<reponame>ForrestPi/FaceProjects<gh_stars>1-10
import torch
import torch.nn as nn
import torch.nn.functional as F



class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.shape[0], -1)

class ConvBn(nn.Module):

    def __init__(self, in_c, out_c, kernel=(1, 1), stride=1, padding=0, groups=1):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_c)
        )
        
    def forward(self, x):
        return self.net(x)


class ConvBnPrelu(nn.Module):

    def __init__(self, in_c, out_c, kernel=(1, 1), stride=1, padding=0, groups=1):
        super().__init__()
        self.net = nn.Sequential(
            ConvBn(in_c, out_c, kernel, stride, padding, groups),
            nn.PReLU(out_c)
        )

    def forward(self, x):
        return self.net(x)


class DepthWise(nn.Module):

    def __init__(self, in_c, out_c, kernel=(3, 3), stride=2, padding=1, groups=1):
        super().__init__()
        self.net = nn.Sequential(
            ConvBnPrelu(in_c, groups, kernel=(1, 1), stride=1, padding=0),
            ConvBnPrelu(groups, groups, kernel=kernel, stride=stride, padding=padding, groups=groups),
            ConvBn(groups, out_c, kernel=(1, 1), stride=1, padding=0),
        )

    def forward(self, x):
        return self.net(x)


class DepthWiseRes(nn.Module):
    """DepthWise with Residual"""

    def __init__(self, in_c, out_c, kernel=(3, 3), stride=2, padding=1, groups=1):
        super().__init__()
        self.net = DepthWise(in_c, out_c, kernel, stride, padding, groups)

    def forward(self, x):
        return self.net(x) + x


class MultiDepthWiseRes(nn.Module):

    def __init__(self, num_block, channels, kernel=(3, 3), stride=1, padding=1, groups=1):
        super().__init__()

        self.net = nn.Sequential(*[
            DepthWiseRes(channels, channels, kernel, stride, padding, groups)
            for _ in range(num_block)
        ])

    def forward(self, x):
        return self.net(x)


class FaceMobileNet(nn.Module):

    def __init__(self, embedding_size):
        super().__init__()
        self.conv1 = ConvBnPrelu(1, 64, kernel=(3, 3), stride=2, padding=1)
        self.conv2 = ConvBn(64, 64, kernel=(3, 3), stride=1, padding=1, groups=64)
        self.conv3 = DepthWise(64, 64, kernel=(3, 3), stride=2, padding=1, groups=128)
        self.conv4 = MultiDepthWiseRes(num_block=4, channels=64, kernel=3, stride=1, padding=1, groups=128)
        self.conv5 = DepthWise(64, 128, kernel=(3, 3), stride=2, padding=1, groups=256)
        self.conv6 = MultiDepthWiseRes(num_block=6, channels=128, kernel=(3, 3), stride=1, padding=1, groups=256)
        self.conv7 = DepthWise(128, 128, kernel=(3, 3), stride=2, padding=1, groups=512)
        self.conv8 = MultiDepthWiseRes(num_block=2, channels=128, kernel=(3, 3), stride=1, padding=1, groups=256)
        self.conv9 = ConvBnPrelu(128, 512, kernel=(1, 1))
        self.conv10 = ConvBn(512, 512, groups=512, kernel=(7, 7))
        self.flatten = Flatten()
        self.linear = nn.Linear(2048, embedding_size, bias=False)
        self.bn = nn.BatchNorm1d(embedding_size)
        
    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        out = self.conv6(out)
        out = self.conv7(out)
        out = self.conv8(out)
        out = self.conv9(out)
        out = self.conv10(out)
        out = self.flatten(out)
        out = self.linear(out)
        out = self.bn(out)
        return out

if __name__ == "__main__":
    from PIL import Image
    import numpy as np

    x = Image.open("../samples/009.jpg").convert('L')
    x = x.resize((128, 128))
    x = np.asarray(x, dtype=np.float32)
    x = x[None, None, ...]
    x = torch.from_numpy(x)
    net = FaceMobileNet(512)
    net.eval()
    with torch.no_grad():
        out = net(x)
    print(out.shape)
import torch
import torch.nn as nn
import torch.nn.functional as F



class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.shape[0], -1)

class ConvBn(nn.Module):

    def __init__(self, in_c, out_c, kernel=(1, 1), stride=1, padding=0, groups=1):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_c)
        )
        
    def forward(self, x):
        return self.net(x)


class ConvBnPrelu(nn.Module):

    def __init__(self, in_c, out_c, kernel=(1, 1), stride=1, padding=0, groups=1):
        super().__init__()
        self.net = nn.Sequential(
            ConvBn(in_c, out_c, kernel, stride, padding, groups),
            nn.PReLU(out_c)
        )

    def forward(self, x):
        return self.net(x)


class DepthWise(nn.Module):

    def __init__(self, in_c, out_c, kernel=(3, 3), stride=2, padding=1, groups=1):
        super().__init__()
        self.net = nn.Sequential(
            ConvBnPrelu(in_c, groups, kernel=(1, 1), stride=1, padding=0),
            ConvBnPrelu(groups, groups, kernel=kernel, stride=stride, padding=padding, groups=groups),
            ConvBn(groups, out_c, kernel=(1, 1), stride=1, padding=0),
        )

    def forward(self, x):
        return self.net(x)


class DepthWiseRes(nn.Module):
    """DepthWise with Residual"""

    def __init__(self, in_c, out_c, kernel=(3, 3), stride=2, padding=1, groups=1):
        super().__init__()
        self.net = DepthWise(in_c, out_c, kernel, stride, padding, groups)

    def forward(self, x):
        return self.net(x) + x


class MultiDepthWiseRes(nn.Module):

    def __init__(self, num_block, channels, kernel=(3, 3), stride=1, padding=1, groups=1):
        super().__init__()

        self.net = nn.Sequential(*[
            DepthWiseRes(channels, channels, kernel, stride, padding, groups)
            for _ in range(num_block)
        ])

    def forward(self, x):
        return self.net(x)


class FaceMobileNet(nn.Module):

    def __init__(self, embedding_size):
        super().__init__()
        self.conv1 = ConvBnPrelu(1, 64, kernel=(3, 3), stride=2, padding=1)
        self.conv2 = ConvBn(64, 64, kernel=(3, 3), stride=1, padding=1, groups=64)
        self.conv3 = DepthWise(64, 64, kernel=(3, 3), stride=2, padding=1, groups=128)
        self.conv4 = MultiDepthWiseRes(num_block=4, channels=64, kernel=3, stride=1, padding=1, groups=128)
        self.conv5 = DepthWise(64, 128, kernel=(3, 3), stride=2, padding=1, groups=256)
        self.conv6 = MultiDepthWiseRes(num_block=6, channels=128, kernel=(3, 3), stride=1, padding=1, groups=256)
        self.conv7 = DepthWise(128, 128, kernel=(3, 3), stride=2, padding=1, groups=512)
        self.conv8 = MultiDepthWiseRes(num_block=2, channels=128, kernel=(3, 3), stride=1, padding=1, groups=256)
        self.conv9 = ConvBnPrelu(128, 512, kernel=(1, 1))
        self.conv10 = ConvBn(512, 512, groups=512, kernel=(7, 7))
        self.flatten = Flatten()
        self.linear = nn.Linear(2048, embedding_size, bias=False)
        self.bn = nn.BatchNorm1d(embedding_size)
        
    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        out = self.conv6(out)
        out = self.conv7(out)
        out = self.conv8(out)
        out = self.conv9(out)
        out = self.conv10(out)
        out = self.flatten(out)
        out = self.linear(out)
        out = self.bn(out)
        return out

if __name__ == "__main__":
    from PIL import Image
    import numpy as np

    x = Image.open("../samples/009.jpg").convert('L')
    x = x.resize((128, 128))
    x = np.asarray(x, dtype=np.float32)
    x = x[None, None, ...]
    x = torch.from_numpy(x)
    net = FaceMobileNet(512)
    net.eval()
    with torch.no_grad():
        out = net(x)
    print(out.shape)
DepthWise with Residual