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[Frontend][Keras] Support nested layers recursively in keras frontend (…
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…#7949)

* Support nested layers recursively in keras frontend

* Fix lint

* Fix issue

* Fix formatting

* Fix unit test
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Trevor Morris committed May 6, 2021
1 parent 9bf213d commit d181867
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Showing 2 changed files with 96 additions and 51 deletions.
133 changes: 82 additions & 51 deletions python/tvm/relay/frontend/keras.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1101,6 +1101,7 @@ def keras_op_to_relay(inexpr, keras_layer, outname, etab):
for t_idx, out in enumerate(outs):
name = outname + ":" + str(t_idx)
etab.set_expr(name, out)
return outs


def from_keras(model, shape=None, layout="NCHW"):
Expand Down Expand Up @@ -1136,6 +1137,85 @@ def _convert_input_layer(keras_layer):
input_shape = shape[input_name] if shape is not None and input_name in shape else None
etab.set_expr(input_name, new_var(input_name, shape=input_shape))

def _convert_layer(keras_layer, etab, scope=""):
inbound_nodes = (
keras_layer.inbound_nodes
if hasattr(keras_layer, "inbound_nodes")
else keras_layer._inbound_nodes
if hasattr(keras_layer, "_inbound_nodes")
else None
)
if inbound_nodes is None:
raise TypeError(
"Unknown layer type or unsupported Keras version : {}".format(keras_layer)
)
outs = []
for node_idx, node in enumerate(inbound_nodes):
# If some nodes in imported model are not relevant to the current model,
# skip such layers.
# - In Keras, model._network_nodes contains keys of all nodes relevant to the
# current model;
# - In tf.Keras, this is already done as part of tensorflow.keras.network.get_config
if (
not is_tf_keras
and not model._node_key(keras_layer, node_idx) in model._network_nodes
):
continue
inexpr = []
# Since Keras allows creating multiple layers from the same name instance,
# we append node index to the expr name to make it unique.
# The one exception is InputLayer. Changing input variable names after conversion
# would confuse users, so we should keep them as far as possible. Fortunately,
# they are named uniquely to input_1, input_2, input_3... by default.
# node_indices attribute removed in tensorflow 2.3, however iterate_inbound() can
# be used
if hasattr(node, "node_indices"):
zip_node = zip(
_as_list(node.inbound_layers),
_as_list(node.node_indices),
_as_list(node.tensor_indices),
_as_list(node.input_tensors),
)
node_attributes = zip_node
else:
node_attributes = node.iterate_inbound()
for inbound_layer, n_idx, t_idx, _ in node_attributes:
if isinstance(inbound_layer, input_layer_class):
expr_name = inbound_layer.name
_convert_input_layer(inbound_layer)
else:
expr_name = scope + inbound_layer.name + ":" + str(n_idx) + ":" + str(t_idx)
expr = etab.get_expr(expr_name)
inexpr.append(expr)

# Handle nested layers
if hasattr(keras_layer, "layers"):
input_index = 0
for layer in keras_layer.layers:
if isinstance(layer, input_layer_class):
# Replace input layer with inbound node
etab.set_expr(layer.name, inexpr[input_index])
input_index += 1
else:
# Convert child layer. Prepend scope with parent layer name.
layer_outs = _convert_layer(layer, etab, keras_layer.name + "_" + scope)

# Get output of last child layer and mark as output of parent.
outname = keras_layer.name + ":" + str(node_idx)
for t_idx, out in enumerate(layer_outs):
name = outname + ":" + str(t_idx)
etab.set_expr(name, out)
outs.extend(layer_outs)
else:
if len(inexpr) == 1:
inexpr = inexpr[0]
outs.extend(
keras_op_to_relay(
inexpr, keras_layer, scope + keras_layer.name + ":" + str(node_idx), etab
)
)
return outs

is_tf_keras = _check_model_is_tf_keras()

if not is_tf_keras:
Expand Down Expand Up @@ -1174,57 +1254,8 @@ def _convert_input_layer(keras_layer):
if isinstance(keras_layer, input_layer_class):
_convert_input_layer(keras_layer)
else:
inbound_nodes = (
keras_layer.inbound_nodes
if hasattr(keras_layer, "inbound_nodes")
else keras_layer._inbound_nodes
if hasattr(keras_layer, "_inbound_nodes")
else None
)
if inbound_nodes is None:
raise TypeError(
"Unknown layer type or unsupported Keras version : {}".format(keras_layer)
)
for node_idx, node in enumerate(inbound_nodes):
# If some nodes in imported model are not relevant to the current model,
# skip such layers.
# - In Keras, model._network_nodes contains keys of all nodes relevant to the
# current model;
# - In tf.Keras, this is already done as part of tensorflow.keras.network.get_config
if (
not is_tf_keras
and not model._node_key(keras_layer, node_idx) in model._network_nodes
):
continue
inexpr = []
# Since Keras allows creating multiple layers from the same name instance,
# we append node index to the expr name to make it unique.
# The one exception is InputLayer. Changing input variable names after conversion
# would confuse users, so we should keep them as far as possible. Fortunately,
# they are named uniquely to input_1, input_2, input_3... by default.
# node_indices attribute removed in tensorflow 2.3, however iterate_inbound() can
# be used
if hasattr(node, "node_indices"):
zip_node = zip(
_as_list(node.inbound_layers),
_as_list(node.node_indices),
_as_list(node.tensor_indices),
_as_list(node.input_tensors),
)
node_attributes = zip_node
else:
node_attributes = node.iterate_inbound()
for inbound_layer, n_idx, t_idx, _ in node_attributes:
if isinstance(inbound_layer, input_layer_class):
expr_name = inbound_layer.name
_convert_input_layer(inbound_layer)
else:
expr_name = inbound_layer.name + ":" + str(n_idx) + ":" + str(t_idx)
expr = etab.get_expr(expr_name)
inexpr.append(expr)
if len(inexpr) == 1:
inexpr = inexpr[0]
keras_op_to_relay(inexpr, keras_layer, keras_layer.name + ":" + str(node_idx), etab)
_convert_layer(keras_layer, etab)

# model._output_coordinates contains out_node(oc[0]), node_index(oc[1]) and tensor_index(oc[2])
# Get all output nodes in etab using the name made from above values.
# The out exprs were added to etab in keras_op_to_relay using this name.
Expand Down
14 changes: 14 additions & 0 deletions tests/python/frontend/keras/test_forward.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -579,6 +579,20 @@ def test_forward_global_pool3d(self, keras):
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, layout="NDHWC")

def test_forward_nested_layers(self, keras):
sub_model = keras.applications.MobileNet(
include_top=False, weights="imagenet", input_shape=(224, 224, 3)
)
keras_model = keras.Sequential(
[
sub_model,
keras.layers.GlobalAveragePooling2D(),
keras.layers.Dense(1024, activation="relu"),
keras.layers.Dense(2, activation="sigmoid"),
]
)
verify_keras_frontend(keras_model)


if __name__ == "__main__":
for k in [keras, tf_keras]:
Expand Down

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