reorg optimizer (#6016)

ppdet/optimizer/__init__.py

+# Copyright (c) 2022 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 .optimizer import *
+from .ema import ModelEMA

ppdet/optimizer/ema.py

+# Copyright (c) 2022 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 math
+import paddle
+import weakref
+
+
+class ModelEMA(object):
+    """
+    Exponential Weighted Average for Deep Neutal Networks
+    Args:
+        model (nn.Layer): Detector of model.
+        decay (int):  The decay used for updating ema parameter.
+            Ema's parameter are updated with the formula:
+           `ema_param = decay * ema_param + (1 - decay) * cur_param`.
+            Defaults is 0.9998.
+        ema_decay_type (str): type in ['threshold', 'normal', 'exponential'],
+            'threshold' as default.
+        cycle_epoch (int): The epoch of interval to reset ema_param and
+            step. Defaults is -1, which means not reset. Its function is to
+            add a regular effect to ema, which is set according to experience
+            and is effective when the total training epoch is large.
+    """
+
+    def __init__(self,
+                 model,
+                 decay=0.9998,
+                 ema_decay_type='threshold',
+                 cycle_epoch=-1):
+        self.step = 0
+        self.epoch = 0
+        self.decay = decay
+        self.state_dict = dict()
+        for k, v in model.state_dict().items():
+            self.state_dict[k] = paddle.zeros_like(v)
+        self.ema_decay_type = ema_decay_type
+        self.cycle_epoch = cycle_epoch
+
+        self._model_state = {
+            k: weakref.ref(p)
+            for k, p in model.state_dict().items()
+        }
+
+    def reset(self):
+        self.step = 0
+        self.epoch = 0
+        for k, v in self.state_dict.items():
+            self.state_dict[k] = paddle.zeros_like(v)
+
+    def resume(self, state_dict, step=0):
+        for k, v in state_dict.items():
+            if k in self.state_dict:
+                self.state_dict[k] = v
+        self.step = step
+
+    def update(self, model=None):
+        if self.ema_decay_type == 'threshold':
+            decay = min(self.decay, (1 + self.step) / (10 + self.step))
+        elif self.ema_decay_type == 'exponential':
+            decay = self.decay * (1 - math.exp(-(self.step + 1) / 2000))
+        else:
+            decay = self.decay
+        self._decay = decay
+
+        if model is not None:
+            model_dict = model.state_dict()
+        else:
+            model_dict = {k: p() for k, p in self._model_state.items()}
+            assert all(
+                [v is not None for _, v in model_dict.items()]), 'python gc.'
+
+        for k, v in self.state_dict.items():
+            v = decay * v + (1 - decay) * model_dict[k]
+            v.stop_gradient = True
+            self.state_dict[k] = v
+        self.step += 1
+
+    def apply(self):
+        if self.step == 0:
+            return self.state_dict
+        state_dict = dict()
+        for k, v in self.state_dict.items():
+            if self.ema_decay_type != 'exponential':
+                v = v / (1 - self._decay**self.step)
+            v.stop_gradient = True
+            state_dict[k] = v
+        self.epoch += 1
+        if self.cycle_epoch > 0 and self.epoch == self.cycle_epoch:
+            self.reset()
+
+        return state_dict

ppdet/optimizer.py → ppdet/optimizer/optimizer.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 
 import sys
 import math
-import weakref
 import paddle
 import paddle.nn as nn
 
@@ -360,89 +359,3 @@ class OptimizerBuilder():
                   parameters=params,
                   grad_clip=grad_clip,
                   **optim_args)
-
-
-class ModelEMA(object):
-    """
-    Exponential Weighted Average for Deep Neutal Networks
-    Args:
-        model (nn.Layer): Detector of model.
-        decay (int):  The decay used for updating ema parameter.
-            Ema's parameter are updated with the formula:
-           `ema_param = decay * ema_param + (1 - decay) * cur_param`.
-            Defaults is 0.9998.
-        ema_decay_type (str): type in ['threshold', 'normal', 'exponential'],
-            'threshold' as default.
-        cycle_epoch (int): The epoch of interval to reset ema_param and
-            step. Defaults is -1, which means not reset. Its function is to
-            add a regular effect to ema, which is set according to experience
-            and is effective when the total training epoch is large.
-    """
-
-    def __init__(self,
-                 model,
-                 decay=0.9998,
-                 ema_decay_type='threshold',
-                 cycle_epoch=-1):
-        self.step = 0
-        self.epoch = 0
-        self.decay = decay
-        self.state_dict = dict()
-        for k, v in model.state_dict().items():
-            self.state_dict[k] = paddle.zeros_like(v)
-        self.ema_decay_type = ema_decay_type
-        self.cycle_epoch = cycle_epoch
-
-        self._model_state = {
-            k: weakref.ref(p)
-            for k, p in model.state_dict().items()
-        }
-
-    def reset(self):
-        self.step = 0
-        self.epoch = 0
-        for k, v in self.state_dict.items():
-            self.state_dict[k] = paddle.zeros_like(v)
-
-    def resume(self, state_dict, step=0):
-        for k, v in state_dict.items():
-            if k in self.state_dict:
-                self.state_dict[k] = v
-        self.step = step
-
-    def update(self, model=None):
-        if self.ema_decay_type == 'threshold':
-            decay = min(self.decay, (1 + self.step) / (10 + self.step))
-        elif self.ema_decay_type == 'exponential':
-            decay = self.decay * (1 - math.exp(-(self.step + 1) / 2000))
-        else:
-            decay = self.decay
-        self._decay = decay
-
-        if model is not None:
-            model_dict = model.state_dict()
-        else:
-            model_dict = {k: p() for k, p in self._model_state.items()}
-            assert all(
-                [v is not None for _, v in model_dict.items()]), 'python gc.'
-
-        for k, v in self.state_dict.items():
-            v = decay * v + (1 - decay) * model_dict[k]
-            v.stop_gradient = True
-            self.state_dict[k] = v
-        self.step += 1
-
-    def apply(self):
-        if self.step == 0:
-            return self.state_dict
-        state_dict = dict()
-        for k, v in self.state_dict.items():
-            if self.ema_decay_type != 'exponential':
-                v = v / (1 - self._decay**self.step)
-            v.stop_gradient = True
-            state_dict[k] = v
-        self.epoch += 1
-        if self.cycle_epoch > 0 and self.epoch == self.cycle_epoch:
-            self.reset()
-
-        return state_dict