op_test_util.py

import numpy
import paddle.v2.framework.core as core
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, "inputs") and in_name in self.inputs:
                        kwargs[in_name] = in_name
                        var = scope.new_var(in_name).get_tensor()
                        arr = self.inputs[in_name]
                        var.set_dims(arr.shape)
                        var.set(arr, place)
                        print "var: %s" % in_name
                    else:
                        kwargs[in_name] = "@EMPTY@"
                        print "var: %s=EMPTY" % in_name

                for out_name in Operator.get_op_output_names(self.type):
                    if not hasattr(self, "outputs"):
                        raise ValueError(
                            "The test op must set self.outputs dict.")
                    if out_name not in self.outputs:
                        raise ValueError("The %s is not in self.outputs dict." %
                                         (out_name))
                    kwargs[out_name] = out_name
                    scope.new_var(out_name).get_tensor()
                    print "var: %s" % out_name

                for attr_name in Operator.get_op_attr_names(self.type):
                    if hasattr(self, "attrs") and attr_name in self.attrs:
                        kwargs[attr_name] = self.attrs[attr_name]

                op = Operator(self.type, **kwargs)
                if isinstance(place, core.GPUPlace) and not op.support_gpu():
                    return

                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())
                    print "actual: %s" % actual
                    expect = self.outputs[out_name]
                    print "expect: %s" % expect
                    self.assertTrue(
                        numpy.allclose(
                            actual, expect, atol=1e-05),
                        "output name: " + out_name + "has diff")

        obj.test_all = test_all
        return obj