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Optimize data augmentation for 3D kernel #57
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Terminal output{
"command": "train",
"gpu_ids": [4],
"path_output": "/home/GRAMES.POLYMTL.CA/p101317/data_nvme_p101317/model_seg_lesion_mp2rage_",
"model_name": "model_seg_lesion_mp2rage",
"debugging": true,
"object_detection_params": {
"object_detection_path": null,
"safety_factor": [1.0, 1.0, 1.0]
},
"wandb": {
"wandb_api_key": "9095e2bc9e4ab445d478c9c8a81759ae908be8c6",
"project_name": "basel-mp2rage-lesion",
"group_name": "3D",
"run_name": "run-1",
"log_grads_every": 100
},
"loader_parameters": {
"path_data": ["/home/GRAMES.POLYMTL.CA/p101317/data_nvme_p101317/data_seg_mp2rage_20221217_170634/data_processed_lesionseg"],
"subject_selection": {"n": [], "metadata": [], "value": []},
"target_suffix": ["_lesion-manualHaris"],
"extensions": [".nii.gz"],
"roi_params": {
"suffix": null,
"slice_filter_roi": null
},
"contrast_params": {
"training_validation": ["UNIT1"],
"testing": ["UNIT1"],
"balance": {}
},
"slice_filter_params": {
"filter_empty_mask": true,
"filter_empty_input": true
},
"slice_axis": "axial",
"multichannel": false,
"soft_gt": false,
"bids_validate": true
},
"split_dataset": {
"fname_split": null,
"random_seed": 42,
"split_method" : "participant_id",
"data_testing": {"data_type": null, "data_value":[]},
"balance": null,
"train_fraction": 0.6,
"test_fraction": 0.2
},
"training_parameters": {
"batch_size": 16,
"loss": {
"name": "DiceLoss"
},
"training_time": {
"num_epochs": 50,
"early_stopping_patience": 50,
"early_stopping_epsilon": 0.001
},
"scheduler": {
"initial_lr": 0.001,
"lr_scheduler": {
"name": "CosineAnnealingLR",
"base_lr": 1e-5,
"max_lr": 1e-3
}
},
"balance_samples": {
"applied": false,
"type": "gt"
},
"mixup_alpha": null,
"transfer_learning": {
"retrain_model": null,
"retrain_fraction": 1.0,
"reset": true
}
},
"default_model": {
"name": "Unet",
"dropout_rate": 0.3,
"bn_momentum": 0.1,
"final_activation": "sigmoid",
"depth": 3
},
"FiLMedUnet": {
"applied": false,
"metadata": "contrasts",
"film_layers": [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
},
"Modified3DUNet": {
"applied": true,
"length_3D": [32, 32, 64],
"stride_3D": [32, 32, 64],
"attention": false,
"n_filters": 8
},
"uncertainty": {
"epistemic": false,
"aleatoric": false,
"n_it": 0
},
"postprocessing": {
"remove_noise": {"thr": -1},
"keep_largest": {},
"binarize_prediction": {"thr": 0.5},
"uncertainty": {"thr": -1, "suffix": "_unc-vox.nii.gz"},
"fill_holes": {},
"remove_small": {"unit": "vox", "thr": 3}
},
"evaluation_parameters": {
"target_size": {"unit": "vox", "thr": [20, 100]},
"overlap": {"unit": "vox", "thr": 3}
},
"transformation": {
"Resample":
{
"hspace": 0.75,
"wspace": 0.75,
"dspace": 0.75
},
"CenterCrop": {
"size": [128, 128, 128]},
"NormalizeInstance": {"applied_to": ["im"]}
}
}
|
Investigating the orientation and kernel size. With the following config: "slice_axis": "axial",
...
"Modified3DUNet": {
"applied": true,
"length_3D": [
32,
32,
16
], Here is a patch: imgplot = plt.imshow(img[4, 0, :, :, 8]) So, it seems like the orientation is consistent, ie: the superior-inferior direction is along the 3rd dim (the "8"). |
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Useful syntax
To look at predictions with GT. Run under the
data_seg_mp2rage_20221220_124553/data_processed_lesionseg
folder:TODO:
config file
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