test_dropout_op.py

import unittest
import numpy as np
from op_test import OpTest


class TestDropoutOp(OpTest):
    def setUp(self):
        self.op_type = "dropout"
        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
        self.attrs = {'dropout_prob': 0.0, 'is_training': True}
        self.outputs = {'Out': self.inputs['X'], 'Mask': np.ones((32, 64))}

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out', max_relative_error=0.05)


class TestDropoutOp2(TestDropoutOp):
    def setUp(self):
        self.op_type = "dropout"
        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
        self.attrs = {'dropout_prob': 1.0, 'is_training': True}
        self.outputs = {'Out': np.zeros((32, 64)), 'Mask': np.zeros((32, 64))}


class TestDropoutOp3(TestDropoutOp):
    def setUp(self):
        self.op_type = "dropout"
        self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
        self.attrs = {'dropout_prob': 0.0, 'is_training': True}
        self.outputs = {'Out': self.inputs['X'], 'Mask': np.ones((32, 64, 2))}


class TestDropoutOp4(OpTest):
    def setUp(self):
        self.op_type = "dropout"
        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
        self.attrs = {'dropout_prob': 0.35, 'is_training': False}
        self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}

    def test_check_output(self):
        self.check_output()


class TestDropoutOp5(OpTest):
    def setUp(self):
        self.op_type = "dropout"
        self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
        self.attrs = {'dropout_prob': 0.75, 'is_training': False}
        self.outputs = {'Out': self.inputs['X'] * self.attrs['dropout_prob']}

    def test_check_output(self):
        self.check_output()


if __name__ == '__main__':
    unittest.main()