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)).astype('float32') } 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)).astype('float32'), 'Mask': np.zeros((32, 64)).astype('float32') } 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)).astype('float32') } 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()