def __init__(self, cfg = None, A=2):
super(Net, self).__init__()
if cfg is None:
# 模型结构
cfg = [192, 160, 96, 192, 192, 192, 192, 192]
self.tnn_bin = nn.Sequential(
nn.Conv2d(3, cfg[0], kernel_size=5, stride=1, padding=2),
bn.BatchNorm2d_bin(cfg[0], affine_flag=2),
Tnn_Bin_Conv2d(cfg[0], cfg[1], kernel_size=1, stride=1, padding=0, A=A),
Tnn_Bin_Conv2d(cfg[1], cfg[2], kernel_size=1, stride=1, padding=0, activation_mp=1, A=A),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
Tnn_Bin_Conv2d(cfg[2], cfg[3], kernel_size=5, stride=1, padding=2, activation_nor=0, A=A),
Tnn_Bin_Conv2d(cfg[3], cfg[4], kernel_size=1, stride=1, padding=0, A=A),
Tnn_Bin_Conv2d(cfg[4], cfg[5], kernel_size=1, stride=1, padding=0, activation_mp=1, A=A),
nn.AvgPool2d(kernel_size=3, stride=2, padding=1),
Tnn_Bin_Conv2d(cfg[5], cfg[6], kernel_size=3, stride=1, padding=1, activation_nor=0, A=A),
Tnn_Bin_Conv2d(cfg[6], cfg[7], kernel_size=1, stride=1, padding=0, last=0, last_relu=1, A=A),
nn.Conv2d(cfg[7], 10, kernel_size=1, stride=1, padding=0),
bn.BatchNorm2d_bin(10, affine_flag=2),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=8, stride=1, padding=0),
)
def __init__(self, cfg = None, A=2):
super(Net, self).__init__()
if cfg is None:
# 模型结构
cfg = [256, 256, 256, 512, 512, 512, 1024, 1024]
self.tnn_bin = nn.Sequential(
nn.Conv2d(3, cfg[0], kernel_size=5, stride=1, padding=2),
bn.BatchNorm2d_bin(cfg[0], affine_flag=2),
Tnn_Bin_Conv2d(cfg[0], cfg[1], kernel_size=1, stride=1, padding=0, groups=2, channel_shuffle=0, A=A),
Tnn_Bin_Conv2d(cfg[1], cfg[2], kernel_size=1, stride=1, padding=0, groups=2, channel_shuffle=1, shuffle_groups=2, activation_mp=1, A=A),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
Tnn_Bin_Conv2d(cfg[2], cfg[3], kernel_size=3, stride=1, padding=1, groups=16, channel_shuffle=1, shuffle_groups=2, activation_nor=0, A=A),
Tnn_Bin_Conv2d(cfg[3], cfg[4], kernel_size=1, stride=1, padding=0, groups=4, channel_shuffle=1, shuffle_groups=16, A=A),
Tnn_Bin_Conv2d(cfg[4], cfg[5], kernel_size=1, stride=1, padding=0, groups=4, channel_shuffle=1, shuffle_groups=4, activation_mp=1, A=A),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
Tnn_Bin_Conv2d(cfg[5], cfg[6], kernel_size=3, stride=1, padding=1, groups=32, channel_shuffle=1, shuffle_groups=4, activation_nor=0, A=A),
Tnn_Bin_Conv2d(cfg[6], cfg[7], kernel_size=1, stride=1, padding=0, groups=8, channel_shuffle=1, shuffle_groups=32, last=0, last_relu=1, A=A),
nn.Conv2d(cfg[7], 10, kernel_size=1, stride=1, padding=0),
bn.BatchNorm2d_bin(10, affine_flag=2),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=8, stride=1, padding=0),
)
def __init__(self,
input_channels,
output_channels,
kernel_size=-1,
stride=-1,
padding=-1,
dropout=0,
last=0,
activation_mp=0,
activation_nor=1,
last_relu=0,
A=2):
super(Tnn_Bin_Conv2d, self).__init__()
self.A = A
self.dropout_ratio = dropout
self.last = last
self.activation_mp = activation_mp
self.activation_nor = activation_nor
self.last_relu = last_relu
if dropout != 0:
self.dropout = nn.Dropout(dropout)
self.conv = nn.Conv2d(input_channels,
output_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding)
#self.bn = nn.BatchNorm2d(output_channels)
self.bn = bn.BatchNorm2d_bin(
output_channels, momentum=0.1,
affine_flag=2) #自定义BN_γ=1、β-train;WbAb_momentum=0.8
self.relu = nn.ReLU(inplace=True)
def __init__(self,
input_channels,
output_channels,
kernel_size=-1,
stride=-1,
padding=-1,
dropout=0,
groups=1,
last_relu=0,
A=2,
W=2):
super(Tnn_Bin_Conv2d, self).__init__()
self.A = A
self.W = W
self.dropout_ratio = dropout
self.last_relu = last_relu
if self.dropout_ratio != 0:
self.dropout = nn.Dropout(dropout)
# ********************* 量化(三/二值)卷积 *********************
self.tnn_bin_conv = Conv2d_Q(input_channels,
output_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
A=A,
W=W)
#self.bn = nn.BatchNorm2d(output_channels)
self.bn = bn.BatchNorm2d_bin(output_channels,
affine_flag=2) #自定义BN_γ=1、β-train
self.relu = nn.ReLU(inplace=True)
def __init__(self, input_channels, output_channels,
kernel_size=-1, stride=-1, padding=-1, dropout=0, groups=1, channel_shuffle=0, shuffle_groups=1, last=0, activation_mp=0, activation_nor=1, last_relu=0, A=2):
super(Tnn_Bin_Conv2d, self).__init__()
self.A = A
self.dropout_ratio = dropout
self.channel_shuffle_flag = channel_shuffle
self.shuffle_groups = shuffle_groups
self.last = last
self.activation_mp = activation_mp
self.activation_nor = activation_nor
self.last_relu = last_relu
if dropout!=0:
self.dropout = nn.Dropout(dropout)
self.conv = nn.Conv2d(input_channels, output_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
#self.bn = nn.BatchNorm2d(output_channels)
self.bn = bn.BatchNorm2d_bin(output_channels, affine_flag=1)#自定义BN_γ=1、β-train
self.relu = nn.ReLU(inplace=True)
