def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
out_padding=0,
dilation=1,
groups=1,
bias=True,
weight_init=Kaiming_Normal(),
bias_init=Zeros()):
"""
:param in_channels:
:param out_channels:
:param kernel_size:
:param stride:
:param padding:
:param dilation:
:param groups:
:param bias:
:param weight_init:
:param bias_init:
"""
super(ConvTranspose1d,
self).__init__(in_channels, out_channels, _single(kernel_size),
_single(stride), _single(padding),
_single(dilation), groups, bias,
_single(out_padding), weight_init, bias_init)
def __init__(self,
in_channels,
kernel_size,
stride=1,
padding=0,
out_padding=0,
dilation=1,
bias=True,
multiplier=1,
weight_init=Kaiming_Normal(),
bias_init=Zeros()):
"""
:param in_channels:
:param kernel_size:
:param stride:
:param padding:
:param dilation:
:param bias:
:param multiplier:
:param weight_init:
:param bias_init:
"""
super(DepthwiseConvTranspose1d,
self).__init__(in_channels, in_channels * multiplier,
_single(kernel_size), _single(stride),
_single(padding),
_single(dilation), in_channels, bias,
_single(out_padding), weight_init, bias_init)
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t = 1,
padding: _size_1_t = 0,
dilation: _size_1_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros'):
kernel_size = _single(kernel_size)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, False,
_single(0), groups, bias, padding_mode)