def setUp(self):
self.layer_cuda = ConvLayerCUDA(2, (2, 3), 1, padding_mode=False)
self.layer_cuda.set_input_shape((2, 8))
self.layer_cuda._filter_weights = np.ones((2, 2, 3), dtype=np.double)
self.layer_cuda._filter_weights[1, :, :] /= 2.0
self.input = np.array([[2, 2, 2, 2, 2, 2, 2, 4],
[4, 4, 4, 4, 4, 4, 4, 8]], dtype=np.double)
class TestConvLayerCUDA(unittest.TestCase):
def setUp(self):
self.layer_cuda = ConvLayerCUDA(2, (2, 3), 1, padding_mode=False)
self.layer_cuda.set_input_shape((2, 8))
self.layer_cuda._filter_weights = np.ones((2, 2, 3), dtype=np.double)
self.layer_cuda._filter_weights[1, :, :] /= 2.0
self.input = np.array([[2, 2, 2, 2, 2, 2, 2, 4],
[4, 4, 4, 4, 4, 4, 4, 8]], dtype=np.double)
def test_forward_prop(self):
expected_output = np.array([[18, 18, 18, 18, 18, 24],
[9, 9, 9, 9, 9, 12]], dtype=np.double)
output = self.layer_cuda.forward_prop(self.input)
numpy.testing.assert_array_equal(output, expected_output)
def test_back_prop(self):
self.layer_cuda.forward_prop(self.input)
out_grad = np.ones((2, 6), dtype=np.double)
expected_in_grad = np.array([[1.5, 3, 4.5, 4.5, 4.5, 4.5, 3, 1.5],
[1.5, 3, 4.5, 4.5, 4.5, 4.5, 3, 1.5]], dtype=np.double)
in_grad = self.layer_cuda.back_prop(out_grad)
numpy.testing.assert_array_equal(in_grad, expected_in_grad)
def test_get_output_shape(self):
self.assertEqual(self.layer_cuda.get_output_shape(), (2, 6))
def test_update_parameters(self):
self.layer_cuda.forward_prop(self.input)
out_grad = np.ones((2, 6), dtype=np.double)
self.layer_cuda.back_prop(out_grad)
self.layer_cuda.update_parameters(0.1)
expected_output = np.array([[-20.6, -20.6, -20.6, -20.6, -20.6, -28.6],
[-29.6, -29.6, -29.6, -29.6, -29.6, -40.6]], dtype=np.double)
output = self.layer_cuda.forward_prop(self.input)
numpy.testing.assert_array_almost_equal(output, expected_output)
