TFLite 2.16.1 conversions fail with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'" #63867

schwefeljm · 2024-03-18T14:34:37Z

1. System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Debian 13 - Trixie
TensorFlow installation (pip package or built from source):
pip
TensorFlow library (version, if pip package or github SHA, if built from source):
2.16.0-rc0 & 2.16.1

I am brand new to TensorFlow and welcome any suggestions.
I tested the exact same code on 2.15.0.post1 and 2.16.x. It runs on 15 and errors out on 16

2. Code

Provide code to help us reproduce your issues using one of the following options:

Option B: Paste your code here or provide a link to a custom end-to-end colab

def main():

    # Removed dependance on argsparser()
    # args = argsparer()

    # batch_size = args.batchSize # 32
    batch_size = 32
    # img_height = args.height    # 128
    img_height = 128
    # img_width = args.width      # 128
    img_width = 128
    activation1 = 'swish'
    activation2 = 'elu'
    optimizer = tf.keras.optimizers.AdamW(learning_rate=0.0005)
    # epochs = args.epochs        # 1
    epochs = 1

    train_data_dir = pathlib.Path("{}/{}/".format(args.inputDir, args.trainingSubDir)).with_suffix('')
    val_data_dir = pathlib.Path("birds/birds/valid/".format(args.inputDir, args.validationSubDir)).with_suffix('')

    # assume all files in training directory are readable image files.
    image_count = len(list(train_data_dir.glob('*/*.*')))

    AUTOTUNE = tf.data.AUTOTUNE

    train_ds = tf.keras.utils.image_dataset_from_directory(
        train_data_dir,
        seed=random.randint(1, 10000),
        image_size=(img_height, img_width),
        batch_size=batch_size)

    val_ds = tf.keras.utils.image_dataset_from_directory(
        val_data_dir,
        # validation_split=0.1,
        # subset="validation",
        seed=random.randint(1, 10000),
        image_size=(img_height, img_width),
        batch_size=batch_size)

    num_classes = len(train_ds.class_names)
    print("Training directory '{}' contains {} images in {} categories.".format(train_data_dir, image_count, num_classes))

    normalization_layer = layers.Rescaling(1. / 255)
    train_ds_norm = train_ds.map(lambda x, y: (normalization_layer(x), y),
                                num_parallel_calls=tf.data.AUTOTUNE,
                                deterministic=False)
    val_ds_norm = val_ds.map(lambda x, y: (normalization_layer(x), y),
                                num_parallel_calls=tf.data.AUTOTUNE,
                                deterministic=False)

    # train_ds = train_ds.shuffle(1000).prefetch(buffer_size=AUTOTUNE)
    # val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)

    data_augmentation = tf.keras.Sequential(
        [
            layers.RandomFlip("horizontal",
                              input_shape=(img_height,
                                           img_width,
                                           3)),
            layers.RandomRotation(0.1),
            layers.RandomZoom(0.1),
        ]
    )

    model = Sequential([
        data_augmentation,
        # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
        # layers.Conv2D(16, 3, padding='same', activation=activation1),
        # layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Conv2D(48, 3, padding='same', activation=activation2),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Dropout(0.15),
        layers.Flatten(),
        layers.Dense(128, activation=activation2),
        layers.Dense(num_classes)
    ])

    model.compile(optimizer=optimizer,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

    history = model.fit(
        train_ds_norm,
        validation_data=val_ds_norm,
        epochs=epochs)


    # Convert the model.
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    converter.experimental_new_converter = True
    converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.
        tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.
    ]
    tflite_model = converter.convert()

    # Save the model.
    with open('model.tflite', 'wb') as f:
        f.write(tflite_model)

(You can paste links or attach files by dragging & dropping them below)

Include code to invoke the TFLite Converter Python API and the errors.
Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.

3. Failure after conversion

Conversion completely fails with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'. Did you mean: '_set_save_spec'?" See below for complete log and traceback.

I have had to revert to 2.15.0.post1 to get model to convert and save as TFLite.

5. (optional) Any other info / logs

024-03-18 09:24:25.859843: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-18 09:24:25.884237: 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 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-18 09:24:26.564689: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.569165: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.569272: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.570638: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.570740: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.570825: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616558: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616674: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616763: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-18 09:24:26.616839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11308 MB memory:  -> device: 0, name: NVIDIA RTX A4000, pci bus id: 0000:01:00.0, compute capability: 8.6
Found 84635 files belonging to 525 classes.
Found 2625 files belonging to 525 classes.
Training directory 'birds/birds/train' contains 84635 images in 525 categories.
/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/keras/src/layers/preprocessing/tf_data_layer.py:19: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)
2024-03-18 09:24:29.337903: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inStatefulPartitionedCall/sequential_1_1/dropout_1/stateless_dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer
2024-03-18 09:24:29.937251: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907
2645/2645 ━━━━━━━━━━━━━━━━━━━━ 46s 17ms/step - accuracy: 0.0066 - loss: 6.1631 - val_accuracy: 0.0579 - val_loss: 5.2888
Traceback (most recent call last):
  File "/home/jschwefel/repositories/aiml/TensorFlow/Test001/imageclassifier.py", line 129, in <module>
    main()
  File "/home/jschwefel/repositories/aiml/TensorFlow/Test001/imageclassifier.py", line 122, in main
    tflite_model = converter.convert()
                   ^^^^^^^^^^^^^^^^^^^
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1175, in wrapper
    return self._convert_and_export_metrics(convert_func, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1129, in _convert_and_export_metrics
    result = convert_func(self, *args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1641, in convert
    self._freeze_keras_model()
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 215, in wrapper
    raise error from None  # Re-throws the exception.
    ^^^^^^^^^^^^^^^^^^^^^
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/convert_phase.py", line 205, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/lite.py", line 1582, in _freeze_keras_model
    input_signature = _model_input_signature(
                      ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jschwefel/repositories/aiml/TensorFlow/tfbase/lib/python3.11/site-packages/tensorflow/lite/python/tflite_keras_util.py", line 84, in model_input_signature
    input_specs = model._get_save_spec(  # pylint: disable=protected-access
                  ^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Sequential' object has no attribute '_get_save_spec'. Did you mean: '_set_save_spec'?

Aloqeely · 2024-03-20T03:23:50Z

    model = Sequential([
        data_augmentation,
        # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
        # layers.Conv2D(16, 3, padding='same', activation=activation1),
        # layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Conv2D(48, 3, padding='same', activation=activation2),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Dropout(0.15),
        layers.Flatten(),
        layers.Dense(128, activation=activation2),
        layers.Dense(num_classes)
    ])

Sequential here might be imported from something other than tf.keras. Please take a look at #39001

LakshmiKalaKadali · 2024-03-20T05:22:55Z

Hi @schwefeljm,

I am trying to reproduce the code meanwhile I encountered some other error. Is it `args = argparser(). Please provide complete reproducible code/link to debug the issue.

Thank You

schwefeljm · 2024-03-20T15:37:58Z

Hi @schwefeljm,

I am trying to reproduce the code meanwhile I encountered some other error. Is it `args = argparser(). Please provide complete reproducible code/link to debug the issue.

Thank You
Hi @LakshmiKalaKadali ,

I updated to code to remove the depency on 'argsparser()'

The dataset I used is from: https://www.kaggle.com/datasets/gpiosenka/100-bird-species Though, I expect it work on any dataset.

Jason

schwefeljm · 2024-03-20T15:56:20Z

    model = Sequential([
        data_augmentation,
        # layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
        # layers.Conv2D(16, 3, padding='same', activation=activation1),
        # layers.MaxPooling2D(),
        layers.Conv2D(32, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Conv2D(48, 3, padding='same', activation=activation2),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, padding='same', activation=activation1),
        layers.MaxPooling2D(),
        layers.Dropout(0.15),
        layers.Flatten(),
        layers.Dense(128, activation=activation2),
        layers.Dense(num_classes)
    ])

Sequential here might be imported from something other than tf.keras. Please take a look at #39001
@Aloqeely

I went through and forced 'tf.keras.models.Sequential' and it had no effect. Thank you for the suggestions, though.

Jason

pkgoogle · 2024-03-27T18:04:52Z

I was able to replicate on tf-nightly as well as 2.16.1. gist, I reduced the reproducible sample to what mattered i.e. the training process is actually irrelevant. This appears to happen when using Keras with the TFLite converter in 2.16.1 onward.

Hi @haozha111, can you please take a look?

Takudzwamz · 2024-04-10T00:19:46Z

I'm experiencing the same error here

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tfmodel = converter.convert()
open ("mobilenetv2_finetuned.tflite" , "wb") .write(tfmodel)

Is it gonna help if I downgrade to an older version of Tensorflow?

schwefeljm · 2024-04-11T13:24:54Z

I'm experiencing the same error here
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tfmodel = converter.convert()
open ("mobilenetv2_finetuned.tflite" , "wb") .write(tfmodel)
Is it gonna help if I downgrade to an older version of Tensorflow?

@Takudzwamz

For me it did.

pkgoogle · 2024-04-11T20:38:51Z

Hi @Takudzwamz, it seems like 2.15 does not exhibit this behavior currently.

Takudzwamz · 2024-04-12T09:57:22Z

@pkgoogle I downgraded to 2.15 and it worked, thanks.

Lerxp · 2024-04-16T12:46:30Z

Referencing legacy Keras worked for me: https://blog.tensorflow.org/2024/03/whats-new-in-tensorflow-216.html

pkgoogle · 2024-04-19T17:30:20Z

As @Lerxp stated, using Legacy Keras is the preferred workaround for now:

import os;os.environ["TF_USE_LEGACY_KERAS"]="1"

gusbernard · 2024-04-27T21:20:22Z

I'm having the same issue, but Legacy Keras isn't working either.

pkgoogle · 2024-04-29T17:35:26Z

Hi @gusbernard, do you have a notebook or code snippet to share? Additionally have you tried using 2.15 for now? Thanks for any information you can provide.

HCzou · 2024-04-30T02:11:00Z

I met the same problem when I using tensorflow2.6.1 in python3.12.3, I tried to downgrade tensorflow to 2.15.0, while it not support python3.12.3. I am trying to downgrade python now...

rudolflovrencic · 2024-05-10T12:41:17Z

I have the same issue when trying to convert a keras model to TFLite exactly like in the docs.

Python     3.12.3
Keras      3.3.3
Tensorflow 2.16.1

kariiho · 2024-05-24T08:39:14Z

I used model.export() to deal with the problem.

# Export the keras model to a saved model format
model.export("saved_model")

# Convert the saved model to TensorFlow Lite
converter = tf.lite.TFLiteConverter.from_saved_model("saved_model")
tflite_model = converter.convert()

# Save the TensorFlow Lite model to a file
with open("model.tflite", "wb") as f:
    f.write(tflite_model)

pkgoogle · 2024-06-11T18:58:00Z

Hi @schwefeljm, have you heard of AI-Edge-Torch?, you can find more information here: googleblog. You will not run into this issue if you go this route.

I have created a script for converting your model here:

import torch
import torch.nn as nn
import ai_edge_torch


class CustomCVModel(nn.Module):
    def __init__(self, num_classes, img_height, img_width, activation1=nn.SiLU, activation2=nn.ELU):
        super().__init__()
        
        self.feature_extractor = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding="same"),
            activation1(),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 48, kernel_size=3, padding="same"),
            activation2(),
            nn.MaxPool2d(2),
            nn.Conv2d(48, 64, kernel_size=3, padding="same"),
            activation1(),
            nn.MaxPool2d(2),
            nn.Dropout(0.15),
            nn.Flatten(),
        )

        self.classifier = nn.Sequential(
            nn.Linear(64 * (img_height // 8) * (img_width // 8), 128),  
            activation2(),
            nn.Linear(128, num_classes)
        )

    def forward(self, x):
        x = self.feature_extractor(x)
        x = self.classifier(x)
        return x


img_height = 128
img_width = 128
model = CustomCVModel(1000, img_height, img_width, nn.SiLU, nn.ELU)
sample_inputs = (torch.randn(1, 3, img_height, img_width),)

edge_model = ai_edge_torch.convert(model.eval(), sample_inputs)
edge_model.export("custom_cv_model.tflite")

You will still need to modify your training code but I have tested this and the conversion does work w/o issue.

If you want to, you can actually try visualizing the result in model-explorer as well.

Please try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo.

github-actions · 2024-06-19T01:51:19Z

This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.

roboticvedant · 2024-06-23T17:18:34Z

@pkgoogle I downgraded to 2.15 and it worked, thanks.
I am facing trouble in downgrading my TF, can you share how you did and what was your python version?

GuidoBartoli · 2024-06-28T07:56:48Z

I have the same issue with a Keras 3 model saved into .keras format and reloaded with keras.models.load_model():

import keras as ks
import tensorflow as tf

model = ks.models.load_model("model.keras")
converter = tf.lite.TFLiteConverter.from_keras_model(model)

This is the error message:

Traceback (most recent call last):
  File "/home/bartoli/Projects/deeplab/deploy.py", line 29, in <module>
    deployment.deploy(args.model, args.output, args.quantize, quant_ds)
  File "/home/bartoli/Projects/deeplab/lib/deployment.py", line 67, in deploy
    converted = converter.convert()
                ^^^^^^^^^^^^^^^^^^^
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/lite.py", line 1175, in wrapper
    return self._convert_and_export_metrics(convert_func, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/lite.py", line 1129, in _convert_and_export_metrics
    result = convert_func(self, *args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/lite.py", line 1641, in convert
    self._freeze_keras_model()
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/convert_phase.py", line 215, in wrapper
    raise error from None  # Re-throws the exception.
    ^^^^^^^^^^^^^^^^^^^^^
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/convert_phase.py", line 205, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/lite.py", line 1582, in _freeze_keras_model
    input_signature = _model_input_signature(
                      ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bartoli/miniconda3/envs/dl/lib/python3.12/site-packages/tensorflow/lite/python/tflite_keras_util.py", line 84, in model_input_signature
    input_specs = model._get_save_spec(  # pylint: disable=protected-access
                  ^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Sequential' object has no attribute '_get_save_spec'. Did you mean: '_set_save_spec'?

schwefeljm added the TFLiteConverter For issues related to TFLite converter label Mar 18, 2024

google-ml-butler bot assigned tilakrayal Mar 18, 2024

tilakrayal added comp:lite TF Lite related issues TF 2.16 type:bug Bug labels Mar 19, 2024

tilakrayal assigned LakshmiKalaKadali and unassigned tilakrayal Mar 19, 2024

LakshmiKalaKadali added the stat:awaiting response Status - Awaiting response from author label Mar 20, 2024

google-ml-butler bot removed the stat:awaiting response Status - Awaiting response from author label Mar 20, 2024

anliec mentioned this issue Mar 25, 2024

cannot convert keras model to onnx : 'Sequential' object has no attribute 'output_names'I onnx/tensorflow-onnx#2319

Closed

LakshmiKalaKadali assigned pkgoogle and unassigned LakshmiKalaKadali Mar 27, 2024

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TFLite 2.16.1 conversions fail with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'" #63867

TFLite 2.16.1 conversions fail with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'" #63867

TFLite 2.16.1 conversions fail with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'" #63867

TFLite 2.16.1 conversions fail with "AttributeError: 'Sequential' object has no attribute '_get_save_spec'" #63867

Comments

1. System information

2. Code

Option B: Paste your code here or provide a link to a custom end-to-end colab

3. Failure after conversion

5. (optional) Any other info / logs