def test_eran_unimplemented():
"""Tests loading a convolutional Network from ERAN format.
"""
path = "eran_unimplemented.eran"
with open(path, "w") as netfile:
netfile.write("Sin\n")
netfile.write("[[1, 2], [3, 4]]")
try:
network = Network.from_file(path)
assert False
except NotImplementedError:
assert True
with open(path, "w") as netfile:
netfile.write("Conv2D\nSin, filters=1, kernel_size=[1, 1], ")
netfile.write("input_shape=[1, 1, 2], stride=[1, 1], padding=0\n")
netfile.write("[[[[1], [2]]]]\n")
netfile.write("[-10]\n")
try:
network = Network.from_file(path)
assert False
except NotImplementedError:
assert True
def test_conv_from_eran():
"""Tests loading a convolutional Network from ERAN format.
"""
path = "conv_test.eran"
with open(path, "w") as netfile:
netfile.write("Conv2D\nReLU, filters=2, kernel_size=[2, 2], ")
netfile.write("input_shape=[16, 16, 2], stride=[10, 10], padding=2\n")
netfile.write("[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],")
netfile.write(" [[[8, 7], [6, 5]], [[4, 3], [2, 1]]]]\n")
netfile.write("[-1, -2]\n")
netfile.write("Affine\n[[1, 2, 3, 4, 5, 6, 7, 8], ")
netfile.write("[5, 6, 7, 8, 9, 10, 11, 12]]\n[-1, -2]\n")
netfile.write("Conv2D\nHardTanh, filters=1, kernel_size=[1, 1], ")
netfile.write("input_shape=[1, 1, 2], stride=[1, 1], padding=0\n")
netfile.write("[[[[1], [2]]]]\n")
netfile.write("[-10]\n")
network = Network.from_file(path)
assert len(network.layers) == 5
assert isinstance(network.layers[0], Conv2DLayer)
assert np.allclose(
network.layers[0].filter_weights,
np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
[[[8, 7], [6, 5]], [[4, 3], [2, 1]]]]))
assert np.allclose(network.layers[0].biases, np.array([-1, -2]))
assert network.layers[0].window_data.input_shape == (16, 16, 2)
assert network.layers[0].window_data.window_shape == (2, 2)
assert network.layers[0].window_data.strides == (10, 10)
assert network.layers[0].window_data.padding == (2, 2)
assert network.layers[0].window_data.out_channels == 2
assert isinstance(network.layers[1], ReluLayer)
assert isinstance(network.layers[2], FullyConnectedLayer)
assert np.allclose(
network.layers[2].weights,
np.array([[1, 5, 2, 6, 3, 7, 4, 8], [5, 9, 6, 10, 7, 11, 8, 12]]).T)
assert np.allclose(network.layers[2].biases, np.array([[-1, -2]]))
assert isinstance(network.layers[3], Conv2DLayer)
assert np.allclose(network.layers[3].filter_weights,
np.array([[[[1], [2]]]]))
assert np.allclose(network.layers[3].biases, np.array([-10]))
assert network.layers[3].window_data.input_shape == (1, 1, 2)
assert network.layers[3].window_data.window_shape == (1, 1)
assert network.layers[3].window_data.strides == (1, 1)
assert network.layers[3].window_data.padding == (0, 0)
assert network.layers[3].window_data.out_channels == 1
assert isinstance(network.layers[4], HardTanhLayer)
def test_fcn_from_eran():
"""Tests loading a fully-connected Network from ERAN format.
"""
path = "fcn_test.eran"
with open(path, "w") as netfile:
netfile.write("ReLU\n[[-1, 2, -3], [-4, 5, -6]]\n[7, 8]\n")
netfile.write("Normalize mean=[1, 2] std=[3, 4]\n")
netfile.write("HardTanh\n[[-8, 7, -6], [-5, 4, -3]]\n[2, 1]\n")
network = Network.from_file(path)
assert len(network.layers) == 5
assert isinstance(network.layers[0], FullyConnectedLayer)
assert np.allclose(network.layers[0].weights,
np.array([[-1, -4], [2, 5], [-3, -6]]))
assert np.allclose(network.layers[0].biases, np.array([[7, 8]]))
assert isinstance(network.layers[1], ReluLayer)
assert isinstance(network.layers[2], NormalizeLayer)
assert np.allclose(network.layers[2].means, np.array([[1, 2]]))
assert np.allclose(network.layers[2].standard_deviations, np.array([[3,
4]]))
assert isinstance(network.layers[3], FullyConnectedLayer)
assert np.allclose(network.layers[3].weights,
np.array([[-8, -5], [7, 4], [-6, -3]]))
assert np.allclose(network.layers[3].biases, np.array([[2, 1]]))
assert isinstance(network.layers[4], HardTanhLayer)
def test_squeezenet_from_onnx():
"""Tests loading a SqueezeNet Network from ONNX format.
"""
network = Network.from_file("external/onnx_squeezenet/squeezenet1.1.onnx")
assert len(network.layers) == 40