qijun
8 years ago
parent
c1696696a3
commit
2d807f2b4c
@ -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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@ -1,16 +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):
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__metaclass__ = OpTestMeta
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class TestSGD(OpTest):
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def setUp(self):
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self.type = "sgd"
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w = numpy.random.random((102, 105)).astype("float32")
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g = numpy.random.random((102, 105)).astype("float32")
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self.op_type = "sgd"
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w = np.random.random((102, 105)).astype("float32")
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g = np.random.random((102, 105)).astype("float32")
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lr = 0.1
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self.inputs = {'param': w, 'grad': g}
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self.attrs = {'learning_rate': lr}
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self.outputs = {'param_out': w - lr * g}
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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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