init

backend/ppocr/optimizer/__init__.py

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+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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
+from __future__ import unicode_literals
+import copy
+import paddle
+
+__all__ = ['build_optimizer']
+
+
+def build_lr_scheduler(lr_config, epochs, step_each_epoch):
+    from . import learning_rate
+    lr_config.update({'epochs': epochs, 'step_each_epoch': step_each_epoch})
+    lr_name = lr_config.pop('name', 'Const')
+    lr = getattr(learning_rate, lr_name)(**lr_config)()
+    return lr
+
+
+def build_optimizer(config, epochs, step_each_epoch, model):
+    from . import regularizer, optimizer
+    config = copy.deepcopy(config)
+    # step1 build lr
+    lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)
+
+    # step2 build regularization
+    if 'regularizer' in config and config['regularizer'] is not None:
+        reg_config = config.pop('regularizer')
+        reg_name = reg_config.pop('name')
+        if not hasattr(regularizer, reg_name):
+            reg_name += 'Decay'
+        reg = getattr(regularizer, reg_name)(**reg_config)()
+    elif 'weight_decay' in config:
+        reg = config.pop('weight_decay')
+    else:
+        reg = None
+
+    # step3 build optimizer
+    optim_name = config.pop('name')
+    if 'clip_norm' in config:
+        clip_norm = config.pop('clip_norm')
+        grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
+    else:
+        grad_clip = None
+    optim = getattr(optimizer, optim_name)(learning_rate=lr,
+                                           weight_decay=reg,
+                                           grad_clip=grad_clip,
+                                           **config)
+    return optim(model), lr