732 lines
26 KiB
Python
732 lines
26 KiB
Python
# 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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from ..base.theseus_layer import TheseusLayer
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"PPHGNetV2_B0":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B0_ssld_pretrained.pdparams",
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"PPHGNetV2_B1":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B1_ssld_pretrained.pdparams",
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"PPHGNetV2_B2":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B2_ssld_pretrained.pdparams",
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"PPHGNetV2_B3":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B3_ssld_pretrained.pdparams",
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"PPHGNetV2_B4":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B4_ssld_pretrained.pdparams",
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"PPHGNetV2_B5":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B5_ssld_pretrained.pdparams",
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"PPHGNetV2_B6":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B6_ssld_pretrained.pdparams",
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"PPHGNetV2_B7":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNetV2_B7_ssld_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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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(TheseusLayer):
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"""
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Create a learnable affine block module. This module can significantly improve accuracy on smaller models.
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Args:
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scale_value (float): The initial value of the scale parameter, default is 1.0.
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bias_value (float): The initial value of the bias parameter, default is 0.0.
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lr_mult (float): The learning rate multiplier, default is 1.0.
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lab_lr (float): The learning rate, default is 0.01.
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"""
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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(TheseusLayer):
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"""
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ConvBNAct is a combination of convolution and batchnorm layers.
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Args:
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in_channels (int): Number of input channels.
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out_channels (int): Number of output channels.
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kernel_size (int): Size of the convolution kernel. Defaults to 3.
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stride (int): Stride of the convolution. Defaults to 1.
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padding (int/str): Padding or padding type for the convolution. Defaults to 1.
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groups (int): Number of groups for the convolution. Defaults to 1.
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use_act: (bool): Whether to use activation function. Defaults to True.
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use_lab (bool): Whether to use the LAB operation. Defaults to False.
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lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0.
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"""
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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(TheseusLayer):
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"""
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LightConvBNAct is a combination of pw and dw layers.
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Args:
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in_channels (int): Number of input channels.
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out_channels (int): Number of output channels.
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kernel_size (int): Size of the depth-wise convolution kernel.
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use_lab (bool): Whether to use the LAB operation. Defaults to False.
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lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0.
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"""
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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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use_lab=False,
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lr_mult=1.0,
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**kwargs):
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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(TheseusLayer):
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"""
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StemBlock for PP-HGNetV2.
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Args:
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in_channels (int): Number of input channels.
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mid_channels (int): Number of middle channels.
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out_channels (int): Number of output channels.
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use_lab (bool): Whether to use the LAB operation. Defaults to False.
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lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0.
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"""
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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 HGV2_Block(TheseusLayer):
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"""
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HGV2_Block, the basic unit that constitutes the HGV2_Stage.
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Args:
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in_channels (int): Number of input channels.
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mid_channels (int): Number of middle channels.
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out_channels (int): Number of output channels.
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kernel_size (int): Size of the convolution kernel. Defaults to 3.
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layer_num (int): Number of layers in the HGV2 block. Defaults to 6.
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stride (int): Stride of the convolution. Defaults to 1.
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padding (int/str): Padding or padding type for the convolution. Defaults to 1.
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groups (int): Number of groups for the convolution. Defaults to 1.
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use_act (bool): Whether to use activation function. Defaults to True.
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use_lab (bool): Whether to use the LAB operation. Defaults to False.
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lr_mult (float): Learning rate multiplier for the layer. Defaults to 1.0.
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"""
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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 HGV2_Stage(TheseusLayer):
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"""
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HGV2_Stage, the basic unit that constitutes the PPHGNetV2.
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Args:
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in_channels (int): Number of input channels.
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mid_channels (int): Number of middle channels.
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out_channels (int): Number of output channels.
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block_num (int): Number of blocks in the HGV2 stage.
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layer_num (int): Number of layers in the HGV2 block. Defaults to 6.
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is_downsample (bool): Whether to use downsampling operation. Defaults to False.
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light_block (bool): Whether to use light block. Defaults to True.
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kernel_size (int): Size of the convolution kernel. Defaults to 3.
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use_lab (bool, optional): Whether to use the LAB operation. Defaults to False.
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lr_mult (float, optional): Learning rate multiplier for the layer. Defaults to 1.0.
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"""
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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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is_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.is_downsample = is_downsample
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if self.is_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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HGV2_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.is_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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class PPHGNetV2(TheseusLayer):
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"""
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PPHGNetV2
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Args:
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stage_config (dict): Config for PPHGNetV2 stages. such as the number of channels, stride, etc.
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stem_channels: (list): Number of channels of the stem of the PPHGNetV2.
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use_lab (bool): Whether to use the LAB operation. Defaults to False.
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use_last_conv (bool): Whether to use the last conv layer as the output channel. Defaults to True.
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class_expand (int): Number of channels for the last 1x1 convolutional layer.
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drop_prob (float): Dropout probability for the last 1x1 convolutional layer. Defaults to 0.0.
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class_num (int): The number of classes for the classification layer. Defaults to 1000.
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lr_mult_list (list): Learning rate multiplier for the stages. Defaults to [1.0, 1.0, 1.0, 1.0, 1.0].
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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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def __init__(self,
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stage_config,
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stem_channels=[3, 32, 64],
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use_lab=False,
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use_last_conv=True,
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class_expand=2048,
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dropout_prob=0.0,
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class_num=1000,
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lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
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**kwargs):
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super().__init__()
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self.use_lab = use_lab
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self.use_last_conv = use_last_conv
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self.class_expand = class_expand
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self.class_num = class_num
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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, is_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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HGV2_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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is_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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self.avg_pool = AdaptiveAvgPool2D(1)
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if self.use_last_conv:
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self.last_conv = Conv2D(
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in_channels=out_channels,
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out_channels=self.class_expand,
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kernel_size=1,
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stride=1,
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padding=0,
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bias_attr=False)
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self.act = ReLU()
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if self.use_lab:
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self.lab = LearnableAffineBlock()
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self.dropout = nn.Dropout(
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p=dropout_prob, mode="downscale_in_infer")
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self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
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self.fc = nn.Linear(self.class_expand if self.use_last_conv else
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out_channels, self.class_num)
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self._init_weights()
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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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def forward(self, x):
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x = self.stem(x)
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for stage in self.stages:
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x = stage(x)
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x = self.avg_pool(x)
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if self.use_last_conv:
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x = self.last_conv(x)
|
|
x = self.act(x)
|
|
if self.use_lab:
|
|
x = self.lab(x)
|
|
x = self.dropout(x)
|
|
x = self.flatten(x)
|
|
x = self.fc(x)
|
|
|
|
return x
|
|
|
|
|
|
def _load_pretrained(pretrained, model, model_url, use_ssld):
|
|
if pretrained is False:
|
|
pass
|
|
elif pretrained is True:
|
|
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
|
|
elif isinstance(pretrained, str):
|
|
load_dygraph_pretrain(model, pretrained)
|
|
else:
|
|
raise RuntimeError(
|
|
"pretrained type is not available. Please use `string` or `boolean` type."
|
|
)
|
|
|
|
|
|
def PPHGNetV2_B0(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B0
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B0` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [16, 16, 64, 1, False, False, 3, 3],
|
|
"stage2": [64, 32, 256, 1, True, False, 3, 3],
|
|
"stage3": [256, 64, 512, 2, True, True, 5, 3],
|
|
"stage4": [512, 128, 1024, 1, True, True, 5, 3],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 16, 16],
|
|
stage_config=stage_config,
|
|
use_lab=True,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B0"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B1(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B1
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B1` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [32, 32, 64, 1, False, False, 3, 3],
|
|
"stage2": [64, 48, 256, 1, True, False, 3, 3],
|
|
"stage3": [256, 96, 512, 2, True, True, 5, 3],
|
|
"stage4": [512, 192, 1024, 1, True, True, 5, 3],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 24, 32],
|
|
stage_config=stage_config,
|
|
use_lab=True,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B1"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B2(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B2
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B2` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [32, 32, 96, 1, False, False, 3, 4],
|
|
"stage2": [96, 64, 384, 1, True, False, 3, 4],
|
|
"stage3": [384, 128, 768, 3, True, True, 5, 4],
|
|
"stage4": [768, 256, 1536, 1, True, True, 5, 4],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 24, 32],
|
|
stage_config=stage_config,
|
|
use_lab=True,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B2"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B3(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B3
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B3` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [32, 32, 128, 1, False, False, 3, 5],
|
|
"stage2": [128, 64, 512, 1, True, False, 3, 5],
|
|
"stage3": [512, 128, 1024, 3, True, True, 5, 5],
|
|
"stage4": [1024, 256, 2048, 1, True, True, 5, 5],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 24, 32],
|
|
stage_config=stage_config,
|
|
use_lab=True,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B3"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B4(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B4
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B4` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [48, 48, 128, 1, False, False, 3, 6],
|
|
"stage2": [128, 96, 512, 1, True, False, 3, 6],
|
|
"stage3": [512, 192, 1024, 3, True, True, 5, 6],
|
|
"stage4": [1024, 384, 2048, 1, True, True, 5, 6],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 32, 48],
|
|
stage_config=stage_config,
|
|
use_lab=False,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B4"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B5(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B5
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B5` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [64, 64, 128, 1, False, False, 3, 6],
|
|
"stage2": [128, 128, 512, 2, True, False, 3, 6],
|
|
"stage3": [512, 256, 1024, 5, True, True, 5, 6],
|
|
"stage4": [1024, 512, 2048, 2, True, True, 5, 6],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 32, 64],
|
|
stage_config=stage_config,
|
|
use_lab=False,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B5"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B6(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B6
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B6` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [96, 96, 192, 2, False, False, 3, 6],
|
|
"stage2": [192, 192, 512, 3, True, False, 3, 6],
|
|
"stage3": [512, 384, 1024, 6, True, True, 5, 6],
|
|
"stage4": [1024, 768, 2048, 3, True, True, 5, 6],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 48, 96],
|
|
stage_config=stage_config,
|
|
use_lab=False,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B6"], use_ssld)
|
|
return model
|
|
|
|
|
|
def PPHGNetV2_B7(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
PPHGNetV2_B7
|
|
Args:
|
|
pretrained (bool/str): If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld (bool) Whether using ssld pretrained model when pretrained is True.
|
|
Returns:
|
|
model: nn.Layer. Specific `PPHGNetV2_B7` model depends on args.
|
|
"""
|
|
stage_config = {
|
|
# in_channels, mid_channels, out_channels, num_blocks, is_downsample, light_block, kernel_size, layer_num
|
|
"stage1": [128, 128, 256, 2, False, False, 3, 7],
|
|
"stage2": [256, 256, 512, 4, True, False, 3, 7],
|
|
"stage3": [512, 512, 1024, 12, True, True, 5, 7],
|
|
"stage4": [1024, 1024, 2048, 4, True, True, 5, 7],
|
|
}
|
|
|
|
model = PPHGNetV2(
|
|
stem_channels=[3, 64, 128],
|
|
stage_config=stage_config,
|
|
use_lab=False,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["PPHGNetV2_B7"], use_ssld)
|
|
return model
|