def register_op(self):
self.op_meta = {
'op_type': 'DepthwiseConv{}d'.format(len(self.kernel_size)),
'arguments': {
'num_output': self.weight.shape[0],
'kernel_shape': self.weight.shape[2:],
'strides': _pair(self.stride),
'pads': _pair(self.padding),
'dilations': _pair(self.dilation),
'data_format': 'NCHW',
}
}
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
bias=True):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
super(DepthwiseConv2d, self).__init__(in_channels, out_channels,
kernel_size, stride, padding,
_pair(0), bias)
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2d,
self).__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, False, _pair(0), groups, bias)
def register_op(self):
self.op_meta = {
'op_type': 'Pool2d',
'arguments': {
'kernel_shape':
_pair(self.kernel_size),
'strides':
_pair(self.stride) if self.stride else _pair(self.kernel_size),
'pads':
_pair(self.padding),
'mode':
'AVG',
'data_format':
'NCHW',
'ceil_mode':
self.ceil_mode,
}
}
def register_op(self):
self.op_meta = {
'op_type':
'Conv{}d{}'.format(len(self.kernel_size),
'Transpose' if self.transposed else ''),
'n_inputs':
3 if self.bias else 2,
'n_outputs':
1,
'arguments': {
'num_output': self.weight.shape[0],
'kernel_size': self.weight.shape[2:],
'stride': _pair(self.stride),
'pad': _pair(self.padding),
'dilation': _pair(self.dilation),
'group': self.groups,
'data_format': 'NCHW',
}
}
def register_op(self):
self.op_meta = {
'op_type': 'Pooling2d',
'n_inputs': 1,
'n_outputs': 1,
'arguments': {
'kernel_size':
_pair(self.kernel_size),
'stride':
_pair(self.stride) if self.stride else _pair(self.kernel_size),
'pad':
_pair(self.padding),
'mode':
'AVG',
'data_format':
'NCHW',
'ceil':
self.ceil_mode
}
}
def register_op(self):
self.op_meta = {
'op_type':
'Conv{}{}d'.format('Transpose' if self.transposed else '',
len(self.kernel_size)),
'arguments': {
'num_output':
self.weight.shape[1]
if self.transposed else self.weight.shape[0],
'kernel_shape':
self.weight.shape[2:],
'strides':
_pair(self.stride),
'pads':
_pair(self.padding),
'dilations':
_pair(self.dilation),
'group':
self.groups,
'data_format':
'NCHW',
}
}
