Início / coding / Perguntas / 79565183
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Shinobu HUYUGIRI
Asked: 2025-04-10 03:22:18 +0800 CST

Como posso converter um modelo "BigGAN" do TensorFlow 1 no Kaggle para o formato TensorFlow Lite?

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Como posso converter um modelo "BigGAN" do TensorFlow 1 no Kaggle para o formato TensorFlow Lite?

https://www.tensorflow.org/hub/tutorials/biggan_generation_with_tf_hub?hl=ja

Estou tentando implementar o seguinte código-fonte para definir um modelo TensorFlow.Keras e convertê-lo para o formato TensorFlow Lite, mas quando tento definir um modelo na API de função usando a camada criada com hub.KerasLayer, ocorre a seguinte exceção:

E também não sei como especificar os argumentos para model.build().

Este modelo requer múltiplas entradas, então não consegui usar a solução para construir um modelo usando a API Sequencial mostrada na URL abaixo. Como posso salvar um modelo com formato de entrada (1, Nenhum, Nenhum, 3) com Nenhum definido como 256?

Existem boas soluções?

Código-fonte não funcional

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tensorflow.compat.v1 import keras
from tensorflow.compat.v1.keras import layers
import tensorflow_hub as hub

input_truncation = keras.Input(shape=(), name='truncation')
input_y = keras.Input(shape=(1000, ), name='y')
input_z = keras.Input(shape=(128, ), name='z')
hub_layer = hub.KerasLayer(
            "https://www.kaggle.com/models/deepmind/biggan/TensorFlow1/128/2",
            trainable=False,
            signature="default", 
            signature_outputs_as_dict=True,
            input_shape = [[], [1000], [128]],
            output_shape = [128, 128, 3],
        )

### -->> TypeError occur HERE
output = hub_layer([input_truncation, input_y, input_z])
### <<-- TypeError occur HERE

model = tf.keras.models.Model(inputs=[input_truncation, input_y, input_z], outputs=[output])

### -->> How to build this model 
model.build([1], [1, 1000], [1, 128])
### <<-- How to build this model

model.summary()
model.save("biggan-128")

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

a exceção que ocorreu

 TypeError
in user code:

    File "/home/shino/anaconda3/envs/movenet/lib/python3.10/site-packages/tensorflow_hub/keras_layer.py", line 242, in call  *
        result = f()

    TypeError: pruned(truncation, y, z) takes 0 positional arguments, got 1.
  File "/tmp/__autograph_generated_fileedgegq8b.py", line 74, in tf__call
    ag__.if_stmt(ag__.not_(ag__.ld(self)._has_training_argument), if_body_3, else_body_3, get_state_3, set_state_3, ('result', 'training'), 1)
  File "/tmp/__autograph_generated_fileedgegq8b.py", line 37, in if_body_3
    result = ag__.converted_call(ag__.ld(f), (), None, fscope)
TypeError: pruned(truncation, y, z) takes 0 positional arguments, got 1.

During handling of the above exception, another exception occurred:

  File "/tmp/__autograph_generated_fileedgegq8b.py", line 37, in if_body_3
    result = ag__.converted_call(ag__.ld(f), (), None, fscope)
  File "/tmp/__autograph_generated_fileedgegq8b.py", line 74, in tf__call
    ag__.if_stmt(ag__.not_(ag__.ld(self)._has_training_argument), if_body_3, else_body_3, get_state_3, set_state_3, ('result', 'training'), 1)
  File "/home/shino/sandbox/python/biggan/biggan_export.py", line 21, in <module>
    output = hub_layer([input_truncation, input_y, input_z])
TypeError: in user code:

    File "/home/shino/anaconda3/envs/movenet/lib/python3.10/site-packages/tensorflow_hub/keras_layer.py", line 242, in call  *
        result = f()

    TypeError: pruned(truncation, y, z) takes 0 positional arguments, got 1.

requisitos.txt

absl-py==2.2.1
astunparse==1.6.3
cachetools==5.5.2
certifi==2025.1.31
charset-normalizer==3.4.1
coloredlogs==15.0.1
flatbuffers==1.12
gast==0.4.0
google-auth==2.38.0
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
grpcio==1.71.0
h5py==3.13.0
humanfriendly==10.0
idna==3.10
keras==2.9.0
Keras-Preprocessing==1.1.2
libclang==18.1.1
Markdown==3.7
MarkupSafe==3.0.2
mpmath==1.3.0
numpy==1.26.4
oauthlib==3.2.2
onnx==1.14.1
onnx-graphsurgeon==0.5.7
onnx2tf==1.26.9
onnxruntime==1.21.0
opt_einsum==3.4.0
packaging==24.2
protobuf==3.20.3
psutil==7.0.0
pyasn1==0.6.1
pyasn1_modules==0.4.2
requests==2.32.3
requests-oauthlib==2.0.0
rsa==4.9
six==1.17.0
sng4onnx==1.0.4
sympy==1.13.3
tensorboard==2.9.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.9.0
tensorflow-estimator==2.9.0
tensorflow-hub==0.16.1
tensorflow-io-gcs-filesystem==0.37.1
tensorflow-neuron==1.0
termcolor==3.0.0
tf-keras==2.14.1
tf2onnx==1.13.0
typing_extensions==4.13.0
urllib3==2.3.0
Werkzeug==3.1.3
wrapt==1.17.2
tensorflow

1 respostas

  1. Best Answer

    Eu resolvi sozinho.

    Modificando o código-fonte da seguinte forma, conseguimos converter com sucesso o BigGAN para o formato TensorFlow Lite.

    Obrigado.

    import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_hub as hub

model_url = "https://www.kaggle.com/models/deepmind/biggan/TensorFlow1/128/2"
input_truncation = keras.Input(batch_shape=(), name='truncation')
input_y = keras.Input(shape=(1000, ), name='y')
input_z = keras.Input(shape=(120, ), name='z')
hub_layer = hub.KerasLayer(
            model_url,
            trainable=False,
            signature="default", 
            signature_outputs_as_dict=True,
        )

inputs = dict(y=input_y, z=input_z, truncation=input_truncation)
output = hub_layer(inputs)
model = tf.keras.models.Model(inputs=[input_truncation, input_y, input_z], outputs=[output])
model.build(([1], [1, 1000], [1, 120]))
model.summary()
model.save("biggan-128")

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("biggan-128.tflite", "wb").write(tflite_model)
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