def test_efficientnet():
l = ext.layer('EfficientNet')
n = l((None, None, 8))
assert len(n.layers) == 334
assert n.layers[0].input_shape == [(None, None, None, 8)]
out_shape = n.compute_output_shape((None, 512, 512, 8)).as_list()
assert out_shape == [None, 16, 16, 352]
def test_gaussian_sample():
l = ext.layer('GaussianSample')
n = l()
assert n.get_config()['kl_loss']
result = n((tf.zeros((1, 3, 3, 3)), tf.ones((1, 3, 3, 3))))
assert result.shape == (1, 3, 3, 3)
assert isinstance(n.callback(), tf.keras.callbacks.Callback)
def __init__(self, layer_type, layer_name, inputs, params, all_layers):
"""
all_layers is a name indexed dictionary of LayerWrappers for all the layers,
shared between them.
"""
self._layer_type = layer_type
self.name = layer_name
self._inputs = inputs
lc = extensions.layer(layer_type)
if lc is None:
lc = getattr(tensorflow.keras.layers, layer_type, None)
if lc is None:
raise ValueError('Unknown layer type %s.' % (layer_type))
self.layer = lc(**params)
self._sub_layers = None
self._tensor = None
all_layers[layer_name] = self
self._all_layers = all_layers