diff --git a/2d_classification/mednist_tutorial.ipynb b/2d_classification/mednist_tutorial.ipynb index 34183277ca..23bfb312a9 100644 --- a/2d_classification/mednist_tutorial.ipynb +++ b/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", diff --git a/3d_classification/ignite/densenet_evaluation_array.py b/3d_classification/ignite/densenet_evaluation_array.py index 8af71a9146..2a13b013d4 100644 --- a/3d_classification/ignite/densenet_evaluation_array.py +++ b/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 diff --git a/3d_classification/ignite/densenet_evaluation_dict.py b/3d_classification/ignite/densenet_evaluation_dict.py index 711be8774f..be008c0951 100644 --- a/3d_classification/ignite/densenet_evaluation_dict.py +++ b/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) diff --git a/3d_classification/ignite/densenet_training_array.py b/3d_classification/ignite/densenet_training_array.py index 8400970bba..96d8625eb8 100644 --- a/3d_classification/ignite/densenet_training_array.py +++ b/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) diff --git a/3d_classification/ignite/densenet_training_dict.py b/3d_classification/ignite/densenet_training_dict.py index 0305d55e10..7f50dd496b 100644 --- a/3d_classification/ignite/densenet_training_dict.py +++ b/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) diff --git a/3d_classification/torch/densenet_evaluation_array.py b/3d_classification/torch/densenet_evaluation_array.py index d090fa0bcf..8a7d4c434f 100644 --- a/3d_classification/torch/densenet_evaluation_array.py +++ b/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() diff --git a/3d_classification/torch/densenet_evaluation_dict.py b/3d_classification/torch/densenet_evaluation_dict.py index 5c9fdfc78d..a5fc74d206 100644 --- a/3d_classification/torch/densenet_evaluation_dict.py +++ b/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() diff --git a/3d_classification/torch/densenet_training_array.ipynb b/3d_classification/torch/densenet_training_array.ipynb index 44264e8d07..39b2ffe3d7 100644 --- a/3d_classification/torch/densenet_training_array.ipynb +++ b/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", diff --git a/3d_classification/torch/densenet_training_array.py b/3d_classification/torch/densenet_training_array.py index 8a4c116902..8dcd587846 100644 --- a/3d_classification/torch/densenet_training_array.py +++ b/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) diff --git a/3d_classification/torch/densenet_training_dict.py b/3d_classification/torch/densenet_training_dict.py index a14ddc7e8e..092d34e832 100644 --- a/3d_classification/torch/densenet_training_dict.py +++ b/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) diff --git a/modules/interpretability/class_lung_lesion.ipynb b/modules/interpretability/class_lung_lesion.ipynb index 9eeed8aa71..4b5db35e02 100644 --- a/modules/interpretability/class_lung_lesion.ipynb +++ b/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", diff --git a/modules/interpretability/covid_classification.ipynb b/modules/interpretability/covid_classification.ipynb index 795318e335..b7b9980ac2 100644 --- a/modules/interpretability/covid_classification.ipynb +++ b/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", diff --git a/modules/layer_wise_learning_rate.ipynb b/modules/layer_wise_learning_rate.ipynb index f88d56dc11..b4c196fe3c 100644 --- a/modules/layer_wise_learning_rate.ipynb +++ b/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()" ] diff --git a/modules/public_datasets.ipynb b/modules/public_datasets.ipynb index e4a29237f1..239050bd0b 100644 --- a/modules/public_datasets.ipynb +++ b/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)" ]