Merge branch 'master' into hf_cli4

GANDLF/models/dynunet_wrapper.py

@@ -2,6 +2,41 @@
 import monai.networks.nets.dynunet as dynunet
 
 
+def get_kernels_strides(sizes, spacings):
+    """
+    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)
+        ]
+        kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio]
+        if all(s == 1 for s in stride):
+            break
+        for idx, (i, j) in enumerate(zip(sizes, stride)):
+            assert (
+                i % j == 0
+            ), f"Patch size is not supported, please try to modify the size {input_size[idx]} in the spatial dimension {idx}."
+        sizes = [i / j for i, j in zip(sizes, stride)]
+        spacings = [i * j for i, j in zip(spacings, stride)]
+        kernels.append(kernel)
+        strides.append(stride)
+
+    strides.insert(0, len(spacings) * [1])
+    kernels.append(len(spacings) * [3])
+    return kernels, strides
+
+
 class dynunet_wrapper(ModelBase):
     """
     More info: https://docs.monai.io/en/stable/networks.html#dynunet
@@ -26,35 +61,33 @@ 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_for_internal_computations",
+            [1.0 for i in range(parameters["model"]["dimension"])],
+        )
+        parameters["model"]["kernel_size"] = parameters["model"].get(
+            "kernel_size", None
+        )
+        parameters["model"]["strides"] = parameters["model"].get("strides", None)
+        if (parameters["model"]["kernel_size"] is None) or (
+            parameters["model"]["strides"] is None
+        ):
+            kernel_size, strides = get_kernels_strides(patch_size, spacing)
+            parameters["model"]["kernel_size"] = kernel_size
+            parameters["model"]["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)
 

testing/test_full.py

@@ -278,12 +278,6 @@ 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)
-            parameters["model"]["strides"] = (1, 1, 1, 1)
-            parameters["model"]["deep_supervision"] = False
-
         parameters["model"]["architecture"] = model
         parameters["nested_training"]["testing"] = -5
         parameters["nested_training"]["validation"] = -5
@@ -376,12 +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
         parameters["nested_training"]["validation"] = -5