softseg_unet3D_balanced.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,14 @@
{
"command": "train",
"gpu_ids": [1],
"log_directory": "msc2021_softseg_unet3D_balanced",
"gpu_ids": [0],
"log_directory": "/home/karthik7/projects/def-laporte1/karthik7/ivadomed_older_version/ivadomed/results/msc2021_softseg_unet3D_balanced",
"model_name": "ms_brain",
"debugging": true,
"object_detection_params": {
"object_detection_path": null
},
"loader_parameters": {
"bids_path": "duke/projects/ivadomed/tmp_ms_challenge_2021_preprocessed",
"path_data": "/home/karthik7/projects/def-laporte1/karthik7/ivadomed_older_version/ivadomed/datasets/tmp_ms_challenge_2021_preprocessed",
"target_suffix": ["_seg-lesion"],
"roi_params": {
"suffix": null,
Expand All @@ -32,19 +32,19 @@
"fname_split": null,
"random_seed": 42,
"center_test": [],
"balance": null,
"balance": null,
"method": "per_patient",
"train_fraction": 0.6,
"test_fraction": 0.2
},
"training_parameters": {
"batch_size": 20,
"batch_size": 16,
"loss": {
"name": "AdapWingLoss"
},
"training_time": {
"num_epochs": 200,
"early_stopping_patience": 50,
"early_stopping_patience": 75,
"early_stopping_epsilon": 0.001
},
"scheduler": {
Expand All @@ -55,7 +55,10 @@
"max_lr": 0.01
}
},
"balance_samples": {"applied": true, "type": "gt"},
"balance_samples": {
"applied": true,
"type": "gt"
},
"mixup_alpha": null,
"transfer_learning": {
"retrain_model": null,
Expand All @@ -67,53 +70,38 @@
"length_3D": [128, 128, 128],
"stride_3D": [64, 64, 64],
"attention": true,
"n_filters": 8
"n_filters": 16
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I would actually be very interested to know how much difference this makes; we should definitely do an experiment on this!

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Sure, definitely! From my experience, increasing the number of feature maps/filters helps in improving performance! Hence, I try to fit as many as possible into the memory.

},
"default_model": {
"name": "Unet",
"dropout_rate": 0.3,
"dropout_rate": 0.2,
"bn_momentum": 0.9,
"depth": 4,
"folder_name": "ms_brain",
"in_channel": 2,
"out_channel": 1,
"final_activation": "relu"
},
"postprocessing": {"binarize_prediction": {"thr": 0.5}},
"postprocessing": {
"binarize_prediction": {"thr": 0.5}
},
"transformation": {
"CenterCrop": {
"size": [
320,
384,
512
],
"size": [ 320, 384, 512 ],
"preprocessing": true
},
"RandomAffine": {
"degrees": 20,
"scale": [
0.1,
0.1,
0.1
],
"translate": [
0.1,
0.1,
0.1
],
"applied_to": [
"im",
"gt"
],
"dataset_type": [
"training"
]
"scale": [ 0.1, 0.1, 0.1 ],
"translate": [ 0.1, 0.1, 0.1 ],
"applied_to": [ "im","gt" ],
"dataset_type": [ "training" ]
},
"NumpyToTensor": {
"applied_to": ["im", "gt"]
},
"NumpyToTensor": {},
"NormalizeInstance": {
"applied_to": [
"im"
]
"applied_to": ["im"]
}
}
}
70 changes: 29 additions & 41 deletions config/softseg_unet3D_unbalanced.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,14 @@
{
"command": "train",
"gpu_ids": [0],
"log_directory": "msc2021_softseg_unet3D_unbalanced",
"gpu_ids": [2],
"log_directory": "results/msc2021_softseg_unet3D_unbalanced",
"model_name": "ms_brain",
"debugging": true,
"object_detection_params": {
"object_detection_path": null
},
"loader_parameters": {
"bids_path": "duke/projects/ivadomed/tmp_ms_challenge_2021_preprocessed",
"path_data": "duke/projects/ivadomed/tmp_ms_challenge_2021_preprocessed",
"target_suffix": ["_seg-lesion"],
"roi_params": {
"suffix": null,
Expand All @@ -32,30 +32,33 @@
"fname_split": null,
"random_seed": 42,
"center_test": [],
"balance": null,
"balance": null,
"method": "per_patient",
"train_fraction": 0.6,
"test_fraction": 0.2
},
"training_parameters": {
"batch_size": 20,
"batch_size": 16,
"loss": {
"name": "AdapWingLoss"
},
"training_time": {
"num_epochs": 200,
"early_stopping_patience": 50,
"early_stopping_patience": 75,
"early_stopping_epsilon": 0.001
},
"scheduler": {
"initial_lr": 5e-05,
"initial_lr": 2e-04,
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These might be old experiments and highly dependent on the specific hyperparameters (e.g. batch size) but in my experience with the challenge lowering the learning rate was very helpful. I have seen cases where lowering the learning rate from 5e-05 to 3e-05 made a huge difference!

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I second this!

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Thanks for this! I really didn't know that lowering the learning rate could make that much of a difference. I am struggling to get PU-Net working for this dataset, maybe this might help to some extent! :)

"lr_scheduler": {
"name": "CosineAnnealingLR",
"base_lr": 1e-05,
"base_lr": 5e-05,
"max_lr": 0.01
}
},
"balance_samples": {"applied": false, "type": "gt"},
"balance_samples": {
"applied": false,
"type": "gt"
},
"mixup_alpha": null,
"transfer_learning": {
"retrain_model": null,
Expand All @@ -64,56 +67,41 @@
},
"Modified3DUNet": {
"applied": true,
"length_3D": [128, 128, 128],
"stride_3D": [64, 64, 64],
"length_3D": [64, 64, 64],
"stride_3D": [32, 32, 32],
"attention": true,
"n_filters": 8
"n_filters": 16
},
"default_model": {
"name": "Unet",
"dropout_rate": 0.3,
"dropout_rate": 0.2,
"bn_momentum": 0.9,
"depth": 4,
"folder_name": "ms_brain",
"in_channel": 2,
"out_channel": 1,
"final_activation": "relu"
},
"postprocessing": {"binarize_prediction": {"thr": 0.5}},
"postprocessing": {
"binarize_prediction": {"thr": 0.5}
},
"transformation": {
"CenterCrop": {
"size": [
320,
384,
512
],
"size": [ 320, 384, 512 ],
"preprocessing": true
},
"RandomAffine": {
"degrees": 20,
"scale": [
0.1,
0.1,
0.1
],
"translate": [
0.1,
0.1,
0.1
],
"applied_to": [
"im",
"gt"
],
"dataset_type": [
"training"
]
"scale": [ 0.1, 0.1, 0.1 ],
"translate": [ 0.1, 0.1, 0.1 ],
"applied_to": [ "im", "gt" ],
"dataset_type": [ "training" ]
},
"NumpyToTensor": {
"applied_to": ["im", "gt"]
},
"NumpyToTensor": {},
"NormalizeInstance": {
"applied_to": [
"im"
]
"applied_to": [ "im" ]
}
}
}
}
9 changes: 9 additions & 0 deletions modeling/ReadMe.md
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Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
### Probabilistic U-Net for Longitudinal MS Lesion Segmentation
This contains the first attempt of using PU-Net for segmenting MS Lesions.

The current (tested) version supports single-GPU training. How to run:

```
$ export CUDA_VISIBLE_DEVICES=<your-GPU-id-here>
$ python training.py -fd 1.0
```
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