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MobileNet_v2.py
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MobileNet_v2.py
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'''
MobileNet_v2_OS_32
these codes brow from https://github.com/tonylins/pytorch-mobilenet-v2
but little different from original architecture :
+-------------------------------------------+-------------------------+
| output stride
+===========================================+=========================+
| original MobileNet_v2_OS_32 | 32 |
+-------------------------------------------+-------------------------+
| self.interverted_residual_setting = [ | |
| # t, c, n, s | |
| [1, 16, 1, 1], | pw -> dw -> pw-linear |
| [6, 24, 2, 2], | |
| [6, 32, 3, 2], | |
| [6, 64, 4, 2], | stride = 2 |
| [6, 96, 3, 1], | |
| [6, 160, 3, 2], | stride = 2 |
| [6, 320, 1, 1], | |
| ] | |
+-------------------------------------------+-------------------------+
| MobileNet_v2_OS_8 | 8 |
+-------------------------------------------+-------------------------+
| self.interverted_residual_setting = [ | |
| # t, c, n, s | |
| [1, 16, 1, 1], | dw -> pw-linear |
| [6, 24, 2, 2], | |
| [6, 32, 3, 2], | |
| [6, 64, 4, 1], | stride = 1 |
| [6, 96, 3, 1], | |
| [6, 160, 3, 1], | stride = 1 |
| [6, 320, 1, 1], | |
| ] | |
+-------------------------------------------+-------------------------+
Notation! I throw away last layers.
Author: Zhengwei Li
Data: July 1 2018
'''
import torch
import torch.nn as nn
import math
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)
)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
self.use_res_connect = self.stride == 1 and inp == oup
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, inp * expand_ratio, 1, 1, 0, bias=False),
nn.BatchNorm2d(inp * expand_ratio),
nn.ReLU6(inplace=True),
# dw
nn.Conv2d(inp * expand_ratio, inp * expand_ratio, 3, stride, 1, groups=inp * expand_ratio, bias=False),
nn.BatchNorm2d(inp * expand_ratio),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(inp * expand_ratio, 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)
#-------------------------------------------------------------------------------------------------
# MobileNet_v2_os_32
#--------------------
class MobileNet_v2_os_32(nn.Module):
def __init__(self, nInputChannels=3):
super(MobileNet_v2_os_32, self).__init__()
# 1/2
# 256 x 256
self.head_conv = conv_bn(nInputChannels, 32, 2)
# 1/2
# 256 x 256
self.block_1 = InvertedResidual(32, 16, 1, 1)
# 1/4 128 x 128
self.block_2 = nn.Sequential(
InvertedResidual(16, 24, 2, 6),
InvertedResidual(24, 24, 1, 6)
)
# 1/8 64 x 64
self.block_3 = nn.Sequential(
InvertedResidual(24, 32, 2, 6),
InvertedResidual(32, 32, 1, 6),
InvertedResidual(32, 32, 1, 6)
)
# 1/16 32 x 32
self.block_4 = nn.Sequential(
InvertedResidual(32, 64, 2, 6),
InvertedResidual(64, 64, 1, 6),
InvertedResidual(64, 64, 1, 6),
InvertedResidual(64, 64, 1, 6)
)
# 1/16 32 x 32
self.block_5 = nn.Sequential(
InvertedResidual(64, 96, 1, 6),
InvertedResidual(96, 96, 1, 6),
InvertedResidual(96, 96, 1, 6)
)
# 1/32 16 x 16
self.block_6 = nn.Sequential(
InvertedResidual(96, 160, 2, 6),
InvertedResidual(160, 160, 1, 6),
InvertedResidual(160, 160, 1, 6)
)
# 1/32 16 x 16
self.block_7 = InvertedResidual(160, 320, 1, 6)
def forward(self, x):
x = self.head_conv(x)
x = self.block_1(x)
x = self.block_2(x)
low_level_feat = x
x = self.block_3(x)
x = self.block_4(x)
x = self.block_5(x)
x = self.block_6(x)
x = self.block_7(x)
return x, low_level_feat
#-------------------------------------------------------------------------------------------------
# MobileNet_v2_os_8
#--------------------
class MobileNet_v2_os_8(nn.Module):
def __init__(self, nInputChannels=3):
super(MobileNet_v2_os_8, self).__init__()
# 1/2 256 x 256
self.head_conv = conv_bn(nInputChannels, 32, 2)
# 1/2 256 x 256
# head bock different form original mobilenetv2
self.block_1 = nn.Sequential(
# dw
nn.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False),
nn.BatchNorm2d(32),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(32, 16, 1, 1, 0, bias=False),
nn.BatchNorm2d(16),
)
# 1/4 128 x 128
self.block_2 = nn.Sequential(
InvertedResidual(16, 24, 2, 6),
InvertedResidual(24, 24, 1, 6)
)
# 1/8 64 x 64
self.block_3 = nn.Sequential(
InvertedResidual(24, 32, 2, 6),
InvertedResidual(32, 32, 1, 6),
InvertedResidual(32, 32, 1, 6)
)
# 1/8 64 x 64
self.block_4 = nn.Sequential(
InvertedResidual(32, 64, 1, 6),
InvertedResidual(64, 64, 1, 6),
InvertedResidual(64, 64, 1, 6),
InvertedResidual(64, 64, 1, 6)
)
# 1/8 64 x 64
self.block_5 = nn.Sequential(
InvertedResidual(64, 96, 1, 6),
InvertedResidual(96, 96, 1, 6),
InvertedResidual(96, 96, 1, 6)
)
# 1/8 64 x 64
self.block_6 = nn.Sequential(
InvertedResidual(96, 160, 1, 6),
InvertedResidual(160, 160, 1, 6),
InvertedResidual(160, 160, 1, 6)
)
# 1/8 64 x 64
self.block_7 = InvertedResidual(160, 320, 1, 6)
def forward(self, x):
x = self.head_conv(x)
x = self.block_1(x)
x = self.block_2(x)
# 1/4 128 x 128
low_level_feat = x
x = self.block_3(x)
x = self.block_4(x)
x = self.block_5(x)
x = self.block_6(x)
x = self.block_7(x)
return x, low_level_feat
if __name__ == "__main__":
model = MobileNet_v2_OS_8()
x = torch.randn(1,3,960,720)
y, low_level_feat = model(x)
print(y.size())