"""MobileNet in PyTorch.

See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F


class Block(nn.Module):
    """Depthwise conv + Pointwise conv"""

    def __init__(self, in_planes, out_planes, stride=1):
        super(Block, self).__init__()
        self.conv1 = nn.Conv2d(
            in_planes,
            in_planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=in_planes,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv2 = nn.Conv2d(
            in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_planes)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        return out


class MobileNet(nn.Module):
    # (128,2) means conv planes=128, conv stride=2, by default conv stride=1
    cfg = [
        64,
        (128, 2),
        128,
        (256, 2),
        256,
        (512, 2),
        512,
        512,
        512,
        512,
        512,
        (1024, 2),
        1024,
    ]

    def __init__(self, num_classes=10):
        super(MobileNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.layers = self._make_layers(in_planes=32)
        self.linear = nn.Linear(1024, num_classes)
        self.relu = nn.ReLU()

    def _make_layers(self, in_planes):
        layers = []
        for x in self.cfg:
            out_planes = x if isinstance(x, int) else x[0]
            stride = 1 if isinstance(x, int) else x[1]
            layers.append(Block(in_planes, out_planes, stride))
            in_planes = out_planes
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.layers(out)
        out = F.avg_pool2d(out, 7)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def test():
    net = MobileNet()
    x = torch.randn(1, 3, 32, 32)
    y = net(x)
    print(y.size())


# test()
