Init

.gitignore

@@ -158,3 +158,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+tb_logs/

BRATS_LA_Pancreas/README.md

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+# Model weights and Visulaizations
+
+You can download the learned weights and their results in the following links.
+   Dataset   | Visulization         | Weights
+  -----------|----------------|----------------
+   BRaTS 2018  | [[Download](https://mega.nz/folder/L4oQmAzL#OxnzxfbQZ8Oanr0iaKJ0qw)]| [[Download](https://mega.nz/file/O1w02BAS#BNK2p2QZ2x2MDE5HKEe7Xwms7J5DMPlyYBMnUEjjwQ8)] 
+   LA (fold 0)      | [[Download](https://mega.nz/folder/SpImhTRD#Rcyh4ernRwpWuma9EOLh0g)]| [[Download](https://mega.nz/file/TkIG0DDD#bTwRrWe_ht3hoitKWsH88LePoN0vokmBHYo8Xhsk8wU)] 
+   Pancreas (fold 0)      | [[Download](https://mega.nz/folder/2sAEiBSb#_apiwqhLrCT-RMDm5Qsg4g)]| [[Download](https://mega.nz/file/z84ijCzA#ZLW7_-qqpfY0qlwoJAbu6bD0hv29iiRBjuFpFe4Sdsc)] 
+
+# BRaTS 2018 dataset
+Download the dataset: [Download](https://mega.nz/file/ShpgECbC#rpZr_lWFr5ZIk8vXe7AVCFEPLZIFxFg7NnQ-Vk16LrM)
+
+## Training 
+```bash
+python src/train_test.py -c brats-2018_lhunet_sgd
+```
+**Before running the code above please adjust the config's paths.** 
+
+## Inference
+Make sure in the config file you set the `test_mode` variable to `true` and give the path to the model you want to test in the `ckpt_path`.
+```bash
+python src/train_test.py -c brats-2018_lhunet_sgd
+```
+
+# Pancreas and LA dataset
+Download the pre-processed datasets with their splits: [Pancreas](https://mega.nz/file/21p3ATLY#IZuAzvqXD8CymZZibN2oqLhfK0nZrBx8hWyk76SZRNk), [LA](https://mega.nz/file/K84Q3RKK#XDKPoSeYerwPJC7mcVyiTOM-Ydfv3TckDnAKkhpEVdY)
+
+## Training
+```bash
+python src/train_test.py -c {dataset}_lhunet_sgd -f {fold_number}
+```
+instead of `{dataset}` you should write `pancreas` or `la` according to which dataset you want to train. regarding the `{fold_number}` for the Pancreas dataset it ranges from 0 till 3 (four-fold cross validation) and for the LA dataset it ranges from 0 till 4 (five-fold cross validation)
+**Before running the code above please adjust the config's paths.** 
+
+## Inference
+Make sure in the config file you set the `just_test` variable to `true` and give the path to the model you want to test in the `ckpt_path`.
+```bash
+python src/train_test.py -c {dataset}_lhunet_sgd -f {fold_number}
+```

BRATS_LA_Pancreas/configs/brats-2018/lhunet/brats-2018_lhunet_sgd.yaml

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+is3d: true
+checkpoints:
+  continue_training: false # continue training from a checkpoint (use it with ckpt_path)
+  test_mode: false # test mode (use it with ckpt_path)
+  ckpt_path: '/home/say26747/Desktop/best.ckpt'
+  save_nifty: true # save nifty files
+dataset:
+  name: "brats2018_loader"
+  input_size: [128,128,128]
+  train:
+    params:
+      data_root: "/home/say26747/Desktop/datasets/Brats 2018"   # path to the dataset
+      normalization: true
+      augmentation: true  
+      p: 0.5
+
+  validation:
+    params:
+      data_root: "/home/say26747/Desktop/datasets/Brats 2018" # path to the dataset
+      normalization: true
+      augmentation: false  
+
+  test:
+    params:
+      data_root: "/home/say26747/Desktop/datasets/Brats 2018" # path to the dataset
+      normalization: true
+      augmentation: false 
+
+  test_split: 0.2
+  validation_split: 0.1
+data_loader:
+  train: 
+    batch_size: 2
+    shuffle: true
+    num_workers: 12
+    pin_memory: true
+    persistent_workers: true
+  validation: 
+    batch_size: 1
+    shuffle: false
+    num_workers: 12
+    pin_memory: true
+    persistent_workers: true
+  test:
+    batch_size: 1
+    shuffle: false
+    num_workers: 12
+    pin_memory: false
+    persistent_workers: true
+training:
+  optimizer:
+    name: 'SGD'
+    params:
+      lr: 0.01
+      momentum: 0.99
+      weight_decay : 0.00003
+      nesterov: true
+  criterion:
+    params: 
+      dice_weight: 0.5
+      bce_weight: 0.5
+  scheduler:
+    name: 'CustomDecayLR'
+    params:
+      max_epochs: 1000
+  epochs: 1000
+
+model: 
+  name: 'lhUNET_SGD_DEV_test' # do not change the lhUNET name at the begining
+  params:
+    spatial_shapes: [128,128,128]
+    do_ds: false # Deep Supervision
+    in_channels: 4
+    out_channels: 4
+
+     # encoder params
+    cnn_kernel_sizes: [3,3]
+    cnn_features: [12,16]
+    cnn_strides: [2,2]
+    cnn_maxpools: [true, true]
+    cnn_dropouts: 0.0
+    cnn_blocks: nn # n: resunet, d: deformconv, b: basicunet
+    hyb_kernel_sizes: [3,3,3]
+    hyb_features: [32,64,128]
+    hyb_strides: [2,2,2]
+    hyb_maxpools: [true,true,true]
+    hyb_cnn_dropouts: 0.0
+    hyb_tf_proj_sizes: [64,32,0]
+    hyb_tf_repeats: [1,1,1]
+    hyb_tf_num_heads: [4,4,8]
+    hyb_tf_dropouts: 0.1
+    hyb_cnn_blocks: nnn # n: resunet, d: deformconv, b: basicunet
+    hyb_vit_blocks: SSC 
+    hyb_skip_mode: "cat" # "sum" or "cat"
+    hyb_arch_mode: "residual" # sequential, residual, parallel, collective
+    hyb_res_mode: "sum" # "sum" or "cat"
+
+    # decoder params
+    dec_hyb_tcv_kernel_sizes: [5,5,5]
+    dec_cnn_tcv_kernel_sizes: [5,5]
+    dec_cnn_blocks: null
+    dec_tcv_bias: false
+    dec_hyb_tcv_bias: false
+    dec_hyb_kernel_sizes: null
+    dec_hyb_features: null
+    dec_hyb_cnn_dropouts: null
+    dec_hyb_tf_proj_sizes: null
+    dec_hyb_tf_repeats: null
+    dec_hyb_tf_num_heads: null
+    dec_hyb_tf_dropouts: null
+    dec_cnn_kernel_sizes: null
+    dec_cnn_features: null
+    dec_cnn_dropouts: null
+    dec_hyb_cnn_blocks: null
+    dec_hyb_vit_blocks: null
+    # dec_hyb_vit_sandwich: null
+    dec_hyb_skip_mode: null
+    dec_hyb_arch_mode: "collective" # sequential, residual, parallel, collective, sequential-lite
+    dec_hyb_res_mode: null
+
+use_sliding_window: true
+sliding_window_params:
+  overlap: 0.5
+  mode: 'gaussian'

BRATS_LA_Pancreas/configs/la/lhunet/la_lhunet_sgd.yaml

@@ -0,0 +1,113 @@
+is3d: true
+find_lr: false
+checkpoints:
+  continue_training: false # continue training from a checkpoint (use it with ckpt_path)
+  test_mode: false # test mode (use it with ckpt_path)
+  ckpt_path: '/home/say26747/Desktop/last.ckpt'
+  log_pictures: false
+  save_nifty: true
+
+dataset:
+  name: "LA_heart_loader"
+  crop_size: [96,96,96]
+  path_to_data: "/home/say26747/Desktop/datasets/Left Atrial" # path to the dataset
+
+datatype: 'LA'
+
+check_val_every_n_epoch: 10
+
+data_loader:
+  train: 
+    batch_size: 2
+    shuffle: true
+    num_workers: 12
+    pin_memory: true
+    persistent_workers: true
+  validation: 
+    batch_size: 1
+    shuffle: false
+    num_workers: 12
+    pin_memory: true
+    persistent_workers: true
+  test:
+    batch_size: 1
+    shuffle: false
+    num_workers: 12
+    pin_memory: false
+    persistent_workers: true
+
+training:
+  optimizer:
+    name: 'SGD'
+    params:
+      lr: 0.01
+      momentum: 0.9
+      weight_decay : 0.00003
+      nesterov: true
+  criterion:
+    params: 
+      dice_weight: 1.0
+      bce_weight: 1.0
+  scheduler:
+    name: 'CustomDecayLR'
+    params:
+      max_epochs: 250
+  epochs: 250
+
+model: 
+  name: 'lhUNET_SGD_LA' # do not change the lhUNET name at the begining
+  params: # the class arguments defined above
+    spatial_shapes: [96, 96, 96]
+    do_ds: false # Deep Supervision
+    in_channels: 1
+    out_channels: 2
+
+      # encoder params
+    cnn_kernel_sizes: [3,3]
+    cnn_features: [12,16]
+    cnn_strides: [2,2]
+    cnn_maxpools: [true, true]
+    cnn_dropouts: 0.0
+    cnn_blocks: nn # n: resunet, d: deformconv, b: basicunet
+    hyb_kernel_sizes: [3,3,3]
+    hyb_features: [32,64,128]
+    hyb_strides: [2,2,2]
+    hyb_maxpools: [true,true,true]
+    hyb_cnn_dropouts: 0.0
+    hyb_tf_proj_sizes: [64,32,0]
+    hyb_tf_repeats: [1,1,1]
+    hyb_tf_num_heads: [4,4,8]
+    hyb_tf_dropouts: 0.0
+    hyb_cnn_blocks: nnn # n: resunet, d: deformconv, b: basicunet
+    hyb_vit_blocks: SSC 
+    hyb_skip_mode: "cat" # "sum" or "cat"
+    hyb_arch_mode: "residual" # sequential, residual, parallel, collective
+    hyb_res_mode: "sum" # "sum" or "cat"
+
+    # decoder params
+    dec_hyb_tcv_kernel_sizes: [5,5,5]
+    dec_cnn_tcv_kernel_sizes: [5,5]
+    dec_cnn_blocks: null
+    dec_tcv_bias: false
+    dec_hyb_tcv_bias: false
+    dec_hyb_kernel_sizes: null
+    dec_hyb_features: null
+    dec_hyb_cnn_dropouts: null
+    dec_hyb_tf_proj_sizes: null
+    dec_hyb_tf_repeats: null
+    dec_hyb_tf_num_heads: null
+    dec_hyb_tf_dropouts: null
+    dec_cnn_kernel_sizes: null
+    dec_cnn_features: null
+    dec_cnn_dropouts: null
+    dec_hyb_cnn_blocks: null
+    dec_hyb_vit_blocks: null
+    # dec_hyb_vit_sandwich: null
+    dec_hyb_skip_mode: null
+    dec_hyb_arch_mode: "collective" # sequential, residual, parallel, collective, sequential-lite
+    dec_hyb_res_mode: null
+
+use_sliding_window: true
+sliding_window_params:
+  overlap: 0.85
+  mode: 'gaussian'