def test_conv3d():
"""
Test the layer.Conv3d class and its default attributes.
"""
conv3d = layer.Conv3d(8)
assert isinstance(conv3d._conv3d, tf.keras.layers.Conv3D)
assert conv3d._conv3d.kernel_size == (3, 3, 3)
assert conv3d._conv3d.strides == (1, 1, 1)
assert conv3d._conv3d.padding == "same"
assert isinstance(conv3d._conv3d.activation, type(tf.keras.activations.linear))
assert conv3d._conv3d.use_bias is True
def test_conv3d():
"""
Test the layer.Conv3d class and its default attributes. No need to test the call() function since its
a tensorflow class
"""
conv3d = layer.Conv3d(8)
assert isinstance(conv3d._conv3d, type(tf.keras.layers.Conv3D(8, 3)))
assert conv3d._conv3d.kernel_size == (3, 3, 3)
assert conv3d._conv3d.strides == (1, 1, 1)
assert conv3d._conv3d.padding == "same"
assert isinstance(conv3d._conv3d.activation,
type(tf.keras.activations.linear))
assert conv3d._conv3d.use_bias is True
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()))
def test_residual3dBlock():
"""
Test the layer.Residual3dBlock class and its default attributes. No need to test the call() function since a
concatenation of tensorflow classes
"""
res3dBlock = layer.Residual3dBlock(8)
assert isinstance(res3dBlock._conv3d_block, type(layer.Conv3dBlock(8)))
assert res3dBlock._conv3d_block._conv3d._conv3d.kernel_size == (3, 3, 3)
assert res3dBlock._conv3d_block._conv3d._conv3d.strides == (1, 1, 1)
assert isinstance(res3dBlock._conv3d, type(layer.Conv3d(8)))
assert res3dBlock._conv3d._conv3d.use_bias is False
assert res3dBlock._conv3d._conv3d.kernel_size == (3, 3, 3)
assert res3dBlock._conv3d._conv3d.strides == (1, 1, 1)
assert isinstance(res3dBlock._act._act, type(tf.keras.activations.relu))
assert isinstance(res3dBlock._norm._norm,
type(tf.keras.layers.BatchNormalization()))
def test_init_conv3dWithResize():
"""
Test the layer.Conv3dWithResize class, its default attributes and it's call function.
"""
batch_size = 5
channels = 4
input_size = (32, 32, 16)
output_size = (62, 62, 24)
filters = 8
input_tensor_size = (batch_size, ) + input_size + (channels, )
output_tensor_size = (batch_size, ) + output_size + (filters, )
model = layer.Conv3dWithResize(output_size, filters)
assert model._output_shape == output_size
assert isinstance(model._conv3d, type(layer.Conv3d(filters)))
# Pass an input of all zeros
inputs = np.zeros(input_tensor_size)
# Get outputs by calling
output = model.call(inputs)
# Expected shape is (5, 1, 2, 3, 3)
assert all(x == y for x, y in zip(output_tensor_size, output.shape))