add ema filter no_grad (#7691)

* add ema filter no_grad

* update file name and default value

ppdet/engine/trainer.py

@@ -187,12 +187,14 @@ class Trainer(object):
             ema_decay_type = self.cfg.get('ema_decay_type', 'threshold')
             cycle_epoch = self.cfg.get('cycle_epoch', -1)
             ema_black_list = self.cfg.get('ema_black_list', None)
+            ema_filter_no_grad = self.cfg.get('ema_filter_no_grad', False)
             self.ema = ModelEMA(
                 self.model,
                 decay=ema_decay,
                 ema_decay_type=ema_decay_type,
                 cycle_epoch=cycle_epoch,
-                ema_black_list=ema_black_list)
+                ema_black_list=ema_black_list,
+                ema_filter_no_grad=ema_filter_no_grad)
 
         self._nranks = dist.get_world_size()
         self._local_rank = dist.get_rank()
@@ -1040,6 +1042,7 @@ class Trainer(object):
 
                     start = end
         return results
+
     def _get_save_image_name(self, output_dir, image_path):
         """
         Get save image name from source image path.

ppdet/optimizer/ema.py

@@ -21,6 +21,8 @@ import paddle
 import weakref
 from copy import deepcopy
 
+from .utils import get_bn_running_state_names
+
 __all__ = ['ModelEMA', 'SimpleModelEMA']
 
 
@@ -49,7 +51,8 @@ class ModelEMA(object):
                  decay=0.9998,
                  ema_decay_type='threshold',
                  cycle_epoch=-1,
-                 ema_black_list=None):
+                 ema_black_list=None,
+                 ema_filter_no_grad=False):
         self.step = 0
         self.epoch = 0
         self.decay = decay
@@ -64,6 +67,12 @@ class ModelEMA(object):
             else:
                 self.state_dict[k] = paddle.zeros_like(v)
 
+        bn_states_names = get_bn_running_state_names(model)
+        if ema_filter_no_grad:
+            for n, p in model.named_parameters():
+                if p.stop_gradient == True and n not in bn_states_names:
+                    self.ema_black_list.append(n)
+
         self._model_state = {
             k: weakref.ref(p)
             for k, p in model.state_dict().items()

ppdet/optimizer/utils.py

+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+
+from typing import List
+
+
+def get_bn_running_state_names(model: nn.Layer) -> List[str]:
+    """Get all bn state full names including running mean and variance
+    """
+    names = []
+    for n, m in model.named_sublayers():
+        if isinstance(m, (nn.BatchNorm2D, nn.SyncBatchNorm)):
+            assert hasattr(m, '_mean'), f'assert {m} has _mean'
+            assert hasattr(m, '_variance'), f'assert {m} has _variance'
+            running_mean = f'{n}._mean'
+            running_var = f'{n}._variance'
+            names.extend([running_mean, running_var])
+
+    return names