205 lines
6.6 KiB
Python
205 lines
6.6 KiB
Python
"""mobilenetv2.py
|
|
|
|
This is a copy of:
|
|
https://github.com/ZHKKKe/MODNet/blob/master/src/models/backbones/mobilenetv2.py
|
|
"""
|
|
|
|
|
|
import math
|
|
import json
|
|
from functools import reduce
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
|
|
#------------------------------------------------------------------------------
|
|
# Useful functions
|
|
#------------------------------------------------------------------------------
|
|
|
|
def _make_divisible(v, divisor, min_value=None):
|
|
if min_value is None:
|
|
min_value = divisor
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
|
# Make sure that round down does not go down by more than 10%.
|
|
if new_v < 0.9 * v:
|
|
new_v += divisor
|
|
return new_v
|
|
|
|
|
|
def conv_bn(inp, oup, stride):
|
|
return nn.Sequential(
|
|
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
|
|
nn.BatchNorm2d(oup),
|
|
nn.ReLU6(inplace=True)
|
|
)
|
|
|
|
|
|
def conv_1x1_bn(inp, oup):
|
|
return nn.Sequential(
|
|
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
|
|
nn.BatchNorm2d(oup),
|
|
nn.ReLU6(inplace=True)
|
|
)
|
|
|
|
|
|
#------------------------------------------------------------------------------
|
|
# Class of Inverted Residual block
|
|
#------------------------------------------------------------------------------
|
|
|
|
class InvertedResidual(nn.Module):
|
|
def __init__(self, inp, oup, stride, expansion, dilation=1):
|
|
super(InvertedResidual, self).__init__()
|
|
self.stride = stride
|
|
assert stride in [1, 2]
|
|
|
|
hidden_dim = round(inp * expansion)
|
|
self.use_res_connect = self.stride == 1 and inp == oup
|
|
|
|
if expansion == 1:
|
|
self.conv = nn.Sequential(
|
|
# dw
|
|
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
|
|
nn.BatchNorm2d(hidden_dim),
|
|
nn.ReLU6(inplace=True),
|
|
# pw-linear
|
|
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
|
|
nn.BatchNorm2d(oup),
|
|
)
|
|
else:
|
|
self.conv = nn.Sequential(
|
|
# pw
|
|
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
|
|
nn.BatchNorm2d(hidden_dim),
|
|
nn.ReLU6(inplace=True),
|
|
# dw
|
|
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
|
|
nn.BatchNorm2d(hidden_dim),
|
|
nn.ReLU6(inplace=True),
|
|
# pw-linear
|
|
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
|
|
nn.BatchNorm2d(oup),
|
|
)
|
|
|
|
def forward(self, x):
|
|
if self.use_res_connect:
|
|
return x + self.conv(x)
|
|
else:
|
|
return self.conv(x)
|
|
|
|
|
|
#------------------------------------------------------------------------------
|
|
# Class of MobileNetV2
|
|
#------------------------------------------------------------------------------
|
|
|
|
class MobileNetV2(nn.Module):
|
|
def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000):
|
|
super(MobileNetV2, self).__init__()
|
|
self.in_channels = in_channels
|
|
self.num_classes = num_classes
|
|
input_channel = 32
|
|
last_channel = 1280
|
|
interverted_residual_setting = [
|
|
# t, c, n, s
|
|
[1 , 16, 1, 1],
|
|
[expansion, 24, 2, 2],
|
|
[expansion, 32, 3, 2],
|
|
[expansion, 64, 4, 2],
|
|
[expansion, 96, 3, 1],
|
|
[expansion, 160, 3, 2],
|
|
[expansion, 320, 1, 1],
|
|
]
|
|
|
|
# building first layer
|
|
input_channel = _make_divisible(input_channel*alpha, 8)
|
|
self.last_channel = _make_divisible(last_channel*alpha, 8) if alpha > 1.0 else last_channel
|
|
self.features = [conv_bn(self.in_channels, input_channel, 2)]
|
|
|
|
# building inverted residual blocks
|
|
for t, c, n, s in interverted_residual_setting:
|
|
output_channel = _make_divisible(int(c*alpha), 8)
|
|
for i in range(n):
|
|
if i == 0:
|
|
self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t))
|
|
else:
|
|
self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t))
|
|
input_channel = output_channel
|
|
|
|
# building last several layers
|
|
self.features.append(conv_1x1_bn(input_channel, self.last_channel))
|
|
|
|
# make it nn.Sequential
|
|
self.features = nn.Sequential(*self.features)
|
|
|
|
# building classifier
|
|
if self.num_classes is not None:
|
|
self.classifier = nn.Sequential(
|
|
nn.Dropout(0.2),
|
|
nn.Linear(self.last_channel, num_classes),
|
|
)
|
|
|
|
# Initialize weights
|
|
self._init_weights()
|
|
|
|
def forward(self, x):
|
|
# Stage1
|
|
x = self.features[0](x)
|
|
x = self.features[1](x)
|
|
# Stage2
|
|
x = self.features[2](x)
|
|
x = self.features[3](x)
|
|
# Stage3
|
|
x = self.features[4](x)
|
|
x = self.features[5](x)
|
|
x = self.features[6](x)
|
|
# Stage4
|
|
x = self.features[7](x)
|
|
x = self.features[8](x)
|
|
x = self.features[9](x)
|
|
x = self.features[10](x)
|
|
x = self.features[11](x)
|
|
x = self.features[12](x)
|
|
x = self.features[13](x)
|
|
# Stage5
|
|
x = self.features[14](x)
|
|
x = self.features[15](x)
|
|
x = self.features[16](x)
|
|
x = self.features[17](x)
|
|
x = self.features[18](x)
|
|
|
|
# Classification
|
|
if self.num_classes is not None:
|
|
x = x.mean(dim=(2,3))
|
|
x = self.classifier(x)
|
|
|
|
# Output
|
|
return x
|
|
|
|
def _load_pretrained_model(self, pretrained_file):
|
|
pretrain_dict = torch.load(pretrained_file, map_location='cpu')
|
|
model_dict = {}
|
|
state_dict = self.state_dict()
|
|
print("[MobileNetV2] Loading pretrained model...")
|
|
for k, v in pretrain_dict.items():
|
|
if k in state_dict:
|
|
model_dict[k] = v
|
|
else:
|
|
print(k, "is ignored")
|
|
state_dict.update(model_dict)
|
|
self.load_state_dict(state_dict)
|
|
|
|
def _init_weights(self):
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
|
m.weight.data.normal_(0, math.sqrt(2. / n))
|
|
if m.bias is not None:
|
|
m.bias.data.zero_()
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
m.weight.data.fill_(1)
|
|
m.bias.data.zero_()
|
|
elif isinstance(m, nn.Linear):
|
|
n = m.weight.size(1)
|
|
m.weight.data.normal_(0, 0.01)
|
|
m.bias.data.zero_()
|