You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
86 lines
2.5 KiB
86 lines
2.5 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
|
# |
|
# 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 paddle.nn as nn |
|
import paddle.nn.functional as F |
|
|
|
|
|
def normal_init(param, *args, **kwargs): |
|
""" |
|
Initialize parameters with a normal distribution. |
|
|
|
Args: |
|
param (Tensor): The tensor that needs to be initialized. |
|
|
|
Returns: |
|
The initialized parameters. |
|
""" |
|
|
|
return nn.initializer.Normal(*args, **kwargs)(param) |
|
|
|
|
|
def kaiming_normal_init(param, *args, **kwargs): |
|
""" |
|
Initialize parameters with the Kaiming normal distribution. |
|
|
|
For more information about the Kaiming initialization method, please refer to |
|
https://arxiv.org/abs/1502.01852 |
|
|
|
Args: |
|
param (Tensor): The tensor that needs to be initialized. |
|
|
|
Returns: |
|
The initialized parameters. |
|
""" |
|
|
|
return nn.initializer.KaimingNormal(*args, **kwargs)(param) |
|
|
|
|
|
def constant_init(param, *args, **kwargs): |
|
""" |
|
Initialize parameters with constants. |
|
|
|
Args: |
|
param (Tensor): The tensor that needs to be initialized. |
|
|
|
Returns: |
|
The initialized parameters. |
|
""" |
|
|
|
return nn.initializer.Constant(*args, **kwargs)(param) |
|
|
|
|
|
class KaimingInitMixin: |
|
""" |
|
A mix-in that provides the Kaiming initialization functionality. |
|
|
|
Examples: |
|
|
|
from paddlers.rs_models.cd.models.param_init import KaimingInitMixin |
|
|
|
class CustomNet(nn.Layer, KaimingInitMixin): |
|
def __init__(self, num_channels, num_classes): |
|
super().__init__() |
|
self.conv = nn.Conv2D(num_channels, num_classes, 3, 1, 0, bias_attr=False) |
|
self.bn = nn.BatchNorm2D(num_classes) |
|
self.init_weight() |
|
""" |
|
|
|
def init_weight(self): |
|
for layer in self.sublayers(): |
|
if isinstance(layer, nn.Conv2D): |
|
kaiming_normal_init(layer.weight) |
|
elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)): |
|
constant_init(layer.weight, value=1) |
|
constant_init(layer.bias, value=0)
|
|
|