【RT-DETR有效改进】华为 | GhostnetV2移动端的特征提取网络效果完爆MobileNet系列

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# 2020.11.06-Changed for building GhostNetV2
#            Huawei Technologies Co., Ltd. <foss@huawei.com>
"""
Creates a GhostNet Model as defined in:
GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu.
https://arxiv.org/abs/1911.11907
Modified from https://github.com/d-li14/mobilenetv3.pytorch and https://github.com/rwightman/pytorch-image-models
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from timm.models import register_model


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


def hard_sigmoid(x, inplace: bool = False):
    if inplace:
        return x.add_(3.).clamp_(0., 6.).div_(6.)
    else:
        return F.relu6(x + 3.) / 6.


class SqueezeExcite(nn.Module):
    def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
                 act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
        super(SqueezeExcite, self).__init__()
        self.gate_fn = gate_fn
        reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
        self.act1 = act_layer(inplace=True)
        self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)

    def forward(self, x):
        x_se = self.avg_pool(x)
        x_se = self.conv_reduce(x_se)
        x_se = self.act1(x_se)
        x_se = self.conv_expand(x_se)
        x = x * self.gate_fn(x_se)
        return x


class ConvBnAct(nn.Module):
    def __init__(self, in_chs, out_chs, kernel_size,
                 stride=1, act_layer=nn.ReLU):
        super(ConvBnAct, self).__init__()
        self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size // 2, bias=False)
        self.bn1 = nn.BatchNorm2d(out_chs)
        self.act1 = act_layer(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn1(x)
        x = self.act1(x)
        return x


class GhostModuleV2(nn.Module):
    def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True, mode=None, args=None):
        super(GhostModuleV2, self).__init__()
        self.mode = mode
        self.gate_fn = nn.Sigmoid()

        if self.mode in ['original']:
            self.oup = oup
            init_channels = math.ceil(oup / ratio)
            new_channels = init_channels * (ratio - 1)
            self.primary_conv = nn.Sequential(
                nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size // 2, bias=False),
                nn.BatchNorm2d(init_channels),
                nn.ReLU(inplace=True) if relu else nn.Sequential(),
            )
            self.cheap_operation = nn.Sequential(
                nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size // 2, groups=init_channels, bias=False),
                nn.BatchNorm2d(new_channels),
                nn.ReLU(inplace=True) if relu else nn.Sequential(),
            )
        elif self.mode in ['attn']:
            self.oup = oup
            init_channels = math.ceil(oup / ratio)
            new_channels = init_channels * (ratio - 1)
            self.primary_conv = nn.Sequential(
                nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size // 2, bias=False),
                nn.BatchNorm2d(init_channels),
                nn.ReLU(inplace=True) if relu else nn.Sequential(),
            )
            self.cheap_operation = nn.Sequential(
                nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size // 2, groups=init_channels, bias=False),
                nn.BatchNorm2d(new_channels),
                nn.ReLU(inplace=True) if relu else nn.Sequential(),
            )
            self.short_conv = nn.Sequential(
                nn.Conv2d(inp, oup, kernel_size, stride, kernel_size // 2, bias=False),
                nn.BatchNorm2d(oup),
                nn.Conv2d(oup, oup, kernel_size=(1, 5), stride=1, padding=(0, 2), groups=oup, bias=False),
                nn.BatchNorm2d(oup),
                nn.Conv2d(oup, oup, kernel_size=(5, 1), stride=1, padding=(2, 0), groups=oup, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):
        if self.mode in ['original']:
            x1 = self.primary_conv(x)
            x2 = self.cheap_operation(x1)
            out = torch.cat([x1, x2], dim=1)
            return out[:, :self.oup, :, :]
        elif self.mode in ['attn']:
            res = self.short_conv(F.avg_pool2d(x, kernel_size=2, stride=2))
            x1 = self.primary_conv(x)
            x2 = self.cheap_operation(x1)
            out = torch.cat([x1, x2], dim=1)
            return out[:, :self.oup, :, :] * F.interpolate(self.gate_fn(res), size=(out.shape[-2], out.shape[-1]),
                                                           mode='nearest')


class GhostBottleneckV2(nn.Module):

    def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
                 stride=1, act_layer=nn.ReLU, se_ratio=0., layer_id=None, args=None):
        super(GhostBottleneckV2, self).__init__()
        has_se = se_ratio is not None and se_ratio > 0.
        self.stride = stride

        # Point-wise expansion
        if layer_id <= 1:
            self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True, mode='original', args=args)
        else:
            self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True, mode='attn', args=args)

            # Depth-wise convolution
        if self.stride > 1:
            self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
                                     padding=(dw_kernel_size - 1) // 2, groups=mid_chs, bias=False)
            self.bn_dw = nn.BatchNorm2d(mid_chs)

        # Squeeze-and-excitation
        if has_se:
            self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
        else:
            self.se = None

        self.ghost2 = GhostModuleV2(mid_chs, out_chs, relu=False, mode='original', args=args)

        # shortcut
        if (in_chs == out_chs and self.stride == 1):
            self.shortcut = nn.Sequential()
        else:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
                          padding=(dw_kernel_size - 1) // 2, groups=in_chs, bias=False),
                nn.BatchNorm2d(in_chs),
                nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(out_chs),
            )

    def forward(self, x):
        residual = x
        x = self.ghost1(x)
        if self.stride > 1:
            x = self.conv_dw(x)
            x = self.bn_dw(x)
        if self.se is not None:
            x = self.se(x)
        x = self.ghost2(x)
        x += self.shortcut(residual)
        return x


class GhostNetV2(nn.Module):
    def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, block=GhostBottleneckV2, args=None):
        super(GhostNetV2, self).__init__()
        self.cfgs = cfgs
        self.dropout = dropout
        self.num_classes = num_classes

        # building first layer
        output_channel = _make_divisible(16 * width, 4)
        self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(output_channel)
        self.act1 = nn.ReLU(inplace=True)
        input_channel = output_channel

        # building inverted residual blocks
        stages = []
        # block = block
        layer_id = 0
        for cfg in self.cfgs:
            layers = []
            for k, exp_size, c, se_ratio, s in cfg:
                output_channel = _make_divisible(c * width, 4)
                hidden_channel = _make_divisible(exp_size * width, 4)
                if block == GhostBottleneckV2:
                    layers.append(block(input_channel, hidden_channel, output_channel, k, s,
                                        se_ratio=se_ratio, layer_id=layer_id, args=args))
                input_channel = output_channel
                layer_id += 1
            stages.append(nn.Sequential(*layers))

        output_channel = _make_divisible(exp_size * width, 4)
        stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
        input_channel = output_channel

        self.blocks = nn.Sequential(*stages)

        self.width_list = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]

    def reset_classifier(self, num_classes, global_avg=''):
        self.num_classes = num_classes
        self.classifier = nn.Linear(1280, self.num_classes) if self.num_classes > 0 else nn.Identity()

    def forward(self, x):
        unique_tensors = {}
        x = self.conv_stem(x)
        x = self.bn1(x)
        x = self.act1(x)
        for model in self.blocks:
            x = model(x)
            if self.dropout > 0.:
                x = F.dropout(x, p=self.dropout, training=self.training)
            width, height = x.shape[2], x.shape[3]
            unique_tensors[(width, height)] = x
        result_list = list(unique_tensors.values())[-4:]
        return result_list



@register_model
def Ghostnetv2(pretrained=False, pretrained_cfg=None, pretrained_cfg_overlay=None, **kwargs):
    cfgs = [
        # k, t, c, SE, s
        [[3,  16,  16, 0, 1]],
        [[3,  48,  24, 0, 2]],
        [[3,  72,  24, 0, 1]],
        [[5,  72,  40, 0.25, 2]],
        [[5, 120,  40, 0.25, 1]],
        [[3, 240,  80, 0, 2]],
        [[3, 200,  80, 0, 1],
         [3, 184,  80, 0, 1],
         [3, 184,  80, 0, 1],
         [3, 480, 112, 0.25, 1],
         [3, 672, 112, 0.25, 1]
        ],
        [[5, 672, 160, 0.25, 2]],
        [[5, 960, 160, 0, 1],
         [5, 960, 160, 0.25, 1],
         [5, 960, 160, 0, 1],
         [5, 960, 160, 0.25, 1]
        ]
    ]


    return GhostNetV2(cfgs)

if __name__=='__main__':
    model = Ghostnetv2()
    model.eval()
    input = torch.randn(16,3,224,224)
    y = model(input)
    print(y.size())

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        elif m in {自行添加对应的模型即可，下面都是一样的}:
            m = m(*args)
            c2 = m.width_list  # 返回通道列表
            backbone = True