def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
momentum=0.01,
a_bits=8,
w_bits=8,
bn_fuse=0,
q_type=1,
q_level=0,
first_layer=0):
super(QuantConvBNReLU, self).__init__()
self.bn_fuse = bn_fuse
if self.bn_fuse == 1:
self.quant_bn_fuse_conv = QuantBNFuseConv2d(in_channels, out_channels,
kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False, padding_mode=padding_mode, a_bits=a_bits, w_bits=w_bits, q_type=q_type, q_level=q_level, first_layer=first_layer)
else:
self.quant_conv = QuantConv2d(in_channels, out_channels,
kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode, a_bits=a_bits, w_bits=w_bits, q_type=q_type, q_level=q_level, first_layer=first_layer)
self.bn = nn.BatchNorm2d(out_channels, momentum=momentum) # 考虑量化带来的抖动影响,对momentum进行调整(0.1 ——> 0.01),削弱batch统计参数占比，一定程度抑制抖动。经实验量化训练效果更好,acc提升1%左右
self.relu = QuantReLU(inplace=True, a_bits=a_bits, q_type=q_type)
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
momentum=0.1,
channel_shuffle=0,
shuffle_groups=1,
a_bits=8,
w_bits=8,
first_layer=0):
super(QuantConvBNReLU, self).__init__()
self.channel_shuffle_flag = channel_shuffle
self.shuffle_groups = shuffle_groups
self.quant_conv = QuantConv2d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode,
a_bits=a_bits,
w_bits=w_bits,
first_layer=first_layer)
self.bn = nn.BatchNorm2d(out_channels, momentum=momentum)
self.relu = nn.ReLU(inplace=True)
def __init__(self, input_channels, output_channels,
kernel_size=-1, stride=-1, padding=-1, groups=1, channel_shuffle=0, shuffle_groups=1, abits=8, wbits=8, first_layer=0):
super(QuantConvBNReLU, self).__init__()
self.channel_shuffle_flag = channel_shuffle
self.shuffle_groups = shuffle_groups
self.quant_conv = QuantConv2d(input_channels, output_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, a_bits=abits, w_bits=wbits, first_layer=first_layer)
self.bn = nn.BatchNorm2d(output_channels)
self.relu = nn.ReLU(inplace=True)
def __init__(self,
input_channels,
output_channels,
kernel_size=-1,
stride=-1,
padding=-1,
groups=1,
channel_shuffle=0,
shuffle_groups=1,
abits=8,
wbits=8,
bn_fuse=0,
q_type=1,
q_level=0,
first_layer=0):
super(QuantConvBNReLU, self).__init__()
self.channel_shuffle_flag = channel_shuffle
self.shuffle_groups = shuffle_groups
self.bn_fuse = bn_fuse
if self.bn_fuse == 1:
self.quant_bn_fuse_conv = QuantBNFuseConv2d(
input_channels,
output_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
a_bits=abits,
w_bits=wbits,
q_type=q_type,
q_level=q_level,
first_layer=first_layer)
else:
self.quant_conv = QuantConv2d(input_channels,
output_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
a_bits=abits,
w_bits=wbits,
q_type=q_type,
q_level=q_level,
first_layer=first_layer)
self.bn = nn.BatchNorm2d(
output_channels, momentum=0.01
) # 考虑量化带来的抖动影响,对momentum进行调整(0.1 ——> 0.01),削弱batch统计参数占比，一定程度抑制抖动。经实验量化训练效果更好,acc提升1%左右
self.relu = QuantReLU(inplace=True, a_bits=abits, q_type=q_type)