Merge pull request #5 from M3DV/a3d

A3d

Author: NotF404

.gitignore

@@ -1,5 +1,5 @@
-<<<<<<< HEAD
 #my setting
+*.so
 .idea
 *pycache*
 *.pyc
@@ -18,8 +18,6 @@ to2dweights.py
 epoch_18.pth
 ttepoch_18.pth
 *.pth
-=======
->>>>>>> 3ff8574077823c9bfc120ac9cd9dab9396a50bcc
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -14,9 +14,9 @@
 
 * ``alignshift``
   the core implementation of AlignShift convolution and TSM convolution, including the operators, models, and 2D-to-3D/AlignShift/TSM model converters. 
-  * ``operators``: include AlignShiftConv, TSMConv.
-  * ``converters.py``: include converters which convert 2D models to 3dConv/AlignShiftConv/TSMConv counterparts.
-  * ``models``: Native AlignShift/TSM models. 
+  * ``operators``: include AlignShiftConv, TSMConv, A3DConv.
+  * ``converters.py``: include converters which convert 2D models to 3DConv/AlignShiftConv/TSMConv/A3DConv counterparts.
+  * ``models``: Native AlignShift/TSM/A3DConv models. 
 * ``deeplesion`` 
   the experiment code is base on [mmdetection](https://github.com/open-mmlab/mmdetection)
 ,this directory consists of compounents used in mmdetection.
@@ -51,10 +51,10 @@ thickness = torch.rand(batch_size, 1)
 out = m(x, thickness)
 ```
 
-## Usage of AlignShiftConv/TSMConv operators
+## Usage of AlignShiftConv/TSMConv/A3DConv operators
 
 ```python
-from alignshift.operators import AlignShiftConv, TSMConv
+from nn.operators import AlignShiftConv, TSMConv, A3DConv
 x = torch.rand(batch_size, 3, D, H, W)
 thickness = torch.rand(batch_size, 1)
 # AlignShiftConv to process 3D volumnes
@@ -63,12 +63,15 @@ out = conv(x, thickness)
 # TSMConv to process 3D volumnes
 conv = TSMConv(in_channels=3, out_channels=10, kernel_size=3, padding=1, n_fold=8, tsm=True)
 out = conv(x)
+# A3DConv to process 3D volumnes
+conv = A3DConv(in_channels=3, out_channels=10, kernel_size=3, padding=1, dimension=3)
+out = conv(x)
 ```
 
 ## Usage of native  AlignShiftConv/TSMConv models
 
 ```python
-from alignshift.models import DenseNetCustomTrunc3dAlign, DenseNetCustomTrunc3dTSM
+from nn.models import DenseNetCustomTrunc3dAlign, DenseNetCustomTrunc3dTSM
 net = DenseNetCustomTrunc3dAlign(num_classes=3)
 B, C_in, D, H, W = (1, 3, 7, 256, 256)
 input_3d = torch.rand(B, C_in, D, H, W)
@@ -109,6 +112,11 @@ output_3d = net(input_3d, thickness)
   ```bash
   ./deeplesion/train_dist.sh ./deeplesion/mconfig/densenet_tsm.py 2
   ```
+    * Train A3DConv models 
+  ```bash
+  ./deeplesion/train_dist.sh ./deeplesion/mconfig/densenet_a3d.py 2
+  ```
+
  * Evaluation 
    ```bash
    ./deeplesion/eval.sh ${mmdetection script} ${checkpoint path}

alignshift/models/__init__.py

@@ -1 +1 @@
-data_root = '/mnt/data/DeepLesion/'
+data_root = '/mnt/data2/deeplesion/dataset/'