def test_basic_attributes(self):
"""Setup network and check basic parameters like TF variable names,
number of layers, size of last feature map...
"""
ft_dim = Shape(None, 64, 64)
num_cls, num_layers = 10, 7
# Compute the image dimensions required for a 2x2 feature size.
im_dim = orpac_net.waveletToImageDim(ft_dim)
net = orpac_net.Orpac(self.sess, im_dim, num_layers, num_cls, None, False)
self.sess.run(tf.global_variables_initializer())
assert net.session() is self.sess
# The feature size must be 1/8 of the image size because the network
# downsamples every second layer, and we specified 7 layers.
assert num_layers == net.numLayers() == 7
assert net.outputShape().hw() == ft_dim.hw()
# Ensure we can query all biases and weights. Also verify the data type
# inside the network.
g = tf.get_default_graph().get_tensor_by_name
for i in range(num_layers):
# These must exist in the graph.
assert g(f'orpac/W{i}:0') is not None
assert g(f'orpac/b{i}:0') is not None
assert net.getBias(i).dtype == np.float32
assert net.getWeight(i).dtype == np.float32
def setup_class(cls):
# Feature dimension will only be 2x2 to simplify testing and debugging.
ft_dim = Shape(None, 64, 64)
num_cls, num_layers = 10, 7
# Compute the image dimensions required for a 2x2 feature size.
im_dim = orpac_net.waveletToImageDim(ft_dim)
# Create Tensorflow session and dummy network. The network is such that
# the feature size is only 2x2 because this makes testing easier.
cls.sess = tf.Session()
cls.net = orpac_net.Orpac(
cls.sess, im_dim, num_layers, num_cls, None, train=False)
assert cls.net.outputShape().hw() == ft_dim.hw()