Paddle/python/paddle/v2/framework/tests/op_test_util.py

64 lines
2.2 KiB

import paddle.v2.framework.core as core
import unittest
import numpy
from paddle.v2.framework.op import Operator
class OpTestMeta(type):
"""
Operator Test ClassMeta.
It injects `test_all` method into user's OperatorTest class, to make Python
unittest module run that method.
The `test_all` read what value is stored in `self`. It use self's values to
create and run a operator, and check whether that op is OK or not.
See `test_add_two_op` for example usage.
"""
def __new__(cls, name, bases, attrs):
obj = super(OpTestMeta, cls).__new__(cls, name, bases, attrs)
def test_all(self):
scope = core.Scope()
kwargs = dict()
places = [core.CPUPlace()]
if core.is_compile_gpu():
places.append(core.GPUPlace(0))
for place in places:
for in_name in Operator.get_op_input_names(self.type):
if hasattr(self, in_name):
kwargs[in_name] = in_name
var = scope.new_var(in_name).get_tensor()
arr = getattr(self, in_name)
var.set_dims(arr.shape)
var.set(arr, place)
else:
kwargs[in_name] = "@EMPTY@"
for out_name in Operator.get_op_output_names(self.type):
if hasattr(self, out_name):
kwargs[out_name] = out_name
scope.new_var(out_name).get_tensor()
for attr_name in Operator.get_op_attr_names(self.type):
if hasattr(self, attr_name):
kwargs[attr_name] = getattr(self, attr_name)
op = Operator(self.type, **kwargs)
op.infer_shape(scope)
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
for out_name in Operator.get_op_output_names(self.type):
actual = numpy.array(scope.find_var(out_name).get_tensor())
expect = getattr(self, out_name)
numpy.isclose(actual, expect)
obj.test_all = test_all
return obj