def __init__(self,
in_channel_list,
out_channel_list,
kernel_size_list=3,
expand_ratio_list=6,
stride=1,
act_func='relu6',
use_se=False):
super(DynamicMBConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size_list = int2list(kernel_size_list, 1)
self.expand_ratio_list = int2list(expand_ratio_list, 1)
self.stride = stride
self.act_func = act_func
self.use_se = use_se
# build modules
max_middle_channel = round(
max(self.in_channel_list) * max(self.expand_ratio_list))
if max(self.expand_ratio_list) == 1:
self.inverted_bottleneck = None
else:
self.inverted_bottleneck = nn.Sequential(
OrderedDict([
('conv',
DynamicPointConv2d(max(self.in_channel_list),
max_middle_channel)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func, inplace=True)),
]))
self.depth_conv = nn.Sequential(
OrderedDict([('conv',
DynamicSeparableConv2d(max_middle_channel,
self.kernel_size_list,
self.stride)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func,
inplace=True))]))
if self.use_se:
self.depth_conv.add_module('se', DynamicSE(max_middle_channel))
self.point_linear = nn.Sequential(
OrderedDict([
('conv',
DynamicPointConv2d(max_middle_channel,
max(self.out_channel_list))),
('bn', DynamicBatchNorm2d(max(self.out_channel_list))),
]))
self.active_kernel_size = max(self.kernel_size_list)
self.active_expand_ratio = max(self.expand_ratio_list)
self.active_out_channel = max(self.out_channel_list)
def __init__(self,
in_channel_list,
out_channel_list,
kernel_size=3,
stride=1,
dilation=1,
use_bn=True,
act_func='relu6'):
super(DynamicConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.use_bn = use_bn
self.act_func = act_func
self.conv = DynamicPointConv2d(
max_in_channels=max(self.in_channel_list),
max_out_channels=max(self.out_channel_list),
kernel_size=self.kernel_size,
stride=self.stride,
dilation=self.dilation,
)
if self.use_bn:
self.bn = DynamicBatchNorm2d(max(self.out_channel_list))
self.act = build_activation(self.act_func, inplace=True)
self.active_out_channel = max(self.out_channel_list)
def __init__(self,
in_channel_list,
out_channel_list,
kernel_size=3,
stride=1,
dilation=1,
act_func='relu6',
w_bit=-1,
a_bit=-1,
half_wave=True):
super(DynamicQConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.act_func = act_func
self.conv = DynamicPointQConv2d(
max_in_channels=max(self.in_channel_list),
max_out_channels=max(self.out_channel_list),
kernel_size=self.kernel_size,
stride=self.stride,
dilation=self.dilation,
w_bit=w_bit,
a_bit=a_bit,
half_wave=half_wave)
self.bn = DynamicBatchNorm2d(max(self.out_channel_list))
self.act = build_activation(self.act_func, inplace=True)
self.active_out_channel = max(self.out_channel_list)
def __init__(self,
in_channel_list,
out_channel_list,
kernel_size_list=3,
expand_ratio_list=6,
stride=1,
act_func='relu6',
pw_w_bit=-1,
pw_a_bit=-1,
dw_w_bit=-1,
dw_a_bit=-1,
**kwargs):
super(DynamicMBQConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size_list = int2list(kernel_size_list, 1)
self.expand_ratio_list = int2list(expand_ratio_list, 1)
self.stride = stride
self.act_func = act_func
# build modules
max_middle_channel = round(
max(self.in_channel_list) * max(self.expand_ratio_list))
if max(self.expand_ratio_list) == 1:
self.inverted_bottleneck = None
else:
self.inverted_bottleneck = nn.Sequential(
OrderedDict([
('conv',
DynamicPointQConv2d(max(self.in_channel_list),
max_middle_channel,
w_bit=pw_w_bit,
a_bit=pw_a_bit,
half_wave=False)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func, inplace=True)),
]))
self.depth_conv = nn.Sequential(
OrderedDict([('conv',
DynamicSeparableQConv2d(max_middle_channel,
self.kernel_size_list,
self.stride,
w_bit=dw_w_bit,
a_bit=dw_a_bit)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func,
inplace=True))]))
self.point_linear = nn.Sequential(
OrderedDict([
('conv',
DynamicPointQConv2d(max_middle_channel,
max(self.out_channel_list),
w_bit=pw_w_bit,
a_bit=pw_a_bit)),
('bn', DynamicBatchNorm2d(max(self.out_channel_list))),
]))
self.active_kernel_size = max(self.kernel_size_list)
self.active_expand_ratio = max(self.expand_ratio_list)
self.active_out_channel = max(self.out_channel_list)