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import torch
import torch.nn as nn
import torch.nn.functional as F
from openrec.modeling.common import Activation
NET_CONFIG_det = {
'blocks2':
# k, in_c, out_c, s, use_se
[[3, 16, 32, 1, False]],
'blocks3': [[3, 32, 64, 2, False], [3, 64, 64, 1, False]],
'blocks4': [[3, 64, 128, 2, False], [3, 128, 128, 1, False]],
'blocks5': [
[3, 128, 256, 2, False],
[5, 256, 256, 1, False],
[5, 256, 256, 1, False],
[5, 256, 256, 1, False],
[5, 256, 256, 1, False],
],
'blocks6': [
[5, 256, 512, 2, True],
[5, 512, 512, 1, True],
[5, 512, 512, 1, False],
[5, 512, 512, 1, False],
],
}
NET_CONFIG_rec = {
'blocks2':
# k, in_c, out_c, s, use_se
[[3, 16, 32, 1, False]],
'blocks3': [[3, 32, 64, 1, False], [3, 64, 64, 1, False]],
'blocks4': [[3, 64, 128, (2, 1), False], [3, 128, 128, 1, False]],
'blocks5': [
[3, 128, 256, (1, 2), False],
[5, 256, 256, 1, False],
[5, 256, 256, 1, False],
[5, 256, 256, 1, False],
[5, 256, 256, 1, False],
],
'blocks6': [
[5, 256, 512, (2, 1), True],
[5, 512, 512, 1, True],
[5, 512, 512, (2, 1), False],
[5, 512, 512, 1, False],
],
}
def make_divisible(v, divisor=16, 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
class LearnableAffineBlock(nn.Module):
def __init__(self,
scale_value=1.0,
bias_value=0.0,
lr_mult=1.0,
lab_lr=0.1):
super().__init__()
self.scale = nn.Parameter(torch.Tensor([scale_value]))
self.bias = nn.Parameter(torch.Tensor([bias_value]))
def forward(self, x):
return self.scale * x + self.bias
class ConvBNLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
groups=1,
lr_mult=1.0):
super().__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
bias=False,
)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class Act(nn.Module):
def __init__(self, act='hard_swish', lr_mult=1.0, lab_lr=0.1):
super().__init__()
assert act in ['hard_swish', 'relu']
self.act = Activation(act)
self.lab = LearnableAffineBlock(lr_mult=lr_mult, lab_lr=lab_lr)
def forward(self, x):
return self.lab(self.act(x))
class LearnableRepLayer(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
num_conv_branches=1,
lr_mult=1.0,
lab_lr=0.1,
):
super().__init__()
self.is_repped = False
self.groups = groups
self.stride = stride
self.kernel_size = kernel_size
self.in_channels = in_channels
self.out_channels = out_channels
self.num_conv_branches = num_conv_branches
self.padding = (kernel_size - 1) // 2
self.identity = (nn.BatchNorm2d(in_channels) if
out_channels == in_channels and stride == 1 else None)
self.conv_kxk = nn.ModuleList([
ConvBNLayer(
in_channels,
out_channels,
kernel_size,
stride,
groups=groups,
lr_mult=lr_mult,
) for _ in range(self.num_conv_branches)
])
self.conv_1x1 = (ConvBNLayer(in_channels,
out_channels,
1,
stride,
groups=groups,
lr_mult=lr_mult)
if kernel_size > 1 else None)
self.lab = LearnableAffineBlock(lr_mult=lr_mult, lab_lr=lab_lr)
self.act = Act(lr_mult=lr_mult, lab_lr=lab_lr)
def forward(self, x):
# for export
if self.is_repped:
out = self.lab(self.reparam_conv(x))
if self.stride != 2:
out = self.act(out)
return out
out = 0
if self.identity is not None:
out += self.identity(x)
if self.conv_1x1 is not None:
out += self.conv_1x1(x)
for conv in self.conv_kxk:
out += conv(x)
out = self.lab(out)
if self.stride != 2:
out = self.act(out)
return out
def rep(self):
if self.is_repped:
return
kernel, bias = self._get_kernel_bias()
self.reparam_conv = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=self.kernel_size,
stride=self.stride,
padding=self.padding,
groups=self.groups,
)
self.reparam_conv.weight.data = kernel
self.reparam_conv.bias.data = bias
self.is_repped = True
def _pad_kernel_1x1_to_kxk(self, kernel1x1, pad):
if not isinstance(kernel1x1, torch.Tensor):
return 0
else:
return nn.functional.pad(kernel1x1, [pad, pad, pad, pad])
def _get_kernel_bias(self):
kernel_conv_1x1, bias_conv_1x1 = self._fuse_bn_tensor(self.conv_1x1)
kernel_conv_1x1 = self._pad_kernel_1x1_to_kxk(kernel_conv_1x1,
self.kernel_size // 2)
kernel_identity, bias_identity = self._fuse_bn_tensor(self.identity)
kernel_conv_kxk = 0
bias_conv_kxk = 0
for conv in self.conv_kxk:
kernel, bias = self._fuse_bn_tensor(conv)
kernel_conv_kxk += kernel
bias_conv_kxk += bias
kernel_reparam = kernel_conv_kxk + kernel_conv_1x1 + kernel_identity
bias_reparam = bias_conv_kxk + bias_conv_1x1 + bias_identity
return kernel_reparam, bias_reparam
def _fuse_bn_tensor(self, branch):
if not branch:
return 0, 0
elif isinstance(branch, ConvBNLayer):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
input_dim = self.in_channels // self.groups
kernel_value = torch.zeros(
(self.in_channels, input_dim, self.kernel_size,
self.kernel_size),
dtype=branch.weight.dtype,
)
for i in range(self.in_channels):
kernel_value[i, i % input_dim, self.kernel_size // 2,
self.kernel_size // 2] = 1
self.id_tensor = kernel_value
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape((-1, 1, 1, 1))
return kernel * t, beta - running_mean * gamma / std
class SELayer(nn.Module):
def __init__(self, channel, reduction=4, lr_mult=1.0):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(
in_channels=channel,
out_channels=channel // reduction,
kernel_size=1,
stride=1,
padding=0,
)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0,
)
self.hardsigmoid = Activation('hard_sigmoid')
def forward(self, x):
identity = x
x = self.avg_pool(x)
x = self.conv1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.hardsigmoid(x)
x = x * identity
return x
class LCNetV3Block(nn.Module):
def __init__(
self,
in_channels,
out_channels,
stride,
dw_size,
use_se=False,
conv_kxk_num=4,
lr_mult=1.0,
lab_lr=0.1,
):
super().__init__()
self.use_se = use_se
self.dw_conv = LearnableRepLayer(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=dw_size,
stride=stride,
groups=in_channels,
num_conv_branches=conv_kxk_num,
lr_mult=lr_mult,
lab_lr=lab_lr,
)
if use_se:
self.se = SELayer(in_channels, lr_mult=lr_mult)
self.pw_conv = LearnableRepLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
num_conv_branches=conv_kxk_num,
lr_mult=lr_mult,
lab_lr=lab_lr,
)
def forward(self, x):
x = self.dw_conv(x)
if self.use_se:
x = self.se(x)
x = self.pw_conv(x)
return x
class PPLCNetV3(nn.Module):
def __init__(self,
scale=1.0,
conv_kxk_num=4,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
lab_lr=0.1,
det=False,
**kwargs):
super().__init__()
self.scale = scale
self.lr_mult_list = lr_mult_list
self.det = det
self.net_config = NET_CONFIG_det if self.det else NET_CONFIG_rec
assert isinstance(
self.lr_mult_list,
(list, tuple
)), 'lr_mult_list should be in (list, tuple) but got {}'.format(
type(self.lr_mult_list))
assert len(self.lr_mult_list
) == 6, 'lr_mult_list length should be 6 but got {}'.format(
len(self.lr_mult_list))
self.conv1 = ConvBNLayer(
in_channels=3,
out_channels=make_divisible(16 * scale),
kernel_size=3,
stride=2,
lr_mult=self.lr_mult_list[0],
)
self.blocks2 = nn.Sequential(*[
LCNetV3Block(
in_channels=make_divisible(in_c * scale),
out_channels=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
conv_kxk_num=conv_kxk_num,
lr_mult=self.lr_mult_list[1],
lab_lr=lab_lr,
) for i, (k, in_c, out_c, s,
se) in enumerate(self.net_config['blocks2'])
])
self.blocks3 = nn.Sequential(*[
LCNetV3Block(
in_channels=make_divisible(in_c * scale),
out_channels=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
conv_kxk_num=conv_kxk_num,
lr_mult=self.lr_mult_list[2],
lab_lr=lab_lr,
) for i, (k, in_c, out_c, s,
se) in enumerate(self.net_config['blocks3'])
])
self.blocks4 = nn.Sequential(*[
LCNetV3Block(
in_channels=make_divisible(in_c * scale),
out_channels=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
conv_kxk_num=conv_kxk_num,
lr_mult=self.lr_mult_list[3],
lab_lr=lab_lr,
) for i, (k, in_c, out_c, s,
se) in enumerate(self.net_config['blocks4'])
])
self.blocks5 = nn.Sequential(*[
LCNetV3Block(
in_channels=make_divisible(in_c * scale),
out_channels=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
conv_kxk_num=conv_kxk_num,
lr_mult=self.lr_mult_list[4],
lab_lr=lab_lr,
) for i, (k, in_c, out_c, s,
se) in enumerate(self.net_config['blocks5'])
])
self.blocks6 = nn.Sequential(*[
LCNetV3Block(
in_channels=make_divisible(in_c * scale),
out_channels=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
conv_kxk_num=conv_kxk_num,
lr_mult=self.lr_mult_list[5],
lab_lr=lab_lr,
) for i, (k, in_c, out_c, s,
se) in enumerate(self.net_config['blocks6'])
])
self.out_channels = make_divisible(512 * scale)
if self.det:
mv_c = [16, 24, 56, 480]
self.out_channels = [
make_divisible(self.net_config['blocks3'][-1][2] * scale),
make_divisible(self.net_config['blocks4'][-1][2] * scale),
make_divisible(self.net_config['blocks5'][-1][2] * scale),
make_divisible(self.net_config['blocks6'][-1][2] * scale),
]
self.layer_list = nn.ModuleList([
nn.Conv2d(self.out_channels[0], int(mv_c[0] * scale), 1, 1, 0),
nn.Conv2d(self.out_channels[1], int(mv_c[1] * scale), 1, 1, 0),
nn.Conv2d(self.out_channels[2], int(mv_c[2] * scale), 1, 1, 0),
nn.Conv2d(self.out_channels[3], int(mv_c[3] * scale), 1, 1, 0),
])
self.out_channels = [
int(mv_c[0] * scale),
int(mv_c[1] * scale),
int(mv_c[2] * scale),
int(mv_c[3] * scale),
]
def forward(self, x):
out_list = []
x = self.conv1(x)
x = self.blocks2(x)
x = self.blocks3(x)
out_list.append(x)
x = self.blocks4(x)
out_list.append(x)
x = self.blocks5(x)
out_list.append(x)
x = self.blocks6(x)
out_list.append(x)
if self.det:
out_list[0] = self.layer_list[0](out_list[0])
out_list[1] = self.layer_list[1](out_list[1])
out_list[2] = self.layer_list[2](out_list[2])
out_list[3] = self.layer_list[3](out_list[3])
return out_list
if self.training:
x = F.adaptive_avg_pool2d(x, [1, 40])
else:
x = F.avg_pool2d(x, [3, 2])
return x
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