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127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
import torch.nn as nn
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import math
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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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nn.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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nn.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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# dw
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nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(inplace=True),
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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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else:
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self.conv = nn.Sequential(
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# pw
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nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(inplace=True),
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# dw
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nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(inplace=True),
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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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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.):
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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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# 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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# building first layer
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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 = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
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self.features = [conv_bn(3, input_channel, 2)]
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# building inverted residual blocks
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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(block(input_channel, output_channel, s, expand_ratio=t))
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else:
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self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
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input_channel = output_channel
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# building last several layers
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self.features.append(conv_1x1_bn(input_channel, self.last_channel))
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# make it nn.Sequential
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self.features = nn.Sequential(*self.features)
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# building classifier
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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. / 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, nn.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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