813 ah net

.github/workflows/cron.yml

@@ -21,9 +21,10 @@ jobs:
         python -m pip install --upgrade pip wheel
         python -m pip uninstall -y torch torchvision
         if [ ${{ matrix.pytorch-version }} == "latest" ]; then
-          python -m pip install torch
+          python -m pip install torch torchvision
         else
           python -m pip install torch==${{ matrix.pytorch-version }}
+          python -m pip install torchvision==0.5.0
         fi
         python -m pip install -r requirements-dev.txt
         python -m pip list

.github/workflows/pythonapp.yml

@@ -81,11 +81,13 @@ jobs:
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
         python -m pip install torch==1.4 -f https://download.pytorch.org/whl/cpu/torch_stable.html
+        python -m pip install torchvision==0.5.0
         # min. requirements for windows instances
         python -c "f=open('requirements-dev.txt', 'r'); txt=f.readlines(); f.close(); print(txt); f=open('requirements-dev.txt', 'w'); f.writelines(txt[1:11]); f.close()"
     - name: Install the dependencies
       run: |
         python -m pip install torch==1.4
+        python -m pip install torchvision==0.5.0
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt
         python -m pip list
@@ -119,7 +121,7 @@ jobs:
         which python
         python -m pip install --upgrade pip wheel
         python -m pip uninstall -y torch torchvision
-        python -m pip install torch==1.4
+        python -m pip install torch==1.4 torchvision==0.5.0
         python -m pip install -r requirements-dev.txt
     - name: Run quick tests (GPU)
       run: |
@@ -162,7 +164,7 @@ jobs:
         # install the latest pytorch for testing
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
-        python -m pip install torch>=1.4
+        python -m pip install torch>=1.4 torchvision
     - name: Test source archive and wheel file
       run: |
         git fetch --depth=1 origin +refs/tags/*:refs/tags/*

.github/workflows/setupapp.yml

@@ -33,7 +33,7 @@ jobs:
         which python
         python -m pip install --upgrade pip wheel
         python -m pip uninstall -y torch torchvision
-        python -m pip install torch==1.4
+        python -m pip install torch==1.4 torchvision==0.5.0
         python -m pip install -r requirements-dev.txt
     - name: Run unit tests report coverage
       run: |
@@ -77,7 +77,7 @@ jobs:
     - name: Install the dependencies
       run: |
         python -m pip install --upgrade pip wheel
-        python -m pip install torch==1.4
+        python -m pip install torch==1.4 torchvision==0.5.0
         python -m pip install -r requirements-dev.txt
     - name: Run quick tests CPU ubuntu
       run: |
@@ -124,10 +124,11 @@ jobs:
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
         python -m pip install torch==1.4 -f https://download.pytorch.org/whl/cpu/torch_stable.html
+        python -m pip install torchvision==0.5.0
     - name: Install the dependencies
       run: |
         # min. requirements for windows instances
-        python -m pip install torch==1.4
+        python -m pip install torch==1.4 torchvision==0.5.0
         python -c "f=open('requirements-dev.txt', 'r'); txt=f.readlines(); f.close(); print(txt); f=open('requirements-dev.txt', 'w'); f.writelines(txt[1:5]); f.close()"
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt

docs/source/networks.rst

@@ -20,6 +20,26 @@ Blocks
 .. autoclass:: ResidualUnit
     :members:
 
+`GCN Module`
+~~~~~~~~~~~~
+.. autoclass:: GCN
+    :members:
+
+`Refinement Module`
+~~~~~~~~~~~~~~~~~~~
+.. autoclass:: Refine
+    :members:
+
+`FCN Module`
+~~~~~~~~~~~~
+.. autoclass:: FCN
+    :members:
+
+`Multi-Channel FCN Module`
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: MCFCN
+    :members:
+
 `Squeeze-and-Excitation`
 ~~~~~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: ChannelSELayer
@@ -144,6 +164,11 @@ Nets
 ----
 .. currentmodule:: monai.networks.nets
 
+`Ahnet`
+~~~~~~~
+.. autoclass:: AHNet
+  :members:
+
 `Densenet3D`
 ~~~~~~~~~~~~
 .. autoclass:: DenseNet

examples/distributed_training/unet_evaluation_horovod.py

@@ -41,21 +41,22 @@
 
 """
 
+import argparse
 import os
 from glob import glob
+
+import horovod.torch as hvd
 import nibabel as nib
 import numpy as np
 import torch
 import torch.multiprocessing as mp
-import argparse
 from torch.utils.data.distributed import DistributedSampler
-import horovod.torch as hvd
 
 import monai
-from monai.data import create_test_image_3d, Dataset, DataLoader
+from monai.data import DataLoader, Dataset, create_test_image_3d
 from monai.inferers import sliding_window_inference
 from monai.metrics import DiceMetric
-from monai.transforms import Compose, LoadNiftid, AsChannelFirstd, ScaleIntensityd, ToTensord
+from monai.transforms import AsChannelFirstd, Compose, LoadNiftid, ScaleIntensityd, ToTensord
 
 
 def evaluate(args):

examples/distributed_training/unet_training_horovod.py

@@ -45,28 +45,29 @@
 
 """
 
+import argparse
 import os
 import sys
 from glob import glob
+
+import horovod.torch as hvd
 import nibabel as nib
 import numpy as np
 import torch
 import torch.multiprocessing as mp
-import argparse
 from torch.utils.data.distributed import DistributedSampler
-import horovod.torch as hvd
 
 import monai
+from monai.data import DataLoader, Dataset, create_test_image_3d
 from monai.transforms import (
+    AsChannelFirstd,
     Compose,
     LoadNiftid,
-    AsChannelFirstd,
-    ScaleIntensityd,
     RandCropByPosNegLabeld,
     RandRotate90d,
+    ScaleIntensityd,
     ToTensord,
 )
-from monai.data import create_test_image_3d, Dataset, DataLoader
 
 
 def train(args):

monai/losses/__init__.py

@@ -13,11 +13,10 @@
     Dice,
     DiceLoss,
     GeneralizedDiceLoss,
+    GeneralizedWassersteinDiceLoss,
+    MaskedDiceLoss,
     dice,
     generalized_dice,
-    MaskedDiceLoss,
-    GeneralizedWassersteinDiceLoss,
 )
-
 from .focal_loss import FocalLoss
 from .tversky import TverskyLoss

monai/losses/tversky.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 import warnings
-from typing import Union, Optional, Callable
+from typing import Callable, Optional, Union
 
 import torch
 from torch.nn.modules.loss import _Loss

monai/metrics/meandice.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 import warnings
-from typing import Optional, Union, Callable
+from typing import Callable, Optional, Union
 
 import torch
 

monai/metrics/rocauc.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 import warnings
-from typing import Union, cast, Callable, Optional
+from typing import Callable, Optional, Union, cast
 
 import numpy as np
 import torch

monai/networks/blocks/__init__.py

@@ -12,6 +12,7 @@
 from .aspp import SimpleASPP
 from .convolutions import Convolution, ResidualUnit
 from .downsample import MaxAvgPool
+from .fcn import FCN, GCN, MCFCN, Refine
 from .squeeze_and_excitation import (
     ChannelSELayer,
     ResidualSELayer,

monai/networks/blocks/convolutions.py

@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Sequence, Union, Optional
+from typing import Optional, Sequence, Union
 
 import numpy as np
 import torch
@@ -38,6 +38,10 @@ class Convolution(nn.Sequential):
         act: activation type and arguments. Defaults to PReLU.
         norm: feature normalization type and arguments. Defaults to instance norm.
         dropout: dropout ratio. Defaults to no dropout.
+        dropout_dim: determine the dimensions of dropout. Defaults to 1.
+            When dropout_dim = 1, randomly zeroes some of the elements for each channel.
+            When dropout_dim = 2, Randomly zero out entire channels (a channel is a 2D feature map).
+            When dropout_dim = 3, Randomly zero out entire channels (a channel is a 3D feature map).
         dilation: dilation rate. Defaults to 1.
         groups: controls the connections between inputs and outputs. Defaults to 1.
         bias: whether to have a bias term. Defaults to True.
@@ -64,6 +68,7 @@ def __init__(
         act: Optional[Union[tuple, str]] = Act.PRELU,
         norm: Union[tuple, str] = Norm.INSTANCE,
         dropout: Optional[Union[int, float, tuple, str]] = None,
+        dropout_dim: int = 1,
         dilation: Union[Sequence[int], int] = 1,
         groups: int = 1,
         bias: bool = True,
@@ -98,7 +103,7 @@ def __init__(
             else:
                 drop_name, drop_args = split_args(dropout)
 
-            drop_type = Dropout[drop_name, dimensions]
+            drop_type = Dropout[drop_name, dropout_dim]
 
         if is_transposed:
             conv = conv_type(
@@ -167,9 +172,10 @@ def __init__(
         strides: int = 1,
         kernel_size: Union[Sequence[int], int] = 3,
         subunits: int = 2,
-        act=Act.PRELU,
-        norm=Norm.INSTANCE,
-        dropout=None,
+        act: Optional[Union[tuple, str]] = Act.PRELU,
+        norm: Union[tuple, str] = Norm.INSTANCE,
+        dropout: Optional[Union[int, float, tuple, str]] = None,
+        dropout_dim: int = 1,
         dilation: Union[Sequence[int], int] = 1,
         bias: bool = True,
         last_conv_only: bool = False,
@@ -197,6 +203,7 @@ def __init__(
                 act=act,
                 norm=norm,
                 dropout=dropout,
+                dropout_dim=dropout_dim,
                 dilation=dilation,
                 bias=bias,
                 conv_only=conv_only,