def test_localNetUpSampleResnetBlock():
"""
Test the layer.LocalNetUpSampleResnetBlock class, its default attributes and its call() function.
"""
batch_size = 5
channels = 4
input_size = (32, 32, 16)
output_size = (64, 64, 32)
nonskip_tensor_size = (batch_size, ) + input_size + (channels, )
skip_tensor_size = (batch_size, ) + output_size + (channels, )
# Test __init__() and build()
model = layer.LocalNetUpSampleResnetBlock(8)
model.build([nonskip_tensor_size, skip_tensor_size])
assert model._filters == 8
assert model._use_additive_upsampling is True
assert isinstance(model._deconv3d_block, type(layer.Deconv3dBlock(8)))
assert isinstance(model._additive_upsampling,
type(layer.AdditiveUpSampling(output_size)))
assert isinstance(model._conv3d_block, type(layer.Conv3dBlock(8)))
assert isinstance(model._residual_block,
type(layer.LocalNetResidual3dBlock(8)))
def test_local_net_residual3d_block():
"""
Test the layer.LocalNetResidual3dBlock class's,
default attributes and call() function.
"""
# Test __init__()
conv3d_block = layer.LocalNetResidual3dBlock(8)
assert isinstance(conv3d_block._conv3d, layer.Conv3d)
assert conv3d_block._conv3d._conv3d.kernel_size == (3, 3, 3)
assert conv3d_block._conv3d._conv3d.strides == (1, 1, 1)
assert conv3d_block._conv3d._conv3d.padding == "same"
assert conv3d_block._conv3d._conv3d.use_bias is False
assert isinstance(conv3d_block._act._act, type(tf.keras.activations.relu))
assert isinstance(conv3d_block._norm._norm, tf.keras.layers.BatchNormalization)
def test_localNetResidual3dBlock():
"""
Test the layer.LocalNetResidual3dBlock class, its default attributes and its call() function. No need to test
the call() function since its a concatenation of tensorflow classes.
"""
# Test __init__()
conv3dBlock = layer.LocalNetResidual3dBlock(8)
assert isinstance(conv3dBlock._conv3d, type(layer.Conv3d(8)))
assert conv3dBlock._conv3d._conv3d.kernel_size == (3, 3, 3)
assert conv3dBlock._conv3d._conv3d.strides == (1, 1, 1)
assert conv3dBlock._conv3d._conv3d.padding == "same"
assert conv3dBlock._conv3d._conv3d.use_bias is False
assert isinstance(conv3dBlock._act._act, type(tf.keras.activations.relu))
assert isinstance(conv3dBlock._norm._norm,
type(tf.keras.layers.BatchNormalization()))