Patch fix auto3dseg to support PyTorch <= 1.12

auto3dseg/algorithm_templates/dints/scripts/train.py

@@ -32,6 +32,7 @@
 from monai.data import DataLoader, partition_dataset
 from monai.inferers import sliding_window_inference
 from monai.metrics import compute_dice
+from monai.networks.utils import pytorch_after
 from monai.utils import set_determinism
 
 
@@ -208,6 +209,12 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
         print("num_epochs", num_epochs)
         print("num_epochs_per_validation", num_epochs_per_validation)
 
+    # patch fix to support PolynomialLR use in PyTorch <= 1.12
+    if  "PolynomialLR" in parser.get("training#lr_scheduler#_target_") and not pytorch_after(1,13):
+        dints_dir = os.path.dirname(os.path.dirname(__file__))
+        sys.path.insert(0, dints_dir)
+        parser["training#lr_scheduler#_target_"] = "scripts.utils.PolynomialLR"
+
     lr_scheduler_part = parser.get_parsed_content(
         "training#lr_scheduler", instantiate=False
     )

auto3dseg/algorithm_templates/dints/scripts/utils.py

@@ -0,0 +1,125 @@
+# 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 PyTorch:
+
+# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+# Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+# From Caffe2:
+
+# Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+
+# All contributions by Facebook:
+# Copyright (c) 2016 Facebook Inc.
+
+# All contributions by Google:
+# Copyright (c) 2015 Google Inc.
+# All rights reserved.
+
+# All contributions by Yangqing Jia:
+# Copyright (c) 2015 Yangqing Jia
+# All rights reserved.
+
+# All contributions by Kakao Brain:
+# Copyright 2019-2020 Kakao Brain
+
+# All contributions by Cruise LLC:
+# Copyright (c) 2022 Cruise LLC.
+# All rights reserved.
+
+# All contributions from Caffe:
+# Copyright(c) 2013, 2014, 2015, the respective contributors
+# All rights reserved.
+
+# All other contributions:
+# Copyright(c) 2015, 2016 the respective contributors
+# All rights reserved.
+
+# Caffe2 uses a copyright model similar to Caffe: each contributor holds
+# copyright over their contributions to Caffe2. The project versioning records
+# all such contribution and copyright details. If a contributor wants to further
+# mark their specific copyright on a particular contribution, they should
+# indicate their copyright solely in the commit message of the change when it is
+# committed.
+
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+
+# 2. Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+
+# 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+#    and IDIAP Research Institute nor the names of its contributors may be
+#    used to endorse or promote products derived from this software without
+#    specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+import warnings
+
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class PolynomialLR(_LRScheduler):
+    """
+    This code is copied from the PyTorch to extend the support of PolynomialLR in Auto3DSeg with PyTorch <= 1.12
+    reference: https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#PolynomialLR
+
+    """
+    def __init__(self, optimizer, total_iters=5, power=1.0, last_epoch=-1, verbose=False):
+        self.total_iters = total_iters
+        self.power = power
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            warnings.warn("To get the last learning rate computed by the scheduler, "
+                          "please use `get_last_lr()`.", UserWarning)
+
+        if self.last_epoch == 0 or self.last_epoch > self.total_iters:
+            return [group["lr"] for group in self.optimizer.param_groups]
+
+        decay_factor = (
+            (1.0 - self.last_epoch / self.total_iters) / (1.0 - (self.last_epoch - 1) / self.total_iters)
+        ) ** self.power
+        return [group["lr"] * decay_factor for group in self.optimizer.param_groups]
+
+    def _get_closed_form_lr(self):
+        return [
+            (
+                base_lr * (1.0 - min(self.total_iters, self.last_epoch) / self.total_iters) ** self.power
+            )
+            for base_lr in self.base_lrs
+        ]