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ValueError: `prune_low_magnitude` can only prune an object of the following types: keras.models.Sequential, keras functional model, keras.layers.Layer, list of keras.layers.Layer. You passed an object of type: Conv2D.

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Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
38/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Estancado
Stack tecnológico
keras, python, tensorflow
Área
machine-learning

Línea de trabajo

Empieza con la función prune_model y la llamada prune_low_magnitude mostrada en mbv2_prune.py. Reproduce el fallo usando MobileNetV2 con TensorFlow 2.16.2, TensorFlow Model Optimization 0.8.0 y Python 3.10.16, y después inspecciona por qué se rechaza Conv2D a pesar de heredar de Layer. Se considera terminado cuando la reproducción documentada ya no produce este ValueError y las capas afectadas se pueden podar.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

bug

Describe the bug
I am trying to prune MobileNetV2 model using prune_low_magnitude but am running into the following error.

2025-01-19 17:41:03.439078: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Loading MobileNetV2 model...
Pruning the model...
Traceback (most recent call last):
  File "/Users/esp/Projects/gen_models/mbv2_prune.py", line 89, in <module>
    full_workflow()
  File "/Users/esp/Projects/gen_models/mbv2_prune.py", line 81, in full_workflow
    pruned_model = prune_model(model)
  File "/Users/esp/Projects/gen_models/mbv2_prune.py", line 27, in prune_model
    pruned_layer = prune_low_magnitude(tf.keras.layers.Conv2D(
  File "/Users/esp/Projects/gen_models/venv/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/keras/metrics.py", line 74, in inner
    raise error
  File "/Users/esp/Projects/gen_models/venv/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/keras/metrics.py", line 69, in inner
    results = func(*args, **kwargs)
  File "/Users/esp/Projects/gen_models/venv/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/sparsity/keras/prune.py", line 216, in prune_low_magnitude
    raise ValueError(
ValueError: `prune_low_magnitude` can only prune an object of the following types: keras.models.Sequential, keras functional model, keras.layers.Layer, list of keras.layers.Layer. You passed an object of type: Conv2D.

System information

TensorFlow version (installed from source or binary): 2.16.2

TensorFlow Model Optimization version (installed from source or binary): 0.8.0

Python version: 3.10.16

Describe the expected behavior
The layers should be pruned as Conv2D inherits Layer which as the error says is supported.

Describe the current behavior
Even though Conv2D inherits Layer which as the error says is supported, the code fails.

Code to reproduce the issue

def load_model_mobilenetv2():
    model = MobileNetV2(weights="imagenet", include_top=True, alpha=0.35, input_shape=(224, 224, 3))
    return model

def prune_model(base_model):
    pruning_schedule = PolynomialDecay(
        initial_sparsity=0.0,
        final_sparsity=0.5,
        begin_step=0,
        end_step=1000
    )
    
    pruned_model = Sequential()
    for layer in base_model.layers:
        if isinstance(layer, tf.keras.layers.Dense):
            pruned_layer = prune_low_magnitude(layer, pruning_schedule)
            pruned_model.add(pruned_layer)
        elif isinstance(layer, tf.keras.layers.Conv2D):
            pruned_layer = prune_low_magnitude(tf.keras.layers.Conv2D(
                layer.filters,
                layer.kernel_size,
                layer.strides,
                layer.padding,
                layer.data_format,
                layer.dilation_rate,
                layer.groups,
                layer.activation,
                layer.use_bias,
                layer.kernel_initializer,
                layer.bias_initializer,
                layer.kernel_regularizer,
                layer.bias_regularizer,
                layer.activity_regularizer,
                layer.kernel_constraint,
                layer.bias_constraint,
            ), pruning_schedule)
            pruned_model.add(pruned_layer)
        else:
            pruned_model.add(layer)
    
    # This doesn't work too
    # for layer in base_model.layers:
    #     if isinstance(layer, tf.keras.layers.Dense) or isinstance(layer, tf.keras.layers.Conv2D):
    #         pruned_layer = prune_low_magnitude(layer, pruning_schedule)
    #         pruned_model.add(pruned_layer)
    #     else:
    #         pruned_model.add(layer)

    pruned_model.build(input_shape=(None, 224, 224, 3))
    pruned_model.compile(
        optimizer=tf.keras.optimizers.Adam(),
        loss="categorical_crossentropy",
        metrics=["accuracy"]
    )
    return pruned_model
Lenguaje dominante
Python
Estrellas
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Forks
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