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""" |
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This MobileNetV2 implementation is modified from the following repository: |
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https://github.com/tonylins/pytorch-mobilenet-v2 |
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""" |
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import math |
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import torch.nn as nn |
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from .lib.nn import SynchronizedBatchNorm2d |
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from .utils import load_url |
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BatchNorm2d = SynchronizedBatchNorm2d |
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__all__ = ["mobilenetv2"] |
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model_urls = { |
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"mobilenetv2": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/mobilenet_v2.pth.tar", |
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} |
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def conv_bn(inp, oup, stride): |
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return nn.Sequential( |
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nn.Conv2d(inp, oup, 3, stride, 1, bias=False), |
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BatchNorm2d(oup), |
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nn.ReLU6(inplace=True), |
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) |
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def conv_1x1_bn(inp, oup): |
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return nn.Sequential( |
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nn.Conv2d(inp, oup, 1, 1, 0, bias=False), |
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BatchNorm2d(oup), |
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nn.ReLU6(inplace=True), |
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) |
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class InvertedResidual(nn.Module): |
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def __init__(self, inp, oup, stride, expand_ratio): |
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super(InvertedResidual, self).__init__() |
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self.stride = stride |
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assert stride in [1, 2] |
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hidden_dim = round(inp * expand_ratio) |
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self.use_res_connect = self.stride == 1 and inp == oup |
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if expand_ratio == 1: |
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self.conv = nn.Sequential( |
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nn.Conv2d( |
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hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False |
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), |
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BatchNorm2d(hidden_dim), |
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nn.ReLU6(inplace=True), |
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), |
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BatchNorm2d(oup), |
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) |
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else: |
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self.conv = nn.Sequential( |
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nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), |
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BatchNorm2d(hidden_dim), |
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nn.ReLU6(inplace=True), |
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nn.Conv2d( |
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hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False |
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), |
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BatchNorm2d(hidden_dim), |
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nn.ReLU6(inplace=True), |
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), |
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BatchNorm2d(oup), |
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) |
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def forward(self, x): |
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if self.use_res_connect: |
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return x + self.conv(x) |
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else: |
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return self.conv(x) |
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class MobileNetV2(nn.Module): |
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def __init__(self, n_class=1000, input_size=224, width_mult=1.0): |
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super(MobileNetV2, self).__init__() |
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block = InvertedResidual |
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input_channel = 32 |
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last_channel = 1280 |
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interverted_residual_setting = [ |
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[1, 16, 1, 1], |
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[6, 24, 2, 2], |
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[6, 32, 3, 2], |
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[6, 64, 4, 2], |
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[6, 96, 3, 1], |
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[6, 160, 3, 2], |
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[6, 320, 1, 1], |
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] |
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assert input_size % 32 == 0 |
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input_channel = int(input_channel * width_mult) |
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self.last_channel = ( |
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int(last_channel * width_mult) if width_mult > 1.0 else last_channel |
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) |
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self.features = [conv_bn(3, input_channel, 2)] |
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for t, c, n, s in interverted_residual_setting: |
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output_channel = int(c * width_mult) |
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for i in range(n): |
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if i == 0: |
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self.features.append( |
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block(input_channel, output_channel, s, expand_ratio=t) |
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) |
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else: |
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self.features.append( |
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block(input_channel, output_channel, 1, expand_ratio=t) |
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) |
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input_channel = output_channel |
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self.features.append(conv_1x1_bn(input_channel, self.last_channel)) |
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self.features = nn.Sequential(*self.features) |
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self.classifier = nn.Sequential( |
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nn.Dropout(0.2), |
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nn.Linear(self.last_channel, n_class), |
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) |
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self._initialize_weights() |
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def forward(self, x): |
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x = self.features(x) |
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x = x.mean(3).mean(2) |
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x = self.classifier(x) |
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return x |
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def _initialize_weights(self): |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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m.weight.data.normal_(0, math.sqrt(2.0 / n)) |
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if m.bias is not None: |
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m.bias.data.zero_() |
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elif isinstance(m, BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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elif isinstance(m, nn.Linear): |
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n = m.weight.size(1) |
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m.weight.data.normal_(0, 0.01) |
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m.bias.data.zero_() |
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def mobilenetv2(pretrained=False, **kwargs): |
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"""Constructs a MobileNet_V2 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = MobileNetV2(n_class=1000, **kwargs) |
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if pretrained: |
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model.load_state_dict(load_url(model_urls["mobilenetv2"]), strict=False) |
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return model |
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