def test_call_global_net():
"""
Asserting that output shape of globalnet Call method
is correct.
"""
out = 3
im_size = (1, 2, 3)
batch_size = 5
# initialising GlobalNet instance
global_test = g.GlobalNet(
image_size=im_size,
out_channels=out,
num_channel_initial=3,
extract_levels=[1, 2, 3],
out_kernel_initializer="softmax",
out_activation="softmax",
)
# pass an input of all zeros
inputs = tf.constant(
np.zeros(
(batch_size, im_size[0], im_size[1], im_size[2], out), dtype=np.float32
)
)
# get outputs by calling
ddf, theta = global_test.call(inputs)
assert ddf.shape == (batch_size, *im_size, 3)
assert theta.shape == (batch_size, 4, 3)
def test_init_GlobalNet():
"""
Testing init of GlobalNet is built as expected.
"""
# Initialising GlobalNet instance
global_test = g.GlobalNet(
image_size=[1, 2, 3],
out_channels=3,
num_channel_initial=3,
extract_levels=[1, 2, 3],
out_kernel_initializer="softmax",
out_activation="softmax",
)
# Asserting initialised var for extract_levels is the same - Pass
assert global_test._extract_levels == [1, 2, 3]
# Asserting initialised var for extract_max_level is the same - Pass
assert global_test._extract_max_level == 3
# self reference grid
# assert global_test.reference_grid correct shape, Pass
assert global_test.reference_grid.shape == [1, 2, 3, 3]
# assert correct reference grid returned, Pass
expected_ref_grid = tf.convert_to_tensor(
[[
[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 2.0]],
[[0.0, 1.0, 0.0], [0.0, 1.0, 1.0], [0.0, 1.0, 2.0]],
]],
dtype=tf.float32,
)
assert is_equal_tf(global_test.reference_grid, expected_ref_grid)
# Testing constant initializer
# We initialize the expected tensor and initialise another from the
# class variable using tf.Variable
test_tensor_return = tf.convert_to_tensor(
[[1.0, 0.0], [0.0, 0.0], [0.0, 1.0], [0.0, 0.0], [0.0, 0.0],
[1.0, 0.0]],
dtype=tf.float32,
)
global_return = tf.Variable(
global_test.transform_initial(shape=[6, 2], dtype=tf.float32))
# Asserting they are equal - Pass
assert is_equal_tf(test_tensor_return,
tf.convert_to_tensor(global_return, dtype=tf.float32))
# Assert downsample blocks type is correct, Pass
assert all(
isinstance(item, layer.DownSampleResnetBlock)
for item in global_test._downsample_blocks)
# Assert number of downsample blocks is correct (== max level), Pass
assert len(global_test._downsample_blocks) == 3
# Assert conv3dBlock type is correct, Pass
assert isinstance(global_test._conv3d_block, layer.Conv3dBlock)
# Asserting type is dense_layer, Pass
assert isinstance(global_test._dense_layer, layer.Dense)
def test_call_GlobalNet():
"""
Asserting that output shape of globalnet Call method
is correct.
"""
out = 3
im_size = [1, 2, 3]
# Initialising GlobalNet instance
global_test = g.GlobalNet(
image_size=im_size,
out_channels=out,
num_channel_initial=3,
extract_levels=[1, 2, 3],
out_kernel_initializer="softmax",
out_activation="softmax",
)
# Pass an input of all zeros
inputs = np.zeros((5, im_size[0], im_size[1], im_size[2], out))
# Get outputs by calling
output = global_test.call(inputs)
# Expected shape is (5, 1, 2, 3, 3)
assert all(x == y for x, y in zip(inputs.shape, output.shape))
def test_global_return():
"""
Testing that build_backbone func returns an object
of type GlobalNet from backbone module when initialised
with the associated GlobalNet config.
"""
out = util.build_backbone(
image_size=(1, 2, 3),
out_channels=1,
model_config={
"backbone": "global",
"global": {
"num_channel_initial": 4,
"extract_levels": [1, 2, 3]
},
},
method_name="ddf",
)
assert isinstance(
out,
type(
global_net.GlobalNet([1, 2, 3], 4, 4, [1, 2, 3], "he_normal",
"sigmoid")),
)