118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
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from torch import nn
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from .initialization import initialize_resnet
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class ConvBNReLU(nn.Module):
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def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
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super(ConvBNReLU, self).__init__()
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padding = (kernel_size - 1) // 2
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self.conv = nn.Conv2d(
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in_planes,
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out_planes,
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kernel_size,
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stride,
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padding,
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groups=groups,
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bias=False,
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)
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self.bn = nn.BatchNorm2d(out_planes)
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self.relu = nn.ReLU6(inplace=True)
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def forward(self, x):
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out = self.conv(x)
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out = self.bn(out)
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out = self.relu(out)
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return out
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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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[
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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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)
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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__(
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self, num_classes, width_mult, input_channel, last_channel, block_name, dropout
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):
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super(MobileNetV2, self).__init__()
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if block_name == "InvertedResidual":
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block = InvertedResidual
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else:
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raise ValueError("invalid block name : {:}".format(block_name))
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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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# 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(
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block(input_channel, output_channel, stride, expand_ratio=t)
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)
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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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# building classifier
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self.classifier = nn.Sequential(
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nn.Dropout(dropout),
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nn.Linear(self.last_channel, num_classes),
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)
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self.message = "MobileNetV2 : width_mult={:}, in-C={:}, last-C={:}, block={:}, dropout={:}".format(
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width_mult, input_channel, last_channel, block_name, dropout
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)
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# weight initialization
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self.apply(initialize_resnet)
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def get_message(self):
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return self.message
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def forward(self, inputs):
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features = self.features(inputs)
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vectors = features.mean([2, 3])
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predicts = self.classifier(vectors)
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return features, predicts
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