Spaces:
Running
Running
import torch.nn as nn | |
from .det_mobilenet_v3 import ConvBNLayer, ResidualUnit, make_divisible | |
class MobileNetV3(nn.Module): | |
def __init__(self, | |
in_channels=3, | |
model_name='small', | |
scale=0.5, | |
large_stride=None, | |
small_stride=None, | |
**kwargs): | |
super(MobileNetV3, self).__init__() | |
if small_stride is None: | |
small_stride = [2, 2, 2, 2] | |
if large_stride is None: | |
large_stride = [1, 2, 2, 2] | |
assert isinstance( | |
large_stride, | |
list), 'large_stride type must ' 'be list but got {}'.format( | |
type(large_stride)) | |
assert isinstance( | |
small_stride, | |
list), 'small_stride type must ' 'be list but got {}'.format( | |
type(small_stride)) | |
assert len( | |
large_stride | |
) == 4, 'large_stride length must be ' '4 but got {}'.format( | |
len(large_stride)) | |
assert len( | |
small_stride | |
) == 4, 'small_stride length must be ' '4 but got {}'.format( | |
len(small_stride)) | |
if model_name == 'large': | |
cfg = [ | |
# k, exp, c, se, nl, s, | |
[3, 16, 16, False, 'relu', large_stride[0]], | |
[3, 64, 24, False, 'relu', (large_stride[1], 1)], | |
[3, 72, 24, False, 'relu', 1], | |
[5, 72, 40, True, 'relu', (large_stride[2], 1)], | |
[5, 120, 40, True, 'relu', 1], | |
[5, 120, 40, True, 'relu', 1], | |
[3, 240, 80, False, 'hard_swish', 1], | |
[3, 200, 80, False, 'hard_swish', 1], | |
[3, 184, 80, False, 'hard_swish', 1], | |
[3, 184, 80, False, 'hard_swish', 1], | |
[3, 480, 112, True, 'hard_swish', 1], | |
[3, 672, 112, True, 'hard_swish', 1], | |
[5, 672, 160, True, 'hard_swish', (large_stride[3], 1)], | |
[5, 960, 160, True, 'hard_swish', 1], | |
[5, 960, 160, True, 'hard_swish', 1], | |
] | |
cls_ch_squeeze = 960 | |
elif model_name == 'small': | |
cfg = [ | |
# k, exp, c, se, nl, s, | |
[3, 16, 16, True, 'relu', (small_stride[0], 1)], | |
[3, 72, 24, False, 'relu', (small_stride[1], 1)], | |
[3, 88, 24, False, 'relu', 1], | |
[5, 96, 40, True, 'hard_swish', (small_stride[2], 1)], | |
[5, 240, 40, True, 'hard_swish', 1], | |
[5, 240, 40, True, 'hard_swish', 1], | |
[5, 120, 48, True, 'hard_swish', 1], | |
[5, 144, 48, True, 'hard_swish', 1], | |
[5, 288, 96, True, 'hard_swish', (small_stride[3], 1)], | |
[5, 576, 96, True, 'hard_swish', 1], | |
[5, 576, 96, True, 'hard_swish', 1], | |
] | |
cls_ch_squeeze = 576 | |
else: | |
raise NotImplementedError('mode[' + model_name + | |
'_model] is not implemented!') | |
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25] | |
assert scale in supported_scale, 'supported scales are {} but input scale is {}'.format( | |
supported_scale, scale) | |
inplanes = 16 | |
# conv1 | |
self.conv1 = ConvBNLayer( | |
in_channels=in_channels, | |
out_channels=make_divisible(inplanes * scale), | |
kernel_size=3, | |
stride=2, | |
padding=1, | |
groups=1, | |
if_act=True, | |
act='hard_swish', | |
) | |
i = 0 | |
block_list = [] | |
inplanes = make_divisible(inplanes * scale) | |
for k, exp, c, se, nl, s in cfg: | |
block_list.append( | |
ResidualUnit( | |
in_channels=inplanes, | |
mid_channels=make_divisible(scale * exp), | |
out_channels=make_divisible(scale * c), | |
kernel_size=k, | |
stride=s, | |
use_se=se, | |
act=nl, | |
name='conv' + str(i + 2), | |
)) | |
inplanes = make_divisible(scale * c) | |
i += 1 | |
self.blocks = nn.Sequential(*block_list) | |
self.conv2 = ConvBNLayer( | |
in_channels=inplanes, | |
out_channels=make_divisible(scale * cls_ch_squeeze), | |
kernel_size=1, | |
stride=1, | |
padding=0, | |
groups=1, | |
if_act=True, | |
act='hard_swish', | |
) | |
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) | |
self.out_channels = make_divisible(scale * cls_ch_squeeze) | |
def forward(self, x): | |
x = self.conv1(x) | |
x = self.blocks(x) | |
x = self.conv2(x) | |
x = self.pool(x) | |
return x | |