mirror of https://github.com/alibaba/EasyCV.git
338 lines
10 KiB
Python
338 lines
10 KiB
Python
# Modified from https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppocr/modeling/backbones/det_mobilenet_v3.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from easycv.models.registry import BACKBONES
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class Hswish(nn.Module):
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def __init__(self, inplace=True):
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super(Hswish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return x * F.relu6(x + 3., inplace=self.inplace) / 6.
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# out = max(0, min(1, slop*x+offset))
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# paddle.fluid.layers.hard_sigmoid(x, slope=0.2, offset=0.5, name=None)
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class Hsigmoid(nn.Module):
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def __init__(self, inplace=True):
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super(Hsigmoid, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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# torch: F.relu6(x + 3., inplace=self.inplace) / 6.
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# paddle: F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
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return F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
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class GELU(nn.Module):
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def __init__(self, inplace=True):
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super(GELU, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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return torch.nn.functional.gelu(x)
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class Swish(nn.Module):
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def __init__(self, inplace=True):
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super(Swish, self).__init__()
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self.inplace = inplace
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def forward(self, x):
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if self.inplace:
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x.mul_(torch.sigmoid(x))
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return x
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else:
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return x * torch.sigmoid(x)
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class Activation(nn.Module):
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def __init__(self, act_type, inplace=True):
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super(Activation, self).__init__()
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act_type = act_type.lower()
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if act_type == 'relu':
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self.act = nn.ReLU(inplace=inplace)
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elif act_type == 'relu6':
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self.act = nn.ReLU6(inplace=inplace)
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elif act_type == 'sigmoid':
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raise NotImplementedError
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elif act_type == 'hard_sigmoid':
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self.act = Hsigmoid(inplace)
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elif act_type == 'hard_swish':
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self.act = Hswish(inplace=inplace)
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elif act_type == 'leakyrelu':
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self.act = nn.LeakyReLU(inplace=inplace)
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elif act_type == 'gelu':
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self.act = GELU(inplace=inplace)
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elif act_type == 'swish':
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self.act = Swish(inplace=inplace)
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else:
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raise NotImplementedError
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def forward(self, inputs):
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return self.act(inputs)
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def make_divisible(v, divisor=8, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class ConvBNLayer(nn.Module):
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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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padding,
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groups=1,
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if_act=True,
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act=None,
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name=None):
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super(ConvBNLayer, self).__init__()
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self.if_act = if_act
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self.conv = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias=False)
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self.bn = nn.BatchNorm2d(out_channels, )
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if self.if_act:
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self.act = Activation(act_type=act, inplace=True)
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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.if_act:
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x = self.act(x)
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return x
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class SEModule(nn.Module):
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def __init__(self, in_channels, reduction=4, name=''):
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super(SEModule, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.conv1 = nn.Conv2d(
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in_channels=in_channels,
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out_channels=in_channels // reduction,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True)
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self.relu1 = Activation(act_type='relu', inplace=True)
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self.conv2 = nn.Conv2d(
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in_channels=in_channels // reduction,
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out_channels=in_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True)
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self.hard_sigmoid = Activation(act_type='hard_sigmoid', inplace=True)
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def forward(self, inputs):
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outputs = self.avg_pool(inputs)
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outputs = self.conv1(outputs)
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outputs = self.relu1(outputs)
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outputs = self.conv2(outputs)
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outputs = self.hard_sigmoid(outputs)
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outputs = inputs * outputs
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return outputs
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class ResidualUnit(nn.Module):
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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,
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stride,
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use_se,
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act=None,
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name=''):
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super(ResidualUnit, self).__init__()
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self.if_shortcut = stride == 1 and in_channels == out_channels
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self.if_se = use_se
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self.expand_conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=mid_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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if_act=True,
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act=act,
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name=name + '_expand')
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self.bottleneck_conv = ConvBNLayer(
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in_channels=mid_channels,
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out_channels=mid_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=int((kernel_size - 1) // 2),
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groups=mid_channels,
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if_act=True,
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act=act,
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name=name + '_depthwise')
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if self.if_se:
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self.mid_se = SEModule(mid_channels, name=name + '_se')
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self.linear_conv = ConvBNLayer(
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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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padding=0,
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if_act=False,
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act=None,
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name=name + '_linear')
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def forward(self, inputs):
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x = self.expand_conv(inputs)
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x = self.bottleneck_conv(x)
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if self.if_se:
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x = self.mid_se(x)
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x = self.linear_conv(x)
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if self.if_shortcut:
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x = inputs + x
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return x
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@BACKBONES.register_module()
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class OCRDetMobileNetV3(nn.Module):
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def __init__(self,
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in_channels=3,
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model_name='large',
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scale=0.5,
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disable_se=False,
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**kwargs):
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"""
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the MobilenetV3 backbone network for detection module.
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Args:
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params(dict): the super parameters for build network
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"""
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super(OCRDetMobileNetV3, self).__init__()
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self.disable_se = disable_se
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if model_name == 'large':
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cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, False, 'relu', 1],
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[3, 64, 24, False, 'relu', 2],
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[3, 72, 24, False, 'relu', 1],
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[5, 72, 40, True, 'relu', 2],
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[5, 120, 40, True, 'relu', 1],
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[5, 120, 40, True, 'relu', 1],
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[3, 240, 80, False, 'hard_swish', 2],
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[3, 200, 80, False, 'hard_swish', 1],
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[3, 184, 80, False, 'hard_swish', 1],
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[3, 184, 80, False, 'hard_swish', 1],
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[3, 480, 112, True, 'hard_swish', 1],
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[3, 672, 112, True, 'hard_swish', 1],
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[5, 672, 160, True, 'hard_swish', 2],
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[5, 960, 160, True, 'hard_swish', 1],
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[5, 960, 160, True, 'hard_swish', 1],
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]
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cls_ch_squeeze = 960
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elif model_name == 'small':
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cfg = [
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# k, exp, c, se, nl, s,
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[3, 16, 16, True, 'relu', 2],
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[3, 72, 24, False, 'relu', 2],
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[3, 88, 24, False, 'relu', 1],
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[5, 96, 40, True, 'hard_swish', 2],
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[5, 240, 40, True, 'hard_swish', 1],
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[5, 240, 40, True, 'hard_swish', 1],
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[5, 120, 48, True, 'hard_swish', 1],
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[5, 144, 48, True, 'hard_swish', 1],
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[5, 288, 96, True, 'hard_swish', 2],
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[5, 576, 96, True, 'hard_swish', 1],
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[5, 576, 96, True, 'hard_swish', 1],
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]
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cls_ch_squeeze = 576
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else:
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raise NotImplementedError('mode[' + model_name +
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'_model] is not implemented!')
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supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
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assert scale in supported_scale, \
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'supported scale are {} but input scale is {}'.format(supported_scale, scale)
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inplanes = 16
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# conv1
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self.conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=make_divisible(inplanes * scale),
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kernel_size=3,
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stride=2,
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padding=1,
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groups=1,
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if_act=True,
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act='hard_swish',
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name='conv1')
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self.stages = nn.ModuleList()
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self.out_channels = []
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block_list = []
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i = 0
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inplanes = make_divisible(inplanes * scale)
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for (k, exp, c, se, nl, s) in cfg:
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se = se and not self.disable_se
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if s == 2 and i > 2:
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self.out_channels.append(inplanes)
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self.stages.append(nn.Sequential(*block_list))
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block_list = []
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block_list.append(
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ResidualUnit(
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in_channels=inplanes,
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mid_channels=make_divisible(scale * exp),
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out_channels=make_divisible(scale * c),
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kernel_size=k,
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stride=s,
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use_se=se,
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act=nl,
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name='conv' + str(i + 2)))
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inplanes = make_divisible(scale * c)
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i += 1
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block_list.append(
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ConvBNLayer(
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in_channels=inplanes,
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out_channels=make_divisible(scale * cls_ch_squeeze),
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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if_act=True,
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act='hard_swish',
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name='conv_last'))
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self.stages.append(nn.Sequential(*block_list))
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self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
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# for i, stage in enumerate(self.stages):
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# self.add_sublayer(sublayer=stage, name="stage{}".format(i))
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def forward(self, x):
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x = self.conv(x)
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out_list = []
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for stage in self.stages:
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x = stage(x)
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out_list.append(x)
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return out_list
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