def __init__(self, inp, oup, stride, kernel=3, expand_ratio=1):
"""Construct InvertedResidual class.
:param inp: input channel
:param oup: output channel
:param stride: stride
:param kernel: kernel
:param expand_ratio: channel increase multiplier
"""
super(InvertedConv, self).__init__()
hidden_dim = round(inp * expand_ratio)
conv = []
if expand_ratio > 1:
conv = [
ops.Conv2d(in_channels=inp, out_channels=hidden_dim,
kernel_size=1, stride=1, padding=0, bias=False),
ops.BatchNorm2d(num_features=hidden_dim),
ops.Relu6(inplace=True)
]
conv = conv + [
ops.Conv2d(in_channels=hidden_dim, out_channels=hidden_dim, kernel_size=kernel,
stride=stride, padding=kernel // 2, groups=hidden_dim, bias=False, depthwise=True),
ops.BatchNorm2d(num_features=hidden_dim),
ops.Relu6(inplace=True),
ops.Conv2d(in_channels=hidden_dim, out_channels=oup,
kernel_size=1, stride=1, padding=0, bias=False),
ops.BatchNorm2d(num_features=oup)
]
self.models = Sequential(*conv)
def __init__(self,
C_in,
C_out,
kernel_size,
stride,
padding,
affine=True,
activation='relu',
inplace=False):
"""Construct ConvBnAct class."""
super(ConvBnAct, self).__init__()
self.conv2d = ops.Conv2d(C_in,
C_out,
kernel_size,
stride,
padding,
bias=False)
self.batch_norm2d = ops.BatchNorm2d(C_out, affine=affine)
if activation == 'hswish':
self.act = ops.Hswish(inplace=inplace)
elif activation == 'hsigmoid':
self.act = ops.Hsigmoid(inplace=inplace)
elif activation == 'relu6':
self.act = ops.Relu6(inplace=inplace)
else:
self.act = ops.Relu(inplace=inplace)
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True, use_relu6=False):
"""Construct ConvBnRelu class."""
super(ConvBnRelu, self).__init__()
self.conv2d = ops.Conv2d(
C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False)
self.batch_norm2d = ops.BatchNorm2d(C_out, affine=affine)
if use_relu6:
self.relu = ops.Relu6(inplace=False)
else:
self.relu = ops.Relu(inplace=False)
def __init__(self,
C_in,
C_out,
kernel_size,
stride,
padding,
Conv2d='Conv2d',
affine=True,
use_relu6=False,
norm_layer='BN',
has_bn=True,
has_relu=True,
**kwargs):
"""Construct ConvBnRelu class."""
super(ConvBnRelu, self).__init__()
if Conv2d == 'Conv2d':
self.conv2d = ops.Conv2d(C_in,
C_out,
kernel_size,
stride=stride,
padding=padding,
bias=False)
elif Conv2d == 'ConvWS2d':
self.conv2d = ops.ConvWS2d(C_in,
C_out,
kernel_size,
stride=stride,
padding=padding,
bias=False)
if has_bn:
if norm_layer == 'BN':
self.batch_norm2d = ops.BatchNorm2d(C_out, affine=affine)
elif norm_layer == 'GN':
num_groups = kwargs.pop('num_groups')
self.batch_norm2d = ops.GroupNorm(num_groups,
C_out,
affine=affine)
elif norm_layer == 'Sync':
self.batch_norm2d = ops.SyncBatchNorm(C_out, affine=affine)
if has_relu:
if use_relu6:
self.relu = ops.Relu6(inplace=False)
else:
self.relu = ops.Relu(inplace=False)