205 lines
6.6 KiB
Python
205 lines
6.6 KiB
Python
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"""mobilenetv2.py
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This is a copy of:
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https://github.com/ZHKKKe/MODNet/blob/master/src/models/backbones/mobilenetv2.py
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"""
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import math
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import json
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from functools import reduce
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import torch
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from torch import nn
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#------------------------------------------------------------------------------
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# Useful functions
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#------------------------------------------------------------------------------
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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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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#------------------------------------------------------------------------------
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# Class of Inverted Residual block
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#------------------------------------------------------------------------------
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class InvertedResidual(nn.Module):
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def __init__(self, inp, oup, stride, expansion, dilation=1):
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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 * expansion)
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self.use_res_connect = self.stride == 1 and inp == oup
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if expansion == 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, dilation=dilation, 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, dilation=dilation, 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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#------------------------------------------------------------------------------
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# Class of MobileNetV2
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#------------------------------------------------------------------------------
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class MobileNetV2(nn.Module):
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def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000):
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super(MobileNetV2, self).__init__()
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self.in_channels = in_channels
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self.num_classes = num_classes
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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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[expansion, 24, 2, 2],
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[expansion, 32, 3, 2],
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[expansion, 64, 4, 2],
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[expansion, 96, 3, 1],
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[expansion, 160, 3, 2],
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[expansion, 320, 1, 1],
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]
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# building first layer
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input_channel = _make_divisible(input_channel*alpha, 8)
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self.last_channel = _make_divisible(last_channel*alpha, 8) if alpha > 1.0 else last_channel
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self.features = [conv_bn(self.in_channels, 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 = _make_divisible(int(c*alpha), 8)
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for i in range(n):
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if i == 0:
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self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t))
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else:
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self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=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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if self.num_classes is not None:
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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, num_classes),
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)
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# Initialize weights
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self._init_weights()
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def forward(self, x):
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# Stage1
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x = self.features[0](x)
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x = self.features[1](x)
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# Stage2
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x = self.features[2](x)
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x = self.features[3](x)
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# Stage3
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x = self.features[4](x)
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x = self.features[5](x)
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x = self.features[6](x)
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# Stage4
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x = self.features[7](x)
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x = self.features[8](x)
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x = self.features[9](x)
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x = self.features[10](x)
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x = self.features[11](x)
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x = self.features[12](x)
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x = self.features[13](x)
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# Stage5
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x = self.features[14](x)
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x = self.features[15](x)
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x = self.features[16](x)
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x = self.features[17](x)
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x = self.features[18](x)
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# Classification
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if self.num_classes is not None:
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x = x.mean(dim=(2,3))
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x = self.classifier(x)
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# Output
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return x
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def _load_pretrained_model(self, pretrained_file):
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pretrain_dict = torch.load(pretrained_file, map_location='cpu')
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model_dict = {}
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state_dict = self.state_dict()
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print("[MobileNetV2] Loading pretrained model...")
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for k, v in pretrain_dict.items():
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if k in state_dict:
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model_dict[k] = v
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else:
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print(k, "is ignored")
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state_dict.update(model_dict)
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self.load_state_dict(state_dict)
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def _init_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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