123 lines
4.5 KiB
Python
123 lines
4.5 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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, division, print_function
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from collections import defaultdict
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import copy
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ..utils import get_param_attr_dict
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class MetaBN1D(nn.BatchNorm1D):
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def forward(self, inputs, opt={}):
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mode = opt.get("bn_mode", "general") if self.training else "eval"
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if mode == "general": # update, but not apply running_mean/var
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result = F.batch_norm(inputs, self._mean, self._variance,
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self.weight, self.bias, self.training,
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self._momentum, self._epsilon)
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elif mode == "hold": # not update, not apply running_mean/var
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result = F.batch_norm(
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inputs,
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paddle.mean(
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inputs, axis=0),
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paddle.var(inputs, axis=0),
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self.weight,
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self.bias,
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self.training,
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self._momentum,
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self._epsilon)
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elif mode == "eval": # fix and apply running_mean/var,
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if self._mean is None:
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result = F.batch_norm(
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inputs,
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paddle.mean(
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inputs, axis=0),
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paddle.var(inputs, axis=0),
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self.weight,
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self.bias,
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True,
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self._momentum,
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self._epsilon)
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else:
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result = F.batch_norm(inputs, self._mean, self._variance,
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self.weight, self.bias, False,
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self._momentum, self._epsilon)
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return result
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class MetaBNNeck(nn.Layer):
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def __init__(self, num_features, **kwargs):
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super(MetaBNNeck, self).__init__()
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=1.0))
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bias_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.0),
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trainable=False)
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if 'weight_attr' in kwargs:
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weight_attr = get_param_attr_dict(kwargs['weight_attr'])
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bias_attr = None
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if 'bias_attr' in kwargs:
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bias_attr = get_param_attr_dict(kwargs['bias_attr'])
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use_global_stats = None
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if 'use_global_stats' in kwargs:
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use_global_stats = get_param_attr_dict(kwargs['use_global_stats'])
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self.feat_bn = MetaBN1D(
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num_features,
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momentum=0.9,
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epsilon=1e-05,
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weight_attr=weight_attr,
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bias_attr=bias_attr,
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use_global_stats=use_global_stats)
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self.flatten = nn.Flatten()
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self.opt = {}
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def forward(self, x):
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x = self.flatten(x)
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x = self.feat_bn(x, self.opt)
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return x
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def reset_opt(self):
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self.opt = defaultdict()
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def setup_opt(self, opt):
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"""
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Arg:
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opt (dict): Optional setting to change the behavior of MetaBIN during training.
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It includes three settings which are `enable_inside_update`, `lr_gate` and `bn_mode`.
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"""
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self.check_opt(opt)
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self.opt = copy.deepcopy(opt)
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@classmethod
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def check_opt(cls, opt):
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assert isinstance(opt, dict), \
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TypeError('Got the wrong type of `opt`. Please use `dict` type.')
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if opt.get('enable_inside_update', False) and 'lr_gate' not in opt:
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raise RuntimeError('Missing `lr_gate` in opt.')
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assert isinstance(opt.get('lr_gate', 1.0), float), \
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TypeError('Got the wrong type of `lr_gate`. Please use `float` type.')
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assert isinstance(opt.get('enable_inside_update', True), bool), \
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TypeError('Got the wrong type of `enable_inside_update`. Please use `bool` type.')
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assert opt.get('bn_mode', "general") in ["general", "hold", "eval"], \
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TypeError('Got the wrong value of `bn_mode`.')
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