Add freeze_layers

monai/networks/utils.py

@@ -1111,3 +1111,47 @@
         # revert
         for name, module in replaced:
             _replace_modules(parent, name, module, [], strict_match=True, match_device=match_device)
+
+
+def freeze_layers(model: nn.Module, freeze_vars=None, exclude_vars=None):
+    """
+    A utilty function to help freeze specific layers.
+
+    Args:
+        model: a source PyTorch model to freeze layer.
+        freeze_vars: a regular expression to match the `model` variable names,
+            so that their `requires_grad` will set to `False`.
+        exclude_vars: a regular expression to match the `model` variable names,
+            except for matched variable names, other `requires_grad` will set to `False`.
+
+    Raises:
+        ValueError: when freeze_vars and exclude_vars are both specified.
+
+    """
+    if freeze_vars is not None and exclude_vars is not None:
+        raise ValueError("Incompatible values: freeze_vars and exclude_vars are both specified.")
+    src_dict = get_state_dict(model)
+
+    frozen_keys = list()
+    if freeze_vars is not None:
+        to_freeze = {s_key for s_key in src_dict if freeze_vars and re.compile(freeze_vars).search(s_key)}
+        for name, param in model.named_parameters():
+            if name in to_freeze:
+                param.requires_grad = False
+                frozen_keys.append(name)
+            elif not param.requires_grad:
+                param.requires_grad = True
+                warnings.warn(
+                    f"The freeze_vars does not include {param}, but requires_grad is False, change it to True."
+                )
+    if exclude_vars is not None:
+        to_exclude = {s_key for s_key in src_dict if exclude_vars and re.compile(exclude_vars).search(s_key)}
+        for name, param in model.named_parameters():
+            if name not in to_exclude:
+                param.requires_grad = False
+                frozen_keys.append(name)
+            elif not param.requires_grad:
+                param.requires_grad = True
+                warnings.warn(f"The exclude_vars includes {param}, but requires_grad is False, change it to True.")
+
+    logger.info(f"{len(frozen_keys)} of {len(src_dict)} variables frozen.")

tests/test_freeze_layers.py

@@ -0,0 +1,61 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import unittest
+
+import torch
+from parameterized import parameterized
+
+from monai.networks.utils import freeze_layers
+from monai.utils import set_determinism
+from tests.test_copy_model_state import _TestModelOne, _TestModelTwo
+
+TEST_CASES = []
+__devices = ("cpu", "cuda") if torch.cuda.is_available() else ("cpu",)
+for _x in __devices:
+    TEST_CASES.append(_x)
+
+
+class TestModuleState(unittest.TestCase):
+    def tearDown(self):
+        set_determinism(None)
+
+    @parameterized.expand(TEST_CASES)
+    def test_freeze_vars(self, device):
+        set_determinism(0)
+        model = _TestModelOne(10, 20, 3)
+        model.to(device)
+        freeze_layers(model, "class")
+
+        for name, param in model.named_parameters():
+            if "class_layer" in name:
+                self.assertEqual(param.requires_grad, False)
+            else:
+                self.assertEqual(param.requires_grad, True)
+
+    @parameterized.expand(TEST_CASES)
+    def test_exclude_vars(self, device):
+        set_determinism(0)
+        model = _TestModelTwo(10, 20, 10, 4)
+        model.to(device)
+        freeze_layers(model, exclude_vars="class")
+
+        for name, param in model.named_parameters():
+            if "class_layer" in name:
+                self.assertEqual(param.requires_grad, True)
+            else:
+                self.assertEqual(param.requires_grad, False)
+
+
+if __name__ == "__main__":
+    unittest.main()