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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """Util class or function."""
-
- class AverageMeter:
- """Computes and stores the average and current value"""
-
- def __init__(self, name, fmt=':f', tb_writer=None):
- self.name = name
- self.fmt = fmt
- self.reset()
- self.tb_writer = tb_writer
- self.cur_step = 1
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = self.sum / self.count
- if self.tb_writer is not None:
- self.tb_writer.add_scalar(self.name, self.val, self.cur_step)
- self.cur_step += 1
-
- def __str__(self):
- fmtstr = '{name}:{avg' + self.fmt + '}'
- return fmtstr.format(**self.__dict__)
-
- def default_wd_filter(x):
- """default weight decay filter."""
- parameter_name = x.name
- if parameter_name.endswith('.bias'):
- # all bias not using weight decay
- return False
- if parameter_name.endswith('.gamma'):
- # bn weight bias not using weight decay, be carefully for now x not
- # include BN
- return False
- if parameter_name.endswith('.beta'):
- # bn weight bias not using weight decay, be carefully for now x not
- # include BN
- return False
-
- return True
-
-
- def get_param_groups(network):
- """Param groups for optimizer."""
- decay_params = []
- no_decay_params = []
- for x in network.trainable_params():
- parameter_name = x.name
- if parameter_name.endswith('.bias'):
- # all bias not using weight decay
- no_decay_params.append(x)
- elif parameter_name.endswith('.gamma'):
- # bn weight bias not using weight decay, be carefully for now x not
- # include BN
- no_decay_params.append(x)
- elif parameter_name.endswith('.beta'):
- # bn weight bias not using weight decay, be carefully for now x not
- # include BN
- no_decay_params.append(x)
- else:
- decay_params.append(x)
-
- return [{'params': no_decay_params, 'weight_decay': 0.0},
- {'params': decay_params}]
-
-
- class ShapeRecord:
- """Log image shape."""
-
- def __init__(self):
- self.shape_record = {
- 416: 0,
- 448: 0,
- 480: 0,
- 512: 0,
- 544: 0,
- 576: 0,
- 608: 0,
- 640: 0,
- 672: 0,
- 704: 0,
- 736: 0,
- 'total': 0
- }
-
- def set(self, shape):
- if len(shape) > 1:
- shape = shape[0]
- shape = int(shape)
- self.shape_record[shape] += 1
- self.shape_record['total'] += 1
-
- def show(self, logger):
- for key in self.shape_record:
- rate = self.shape_record[key] / float(self.shape_record['total'])
- logger.info('shape {}: {:.2f}%'.format(key, rate * 100))
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