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141 lines
5.0 KiB
141 lines
5.0 KiB
# 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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from paddlers_slim.models.ppdet.core.workspace import register, serializable |
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from .resnet import ResNet, Blocks, BasicBlock, BottleNeck |
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from ..shape_spec import ShapeSpec |
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from .name_adapter import NameAdapter |
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__all__ = ['SENet', 'SERes5Head'] |
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@register |
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@serializable |
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class SENet(ResNet): |
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__shared__ = ['norm_type'] |
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def __init__(self, |
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depth=50, |
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variant='b', |
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lr_mult_list=[1.0, 1.0, 1.0, 1.0], |
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groups=1, |
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base_width=64, |
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norm_type='bn', |
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norm_decay=0, |
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freeze_norm=True, |
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freeze_at=0, |
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return_idx=[0, 1, 2, 3], |
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dcn_v2_stages=[-1], |
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std_senet=True, |
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num_stages=4): |
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""" |
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Squeeze-and-Excitation Networks, see https://arxiv.org/abs/1709.01507 |
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Args: |
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depth (int): SENet depth, should be 50, 101, 152 |
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variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently |
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lr_mult_list (list): learning rate ratio of different resnet stages(2,3,4,5), |
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lower learning rate ratio is need for pretrained model |
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got using distillation(default as [1.0, 1.0, 1.0, 1.0]). |
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groups (int): group convolution cardinality |
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base_width (int): base width of each group convolution |
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norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel' |
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norm_decay (float): weight decay for normalization layer weights |
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freeze_norm (bool): freeze normalization layers |
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freeze_at (int): freeze the backbone at which stage |
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return_idx (list): index of the stages whose feature maps are returned |
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dcn_v2_stages (list): index of stages who select deformable conv v2 |
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std_senet (bool): whether use senet, default True |
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num_stages (int): total num of stages |
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""" |
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super(SENet, self).__init__( |
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depth=depth, |
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variant=variant, |
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lr_mult_list=lr_mult_list, |
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ch_in=128, |
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groups=groups, |
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base_width=base_width, |
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norm_type=norm_type, |
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norm_decay=norm_decay, |
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freeze_norm=freeze_norm, |
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freeze_at=freeze_at, |
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return_idx=return_idx, |
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dcn_v2_stages=dcn_v2_stages, |
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std_senet=std_senet, |
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num_stages=num_stages) |
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@register |
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class SERes5Head(nn.Layer): |
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def __init__(self, |
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depth=50, |
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variant='b', |
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lr_mult=1.0, |
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groups=1, |
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base_width=64, |
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norm_type='bn', |
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norm_decay=0, |
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dcn_v2=False, |
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freeze_norm=False, |
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std_senet=True): |
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""" |
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SERes5Head layer |
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Args: |
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depth (int): SENet depth, should be 50, 101, 152 |
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variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently |
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lr_mult (list): learning rate ratio of SERes5Head, default as 1.0. |
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groups (int): group convolution cardinality |
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base_width (int): base width of each group convolution |
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norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel' |
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norm_decay (float): weight decay for normalization layer weights |
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dcn_v2_stages (list): index of stages who select deformable conv v2 |
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std_senet (bool): whether use senet, default True |
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""" |
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super(SERes5Head, self).__init__() |
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ch_out = 512 |
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ch_in = 256 if depth < 50 else 1024 |
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na = NameAdapter(self) |
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block = BottleNeck if depth >= 50 else BasicBlock |
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self.res5 = Blocks( |
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block, |
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ch_in, |
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ch_out, |
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count=3, |
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name_adapter=na, |
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stage_num=5, |
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variant=variant, |
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groups=groups, |
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base_width=base_width, |
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lr=lr_mult, |
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norm_type=norm_type, |
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norm_decay=norm_decay, |
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freeze_norm=freeze_norm, |
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dcn_v2=dcn_v2, |
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std_senet=std_senet) |
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self.ch_out = ch_out * block.expansion |
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@property |
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def out_shape(self): |
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return [ShapeSpec( |
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channels=self.ch_out, |
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stride=16, )] |
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def forward(self, roi_feat): |
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y = self.res5(roi_feat) |
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return y
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