def test_dimshuffle_false_get_output_for(self, DummyInputLayer):
try:
from lasagne.layers.cuda_convnet import Conv2DCCLayer
except ImportError:
pytest.skip("cuda_convnet not available")
# this implementation is tested against FilterActs instead of
# theano.tensor.nnet.conv.conv2d because using the latter leads to
# numerical precision errors.
from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs
filter_acts = FilterActs(stride=1, pad=0, partial_sum=1)
input = theano.shared(floatX(np.random.random((4, 5, 5, 8))))
kernel = theano.shared(floatX(np.random.random((4, 3, 3, 16))))
input_layer = DummyInputLayer((4, 5, 5, 8)) # c01b instead of bc01
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 3),
dimshuffle=False,
W=kernel,
b=None,
nonlinearity=None)
output = np.array(filter_acts(input, kernel).eval())
actual = layer.get_output_for(input).eval()
actual = np.array(actual)
assert actual.shape == output.shape
assert actual.shape == layer.output_shape
assert np.allclose(actual, output)
def test_dimshuffle_false_shapes(self, DummyInputLayer):
try:
from lasagne.layers.cuda_convnet import Conv2DCCLayer
except ImportError:
pytest.skip("cuda_convnet not available")
input_layer = DummyInputLayer((4, 32, 32, 128)) # c01b instead of bc01
layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3),
dimshuffle=False)
assert layer.W.get_value().shape == (4, 3, 3, 16)
assert layer.b.get_value().shape == (16,)
layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3),
dimshuffle=False, untie_biases=True)
assert layer.W.get_value().shape == (4, 3, 3, 16)
assert layer.b.get_value().shape == (16, 30, 30)
def test_unsupported_settings(self, DummyInputLayer):
try:
from lasagne.layers.cuda_convnet import Conv2DCCLayer
except ImportError:
pytest.skip("cuda_convnet not available")
input_layer = DummyInputLayer((128, 3, 32, 32))
with pytest.raises(RuntimeError) as exc:
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 5))
assert ("Conv2DCCLayer only supports square filters"
in exc.value.args[0])
with pytest.raises(RuntimeError) as exc:
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 3),
stride=(1, 2))
assert ("Conv2DCCLayer only supports square strides"
in exc.value.args[0])
with pytest.raises(RuntimeError) as exc:
layer = Conv2DCCLayer(input_layer,
num_filters=15,
filter_size=(3, 3))
assert ("Conv2DCCLayer requires num_filters to be a multiple of 16"
in exc.value.args[0])
with pytest.raises(RuntimeError) as exc:
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 3),
pad=(1, 2))
assert ("Conv2DCCLayer only supports square padding"
in exc.value.args[0])
input_layer = DummyInputLayer((128, 7, 32, 32))
with pytest.raises(RuntimeError) as exc:
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 3))
assert ("Conv2DCCLayer requires the number of input channels to be "
"1, 2, 3 or a multiple of 4" in exc.value.args[0])
def test_pad(self, DummyInputLayer):
try:
from lasagne.layers.cuda_convnet import Conv2DCCLayer
except ImportError:
pytest.skip("cuda_convnet not available")
input_layer = DummyInputLayer((128, 3, 32, 32))
layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3),
pad=(3, 3))
assert layer.output_shape == (128, 16, 36, 36)
def test_pad(self, DummyInputLayer):
try:
from lasagne.layers.cuda_convnet import Conv2DCCLayer
except ImportError:
pytest.skip("cuda_convnet not available")
input_layer = DummyInputLayer((128, 3, 32, 32))
with pytest.raises(RuntimeError) as exc:
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 3),
border_mode='valid',
pad=(1, 1))
assert ("You cannot specify both 'border_mode' and 'pad'"
in exc.value.args[0])
layer = Conv2DCCLayer(input_layer,
num_filters=16,
filter_size=(3, 3),
pad=(3, 3))
assert layer.output_shape == (128, 16, 36, 36)