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prerakmody committed Nov 30, 2021
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9 changes: 9 additions & 0 deletions .gitignore
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# Python
__pycache__
.pyc


# Modeling
_data
_models
_logs
49 changes: 49 additions & 0 deletions README.md
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# Bayesian Uncertainty for Quality Assessment of Deep Learning Contours
This repository contains Tensorflow2.4 code for the paper(s)
- Comparing Bayesian Models for Organ Contouring in Headand Neck Radiotherapy


## Installation
1. Install [Anaconda](https://docs.anaconda.com/anaconda/install/) with python3.7
2. Install [git](https://git-scm.com/downloads)
3. Open a terminal and follow the commands
- Clone this repository
- `git clone [email protected]:prerakmody/hansegmentation-uncertainty-qa.git`
- Create conda env
- (Specifically For Windows): `conda init powershell` (and restart the terminal)
- (For all plaforms)
```
cd hansegmentation-uncertainty-qa
conda deactivate
conda create --name hansegmentation-uncertainty-qa python=3.8
conda activate hansegmentation-uncertainty-qa
conda develop . # check for conda.pth file in $ANACONDA_HOME/envs/hansegmentation-uncertainty-qa/lib/python3.8/site-packages
```
- Install packages
- Tensorflow (check [here]((https://www.tensorflow.org/install/source#tested_build_configurations)) for CUDA/cuDNN requirements)
- (stick to the exact commands)
- For tensorflow2.4
```
conda install -c nvidia cudnn=8.0.0=cuda11.0_0
pip install tensorflow==2.4
```
- Check tensorflow installation
```
python -c "import tensorflow as tf;print('\n\n\n====================== \n GPU Devices: ',tf.config.list_physical_devices('GPU'), '\n======================')"
python -c "import tensorflow as tf;print('\n\n\n====================== \n', tf.reduce_sum(tf.random.normal([1000, 1000])), '\n======================' )"
```
- [unix] upon running either of the above commands, you will see tensorflow searching for library files like libcudart.so, libcublas.so, libcublasLt.so, libcufft.so, libcurand.so, libcusolver.so, libcusparse.so, libcudnn.so in the location `$ANACONDA_HOME/envs/hansegmentation-uncertainty-qa/lib/`
- [windows] upon running either of the above commands, you will see tensorflow searching for library files like cudart64_110.dll ... and so on in the location `$ANACONDA_HOME\envs\hansegmentation-uncertainty-qa\Library\bin`
- Other tensorflow pacakges
```
pip install tensorflow-probability==0.12.1 tensorflow-addons==0.12.1
```
- Other packages
```
pip install scipy seaborn tqdm psutil humanize pynrrd pydicom SimpleITK itk scikit-image
pip install psutil humanize pynvml
```
# Notes
- All the `src/train{}.py` files are the ones used to train the models as shown in the `demo/` folder
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105 changes: 105 additions & 0 deletions demo/demo_dropout_ce.py
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# Import private libraries
import src.config as config
from src.model.trainer import Trainer,Validator

# Import public libraries
import os
import pdb
import traceback
import tensorflow as tf
from pathlib import Path



if __name__ == "__main__":

exp_name = 'HansegmentationUncertaintyQA-Dropout-CE'

data_dir = Path(config.MAIN_DIR).joinpath('medical_dataloader', '_data')
resampled = True
crop_init = True
grid = True
batch_size = 2

model = config.MODEL_FOCUSNET_DROPOUT

# To train
params = {
'exp_name': exp_name
, 'random_seed':42
, 'dataloader':{
'data_dir': data_dir
, 'dir_type' : [config.DATALOADER_MICCAI2015_TRAIN, config.DATALOADER_MICCAI2015_TRAIN_ADD]
, 'resampled' : resampled
, 'crop_init' : crop_init
, 'grid' : grid
, 'random_grid': True
, 'filter_grid': False
, 'centred_prob' : 0.3
, 'batch_size' : batch_size
, 'shuffle' : 5
, 'prefetch_batch': 4
, 'parallel_calls': 3
}
, 'model': {
'name': model
, 'optimizer' : config.OPTIMIZER_ADAM
, 'init_lr' : 0.001
, 'fixed_lr' : True
, 'epochs' : 1500
, 'epochs_save': 50
, 'epochs_eval': 50
, 'epochs_viz' : 500
, 'load_model':{
'load':False, 'load_exp_name': None, 'load_epoch':-1, 'load_optimizer_lr':None
}
, 'profiler': {
'profile': False
, 'epochs': [2,3]
, 'steps_per_epoch': 60
, 'starting_step': 4
}
, 'model_tboard': False
}
, 'metrics' : {
'logging_tboard': True
# for full 3D volume
, 'metrics_eval': {'Dice': config.LOSS_DICE}
## for smaller grid/patch
, 'metrics_loss' : {'CE': config.LOSS_CE} # [config.LOSS_CE, config.LOSS_DICE]
, 'loss_weighted' : {'CE': True}
, 'loss_mask' : {'CE': True}
, 'loss_combo' : {'CE': 1.0}
}
, 'others': {
'epochs_timer': 20
, 'epochs_memory':5
}
}

# Call the trainer
trainer = Trainer(params)
trainer.train()

# To evaluate on MICCAI2015
params = {
'exp_name': exp_name
, 'pid' : os.getpid()
, 'dataloader': {
'data_dir' : data_dir
, 'resampled' : resampled
, 'grid' : grid
, 'crop_init' : crop_init
, 'batch_size' : batch_size
, 'prefetch_batch': 1
, 'dir_type' : [config.DATALOADER_MICCAI2015_TEST] # [config.DATALOADER_MICCAI2015_TESTONSITE]
, 'eval_type' : config.MODE_TEST
}
, 'model': {
'name': model
, 'load_epoch' : 1000
, 'MC_RUNS' : 30
, 'training_bool' : True # [True=dropout-at-test-time, False=no-dropout-at-test-time]
}
, 'save': True
}
105 changes: 105 additions & 0 deletions demo/demo_dropout_cebasic.py
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# Import private libraries
import src.config as config
from src.model.trainer import Trainer,Validator

# Import public libraries
import os
import pdb
import traceback
import tensorflow as tf
from pathlib import Path



if __name__ == "__main__":

exp_name = 'HansegmentationUncertaintyQA-Dropout-CEBasic'

data_dir = Path(config.MAIN_DIR).joinpath('medical_dataloader', '_data')
resampled = True
crop_init = True
grid = True
batch_size = 2

model = config.MODEL_FOCUSNET_DROPOUT

# To train
params = {
'exp_name': exp_name
, 'random_seed':42
, 'dataloader':{
'data_dir': data_dir
, 'dir_type' : [config.DATALOADER_MICCAI2015_TRAIN, config.DATALOADER_MICCAI2015_TRAIN_ADD]
, 'resampled' : resampled
, 'crop_init' : crop_init
, 'grid' : grid
, 'random_grid': True
, 'filter_grid': False
, 'centred_prob' : 0.3
, 'batch_size' : batch_size
, 'shuffle' : 5
, 'prefetch_batch': 4
, 'parallel_calls': 3
}
, 'model': {
'name': model
, 'optimizer' : config.OPTIMIZER_ADAM
, 'init_lr' : 0.001
, 'fixed_lr' : True
, 'epochs' : 1500
, 'epochs_save': 50
, 'epochs_eval': 50
, 'epochs_viz' : 500
, 'load_model':{
'load':False, 'load_exp_name': None, 'load_epoch':-1, 'load_optimizer_lr':None
}
, 'profiler': {
'profile': False
, 'epochs': [2,3]
, 'steps_per_epoch': 60
, 'starting_step': 4
}
, 'model_tboard': False
}
, 'metrics' : {
'logging_tboard': True
# for full 3D volume
, 'metrics_eval': {'Dice': config.LOSS_DICE}
## for smaller grid/patch
, 'metrics_loss' : {'CE-Basic': config.LOSS_CE_BASIC}
, 'loss_weighted' : {'CE-Basic': True}
, 'loss_mask' : {'CE-Basic': True}
, 'loss_combo' : {'CE-Basic': 1.0}
}
, 'others': {
'epochs_timer': 20
, 'epochs_memory':5
}
}

# Call the trainer
trainer = Trainer(params)
trainer.train()

# To evaluate on MICCAI2015
params = {
'exp_name': exp_name
, 'pid' : os.getpid()
, 'dataloader': {
'data_dir' : data_dir
, 'resampled' : resampled
, 'grid' : grid
, 'crop_init' : crop_init
, 'batch_size' : batch_size
, 'prefetch_batch': 1
, 'dir_type' : [config.DATALOADER_MICCAI2015_TEST] # [config.DATALOADER_MICCAI2015_TESTONSITE]
, 'eval_type' : config.MODE_TEST
}
, 'model': {
'name': model
, 'load_epoch' : 1000
, 'MC_RUNS' : 30
, 'training_bool' : True # [True=dropout-at-test-time, False=no-dropout-at-test-time]
}
, 'save': True
}
105 changes: 105 additions & 0 deletions demo/demo_dropout_dice.py
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# Import private libraries
import src.config as config
from src.model.trainer import Trainer,Validator

# Import public libraries
import os
import pdb
import traceback
import tensorflow as tf
from pathlib import Path



if __name__ == "__main__":

exp_name = 'HansegmentationUncertaintyQA-Dropout-DICE'

data_dir = Path(config.MAIN_DIR).joinpath('medical_dataloader', '_data')
resampled = True
crop_init = True
grid = True
batch_size = 2

model = config.MODEL_FOCUSNET_DROPOUT

# To train
params = {
'exp_name': exp_name
, 'random_seed':42
, 'dataloader':{
'data_dir': data_dir
, 'dir_type' : [config.DATALOADER_MICCAI2015_TRAIN, config.DATALOADER_MICCAI2015_TRAIN_ADD]
, 'resampled' : resampled
, 'crop_init' : crop_init
, 'grid' : grid
, 'random_grid': True
, 'filter_grid': False
, 'centred_prob' : 0.3
, 'batch_size' : batch_size
, 'shuffle' : 5
, 'prefetch_batch': 4
, 'parallel_calls': 3
}
, 'model': {
'name': model
, 'optimizer' : config.OPTIMIZER_ADAM
, 'init_lr' : 0.001
, 'fixed_lr' : True
, 'epochs' : 1500
, 'epochs_save': 50
, 'epochs_eval': 50
, 'epochs_viz' : 500
, 'load_model':{
'load':False, 'load_exp_name': None, 'load_epoch':-1, 'load_optimizer_lr':None
}
, 'profiler': {
'profile': False
, 'epochs': [2,3]
, 'steps_per_epoch': 60
, 'starting_step': 4
}
, 'model_tboard': False
}
, 'metrics' : {
'logging_tboard': True
# for full 3D volume
, 'metrics_eval': {'Dice': config.LOSS_DICE}
## for smaller grid/patch
, 'metrics_loss' : {'Dice': config.LOSS_DICE}
, 'loss_weighted' : {'Dice': True}
, 'loss_mask' : {'Dice': True}
, 'loss_combo' : {'Dice': 1.0}
}
, 'others': {
'epochs_timer': 20
, 'epochs_memory':5
}
}

# Call the trainer
trainer = Trainer(params)
trainer.train()

# To evaluate on MICCAI2015
params = {
'exp_name': exp_name
, 'pid' : os.getpid()
, 'dataloader': {
'data_dir' : data_dir
, 'resampled' : resampled
, 'grid' : grid
, 'crop_init' : crop_init
, 'batch_size' : batch_size
, 'prefetch_batch': 1
, 'dir_type' : [config.DATALOADER_MICCAI2015_TEST] # [config.DATALOADER_MICCAI2015_TESTONSITE]
, 'eval_type' : config.MODE_TEST
}
, 'model': {
'name': model
, 'load_epoch' : 1000
, 'MC_RUNS' : 30
, 'training_bool' : True # [True=dropout-at-test-time, False=no-dropout-at-test-time]
}
, 'save': True
}
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