add auto configure kernel and strides

GANDLF/models/dynunet_wrapper.py

@@ -1,18 +1,25 @@
-from testing.test_full import patch_size
 from .modelBase import ModelBase
 import monai.networks.nets.dynunet as dynunet
 
 
-def get_kernels_strides(sizes,spacings):
+def get_kernels_strides(sizes, spacings):
     """
-    https://github.com/Project-MONAI/tutorials/blob/main/modules/dynunet_pipeline/create_network.py#L19
+    More info: https://github.com/Project-MONAI/tutorials/blob/main/modules/dynunet_pipeline/create_network.py#L19
+
+    When refering this method for other tasks, please ensure that the patch size for each spatial dimension should
+    be divisible by the product of all strides in the corresponding dimension.
+    In addition, the minimal spatial size should have at least one dimension that has twice the size of
+    the product of all strides. For patch sizes that cannot find suitable strides, an error will be raised.
 
     """
     input_size = sizes
     strides, kernels = [], []
     while True:
         spacing_ratio = [sp / min(spacings) for sp in spacings]
-        stride = [2 if ratio <= 2 and size >= 8 else 1 for (ratio, size) in zip(spacing_ratio, sizes)]
+        stride = [
+            2 if ratio <= 2 and size >= 8 else 1
+            for (ratio, size) in zip(spacing_ratio, sizes)
+        ]
         kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio]
         if all(s == 1 for s in stride):
             break
@@ -30,6 +37,7 @@ def get_kernels_strides(sizes,spacings):
     kernels.append(len(spacings) * [3])
     return kernels, strides
 
+
 class dynunet_wrapper(ModelBase):
     """
     More info: https://docs.monai.io/en/stable/networks.html#dynunet
@@ -54,42 +62,29 @@ class dynunet_wrapper(ModelBase):
     def __init__(self, parameters: dict):
         super(dynunet_wrapper, self).__init__(parameters)
 
-        # checking for validation
-        assert (
-            "kernel_size" in parameters["model"]
-        ) == True, "\033[0;31m`kernel_size` key missing in parameters"
-        assert (
-            "strides" in parameters["model"]
-        ) == True, "\033[0;31m`strides` key missing in parameters"
-
-        # defining some defaults
-        # if not ("upsample_kernel_size" in parameters["model"]):
-        #     parameters["model"]["upsample_kernel_size"] = parameters["model"][
-        #         "strides"
-        #     ][1:]
-
+        patch_size = parameters.get("patch_size", None)
+        spacing = parameters.get(
+            "spacing", [1.0 for i in range(parameters["model"]["dimension"])]
+        )
+        kernel_size, strides = get_kernels_strides(patch_size, spacing)
+        parameters["model"]["kernel_size"] = parameters["model"].get(
+            "kernel_size", kernel_size
+        )
+        parameters["model"]["strides"] = parameters["model"].get("strides", strides)
         parameters["model"]["filters"] = parameters["model"].get("filters", None)
         parameters["model"]["act_name"] = parameters["model"].get(
             "act_name", ("leakyrelu", {"inplace": True, "negative_slope": 0.01})
         )
-
         parameters["model"]["deep_supervision"] = parameters["model"].get(
-            "deep_supervision", True
+            "deep_supervision", False
         )
-
         parameters["model"]["deep_supr_num"] = parameters["model"].get(
             "deep_supr_num", 1
         )
-
         parameters["model"]["res_block"] = parameters["model"].get("res_block", True)
-
         parameters["model"]["trans_bias"] = parameters["model"].get("trans_bias", False)
         parameters["model"]["dropout"] = parameters["model"].get("dropout", None)
-        parameters["model"]["auto_calculation_kernel_stripes"] = parameters["model"].get("auto_calculation_kernel_stripes", True)
-        if parameters["model"]["auto_calculation_kernel_stripes"] == True:
-            patch_size = parameters.get("patch_size", None)
-            spacing = parameters.get("spacing", None)
-            parameters["model"]["kernel_size"], parameters["model"]["strides"] = get_kernels_strides(patch_size, spacing)
+
         if not ("norm_type" in parameters["model"]):
             self.norm_type = "INSTANCE"
 

testing/test_full.py

@@ -52,15 +52,15 @@
 ## global defines
 # pre-defined segmentation model types for testing
 all_models_segmentation = [
-    # "lightunet",
-    # "lightunet_multilayer",
-    # "unet",
-    # "unet_multilayer",
-    # "deep_resunet",
-    # "fcn",
-    # "uinc",
-    # "msdnet",
-    # "imagenet_unet",
+    "lightunet",
+    "lightunet_multilayer",
+    "unet",
+    "unet_multilayer",
+    "deep_resunet",
+    "fcn",
+    "uinc",
+    "msdnet",
+    "imagenet_unet",
     "dynunet",
 ]
 # pre-defined regression/classification model types for testing
@@ -276,13 +276,6 @@ def test_train_segmentation_rad_2d(device):
                 ["acs", "soft", "conv3d"]
             )
 
-        if model == "dynunet":
-            # More info: https://github.com/Project-MONAI/MONAI/blob/96bfda00c6bd290297f5e3514ea227c6be4d08b4/tests/test_dynunet.py
-            # parameters["model"]["kernel_size"] = (3, 3, 3, 1)
-            ["model"]["auto_calculation_kernel_stripes"] = True
-            parameters["spacing"] = [[1.0, 1.0, 1.0]]
-            # parameters["model"]["strides"] = (1, 1, 1, 1)
-            parameters["model"]["deep_supervision"] = False
 
         parameters["model"]["architecture"] = model
         parameters["nested_training"]["testing"] = -5
@@ -296,6 +289,7 @@ def test_train_segmentation_rad_2d(device):
             resume=False,
             reset=True,
         )
+        print(parameters["model"]["strides"])
 
     sanitize_outputDir()
 
@@ -376,11 +370,6 @@ def test_train_segmentation_rad_3d(device):
                 ["acs", "soft", "conv3d"]
             )
 
-        if model == "dynunet":
-            # More info: https://github.com/Project-MONAI/MONAI/blob/96bfda00c6bd290297f5e3514ea227c6be4d08b4/tests/test_dynunet.py
-            parameters["model"]["kernel_size"] = (3, 3, 3, 1)
-            parameters["model"]["strides"] = (1, 1, 1, 1)
-            parameters["model"]["deep_supervision"] = False
 
         parameters["model"]["architecture"] = model
         parameters["nested_training"]["testing"] = -5