mobilenetv1.py

import torch.nn as nn
from utils.builder import get_builder


class MobileNetV1(nn.Module):
    def __init__(self):
        super(MobileNetV1, self).__init__()
        builder = get_builder()

        def conv_bn(inp, oup, stride):
            return nn.Sequential(
                builder.conv2d(inp, oup, 3, stride, 1, bias=False),
                builder.batchnorm(oup),
                nn.ReLU(inplace=True)
            )

        def conv_dw(inp, oup, stride):
            return nn.Sequential(
                builder.conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                builder.batchnorm(inp),
                nn.ReLU(inplace=True),

                builder.conv2d(inp, oup, 1, 1, 0, bias=False),
                builder.batchnorm(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 = builder.conv1x1(1024, 1000)

    def forward(self, x):
        x = self.model(x)
        x = self.fc(x)
        x = x.view(-1, 1000)
        return x