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()))