from torch import nn
from utils.torch import *


class MobileNet(nn.Module):
    def __init__(self, out_dim):
        super().__init__()
        self.out_dim = out_dim

        def conv_bn(inp, oup, stride):
            return nn.Sequential(
                nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
                nn.BatchNorm2d(oup),
                nn.ReLU(inplace=True)
            )

        def conv_dw(inp, oup, stride):
            return nn.Sequential(
                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                nn.ReLU(inplace=True),

                nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
                nn.ReLU(inplace=True),
            )

        self.model = nn.Sequential(
            conv_bn(3, 32, 2),
            conv_dw(32, 64, 1),
            conv_dw(64, 128, 2),
            conv_dw(128, 128, 1),
            conv_dw(128, 256, 2),
            conv_dw(256, 256, 1),
            conv_dw(256, 512, 2),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 1024, 2),
            conv_dw(1024, 1024, 1),
            nn.AvgPool2d(7),
        )
        self.fc = nn.Linear(1024, out_dim)

    def forward(self, x):
        x = self.model(x)
        x = x.view(-1, 1024)
        x = self.fc(x)
        return x


if __name__ == '__main__':
    import time
    torch.set_grad_enabled(False)
    net = MobileNet(128)
    input = ones(1, 3, 224, 224)
    for i in range(10):
        t0 = time.time()
        out = net(input)
        print(time.time() - t0)
    print(out.shape)
