def test_operators_variable_builtins(self, mxf_operator, mxnet_operator,
inputs, case):
m = Model()
v1 = Variable()
v2 = Variable()
variables = [v1, v2] if len(inputs) > 1 else [v1]
m.r = mxf_operator(*variables)
vs = [v for v in m.r.factor.inputs]
variables_rt = {v[1].uuid: inputs[i] for i, v in enumerate(vs)}
r_eval = m.r.factor.eval(mx.nd, variables=variables_rt)
m2 = Model()
v12 = Variable()
v22 = Variable()
variables2 = [v12, v22] if len(inputs) > 1 else [v12]
if case == "add":
m2.r = v12 + v22
elif case == "sub":
m2.r = v12 - v22
elif case == "mul":
m2.r = v12 * v22
elif case == "div":
m2.r = v12 / v22
elif case == "pow":
m2.r = v12**v22
elif case == "transpose":
m2.r = transpose(v12)
vs2 = [v for v in m2.r.factor.inputs]
variables_rt2 = {v[1].uuid: inputs[i] for i, v in enumerate(vs2)}
p_eval = m2.r.factor.eval(mx.nd, variables=variables_rt2)
assert np.allclose(r_eval.asnumpy(),
p_eval.asnumpy()), (r_eval, p_eval)
def _test_operator(self, operator, inputs, properties=None):
"""
inputs are mx.nd.array
properties are just the operator properties needed at model def time.
"""
properties = properties if properties is not None else {}
m = Model()
variables = [Variable() for _ in inputs]
m.r = operator(*variables, **properties)
vs = [v for v in m.r.factor.inputs]
variables = {v[1].uuid: inputs[i] for i, v in enumerate(vs)}
evaluation = m.r.factor.eval(mx.nd, variables=variables)
return evaluation
def test_operator_replicate(self, mxf_operator, mxnet_operator, inputs,
properties):
properties = properties if properties is not None else {}
m = Model()
variables = [Variable() for _ in inputs]
m.r = mxf_operator(*variables, **properties)
vs = [v for v in m.r.factor.inputs]
variables = {v[1].uuid: inputs[i] for i, v in enumerate(vs)}
evaluation = m.r.factor.eval(mx.nd, variables=variables)
r_clone = m.extract_distribution_of(m.r)
vs = [v for v in r_clone.factor.inputs]
variables = {v[1].uuid: inputs[i] for i, v in enumerate(vs)}
evaluation2 = r_clone.factor.eval(mx.nd, variables=variables)
assert np.allclose(evaluation.asnumpy(),
evaluation2.asnumpy()), (evaluation, evaluation2)