attacks/CleanSheet/models/mobilenet_v2.py

"""MobileNetV2 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
for more details.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F


class Block(nn.Module):
    """expand + depthwise + pointwise"""

    def __init__(self, in_planes, out_planes, expansion, stride):
        super(Block, self).__init__()
        self.stride = stride

        planes = expansion * in_planes
        self.conv1 = nn.Conv2d(in_planes,
                               planes,
                               kernel_size=1,
                               stride=1,
                               padding=0,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes,
            planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=planes,
            bias=False,
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes,
                               out_planes,
                               kernel_size=1,
                               stride=1,
                               padding=0,
                               bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 1 and in_planes != out_planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes,
                          out_planes,
                          kernel_size=1,
                          stride=1,
                          padding=0,
                          bias=False),
                nn.BatchNorm2d(out_planes),
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out = out + self.shortcut(x) if self.stride == 1 else out
        return out


class MobileNetV2(nn.Module):
    # (expansion, out_planes, num_blocks, stride)
    cfg = [
        (1, 16, 1, 1),
        (6, 24, 2, 1),  # NOTE: change stride 2 -> 1 for CIFAR10
        (6, 32, 3, 2),
        (6, 64, 4, 2),
        (6, 96, 3, 1),
        (6, 160, 3, 2),
        (6, 320, 1, 1),
    ]

    def __init__(self, num_classes: int = 10) -> None:
        super(MobileNetV2, self).__init__()
        # NOTE: change conv1 stride 2 -> 1 for CIFAR10
        self.conv1 = nn.Conv2d(3,
                               32,
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.layers = self._make_layers(in_planes=32)
        self.conv2 = nn.Conv2d(320,
                               1280,
                               kernel_size=1,
                               stride=1,
                               padding=0,
                               bias=False)
        self.bn2 = nn.BatchNorm2d(1280)
        self.linear = nn.Linear(1280, num_classes)

    def _make_layers(self, in_planes):
        layers = []
        for expansion, out_planes, num_blocks, stride in self.cfg:
            strides = [stride] + [1] * (num_blocks - 1)
            for stride in strides:
                layers.append(Block(in_planes, out_planes, expansion, stride))
                in_planes = out_planes
        return nn.Sequential(*layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layers(out)
        out = F.relu(self.bn2(self.conv2(out)))
        # NOTE: change pooling kernel_size 7 -> 4 for CIFAR10
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def mobilenet_v2(num_classes: int) -> MobileNetV2:
    return MobileNetV2(num_classes=num_classes)