85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# Copyright 2021 Samsung Electronics 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.
|
|
# =============================================================================
|
|
|
|
import torch.nn as nn
|
|
|
|
def init_net(net, w_type, b_type):
|
|
if w_type == 'none':
|
|
pass
|
|
elif w_type == 'xavier':
|
|
net.apply(init_weights_vs)
|
|
elif w_type == 'kaiming':
|
|
net.apply(init_weights_he)
|
|
elif w_type == 'zero':
|
|
net.apply(init_weights_zero)
|
|
elif w_type == 'one':
|
|
net.apply(init_weights_one)
|
|
else:
|
|
raise NotImplementedError(f'init_type={w_type} is not supported.')
|
|
|
|
if b_type == 'none':
|
|
pass
|
|
elif b_type == 'xavier':
|
|
net.apply(init_bias_vs)
|
|
elif b_type == 'kaiming':
|
|
net.apply(init_bias_he)
|
|
elif b_type == 'zero':
|
|
net.apply(init_bias_zero)
|
|
elif b_type == 'one':
|
|
net.apply(init_bias_one)
|
|
else:
|
|
raise NotImplementedError(f'init_type={b_type} is not supported.')
|
|
|
|
|
|
|
|
|
|
def init_weights_vs(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
nn.init.xavier_normal_(m.weight)
|
|
|
|
def init_bias_vs(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
if m.bias is not None:
|
|
nn.init.xavier_normal_(m.bias)
|
|
|
|
def init_weights_he(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
nn.init.kaiming_normal_(m.weight)
|
|
|
|
def init_bias_he(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
if m.bias is not None:
|
|
nn.init.kaiming_normal_(m.bias)
|
|
|
|
def init_weights_zero(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
m.weight.data.fill_(.0)
|
|
|
|
def init_weights_one(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
m.weight.data.fill_(1.)
|
|
|
|
def init_bias_zero(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
if m.bias is not None:
|
|
m.bias.data.fill_(.0)
|
|
|
|
|
|
def init_bias_one(m):
|
|
if type(m) == nn.Linear or type(m) == nn.Conv2d:
|
|
if m.bias is not None:
|
|
m.bias.data.fill_(1.)
|
|
|