Merge pull request #4022 from QiJune/refine_op_py_tests
refine all operator python tests using new test frameworkenforce_failed
Yu Yang
8 years ago
committed by
GitHub
commit
d71190f04c
File diff suppressed because it is too large
Load Diff
@ -1,82 +0,0 @@
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import numpy
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import paddle.v2.framework.core as core
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from paddle.v2.framework.op import Operator
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class OpTestMeta(type):
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"""
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Operator Test ClassMeta.
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It injects `test_all` method into user's OperatorTest class, to make Python
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unittest module run that method.
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The `test_all` read what value is stored in `self`. It use self's values to
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create and run a operator, and check whether that op is OK or not.
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See `test_add_two_op` for example usage.
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"""
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def __new__(cls, name, bases, attrs):
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obj = super(OpTestMeta, cls).__new__(cls, name, bases, attrs)
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def test_all(self):
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scope = core.Scope()
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kwargs = dict()
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places = [core.CPUPlace()]
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if core.is_compile_gpu():
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places.append(core.GPUPlace(0))
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for place in places:
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for in_name, in_dup in Operator.get_op_inputs(self.type):
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if hasattr(self, 'inputs') and in_name in self.inputs:
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kwargs[in_name] = []
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if in_dup:
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arrays = self.inputs[in_name]
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for index, arr in enumerate(arrays):
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var = scope.new_var(in_name + str(index))
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tensor = var.get_tensor()
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tensor.set_dims(arr.shape)
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tensor.set(arr, place)
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kwargs[in_name].append(in_name + str(index))
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else:
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kwargs[in_name] = in_name
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var = scope.new_var(in_name).get_tensor()
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arr = self.inputs[in_name]
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var.set_dims(arr.shape)
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var.set(arr, place)
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else:
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kwargs[in_name] = "@EMPTY@"
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for out_name, out_dup in Operator.get_op_outputs(self.type):
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if not hasattr(self, "outputs"):
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raise ValueError(
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"The test op must set self.outputs dict.")
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if out_name not in self.outputs:
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raise ValueError("The %s is not in self.outputs dict." %
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(out_name))
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kwargs[out_name] = out_name
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scope.new_var(out_name).get_tensor()
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for attr_name in Operator.get_op_attr_names(self.type):
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if hasattr(self, "attrs") and attr_name in self.attrs:
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kwargs[attr_name] = self.attrs[attr_name]
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op = Operator(self.type, **kwargs)
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if isinstance(place, core.GPUPlace) and not op.support_gpu():
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return
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op.infer_shape(scope)
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ctx = core.DeviceContext.create(place)
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op.run(scope, ctx)
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for out_name, out_dup in Operator.get_op_outputs(self.type):
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actual = numpy.array(scope.find_var(out_name).get_tensor())
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expect = self.outputs[out_name]
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self.assertTrue(
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numpy.allclose(
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actual, expect, atol=1e-05),
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"output name: " + out_name + " has diff")
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obj.test_all = test_all
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return obj
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@ -1,23 +1,20 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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import numpy
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import paddle.v2.framework.core as core
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from paddle.v2.framework.op import Operator
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from op_test_util import OpTestMeta
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class TestAddOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class TestAddOp(OpTest):
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def setUp(self):
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self.type = "add"
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self.op_type = "add"
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self.inputs = {
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'X': numpy.random.random((102, 105)).astype("float32"),
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'Y': numpy.random.random((102, 105)).astype("float32")
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'X': np.random.random((102, 105)).astype("float32"),
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'Y': np.random.random((102, 105)).astype("float32")
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}
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self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,22 +1,22 @@
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import unittest
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import numpy as np
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from gradient_checker import GradientChecker, create_op
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from op_test_util import OpTestMeta
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from op_test import OpTest
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class TestConcatOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class TestConcatOp(OpTest):
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def setUp(self):
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self.type = "concat"
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self.op_type = "concat"
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x0 = np.random.random((2, 3, 2, 5)).astype('float32')
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x1 = np.random.random((2, 3, 3, 5)).astype('float32')
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x2 = np.random.random((2, 3, 4, 5)).astype('float32')
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axis = 2
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self.inputs = {'X': [x0, x1, x2]}
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self.inputs = {'X': [('x0', x0), ('x1', x1), ('x2', x2)]}
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self.attrs = {'axis': axis}
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self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)}
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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unittest.main()
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@ -1,16 +1,17 @@
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import unittest
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from op_test_util import OpTestMeta
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import numpy
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import numpy as np
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from op_test import OpTest
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class TestFillZerosLikeOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class TestFillZerosLikeOp(OpTest):
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def setUp(self):
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self.type = "fill_zeros_like"
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self.inputs = {'Src': numpy.random.random((219, 232)).astype("float32")}
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self.outputs = {'Dst': numpy.zeros_like(self.inputs['Src'])}
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self.op_type = "fill_zeros_like"
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self.inputs = {'Src': np.random.random((219, 232)).astype("float32")}
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self.outputs = {'Dst': np.zeros_like(self.inputs["Src"])}
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,31 +1,22 @@
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import unittest
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import numpy as np
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from op_test_util import OpTestMeta
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from gradient_checker import GradientChecker, create_op
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from op_test import OpTest
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class TestLookupTableOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class TestLookupTableOp(OpTest):
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def setUp(self):
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self.type = 'lookup_table'
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table = np.random.random((17, 31)).astype('float32')
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ids = np.random.randint(0, 17, 4).astype('int32')
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self.op_type = "lookup_table"
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table = np.random.random((17, 31)).astype("float32")
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ids = np.random.randint(0, 17, 4).astype("int32")
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self.inputs = {'W': table, 'Ids': ids}
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self.outputs = {'Out': table[ids]}
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def test_check_output(self):
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self.check_output()
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class TestLookupTableGradOp(GradientChecker):
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def test_grad(self):
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op = create_op('lookup_table')
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table = np.random.random((17, 31)).astype('float32')
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ids = np.random.randint(0, 17, 4).astype('int32')
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inputs = {'W': table, 'Ids': ids}
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# comapre gradients
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self.compare_grad(op, inputs, set(['Ids']))
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# check gradients
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self.check_grad(op, inputs, set('W'), 'Out')
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def test_check_grad(self):
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self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,24 +1,20 @@
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import unittest
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from op_test_util import OpTestMeta
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from gradient_checker import GradientChecker, create_op
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import numpy as np
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from op_test import OpTest
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class TestMeanOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class TestMeanOp(OpTest):
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def setUp(self):
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self.type = "mean"
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self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
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self.outputs = {'Out': np.mean(self.inputs['X'])}
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self.op_type = "mean"
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self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def test_check_output(self):
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self.check_output()
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class MeanGradOpTest(GradientChecker):
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def test_normal(self):
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op = create_op("mean")
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inputs = {"X": np.random.random((10, 10)).astype("float32")}
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self.check_grad(op, inputs, set("X"), "Out")
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def test_checkout_grad(self):
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self.check_grad(['X'], 'Out')
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,30 +1,23 @@
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import unittest
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import numpy as np
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from gradient_checker import GradientChecker, create_op
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from op_test_util import OpTestMeta
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from op_test import OpTest
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class MinusOpTest(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class MinusOpTest(OpTest):
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def setUp(self):
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self.type = "minus"
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self.op_type = "minus"
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self.inputs = {
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'X': np.random.random((32, 84)).astype("float32"),
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'Y': np.random.random((32, 84)).astype("float32")
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}
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self.outputs = {'Out': (self.inputs['X'] - self.inputs['Y'])}
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def test_check_output(self):
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self.check_output()
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class MinusGradTest(GradientChecker):
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def test_left(self):
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op = create_op("minus")
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inputs = {
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"X": np.random.random((10, 10)).astype("float32"),
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"Y": np.random.random((10, 10)).astype("float32")
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}
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self.check_grad(op, inputs, ["X", 'Y'], "Out")
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def test_check_grad(self):
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self.check_grad(['X', 'Y'], 'Out')
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,68 +1,51 @@
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import unittest
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import numpy as np
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from op_test_util import OpTestMeta
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from gradient_checker import GradientChecker, create_op
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from op_test import OpTest
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class TestRowwiseAddOp(unittest.TestCase):
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__metaclass__ = OpTestMeta
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def setUp(self):
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self.type = "rowwise_add"
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self.inputs = {
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'X': np.random.random((32, 84)).astype("float32"),
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'b': np.random.random(84).astype("float32")
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}
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self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
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class TestRowwiseAddOp2(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class TestRowwiseAddOp(OpTest):
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def setUp(self):
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self.type = "rowwise_add"
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self.op_type = "rowwise_add"
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self.inputs = {
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'X': np.random.random((13, 6, 7, 8)).astype("float32"),
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'b': np.random.random((7, 8)).astype("float32")
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'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
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'b': np.random.uniform(0.1, 1, [10]).astype("float32")
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}
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self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
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def test_check_output(self):
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self.check_output()
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class TestRowwiseAddGradOp(GradientChecker):
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def setUp(self):
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self.op = create_op("rowwise_add")
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self.inputs = {
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"X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
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"b": np.random.uniform(0.1, 1, [10]).astype("float32")
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}
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def test_check_grad_normal(self):
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self.check_grad(['X', 'b'], 'Out')
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def test_normal(self):
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self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
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def test_check_grad_ingore_b(self):
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self.check_grad(['X'], 'Out', no_grad_set=set('b'))
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def test_ignore_b(self):
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self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
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def test_check_grad_ingore_x(self):
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self.check_grad(['b'], 'Out', no_grad_set=set('X'))
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def test_ignore_x(self):
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self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
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class TestRowwiseAddGradOp2(GradientChecker):
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class TestRowwiseAddOp2(OpTest):
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def setUp(self):
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self.op = create_op("rowwise_add")
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self.op_type = "rowwise_add"
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self.inputs = {
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"X": np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
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"b": np.random.uniform(0.1, 1, [2, 5]).astype("float32")
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'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
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'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32")
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}
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self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
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def test_check_output(self):
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self.check_output()
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def test_normal(self):
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self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
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def test_check_grad_normal(self):
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self.check_grad(['X', 'b'], 'Out')
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def test_ignore_b(self):
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self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
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def test_check_grad_ignore_b(self):
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self.check_grad(['X'], 'Out', no_grad_set=set('b'))
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def test_ignore_x(self):
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self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
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def test_check_grad_ignore_x(self):
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self.check_grad(['b'], 'Out', no_grad_set=set('X'))
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,43 +1,34 @@
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import unittest
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from op_test_util import OpTestMeta
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from gradient_checker import GradientChecker, create_op
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import numpy as np
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from paddle.v2.framework.op import Operator
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from op_test import OpTest
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class IdentityTest(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class IdentityTest(OpTest):
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def setUp(self):
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self.type = "identity"
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self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
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self.op_type = "identity"
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self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
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self.outputs = {'Out': self.inputs['X']}
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def test_check_output(self):
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self.check_output()
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class IdentityGradOpTest(GradientChecker):
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def test_normal(self):
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op = create_op("identity")
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inputs = {"X": np.random.random((10, 10)).astype("float32")}
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self.check_grad(op, inputs, set("X"), "Out")
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class ScaleTest(unittest.TestCase):
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__metaclass__ = OpTestMeta
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class ScaleTest(OpTest):
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def setUp(self):
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self.type = "scale"
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self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
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self.op_type = "scale"
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self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
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self.attrs = {'scale': -2.3}
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self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']}
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def test_check_output(self):
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self.check_output()
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class ScaleGradTest(GradientChecker):
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def test_normal(self):
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op = Operator("scale", X="X", Out="Out", scale=3.2)
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self.check_grad(op,
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{"X": np.random.random((10, 10)).astype("float32")},
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set("X"), "Out")
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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@ -1,21 +1,22 @@
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import unittest
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import numpy
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from op_test_util import OpTestMeta
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import numpy as np
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from op_test import OpTest
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||||
|
||||
class TestSGD(unittest.TestCase):
|
||||
__metaclass__ = OpTestMeta
|
||||
|
||||
class TestSGD(OpTest):
|
||||
def setUp(self):
|
||||
self.type = "sgd"
|
||||
w = numpy.random.random((102, 105)).astype("float32")
|
||||
g = numpy.random.random((102, 105)).astype("float32")
|
||||
self.op_type = "sgd"
|
||||
w = np.random.random((102, 105)).astype("float32")
|
||||
g = np.random.random((102, 105)).astype("float32")
|
||||
lr = 0.1
|
||||
|
||||
self.inputs = {'param': w, 'grad': g}
|
||||
self.attrs = {'learning_rate': lr}
|
||||
self.outputs = {'param_out': w - lr * g}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
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Reference in new issue