Yamnet clustering AttributeError: Exception encountered when calling layer "tf.__operators__.add" (type TFOpLambda).
MATTYGILO opened this issue · 1 comments
MATTYGILO commented
Prior to filing: check that this should be a bug instead of a feature request. Everything supported, including the compatible versions of TensorFlow, is listed in the overview page of each technique. For example, the overview page of quantization-aware training is here. An issue for anything not supported should be a feature request.
Describe the bug
I am using clustering to reduce the size of a yamnet model
System information
TensorFlow version (installed from source or binary):
2.7.0
TensorFlow Model Optimization version (installed from source or binary):
Installed using wheel from GitHub on an m1 Mac
Python version:
3.10
Describe the expected behavior
final_model = tfmot.clustering.keras.strip_clustering(clustered_model)
Strip the clustered model
Describe the current behavior
Failing with this error
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [10], in <cell line: 1>()
----> 1 final_model = tfmot.clustering.keras.strip_clustering(clustered_model)
3 final_model.summary()
File ~/.local/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/clustering/keras/cluster.py:356, in strip_clustering(model)
353 return layer
355 # Just copy the model with the right callback
--> 356 return tf.keras.models.clone_model(
357 model, input_tensors=None, clone_function=_strip_clustering_wrapper)
File ~/miniforge3/envs/net/lib/python3.10/site-packages/keras/models.py:456, in clone_model(model, input_tensors, clone_function)
453 return _clone_sequential_model(
454 model, input_tensors=input_tensors, layer_fn=clone_function)
455 else:
--> 456 return _clone_functional_model(
457 model, input_tensors=input_tensors, layer_fn=clone_function)
File ~/miniforge3/envs/net/lib/python3.10/site-packages/keras/models.py:197, in _clone_functional_model(model, input_tensors, layer_fn)
193 model_configs, created_layers = _clone_layers_and_model_config(
194 model, new_input_layers, layer_fn)
195 # Reconstruct model from the config, using the cloned layers.
196 input_tensors, output_tensors, created_layers = (
--> 197 functional.reconstruct_from_config(model_configs,
198 created_layers=created_layers))
199 metrics_names = model.metrics_names
200 model = Model(input_tensors, output_tensors, name=model.name)
File ~/miniforge3/envs/net/lib/python3.10/site-packages/keras/engine/functional.py:1338, in reconstruct_from_config(config, custom_objects, created_layers)
1336 while layer_nodes:
1337 node_data = layer_nodes[0]
-> 1338 if process_node(layer, node_data):
1339 layer_nodes.pop(0)
1340 else:
1341 # If a node can't be processed, stop processing the nodes of
1342 # the current layer to maintain node ordering.
File ~/miniforge3/envs/net/lib/python3.10/site-packages/keras/engine/functional.py:1282, in reconstruct_from_config.<locals>.process_node(layer, node_data)
1279 if not layer._preserve_input_structure_in_config:
1280 input_tensors = (
1281 base_layer_utils.unnest_if_single_tensor(input_tensors))
-> 1282 output_tensors = layer(input_tensors, **kwargs)
1284 # Update node index map.
1285 output_index = (tf.nest.flatten(output_tensors)[0].
1286 _keras_history.node_index)
File ~/miniforge3/envs/net/lib/python3.10/site-packages/keras/utils/traceback_utils.py:67, in filter_traceback.<locals>.error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
File ~/miniforge3/envs/net/lib/python3.10/site-packages/tensorflow/python/ops/math_ops.py:1733, in _add_dispatch(x, y, name)
1712 """The operation invoked by the `Tensor.__add__` operator.
1713
1714 Purpose in the API:
(...)
1729 The result of the elementwise `+` operation.
1730 """
1731 if not isinstance(y, ops.Tensor) and not isinstance(
1732 y, sparse_tensor.SparseTensor):
-> 1733 y = ops.convert_to_tensor(y, dtype_hint=x.dtype.base_dtype, name="y")
1734 if x.dtype == dtypes.string:
1735 return gen_math_ops.add(x, y, name=name)
Code to reproduce the issue
... My custom yamnet, perhaps get one from tf hub ...
import tensorflow_model_optimization as tfmot
cluster_weights = tfmot.clustering.keras.cluster_weights
CentroidInitialization = tfmot.clustering.keras.CentroidInitialization
clustering_params = {
'number_of_clusters': 16,
'cluster_centroids_init': CentroidInitialization.LINEAR
}
# Cluster a whole model
clustered_model = cluster_weights(model, **clustering_params)
clustered_model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.00001),
loss="categorical_crossentropy",
metrics=["accuracy"],
run_eagerly=True
)
clustered_model.summary()
final_model = tfmot.clustering.keras.strip_clustering(clustered_model)
final_model.summary()
Similar problem. Mine is for clustering though:
#867
MATTYGILO commented
Ok I've fixed the problem using this code:
def cluster_model(model):
# Info required for clustering
cluster_weights = tfmot.clustering.keras.cluster_weights
CentroidInitialization = tfmot.clustering.keras.CentroidInitialization
clustering_params = {
'number_of_clusters': 16,
'cluster_centroids_init': CentroidInitialization.LINEAR
}
def apply_clustering_to_dense(layer):
accepted_layers = [
tf.keras.layers.Dense,
tf.keras.layers.GlobalAveragePooling2D,
tf.keras.layers.ReLU,
tf.keras.layers.Conv2D,
tf.keras.layers.BatchNormalization,
tf.keras.layers.DepthwiseConv2D,
tf.keras.layers.Activation,
]
for accepted in accepted_layers:
if isinstance(layer, accepted):
return cluster_weights(layer, **clustering_params)
return layer
return tf.keras.models.clone_model(
model,
clone_function=apply_clustering_to_dense,
)