def test_axes(self):
"""
Test different output axes.
Use different output axes and see whether the output is what we
expect.
"""
default_axes = ('b', 'c', 0, 1)
axes = (0, 'b', 1, 'c')
another_axes = (0, 1, 'c', 'b')
# 1, 3, 0, 2
map_to_default = tuple(axes.index(axis) for axis in default_axes)
# 2, 0, 3, 1
map_to_another_axes = tuple(
default_axes.index(axis) for axis in another_axes)
input_space = Conv2DSpace((3, 3), num_channels=1, axes=another_axes)
# Apply cudnn2d with `axes` as output_axes
cudnn2d = Cudnn2D(self.filters, 1, input_space, output_axes=axes)
f = theano.function([self.image_tensor],
cudnn2d.lmul(self.image_tensor))
# Apply cudnn2d with default axes
f_def = theano.function([self.image_tensor],
self.cudnn2d.lmul(self.image_tensor))
# Apply f on the `another_axes`-shaped image
output = f(np.transpose(self.image, map_to_another_axes))
# Apply f_def on self.image (b,c,0,1)
output_def = np.array(f_def(self.image))
# transpose output to def
output = np.transpose(output, map_to_default)
np.testing.assert_allclose(output_def, output)
np.testing.assert_equal(output_def.shape, output.shape)
def test_value_errors(self):
"""
Check correct errors are raised when bad input is given.
"""
with self.assertRaises(AssertionError):
Cudnn2D(filters=self.filters, batch_size=-1,
input_space=self.input_space)
def test_channels(self):
"""
Go from 2 to 3 channels and see whether the shape is correct.
"""
input_space = Conv2DSpace((3, 3), num_channels=3)
filters_values = np.ones((2, 3, 2, 2), dtype=theano.config.floatX)
filters = sharedX(filters_values)
image = np.random.rand(1, 3, 3, 3).astype(theano.config.floatX)
cudnn2d = Cudnn2D(filters, 1, input_space)
f = theano.function([self.image_tensor],
cudnn2d.lmul(self.image_tensor))
assert f(image).shape == (1, 2, 2, 2)
def setUp(self):
"""
Set up a test image and filter to re-use.
"""
skip_if_no_gpu()
if not dnn_available():
raise SkipTest('Skipping tests cause cudnn is not available')
self.orig_floatX = theano.config.floatX
theano.config.floatX = 'float32'
self.image = np.random.rand(1, 1, 3, 3).astype(theano.config.floatX)
self.image_tensor = tensor.tensor4()
self.input_space = Conv2DSpace((3, 3), 1, axes=('b', 'c', 0, 1))
self.filters_values = np.ones((1, 1, 2, 2), dtype=theano.config.floatX)
self.filters = sharedX(self.filters_values, name='filters')
self.batch_size = 1
self.cudnn2d = Cudnn2D(self.filters, self.batch_size, self.input_space)