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118 lines
3.3 KiB
118 lines
3.3 KiB
import unittest
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import numpy as np
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from op_test import OpTest
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class TestSumOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestMeanOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_mean"
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self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float32")}
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self.attrs = {'dim': 1}
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self.outputs = {'Out': self.inputs['X'].mean(axis=self.attrs['dim'])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestMaxOp(OpTest):
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"""Remove Max with subgradient from gradient check to confirm the success of CI."""
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def setUp(self):
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self.op_type = "reduce_max"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
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self.attrs = {'dim': -1}
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self.outputs = {'Out': self.inputs['X'].max(axis=self.attrs['dim'])}
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def test_check_output(self):
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self.check_output()
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class TestMinOp(OpTest):
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"""Remove Min with subgradient from gradient check to confirm the success of CI."""
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def setUp(self):
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self.op_type = "reduce_min"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
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self.attrs = {'dim': 2}
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self.outputs = {'Out': self.inputs['X'].min(axis=self.attrs['dim'])}
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def test_check_output(self):
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self.check_output()
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class TestKeepDimReduce(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
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self.attrs = {'dim': -2, 'keep_dim': True}
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self.outputs = {
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'Out': self.inputs['X'].sum(axis=self.attrs['dim'], keepdims=True)
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class Test1DReduce(OpTest):
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def setUp(self):
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self.op_type = "reduce_sum"
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self.inputs = {'X': np.random.random(20).astype("float32")}
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self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestNorm(OpTest):
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def setUp(self):
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# use x away from 0 to avoid errors of numerical gradient when gradient near 0
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x = np.random.random((5, 6, 10)).astype("float32") + 0.2
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p = 2
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dim = 1
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keep_dim = False
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abs_out = np.absolute(x)
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pow_out = np.power(x, p)
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sum_out = np.sum(pow_out, axis=dim, keepdims=keep_dim)
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out = np.power(sum_out, 1. / p)
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self.op_type = "norm"
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self.inputs = {'X': x}
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self.attrs = {"p": p, "dim": dim, "keep_dim": keep_dim}
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self.outputs = {
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"AbsOut": abs_out,
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"PowOut": pow_out,
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"SumOut": sum_out,
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"Out": out
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', max_relative_error=0.01)
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if __name__ == '__main__':
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unittest.main()
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