Bobholamovic
3 years ago
parent
bfd2554f98
commit
32babb3418
6 changed files with 459 additions and 2 deletions
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# Copyright (c) 2022 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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|
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from .blocks import * |
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# Copyright (c) 2022 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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|
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import paddle.nn as nn |
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__all__ = [ |
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'BasicConv', 'Conv1x1', 'Conv3x3', 'Conv7x7', |
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'MaxPool2x2', 'MaxUnPool2x2', |
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'ConvTransposed3x3', |
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'Identity' |
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] |
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|
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def get_norm_layer(): |
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# TODO: select appropriate norm layer. |
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return nn.BatchNorm2D |
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|
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def get_act_layer(): |
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# TODO: select appropriate activation layer. |
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return nn.ReLU |
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|
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def make_norm(*args, **kwargs): |
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norm_layer = get_norm_layer() |
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return norm_layer(*args, **kwargs) |
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|
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def make_act(*args, **kwargs): |
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act_layer = get_act_layer() |
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return act_layer(*args, **kwargs) |
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|
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class BasicConv(nn.Layer): |
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def __init__( |
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self, in_ch, out_ch, |
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kernel_size, pad_mode='constant', |
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bias='auto', norm=False, act=False, |
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**kwargs |
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): |
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super().__init__() |
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seq = [] |
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if kernel_size >= 2: |
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seq.append(nn.Pad2D(kernel_size//2, mode=pad_mode)) |
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seq.append( |
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nn.Conv2D( |
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in_ch, out_ch, kernel_size, |
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stride=1, padding=0, |
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bias_attr=(False if norm else None) if bias=='auto' else bias, |
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**kwargs |
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) |
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) |
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if norm: |
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if norm is True: |
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norm = make_norm(out_ch) |
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seq.append(norm) |
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if act: |
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if act is True: |
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act = make_act() |
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seq.append(act) |
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self.seq = nn.Sequential(*seq) |
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|
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def forward(self, x): |
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return self.seq(x) |
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class Conv1x1(BasicConv): |
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def __init__(self, in_ch, out_ch, pad_mode='constant', bias='auto', norm=False, act=False, **kwargs): |
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super().__init__(in_ch, out_ch, 1, pad_mode=pad_mode, bias=bias, norm=norm, act=act, **kwargs) |
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|
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class Conv3x3(BasicConv): |
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def __init__(self, in_ch, out_ch, pad_mode='constant', bias='auto', norm=False, act=False, **kwargs): |
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super().__init__(in_ch, out_ch, 3, pad_mode=pad_mode, bias=bias, norm=norm, act=act, **kwargs) |
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|
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class Conv7x7(BasicConv): |
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def __init__(self, in_ch, out_ch, pad_mode='constant', bias='auto', norm=False, act=False, **kwargs): |
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super().__init__(in_ch, out_ch, 7, pad_mode=pad_mode, bias=bias, norm=norm, act=act, **kwargs) |
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class MaxPool2x2(nn.MaxPool2D): |
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def __init__(self, **kwargs): |
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super().__init__(kernel_size=2, stride=(2,2), padding=(0,0), **kwargs) |
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class MaxUnPool2x2(nn.MaxUnPool2D): |
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def __init__(self, **kwargs): |
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super().__init__(kernel_size=2, stride=(2,2), padding=(0,0), **kwargs) |
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class ConvTransposed3x3(nn.Layer): |
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def __init__( |
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self, in_ch, out_ch, |
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bias='auto', norm=False, act=False, |
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**kwargs |
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): |
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super().__init__() |
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seq = [] |
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seq.append( |
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nn.Conv2DTranspose( |
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in_ch, out_ch, 3, |
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stride=2, padding=1, |
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bias_attr=(False if norm else None) if bias=='auto' else bias, |
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**kwargs |
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) |
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) |
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if norm: |
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if norm is True: |
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norm = make_norm(out_ch) |
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seq.append(norm) |
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if act: |
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if act is True: |
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act = make_act() |
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seq.append(act) |
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self.seq = nn.Sequential(*seq) |
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def forward(self, x): |
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return self.seq(x) |
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class Identity(nn.Layer): |
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"""A placeholder identity operator that accepts exactly one argument.""" |
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def __init__(self, *args, **kwargs): |
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super().__init__() |
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|
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def forward(self, x): |
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return x |
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# Copyright (c) 2022 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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import paddle.nn as nn |
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import paddle.nn.functional as F |
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def normal_init(param, *args, **kwargs): |
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""" |
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Initialize parameters with a normal distribution. |
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Args: |
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param (Tensor): The tensor that needs to be initialized. |
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Returns: |
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The initialized parameters. |
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""" |
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return nn.initializer.Normal(*args, **kwargs)(param) |
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def kaiming_normal_init(param, *args, **kwargs): |
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""" |
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Initialize parameters with the Kaiming normal distribution. |
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For more information about the Kaiming initialization method, please refer to |
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https://arxiv.org/abs/1502.01852 |
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Args: |
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param (Tensor): The tensor that needs to be initialized. |
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Returns: |
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The initialized parameters. |
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""" |
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return nn.initializer.KaimingNormal(*args, **kwargs)(param) |
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def constant_init(param, *args, **kwargs): |
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""" |
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Initialize parameters with constants. |
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Args: |
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param (Tensor): The tensor that needs to be initialized. |
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Returns: |
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The initialized parameters. |
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""" |
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return nn.initializer.Constant(*args, **kwargs)(param) |
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class KaimingInitMixin: |
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""" |
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A mix-in that provides the Kaiming initialization functionality. |
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Examples: |
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from paddlers.models.cd.models.param_init import KaimingInitMixin |
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class CustomNet(nn.Layer, KaimingInitMixin): |
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def __init__(self, num_channels, num_classes): |
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super().__init__() |
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self.conv = nn.Conv2D(num_channels, num_classes, 3, 1, 0, bias_attr=False) |
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self.bn = nn.BatchNorm2D(num_classes) |
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self.init_weight() |
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""" |
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def init_weight(self): |
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for layer in self.sublayers(): |
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if isinstance(layer, nn.Conv2D): |
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kaiming_normal_init(layer.weight) |
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elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)): |
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constant_init(layer.weight, value=1) |
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constant_init(layer.bias, value=0) |
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# Copyright (c) 2022 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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|
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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 .layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity |
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from .param_init import normal_init, constant_init |
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class UNetEarlyFusion(nn.Layer): |
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""" |
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The FC-EF implementation based on PaddlePaddle. |
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The original article refers to |
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Caye Daudt, R., et al. "Fully convolutional siamese networks for change detection" |
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(https://arxiv.org/abs/1810.08462) |
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Args: |
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in_channels (int): The number of bands of the input images. |
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num_classes (int): The number of target classes. |
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use_dropout (bool, optional): A bool value that indicates whether to use dropout layers. |
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When the model is trained on a relatively small dataset, the dropout layers help prevent |
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overfitting. Default: False. |
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""" |
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def __init__( |
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self, |
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in_channels, |
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num_classes, |
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use_dropout=False |
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): |
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super().__init__() |
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C1, C2, C3, C4, C5 = 16, 32, 64, 128, 256 |
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self.use_dropout = use_dropout |
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self.conv11 = Conv3x3(in_channels, C1, norm=True, act=True) |
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self.do11 = self._make_dropout() |
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self.conv12 = Conv3x3(C1, C1, norm=True, act=True) |
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self.do12 = self._make_dropout() |
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self.pool1 = MaxPool2x2() |
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self.conv21 = Conv3x3(C1, C2, norm=True, act=True) |
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self.do21 = self._make_dropout() |
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self.conv22 = Conv3x3(C2, C2, norm=True, act=True) |
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self.do22 = self._make_dropout() |
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self.pool2 = MaxPool2x2() |
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self.conv31 = Conv3x3(C2, C3, norm=True, act=True) |
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self.do31 = self._make_dropout() |
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self.conv32 = Conv3x3(C3, C3, norm=True, act=True) |
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self.do32 = self._make_dropout() |
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self.conv33 = Conv3x3(C3, C3, norm=True, act=True) |
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self.do33 = self._make_dropout() |
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self.pool3 = MaxPool2x2() |
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self.conv41 = Conv3x3(C3, C4, norm=True, act=True) |
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self.do41 = self._make_dropout() |
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self.conv42 = Conv3x3(C4, C4, norm=True, act=True) |
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self.do42 = self._make_dropout() |
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self.conv43 = Conv3x3(C4, C4, norm=True, act=True) |
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self.do43 = self._make_dropout() |
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self.pool4 = MaxPool2x2() |
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self.upconv4 = ConvTransposed3x3(C4, C4, output_padding=1) |
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self.conv43d = Conv3x3(C5, C4, norm=True, act=True) |
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self.do43d = self._make_dropout() |
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self.conv42d = Conv3x3(C4, C4, norm=True, act=True) |
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self.do42d = self._make_dropout() |
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self.conv41d = Conv3x3(C4, C3, norm=True, act=True) |
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self.do41d = self._make_dropout() |
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self.upconv3 = ConvTransposed3x3(C3, C3, output_padding=1) |
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self.conv33d = Conv3x3(C4, C3, norm=True, act=True) |
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self.do33d = self._make_dropout() |
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self.conv32d = Conv3x3(C3, C3, norm=True, act=True) |
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self.do32d = self._make_dropout() |
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self.conv31d = Conv3x3(C3, C2, norm=True, act=True) |
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self.do31d = self._make_dropout() |
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self.upconv2 = ConvTransposed3x3(C2, C2, output_padding=1) |
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self.conv22d = Conv3x3(C3, C2, norm=True, act=True) |
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self.do22d = self._make_dropout() |
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self.conv21d = Conv3x3(C2, C1, norm=True, act=True) |
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self.do21d = self._make_dropout() |
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self.upconv1 = ConvTransposed3x3(C1, C1, output_padding=1) |
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self.conv12d = Conv3x3(C2, C1, norm=True, act=True) |
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self.do12d = self._make_dropout() |
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self.conv11d = Conv3x3(C1, num_classes) |
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self.init_weight() |
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def forward(self, t1, t2): |
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x = paddle.concat([t1, t2], axis=1) |
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# Stage 1 |
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x11 = self.do11(self.conv11(x)) |
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x12 = self.do12(self.conv12(x11)) |
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x1p = self.pool1(x12) |
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# Stage 2 |
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x21 = self.do21(self.conv21(x1p)) |
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x22 = self.do22(self.conv22(x21)) |
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x2p = self.pool2(x22) |
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# Stage 3 |
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x31 = self.do31(self.conv31(x2p)) |
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x32 = self.do32(self.conv32(x31)) |
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x33 = self.do33(self.conv33(x32)) |
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x3p = self.pool3(x33) |
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# Stage 4 |
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x41 = self.do41(self.conv41(x3p)) |
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x42 = self.do42(self.conv42(x41)) |
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x43 = self.do43(self.conv43(x42)) |
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x4p = self.pool4(x43) |
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# Stage 4d |
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x4d = self.upconv4(x4p) |
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pad4 = ( |
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0, |
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paddle.shape(x43)[3]-paddle.shape(x4d)[3], |
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0, |
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paddle.shape(x43)[2]-paddle.shape(x4d)[2] |
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) |
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x4d = paddle.concat([F.pad(x4d, pad=pad4, mode='replicate'), x43], 1) |
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x43d = self.do43d(self.conv43d(x4d)) |
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x42d = self.do42d(self.conv42d(x43d)) |
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x41d = self.do41d(self.conv41d(x42d)) |
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# Stage 3d |
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x3d = self.upconv3(x41d) |
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pad3 = ( |
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0, |
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paddle.shape(x33)[3]-paddle.shape(x3d)[3], |
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0, |
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paddle.shape(x33)[2]-paddle.shape(x3d)[2] |
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) |
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x3d = paddle.concat([F.pad(x3d, pad=pad3, mode='replicate'), x33], 1) |
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x33d = self.do33d(self.conv33d(x3d)) |
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x32d = self.do32d(self.conv32d(x33d)) |
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x31d = self.do31d(self.conv31d(x32d)) |
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# Stage 2d |
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x2d = self.upconv2(x31d) |
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pad2 = ( |
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0, |
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paddle.shape(x22)[3]-paddle.shape(x2d)[3], |
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0, |
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paddle.shape(x22)[2]-paddle.shape(x2d)[2] |
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) |
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x2d = paddle.concat([F.pad(x2d, pad=pad2, mode='replicate'), x22], 1) |
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x22d = self.do22d(self.conv22d(x2d)) |
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x21d = self.do21d(self.conv21d(x22d)) |
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# Stage 1d |
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x1d = self.upconv1(x21d) |
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pad1 = ( |
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0, |
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paddle.shape(x12)[3]-paddle.shape(x1d)[3], |
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0, |
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paddle.shape(x12)[2]-paddle.shape(x1d)[2] |
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) |
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x1d = paddle.concat([F.pad(x1d, pad=pad1, mode='replicate'), x12], 1) |
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x12d = self.do12d(self.conv12d(x1d)) |
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x11d = self.conv11d(x12d) |
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return x11d, |
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def init_weight(self): |
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for sublayer in self.sublayers(): |
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if isinstance(sublayer, nn.Conv2D): |
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normal_init(sublayer.weight, std=0.001) |
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elif isinstance(sublayer, (nn.BatchNorm, nn.SyncBatchNorm)): |
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constant_init(sublayer.weight, value=1.0) |
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constant_init(sublayer.bias, value=0.0) |
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def _make_dropout(self): |
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if self.use_dropout: |
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return nn.Dropout2D(p=0.2) |
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else: |
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return Identity() |
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