135 lines
4.6 KiB
Python
Executable File
135 lines
4.6 KiB
Python
Executable File
from torch import nn
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from ssd.modeling import registry
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from ssd.utils.model_zoo import load_state_dict_from_url
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model_urls = {
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'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
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}
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class ConvBNReLU(nn.Sequential):
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def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
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padding = (kernel_size - 1) // 2
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super(ConvBNReLU, self).__init__(
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nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
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nn.BatchNorm2d(out_planes),
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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 = int(round(inp * expand_ratio))
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self.use_res_connect = self.stride == 1 and inp == oup
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layers = []
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if expand_ratio != 1:
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# pw
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layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
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layers.extend([
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# dw
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ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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])
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self.conv = nn.Sequential(*layers)
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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, width_mult=1.0, inverted_residual_setting=None):
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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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if inverted_residual_setting is None:
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inverted_residual_setting = [
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# t, c, n, s
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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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# only check the first element, assuming user knows t,c,n,s are required
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if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
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raise ValueError("inverted_residual_setting should be non-empty "
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"or a 4-element list, got {}".format(inverted_residual_setting))
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# building first layer
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input_channel = int(input_channel * width_mult)
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self.last_channel = int(last_channel * max(1.0, width_mult))
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features = [ConvBNReLU(3, input_channel, stride=2)]
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# building inverted residual blocks
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for t, c, n, s in inverted_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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stride = s if i == 0 else 1
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features.append(block(input_channel, output_channel, stride, expand_ratio=t))
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input_channel = output_channel
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# building last several layers
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features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
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# make it nn.Sequential
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self.features = nn.Sequential(*features)
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self.extras = nn.ModuleList([
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InvertedResidual(1280, 512, 2, 0.2),
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InvertedResidual(512, 256, 2, 0.25),
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InvertedResidual(256, 256, 2, 0.5),
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InvertedResidual(256, 64, 2, 0.25)
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])
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self.reset_parameters()
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def reset_parameters(self):
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# weight initialization
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.ones_(m.weight)
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.zeros_(m.bias)
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def forward(self, x):
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features = []
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for i in range(14):
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x = self.features[i](x)
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features.append(x)
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for i in range(14, len(self.features)):
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x = self.features[i](x)
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features.append(x)
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for i in range(len(self.extras)):
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x = self.extras[i](x)
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features.append(x)
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return tuple(features)
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@registry.BACKBONES.register('mobilenet_v2')
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def mobilenet_v2(cfg, pretrained=True):
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model = MobileNetV2()
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if pretrained:
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model.load_state_dict(load_state_dict_from_url(model_urls['mobilenet_v2']), strict=False)
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return model
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