def __init__(self, in_channels, result_num=6, **kwargs):
super(PseHead, self).__init__()
self.H = kwargs.get('H', 640)
self.W = kwargs.get('W', 640)
self.scale = kwargs.get('scale', 1)
self.conv = ConvBNACT(in_channels, in_channels // 4, kernel_size=3, padding=1, stride=1, act='relu')
self.out_conv = nn.Conv2d(in_channels // 4, result_num, kernel_size=1, stride=1)
def __init__(self, num_in_filter, num_mid_filter, num_out_filter, stride, kernel_size, act=None, use_se=False):
super().__init__()
self.conv0 = ConvBNACT(in_channels=num_in_filter, out_channels=num_mid_filter, kernel_size=1, stride=1,
padding=0, act=act)
self.conv1 = ConvBNACT(in_channels=num_mid_filter, out_channels=num_mid_filter, kernel_size=kernel_size,
stride=stride,
padding=int((kernel_size - 1) // 2), act=act, groups=num_mid_filter)
if use_se:
self.se = SEBlock(in_channels=num_mid_filter, out_channels=num_mid_filter)
else:
self.se = None
self.conv2 = ConvBNACT(in_channels=num_mid_filter, out_channels=num_out_filter, kernel_size=1, stride=1,
padding=0)
self.not_add = num_in_filter != num_out_filter or stride != 1
def __init__(self,
num_in_filter,
num_mid_filter,
num_out_filter,
stride,
kernel_size,
act=None,
use_se=False):
super().__init__()
self.expand_conv = ConvBNACT(in_channels=num_in_filter,
out_channels=num_mid_filter,
kernel_size=1,
stride=1,
padding=0,
act=act)
self.bottleneck_conv = ConvBNACT(in_channels=num_mid_filter,
out_channels=num_mid_filter,
kernel_size=kernel_size,
stride=stride,
padding=int((kernel_size - 1) // 2),
act=act,
groups=num_mid_filter)
if use_se:
self.se = SEBlock(in_channels=num_mid_filter,
out_channels=num_mid_filter,
hsigmoid_type='paddle')
else:
self.se = None
self.linear_conv = ConvBNACT(in_channels=num_mid_filter,
out_channels=num_out_filter,
kernel_size=1,
stride=1,
padding=0)
self.not_add = num_in_filter != num_out_filter or stride != 1
def __init__(self, in_channels, out_channels=256, inplace=True, **kwargs):
super().__init__()
self.out_channels = out_channels * 4
self.toplayer = ConvBNACT(in_channels=in_channels[3],
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
act='relu')
self.latlayer1 = ConvBNACT(in_channels=in_channels[2],
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
act='relu')
self.latlayer2 = ConvBNACT(in_channels=in_channels[1],
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
act='relu')
self.latlayer3 = ConvBNACT(in_channels=in_channels[0],
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
act='relu')
# Smooth layers
self.smooth1 = ConvBNACT(in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
act='relu')
self.smooth2 = ConvBNACT(in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
act='relu')
self.smooth3 = ConvBNACT(in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
act='relu')
class ResidualUnit(nn.Module):
def __init__(self, num_in_filter, num_mid_filter, num_out_filter, stride, kernel_size, act=None, use_se=False):
super().__init__()
self.conv0 = ConvBNACT(in_channels=num_in_filter, out_channels=num_mid_filter, kernel_size=1, stride=1,
padding=0, act=act)
self.conv1 = ConvBNACT(in_channels=num_mid_filter, out_channels=num_mid_filter, kernel_size=kernel_size,
stride=stride,
padding=int((kernel_size - 1) // 2), act=act, groups=num_mid_filter)
if use_se:
self.se = SEBlock(in_channels=num_mid_filter, out_channels=num_mid_filter)
else:
self.se = None
self.conv2 = ConvBNACT(in_channels=num_mid_filter, out_channels=num_out_filter, kernel_size=1, stride=1,
padding=0)
self.not_add = num_in_filter != num_out_filter or stride != 1
def load_3rd_state_dict(self, _3rd_name, _state, _convolution_index):
if _3rd_name == 'paddle':
self.conv0.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_expand')
self.conv1.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_depthwise')
if self.se is not None:
self.se.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_se')
self.conv2.load_3rd_state_dict(_3rd_name, _state, f'conv{_convolution_index}_linear')
else:
pass
pass
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
if self.se is not None:
y = self.se(y)
y = self.conv2(y)
if not self.not_add:
y = x + y
return y
def __init__(self, in_channels=3, **kwargs):
super().__init__()
self.scale = kwargs.get('scale', 0.5)
model_name = kwargs.get('model_name', 'small')
self.inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', (2, 1)],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', (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', (2, 1)],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'relu', (1, 1)],
[3, 72, 24, False, 'relu', (2, 1)],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', (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', (2, 1)],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError("mode[" + model_name +
"_model] is not implemented!")
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
assert self.scale in supported_scale, "supported scale are {} but input scale is {}".format(
supported_scale, self.scale)
scale = self.scale
inplanes = self.inplanes
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
# conv1
self.conv1 = ConvBNACT(in_channels=in_channels,
out_channels=self.make_divisible(inplanes *
scale),
kernel_size=3,
stride=2,
padding=1,
groups=1,
act='hard_swish')
inplanes = self.make_divisible(inplanes * scale)
block_list = []
for layer_cfg in cfg:
block = ResidualUnit(
num_in_filter=inplanes,
num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
num_out_filter=self.make_divisible(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
kernel_size=layer_cfg[0],
use_se=layer_cfg[3])
block_list.append(block)
inplanes = self.make_divisible(scale * layer_cfg[2])
self.blocks = nn.Sequential(*block_list)
self.conv2 = ConvBNACT(in_channels=inplanes,
out_channels=self.make_divisible(
scale * cls_ch_squeeze),
kernel_size=1,
stride=1,
padding=0,
groups=1,
act='hard_swish')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.out_channels = self.make_divisible(scale * cls_ch_squeeze)
class MobileNetV3(nn.Module):
def __init__(self, in_channels=3, **kwargs):
super().__init__()
self.scale = kwargs.get('scale', 0.5)
model_name = kwargs.get('model_name', 'small')
self.inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', (2, 1)],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', (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', (2, 1)],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'relu', (1, 1)],
[3, 72, 24, False, 'relu', (2, 1)],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', (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', (2, 1)],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError("mode[" + model_name +
"_model] is not implemented!")
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
assert self.scale in supported_scale, "supported scale are {} but input scale is {}".format(
supported_scale, self.scale)
scale = self.scale
inplanes = self.inplanes
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
# conv1
self.conv1 = ConvBNACT(in_channels=in_channels,
out_channels=self.make_divisible(inplanes *
scale),
kernel_size=3,
stride=2,
padding=1,
groups=1,
act='hard_swish')
inplanes = self.make_divisible(inplanes * scale)
block_list = []
for layer_cfg in cfg:
block = ResidualUnit(
num_in_filter=inplanes,
num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
num_out_filter=self.make_divisible(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
kernel_size=layer_cfg[0],
use_se=layer_cfg[3])
block_list.append(block)
inplanes = self.make_divisible(scale * layer_cfg[2])
self.blocks = nn.Sequential(*block_list)
self.conv2 = ConvBNACT(in_channels=inplanes,
out_channels=self.make_divisible(
scale * cls_ch_squeeze),
kernel_size=1,
stride=1,
padding=0,
groups=1,
act='hard_swish')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.out_channels = self.make_divisible(scale * cls_ch_squeeze)
def make_divisible(self, v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
def load_3rd_state_dict(self, _3rd_name, _state):
if _3rd_name == 'paddle':
self.conv1.load_3rd_state_dict(_3rd_name, _state, 'conv1')
for m_block_index, m_block in enumerate(self.blocks, 2):
m_block.load_3rd_state_dict(_3rd_name, _state, m_block_index)
self.conv2.load_3rd_state_dict(_3rd_name, _state, 'conv_last')
else:
pass
def forward(self, x):
x = self.conv1(x)
x = self.blocks(x)
x = self.conv2(x)
x = self.pool(x)
return x
def __init__(self, in_channels, **kwargs):
"""
the MobilenetV3 backbone network for detection module.
Args:
params(dict): the super parameters for build network
"""
super().__init__()
self.scale = kwargs.get('scale', 0.5)
model_name = kwargs.get('model_name', 'large')
self.inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', 2],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hard_swish', 2],
[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', 2],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'relu', 2],
[3, 72, 24, False, 'relu', 2],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', 2],
[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', 2],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError("mode[" + model_name +
"_model] is not implemented!")
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
assert self.scale in supported_scale, \
"supported scale are {} but input scale is {}".format(supported_scale, self.scale)
scale = self.scale
inplanes = self.inplanes
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
# conv1
self.conv1 = ConvBNACT(in_channels=in_channels,
out_channels=self.make_divisible(inplanes *
scale),
kernel_size=3,
stride=2,
padding=1,
groups=1,
act='hard_swish')
i = 0
inplanes = self.make_divisible(inplanes * scale)
self.stages = nn.ModuleList()
block_list = []
self.out_channels = []
for layer_cfg in cfg:
if layer_cfg[5] == 2 and i > 2:
self.out_channels.append(inplanes)
self.stages.append(nn.Sequential(*block_list))
block_list = []
block = ResidualUnit(
num_in_filter=inplanes,
num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
num_out_filter=self.make_divisible(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
kernel_size=layer_cfg[0],
use_se=layer_cfg[3])
block_list.append(block)
inplanes = self.make_divisible(scale * layer_cfg[2])
i += 1
self.stages.append(nn.Sequential(*block_list))
self.conv2 = ConvBNACT(in_channels=inplanes,
out_channels=self.make_divisible(
scale * cls_ch_squeeze),
kernel_size=1,
stride=1,
padding=0,
groups=1,
act='hard_swish')
self.out_channels.append(self.make_divisible(scale * cls_ch_squeeze))
class MobileNetV3(nn.Module):
def __init__(self, in_channels, **kwargs):
"""
the MobilenetV3 backbone network for detection module.
Args:
params(dict): the super parameters for build network
"""
super().__init__()
self.scale = kwargs.get('scale', 0.5)
model_name = kwargs.get('model_name', 'large')
self.inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', 2],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hard_swish', 2],
[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', 2],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'relu', 2],
[3, 72, 24, False, 'relu', 2],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', 2],
[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', 2],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError("mode[" + model_name +
"_model] is not implemented!")
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
assert self.scale in supported_scale, \
"supported scale are {} but input scale is {}".format(supported_scale, self.scale)
scale = self.scale
inplanes = self.inplanes
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
# conv1
self.conv1 = ConvBNACT(in_channels=in_channels,
out_channels=self.make_divisible(inplanes *
scale),
kernel_size=3,
stride=2,
padding=1,
groups=1,
act='hard_swish')
i = 0
inplanes = self.make_divisible(inplanes * scale)
self.stages = nn.ModuleList()
block_list = []
self.out_channels = []
for layer_cfg in cfg:
if layer_cfg[5] == 2 and i > 2:
self.out_channels.append(inplanes)
self.stages.append(nn.Sequential(*block_list))
block_list = []
block = ResidualUnit(
num_in_filter=inplanes,
num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
num_out_filter=self.make_divisible(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
kernel_size=layer_cfg[0],
use_se=layer_cfg[3])
block_list.append(block)
inplanes = self.make_divisible(scale * layer_cfg[2])
i += 1
self.stages.append(nn.Sequential(*block_list))
self.conv2 = ConvBNACT(in_channels=inplanes,
out_channels=self.make_divisible(
scale * cls_ch_squeeze),
kernel_size=1,
stride=1,
padding=0,
groups=1,
act='hard_swish')
self.out_channels.append(self.make_divisible(scale * cls_ch_squeeze))
def make_divisible(self, v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
def load_3rd_state_dict(self, _3rd_name, _state):
if _3rd_name == 'paddle':
self.conv1.load_3rd_state_dict(_3rd_name, _state, 'conv1')
m_block_index = 2
for m_stage in self.stages:
for m_block in m_stage:
m_block.load_3rd_state_dict(_3rd_name, _state,
m_block_index)
m_block_index += 1
self.conv2.load_3rd_state_dict(_3rd_name, _state, 'conv_last')
else:
pass
def forward(self, x):
x = self.conv1(x)
out = []
for stage in self.stages:
x = stage(x)
out.append(x)
out[-1] = self.conv2(out[-1])
return out
def __init__(self, in_channels, pretrained=True, **kwargs):
"""
the MobilenetV3 backbone network for detection module.
Args:
params(dict): the super parameters for build network
"""
super().__init__()
self.scale = kwargs.get('scale', 0.5)
model_name = kwargs.get('model_name', 'large')
self.disable_se = kwargs.get('disable_se', True)
self.inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', 2],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hard_swish', 2],
[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', 2],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'relu', 2],
[3, 72, 24, False, 'relu', 2],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', 2],
[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', 2],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError("mode[" + model_name +
"_model] is not implemented!")
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
assert self.scale in supported_scale, \
"supported scale are {} but input scale is {}".format(supported_scale, self.scale)
scale = self.scale
inplanes = self.inplanes
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
# conv1
self.conv1 = ConvBNACT(in_channels=in_channels,
out_channels=self.make_divisible(inplanes * scale),
kernel_size=3,
stride=2,
padding=1,
groups=1,
act='hard_swish')
i = 0
inplanes = self.make_divisible(inplanes * scale)
self.stages = nn.ModuleList()
block_list = []
self.out_channels = []
for layer_cfg in cfg:
se = layer_cfg[3] and not self.disable_se
if layer_cfg[5] == 2 and i > 2:
self.out_channels.append(inplanes)
self.stages.append(nn.Sequential(*block_list))
block_list = []
block = ResidualUnit(num_in_filter=inplanes,
num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
num_out_filter=self.make_divisible(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
kernel_size=layer_cfg[0],
use_se=se)
block_list.append(block)
inplanes = self.make_divisible(scale * layer_cfg[2])
i += 1
block_list.append(ConvBNACT(
in_channels=inplanes,
out_channels=self.make_divisible(scale * cls_ch_squeeze),
kernel_size=1,
stride=1,
padding=0,
groups=1,
act='hard_swish'))
self.stages.append(nn.Sequential(*block_list))
self.out_channels.append(self.make_divisible(scale * cls_ch_squeeze))
if pretrained:
ckpt_path = f'./weights/MobileNetV3_{model_name}_x{str(scale).replace(".", "_")}.pth'
logger = logging.getLogger('torchocr')
if os.path.exists(ckpt_path):
logger.info('load imagenet weights')
dic_ckpt = torch.load(ckpt_path)
filtered_dict = OrderedDict()
for key in dic_ckpt.keys():
flag = key.find('se') != -1
if self.disable_se and flag:
continue
filtered_dict[key] = dic_ckpt[key]
self.load_state_dict(filtered_dict)
else:
logger.info(f'{ckpt_path} not exists')