add an toy example for nnunetv2.utilities.get_network_from_plans

nnunetv2/utilities/get_network_from_plans.py

@@ -41,3 +41,34 @@ def get_network_from_plans(arch_class_name, arch_kwargs, arch_kwargs_req_import,
         network.apply(network.initialize)
 
     return network
+
+if __name__ == "__main__":
+    import torch
+
+    model = get_network_from_plans(
+        arch_class_name="dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        arch_kwargs={
+            "n_stages": 7,
+            "features_per_stage": [32, 64, 128, 256, 512, 512, 512],
+            "conv_op": "torch.nn.modules.conv.Conv2d",
+            "kernel_sizes": [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]],
+            "strides": [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]],
+            "n_blocks_per_stage": [1, 3, 4, 6, 6, 6, 6],
+            "n_conv_per_stage_decoder": [1, 1, 1, 1, 1, 1],
+            "conv_bias": True,
+            "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+            "norm_op_kwargs": {"eps": 1e-05, "affine": True},
+            "dropout_op": None,
+            "dropout_op_kwargs": None,
+            "nonlin": "torch.nn.LeakyReLU",
+            "nonlin_kwargs": {"inplace": True},
+        },
+        arch_kwargs_req_import=["conv_op", "norm_op", "dropout_op", "nonlin"],
+        input_channels=1,
+        output_channels=4,
+        allow_init=True,
+        deep_supervision=True,
+    )
+    data = torch.rand((8, 1, 256, 256))
+    target = torch.rand(size=(8, 1, 256, 256))
+    outputs = model(data) # this should be a list of torch.Tensor