mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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3f65b360ef
* add vl * add vl * add vl * add ref * fix head out * add visionlan doc * fix vl infer * update dict
286 lines
9.8 KiB
Python
286 lines
9.8 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from paddle import optimizer as optim
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class Momentum(object):
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"""
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Simple Momentum optimizer with velocity state.
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Args:
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learning_rate (float|Variable) - The learning rate used to update parameters.
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Can be a float value or a Variable with one float value as data element.
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momentum (float) - Momentum factor.
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regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
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"""
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def __init__(self,
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learning_rate,
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momentum,
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weight_decay=None,
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grad_clip=None,
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**args):
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super(Momentum, self).__init__()
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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def __call__(self, model):
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train_params = [
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param for param in model.parameters() if param.trainable is True
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]
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opt = optim.Momentum(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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parameters=train_params)
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return opt
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class Adam(object):
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-08,
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parameter_list=None,
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weight_decay=None,
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grad_clip=None,
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name=None,
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lazy_mode=False,
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**kwargs):
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
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self.epsilon = epsilon
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self.parameter_list = parameter_list
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self.learning_rate = learning_rate
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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self.name = name
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self.lazy_mode = lazy_mode
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self.group_lr = kwargs.get('group_lr', False)
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self.training_step = kwargs.get('training_step', None)
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def __call__(self, model):
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if self.group_lr:
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if self.training_step == 'LF_2':
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import paddle
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if isinstance(model, paddle.fluid.dygraph.parallel.
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DataParallel): # multi gpu
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mlm = model._layers.head.MLM_VRM.MLM.parameters()
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pre_mlm_pp = model._layers.head.MLM_VRM.Prediction.pp_share.parameters(
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)
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pre_mlm_w = model._layers.head.MLM_VRM.Prediction.w_share.parameters(
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)
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else: # single gpu
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mlm = model.head.MLM_VRM.MLM.parameters()
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pre_mlm_pp = model.head.MLM_VRM.Prediction.pp_share.parameters(
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)
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pre_mlm_w = model.head.MLM_VRM.Prediction.w_share.parameters(
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)
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total = []
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for param in mlm:
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total.append(id(param))
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for param in pre_mlm_pp:
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total.append(id(param))
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for param in pre_mlm_w:
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total.append(id(param))
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group_base_params = [
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param for param in model.parameters() if id(param) in total
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]
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group_small_params = [
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param for param in model.parameters()
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if id(param) not in total
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]
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train_params = [{
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'params': group_base_params
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}, {
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'params': group_small_params,
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'learning_rate': self.learning_rate.values[0] * 0.1
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}]
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else:
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print(
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'group lr currently only support VisionLAN in LF_2 training step'
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)
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train_params = [
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param for param in model.parameters()
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if param.trainable is True
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]
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else:
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train_params = [
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param for param in model.parameters() if param.trainable is True
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]
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opt = optim.Adam(
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learning_rate=self.learning_rate,
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beta1=self.beta1,
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beta2=self.beta2,
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epsilon=self.epsilon,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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name=self.name,
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lazy_mode=self.lazy_mode,
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parameters=train_params)
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return opt
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class RMSProp(object):
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"""
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Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
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Args:
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learning_rate (float|Variable) - The learning rate used to update parameters.
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Can be a float value or a Variable with one float value as data element.
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momentum (float) - Momentum factor.
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rho (float) - rho value in equation.
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epsilon (float) - avoid division by zero, default is 1e-6.
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regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
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"""
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def __init__(self,
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learning_rate,
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momentum=0.0,
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rho=0.95,
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epsilon=1e-6,
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weight_decay=None,
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grad_clip=None,
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**args):
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super(RMSProp, self).__init__()
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.rho = rho
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self.epsilon = epsilon
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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def __call__(self, model):
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train_params = [
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param for param in model.parameters() if param.trainable is True
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]
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opt = optim.RMSProp(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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rho=self.rho,
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epsilon=self.epsilon,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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parameters=train_params)
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return opt
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class Adadelta(object):
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def __init__(self,
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learning_rate=0.001,
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epsilon=1e-08,
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rho=0.95,
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parameter_list=None,
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weight_decay=None,
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grad_clip=None,
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name=None,
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**kwargs):
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self.learning_rate = learning_rate
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self.epsilon = epsilon
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self.rho = rho
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self.parameter_list = parameter_list
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self.learning_rate = learning_rate
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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self.name = name
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def __call__(self, model):
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train_params = [
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param for param in model.parameters() if param.trainable is True
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]
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opt = optim.Adadelta(
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learning_rate=self.learning_rate,
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epsilon=self.epsilon,
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rho=self.rho,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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name=self.name,
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parameters=train_params)
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return opt
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class AdamW(object):
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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weight_decay=0.01,
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multi_precision=False,
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grad_clip=None,
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no_weight_decay_name=None,
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one_dim_param_no_weight_decay=False,
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name=None,
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lazy_mode=False,
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**args):
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super().__init__()
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
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self.epsilon = epsilon
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self.grad_clip = grad_clip
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self.weight_decay = 0.01 if weight_decay is None else weight_decay
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self.grad_clip = grad_clip
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self.name = name
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self.lazy_mode = lazy_mode
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self.multi_precision = multi_precision
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self.no_weight_decay_name_list = no_weight_decay_name.split(
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) if no_weight_decay_name else []
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self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
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def __call__(self, model):
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parameters = [
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param for param in model.parameters() if param.trainable is True
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]
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self.no_weight_decay_param_name_list = [
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p.name for n, p in model.named_parameters()
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if any(nd in n for nd in self.no_weight_decay_name_list)
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]
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if self.one_dim_param_no_weight_decay:
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self.no_weight_decay_param_name_list += [
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p.name for n, p in model.named_parameters() if len(p.shape) == 1
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]
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opt = optim.AdamW(
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learning_rate=self.learning_rate,
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beta1=self.beta1,
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beta2=self.beta2,
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epsilon=self.epsilon,
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parameters=parameters,
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weight_decay=self.weight_decay,
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multi_precision=self.multi_precision,
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grad_clip=self.grad_clip,
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name=self.name,
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lazy_mode=self.lazy_mode,
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apply_decay_param_fun=self._apply_decay_param_fun)
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return opt
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def _apply_decay_param_fun(self, name):
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return name not in self.no_weight_decay_param_name_list
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