146 Update DenseNet class

2d_classification/mednist_tutorial.ipynb

@@ -103,7 +103,7 @@
     "from monai.apps import download_and_extract\n",
     "from monai.config import print_config\n",
     "from monai.metrics import compute_roc_auc\n",
-    "from monai.networks.nets import densenet121\n",
+    "from monai.networks.nets import DenseNet121\n",
     "from monai.transforms import (\n",
     "    AddChannel,\n",
     "    Compose,\n",
@@ -423,7 +423,7 @@
    "outputs": [],
    "source": [
     "device = torch.device(\"cuda:0\")\n",
-    "model = densenet121(spatial_dims=2, in_channels=1,\n",
+    "model = DenseNet121(spatial_dims=2, in_channels=1,\n",
     "                    out_channels=num_class).to(device)\n",
     "loss_function = torch.nn.CrossEntropyLoss()\n",
     "optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n",

3d_classification/ignite/densenet_evaluation_array.py

@@ -52,7 +52,7 @@ def main():
     val_ds = ImageDataset(image_files=images, labels=labels, transform=val_transforms, image_only=False)
     # create DenseNet121
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    net = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    net = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
 
     metric_name = "Accuracy"
     # add evaluation metric to the evaluator engine

3d_classification/ignite/densenet_evaluation_dict.py

@@ -59,7 +59,7 @@ def main():
 
     # create DenseNet121
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    net = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    net = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
 
     def prepare_batch(batch, device=None, non_blocking=False):
         return _prepare_batch((batch["img"], batch["label"]), device, non_blocking)

3d_classification/ignite/densenet_training_array.py

@@ -69,7 +69,7 @@ def main():
 
     # create DenseNet121, CrossEntropyLoss and Adam optimizer
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    net = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    net = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
     loss = torch.nn.CrossEntropyLoss()
     lr = 1e-5
     opt = torch.optim.Adam(net.parameters(), lr)

3d_classification/ignite/densenet_training_dict.py

@@ -87,7 +87,7 @@ def main():
 
     # create DenseNet121, CrossEntropyLoss and Adam optimizer
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    net = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    net = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
     loss = torch.nn.CrossEntropyLoss()
     lr = 1e-5
     opt = torch.optim.Adam(net.parameters(), lr)

3d_classification/torch/densenet_evaluation_array.py

@@ -53,7 +53,7 @@ def main():
 
     # Create DenseNet121
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
 
     model.load_state_dict(torch.load("best_metric_model_classification3d_array.pth"))
     model.eval()

3d_classification/torch/densenet_evaluation_dict.py

@@ -61,7 +61,7 @@ def main():
 
     # Create DenseNet121
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
 
     model.load_state_dict(torch.load("best_metric_model_classification3d_dict.pth"))
     model.eval()

3d_classification/torch/densenet_training_array.ipynb

@@ -368,7 +368,7 @@
     "\n",
     "# Create DenseNet121, CrossEntropyLoss and Adam optimizer\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model = monai.networks.nets.densenet.densenet121(\n",
+    "model = monai.networks.nets.DenseNet121(\n",
     "    spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
     "loss_function = torch.nn.CrossEntropyLoss()\n",
     "optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n",

3d_classification/torch/densenet_training_array.py

@@ -74,7 +74,7 @@ def main():
 
     # Create DenseNet121, CrossEntropyLoss and Adam optimizer
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
     loss_function = torch.nn.CrossEntropyLoss()
     optimizer = torch.optim.Adam(model.parameters(), 1e-5)
 

3d_classification/torch/densenet_training_dict.py

@@ -93,7 +93,7 @@ def main():
 
     # Create DenseNet121, CrossEntropyLoss and Adam optimizer
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
     loss_function = torch.nn.CrossEntropyLoss()
     optimizer = torch.optim.Adam(model.parameters(), 1e-5)
 

modules/interpretability/class_lung_lesion.ipynb

@@ -311,7 +311,7 @@
    },
    "outputs": [],
    "source": [
-    "model = monai.networks.nets.densenet.densenet121(\n",
+    "model = monai.networks.nets.DenseNet121(\n",
     "    spatial_dims=3, in_channels=1, out_channels=2\n",
     ").to(device)\n",
     "bce = torch.nn.BCEWithLogitsLoss()\n",
@@ -494,7 +494,7 @@
    ],
    "source": [
     "# Reload the best network and display info\n",
-    "model_3d = monai.networks.nets.densenet.densenet121(\n",
+    "model_3d = monai.networks.nets.DenseNet121(\n",
     "    spatial_dims=3, in_channels=1, out_channels=2\n",
     ").to(device)\n",
     "model_3d.load_state_dict(\n",

modules/interpretability/covid_classification.ipynb

@@ -79,7 +79,7 @@
     "\n",
     "import monai\n",
     "from monai.networks.utils import eval_mode\n",
-    "from monai.networks.nets import densenet121\n",
+    "from monai.networks.nets import DenseNet121\n",
     "from monai.transforms import (\n",
     "    Compose, LoadImage, Lambda, AddChannel,\n",
     "    ScaleIntensity, ToTensor, RandRotate,\n",
@@ -301,7 +301,7 @@
    "outputs": [],
    "source": [
     "def create_new_net():\n",
-    "    return densenet121(\n",
+    "    return DenseNet121(\n",
     "        spatial_dims=2,\n",
     "        in_channels=1,\n",
     "        out_channels=num_class\n",

modules/layer_wise_learning_rate.ipynb

@@ -57,7 +57,7 @@
     "    ToTensord,\n",
     ")\n",
     "from monai.optimizers import generate_param_groups\n",
-    "from monai.networks.nets import densenet121\n",
+    "from monai.networks.nets import DenseNet121\n",
     "from monai.inferers import SimpleInferer\n",
     "from monai.handlers import StatsHandler\n",
     "from monai.engines import SupervisedTrainer\n",
@@ -304,7 +304,7 @@
    "outputs": [],
    "source": [
     "device = torch.device(\"cuda:0\")\n",
-    "net = densenet121(pretrained=True, progress=False,\n",
+    "net = DenseNet121(pretrained=True, progress=False,\n",
     "                  spatial_dims=2, in_channels=1, out_channels=6).to(device)\n",
     "loss = torch.nn.CrossEntropyLoss()"
    ]

modules/public_datasets.ipynb

@@ -56,7 +56,7 @@
     "    Spacingd,\n",
     "    ToTensord,\n",
     ")\n",
-    "from monai.networks.nets import UNet, densenet121\n",
+    "from monai.networks.nets import UNet, DenseNet121\n",
     "from monai.networks.layers import Norm\n",
     "from monai.losses import DiceLoss\n",
     "from monai.inferers import SimpleInferer\n",
@@ -316,7 +316,7 @@
    "outputs": [],
    "source": [
     "device = torch.device(\"cuda:0\")\n",
-    "net = densenet121(spatial_dims=2, in_channels=1, out_channels=6).to(device)\n",
+    "net = DenseNet121(spatial_dims=2, in_channels=1, out_channels=6).to(device)\n",
     "loss = torch.nn.CrossEntropyLoss()\n",
     "opt = torch.optim.Adam(net.parameters(), 1e-5)"
    ]