<gh_stars>0
import torch
import torch.nn as nn
import torch.nn.functional as F


class HSwish(nn.Module):
    def forward(self, x):
        out = x * F.relu6(x+3, inplace=True) / 6
        return out


class HardSigmoid(nn.Module):
    def __init__(self, slope=.2, offset=.5):
        super().__init__()
        self.slope = slope
        self.offset = offset

    def forward(self, x):
        x = (self.slope * x) + self.offset
        x = F.threshold(-x, threshold=-1, value=-1)
        x = F.threshold(-x, threshold=0, value=0)
        return x


class ConvBnRelu(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, dilation=1, groups=1,
                 bias=True, padding_mode='zeros', inplace=True):
        super().__init__()
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                              stride=stride, padding=padding, dilation=dilation, groups=groups,
                              bias=bias, padding_mode=padding_mode)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=inplace)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class ConvBNAct(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, groups=1, act=None):
        super(ConvBNAct, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
                              stride=stride, padding=padding, groups=groups, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)
        if act == 'relu':
            self.act = nn.ReLU()
        elif act == 'hard_swish':
            self.act = HSwish()
        elif act == None:
            self.act = None

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        if self.act is not None:
            x = self.act(x)
        return x


class SEBlock(nn.Module):
    def __init__(self, in_channels, out_channels, ratio=4):
        super(SEBlock, self).__init__()
        num_mid_filter = out_channels // ratio
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(in_channels, num_mid_filter, kernel_size=1, bias=True)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(num_mid_filter, out_channels, kernel_size=1, bias=True)
        self.relu2 = HardSigmoid()

    def forward(self, x):
        att = self.pool(x)  # attention
        att = self.conv1(att)
        att = self.relu1(att)
        att = self.conv2(att)
        att = self.relu2(att)
        return x * att

import torch
import torch.nn as nn
import torch.nn.functional as F


class HSwish(nn.Module):
    def forward(self, x):
        out = x * F.relu6(x+3, inplace=True) / 6
        return out


class HardSigmoid(nn.Module):
    def __init__(self, slope=.2, offset=.5):
        super().__init__()
        self.slope = slope
        self.offset = offset

    def forward(self, x):
        x = (self.slope * x) + self.offset
        x = F.threshold(-x, threshold=-1, value=-1)
        x = F.threshold(-x, threshold=0, value=0)
        return x


class ConvBnRelu(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, dilation=1, groups=1,
                 bias=True, padding_mode='zeros', inplace=True):
        super().__init__()
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                              stride=stride, padding=padding, dilation=dilation, groups=groups,
                              bias=bias, padding_mode=padding_mode)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=inplace)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class ConvBNAct(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, groups=1, act=None):
        super(ConvBNAct, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
                              stride=stride, padding=padding, groups=groups, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)
        if act == 'relu':
            self.act = nn.ReLU()
        elif act == 'hard_swish':
            self.act = HSwish()
        elif act == None:
            self.act = None

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        if self.act is not None:
            x = self.act(x)
        return x


class SEBlock(nn.Module):
    def __init__(self, in_channels, out_channels, ratio=4):
        super(SEBlock, self).__init__()
        num_mid_filter = out_channels // ratio
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(in_channels, num_mid_filter, kernel_size=1, bias=True)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(num_mid_filter, out_channels, kernel_size=1, bias=True)
        self.relu2 = HardSigmoid()

    def forward(self, x):
        att = self.pool(x)  # attention
        att = self.conv1(att)
        att = self.relu1(att)
        att = self.conv2(att)
        att = self.relu2(att)
        return x * att
# attention