更换文档检测模型
This commit is contained in:
447
paddle_detection/ppdet/modeling/backbones/hgnet_v2.py
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447
paddle_detection/ppdet/modeling/backbones/hgnet_v2.py
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# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve.
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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
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn.initializer import KaimingNormal, Constant
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from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
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from paddle.regularizer import L2Decay
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from paddle import ParamAttr
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import copy
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from ppdet.core.workspace import register, serializable
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from ..shape_spec import ShapeSpec
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__all__ = ['PPHGNetV2']
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kaiming_normal_ = KaimingNormal()
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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class LearnableAffineBlock(nn.Layer):
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def __init__(self,
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scale_value=1.0,
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bias_value=0.0,
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lr_mult=1.0,
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lab_lr=0.01):
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super().__init__()
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self.scale = self.create_parameter(
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shape=[1, ],
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default_initializer=Constant(value=scale_value),
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attr=ParamAttr(learning_rate=lr_mult * lab_lr))
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self.add_parameter("scale", self.scale)
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self.bias = self.create_parameter(
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shape=[1, ],
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default_initializer=Constant(value=bias_value),
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attr=ParamAttr(learning_rate=lr_mult * lab_lr))
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self.add_parameter("bias", self.bias)
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def forward(self, x):
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return self.scale * x + self.bias
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class ConvBNAct(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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groups=1,
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use_act=True,
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use_lab=False,
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lr_mult=1.0):
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super().__init__()
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self.use_act = use_act
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self.use_lab = use_lab
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self.conv = Conv2D(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding=padding
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if isinstance(padding, str) else (kernel_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(learning_rate=lr_mult),
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bias_attr=False)
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self.bn = BatchNorm2D(
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out_channels,
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weight_attr=ParamAttr(
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regularizer=L2Decay(0.0), learning_rate=lr_mult),
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bias_attr=ParamAttr(
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regularizer=L2Decay(0.0), learning_rate=lr_mult))
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if self.use_act:
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self.act = ReLU()
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if self.use_lab:
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self.lab = LearnableAffineBlock(lr_mult=lr_mult)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.use_act:
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x = self.act(x)
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if self.use_lab:
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x = self.lab(x)
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return x
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class LightConvBNAct(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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groups=1,
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use_lab=False,
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lr_mult=1.0):
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super().__init__()
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self.conv1 = ConvBNAct(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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use_act=False,
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.conv2 = ConvBNAct(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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groups=out_channels,
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use_act=True,
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use_lab=use_lab,
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lr_mult=lr_mult)
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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return x
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class StemBlock(nn.Layer):
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def __init__(self,
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in_channels,
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mid_channels,
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out_channels,
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use_lab=False,
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lr_mult=1.0):
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super().__init__()
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self.stem1 = ConvBNAct(
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in_channels=in_channels,
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out_channels=mid_channels,
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kernel_size=3,
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stride=2,
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.stem2a = ConvBNAct(
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in_channels=mid_channels,
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out_channels=mid_channels // 2,
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kernel_size=2,
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stride=1,
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padding="SAME",
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.stem2b = ConvBNAct(
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in_channels=mid_channels // 2,
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out_channels=mid_channels,
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kernel_size=2,
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stride=1,
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padding="SAME",
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.stem3 = ConvBNAct(
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in_channels=mid_channels * 2,
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out_channels=mid_channels,
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kernel_size=3,
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stride=2,
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.stem4 = ConvBNAct(
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in_channels=mid_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.pool = nn.MaxPool2D(
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kernel_size=2, stride=1, ceil_mode=True, padding="SAME")
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def forward(self, x):
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x = self.stem1(x)
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x2 = self.stem2a(x)
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x2 = self.stem2b(x2)
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x1 = self.pool(x)
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x = paddle.concat([x1, x2], 1)
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x = self.stem3(x)
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x = self.stem4(x)
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return x
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class HG_Block(nn.Layer):
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def __init__(self,
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in_channels,
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mid_channels,
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out_channels,
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kernel_size=3,
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layer_num=6,
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identity=False,
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light_block=True,
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use_lab=False,
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lr_mult=1.0):
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super().__init__()
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self.identity = identity
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self.layers = nn.LayerList()
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block_type = "LightConvBNAct" if light_block else "ConvBNAct"
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for i in range(layer_num):
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self.layers.append(
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eval(block_type)(in_channels=in_channels
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if i == 0 else mid_channels,
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out_channels=mid_channels,
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stride=1,
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kernel_size=kernel_size,
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use_lab=use_lab,
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lr_mult=lr_mult))
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# feature aggregation
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total_channels = in_channels + layer_num * mid_channels
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self.aggregation_squeeze_conv = ConvBNAct(
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in_channels=total_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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use_lab=use_lab,
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lr_mult=lr_mult)
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self.aggregation_excitation_conv = ConvBNAct(
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in_channels=out_channels // 2,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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use_lab=use_lab,
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lr_mult=lr_mult)
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def forward(self, x):
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identity = x
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output = []
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output.append(x)
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for layer in self.layers:
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x = layer(x)
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output.append(x)
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x = paddle.concat(output, axis=1)
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x = self.aggregation_squeeze_conv(x)
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x = self.aggregation_excitation_conv(x)
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if self.identity:
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x += identity
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return x
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class HG_Stage(nn.Layer):
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def __init__(self,
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in_channels,
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mid_channels,
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out_channels,
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block_num,
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layer_num=6,
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downsample=True,
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light_block=True,
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kernel_size=3,
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use_lab=False,
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lr_mult=1.0):
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super().__init__()
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self.downsample = downsample
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if downsample:
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self.downsample = ConvBNAct(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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stride=2,
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groups=in_channels,
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use_act=False,
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use_lab=use_lab,
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lr_mult=lr_mult)
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blocks_list = []
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for i in range(block_num):
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blocks_list.append(
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HG_Block(
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in_channels=in_channels if i == 0 else out_channels,
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mid_channels=mid_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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layer_num=layer_num,
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identity=False if i == 0 else True,
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light_block=light_block,
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use_lab=use_lab,
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lr_mult=lr_mult))
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self.blocks = nn.Sequential(*blocks_list)
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def forward(self, x):
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if self.downsample:
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x = self.downsample(x)
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x = self.blocks(x)
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return x
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def _freeze_norm(m: nn.BatchNorm2D):
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param_attr = ParamAttr(
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learning_rate=0., regularizer=L2Decay(0.), trainable=False)
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bias_attr = ParamAttr(
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learning_rate=0., regularizer=L2Decay(0.), trainable=False)
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global_stats = True
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norm = nn.BatchNorm2D(
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m._num_features,
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weight_attr=param_attr,
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bias_attr=bias_attr,
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use_global_stats=global_stats)
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for param in norm.parameters():
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param.stop_gradient = True
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return norm
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def reset_bn(model: nn.Layer, reset_func=_freeze_norm):
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if isinstance(model, nn.BatchNorm2D):
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model = reset_func(model)
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else:
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for name, child in model.named_children():
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_child = reset_bn(child, reset_func)
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if _child is not child:
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setattr(model, name, _child)
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return model
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@register
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@serializable
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class PPHGNetV2(nn.Layer):
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"""
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PPHGNetV2
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Args:
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stem_channels: list. Number of channels for the stem block.
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stage_type: str. The stage configuration of PPHGNet. such as the number of channels, stride, etc.
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use_lab: boolean. Whether to use LearnableAffineBlock in network.
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lr_mult_list: list. Control the learning rate of different stages.
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Returns:
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model: nn.Layer. Specific PPHGNetV2 model depends on args.
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"""
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arch_configs = {
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'L': {
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'stem_channels': [3, 32, 48],
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'stage_config': {
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# in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num
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"stage1": [48, 48, 128, 1, False, False, 3, 6],
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"stage2": [128, 96, 512, 1, True, False, 3, 6],
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"stage3": [512, 192, 1024, 3, True, True, 5, 6],
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"stage4": [1024, 384, 2048, 1, True, True, 5, 6],
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}
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},
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'X': {
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'stem_channels': [3, 32, 64],
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'stage_config': {
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# in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num
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"stage1": [64, 64, 128, 1, False, False, 3, 6],
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"stage2": [128, 128, 512, 2, True, False, 3, 6],
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"stage3": [512, 256, 1024, 5, True, True, 5, 6],
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"stage4": [1024, 512, 2048, 2, True, True, 5, 6],
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}
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}
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}
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def __init__(self,
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arch,
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use_lab=False,
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lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
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return_idx=[1, 2, 3],
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freeze_stem_only=True,
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freeze_at=0,
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freeze_norm=True):
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super().__init__()
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self.use_lab = use_lab
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self.return_idx = return_idx
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stem_channels = self.arch_configs[arch]['stem_channels']
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stage_config = self.arch_configs[arch]['stage_config']
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self._out_strides = [4, 8, 16, 32]
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self._out_channels = [stage_config[k][2] for k in stage_config]
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# stem
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self.stem = StemBlock(
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in_channels=stem_channels[0],
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mid_channels=stem_channels[1],
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out_channels=stem_channels[2],
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use_lab=use_lab,
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lr_mult=lr_mult_list[0])
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# stages
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self.stages = nn.LayerList()
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for i, k in enumerate(stage_config):
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in_channels, mid_channels, out_channels, block_num, downsample, light_block, kernel_size, layer_num = stage_config[
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k]
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self.stages.append(
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HG_Stage(
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in_channels,
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mid_channels,
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out_channels,
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block_num,
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layer_num,
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downsample,
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light_block,
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kernel_size,
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use_lab,
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lr_mult=lr_mult_list[i + 1]))
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if freeze_at >= 0:
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self._freeze_parameters(self.stem)
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if not freeze_stem_only:
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for i in range(min(freeze_at + 1, len(self.stages))):
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self._freeze_parameters(self.stages[i])
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if freeze_norm:
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reset_bn(self, reset_func=_freeze_norm)
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self._init_weights()
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def _freeze_parameters(self, m):
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for p in m.parameters():
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p.stop_gradient = True
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def _init_weights(self):
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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kaiming_normal_(m.weight)
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elif isinstance(m, (nn.BatchNorm2D)):
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ones_(m.weight)
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zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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zeros_(m.bias)
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@property
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def out_shape(self):
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return [
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ShapeSpec(
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channels=self._out_channels[i], stride=self._out_strides[i])
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for i in self.return_idx
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]
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def forward(self, inputs):
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x = inputs['image']
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x = self.stem(x)
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outs = []
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for idx, stage in enumerate(self.stages):
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x = stage(x)
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if idx in self.return_idx:
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outs.append(x)
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return outs
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