forked from PulseFocusPlatform/PulseFocusPlatform
140 lines
5.0 KiB
Python
140 lines
5.0 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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import paddle.nn as nn
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from ppdet.core.workspace import register, serializable
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from .resnet import ResNet, Blocks, BasicBlock, BottleNeck
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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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