def test_set_value(self):
ts = TensorShape(IntVector(lpx.IntVector([1, 2, 3, 4])))
ts.set_value(0, -1)
assert len(ts) == 4
assert ts[0] == -1
assert ts[1] == 2
def relay_op(op_name, expr, in_xlayers):
# type: (str, tvm.relay.expr.Expr, List[XLayer]) -> XLayer
""" Insert generic relay op operator """
logger.debug("-- op_name: {}".format(op_name))
logger.debug("-- expr: {}".format(expr.op))
ty = expr.checked_type
if isinstance(ty, relay.ty.TensorType):
relay_shape = TensorShape([int(i) for i in list(ty.shape)])
dtype = str(ty.dtype)
else:
relay_shape = TupleShape([
TensorShape([int(i) for i in list(t_ty.shape)])
for t_ty in ty.fields
])
dtype = [str(t_ty.dtype) for t_ty in ty.fields]
# TODO
relay_shape.set_value(axis=0, value=-1)
attrs = {}
for attr in dir(expr.attrs):
value = getattr(expr.attrs, attr)
attrs[attr] = str(value)
if 'dtype' in attrs:
dtype = attrs['dtype']
del attrs['dtype']
X = xlf.get_xop_factory_func('RelayOp')(op_name,
in_xlayers,
relay_shape=relay_shape.tolist(),
dtype=dtype,
relay_id=[hash(expr)],
**attrs)
return X
