def extract(cls, node):
mode = onnx_attr(node,
'mode',
's',
default='constant',
dst_type=lambda x: x.decode())
pads = onnx_attr(node,
'pads',
'ints',
dst_type=lambda x: np.array(x, dtype=np.int64))
value = onnx_attr(node, 'value', 'f', default=0.)
assert pads is not None
# MO Pad op and ONNX Pad op have different format for pads values
# MO Pad has Dx2 where D is the total number of dimensions
# ONNX Pad pads flat layout, so
# need to reshape and transpose
pads = np.transpose(pads.reshape([2, -1]))
Pad.update_node_stat(node, {
'mode': mode,
'pads': pads,
'fill_value': value
})
return cls.enabled
def extract(node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
pads = np.array(list(attrs.tuple('pad_width', int, None)))
pads = pads.reshape([-1, 2])
value = attrs.float('constant_value', 0.0)
node_attrs = {
'pads': pads,
'mode': attrs.str('mode', None),
'fill_value': value,
}
Pad.update_node_stat(node, node_attrs)
return __class__.enabled
def extract(node):
Pad.update_node_stat(node)
return __class__.enabled
def extract(node):
Pad.update_node_stat(node, {'mode': node.pb.attr['mode'].s.decode('utf-8').lower()})
return __class__.enabled
def extract(cls, node):
Pad.update_node_stat(node)
return cls.enabled