Downloaded model deserialization failure

[r-zip]

Describe the bug

Models downloaded through KerasHub fail to deserialize.

To Reproduce

It is not possible to reproduce this bug in Colab. I suspect there is some library version incompatibility; however, the Colab environment is so complicated that I was unable to replicate it locally.

To reproduce the bug, create a new directory and put the following pyproject.toml into it:

[project]
name = "foo"
version = "0.0.1"
description = "Add your description here"
readme = "README.md"
requires-python = "==3.11.*"
dependencies = [
    "jax==0.4.33",
    "jax-cuda12-pjrt==0.4.33",
    "jax-cuda12-plugin==0.4.33",
    "jaxlib==0.4.33",
    "jupyter>=1.1.1",
    "kaggle==1.6.17",
    "kagglehub==0.3.6",
    "keras==3.8.0",
    "keras-hub==0.18.1",
    "matplotlib>=3.10.0",
    "tensorflow==2.18.0",
    "tensorflow-datasets==4.9.7",
    "tensorflow-hub==0.16.1",
    "tensorflow-io-gcs-filesystem==0.37.1",
    "tensorflow-metadata==1.16.1",
    "tensorflow-probability==0.24.0",
    "tensorflow-text==2.18.1",
    "tf-keras==2.18.0",
    "tf-slim==1.1.0",
    "torch",
    "torchaudio",
    "torchsummary==1.5.1",
    "torchvision",
]

[tool.uv.sources]
torchaudio = { url = "https://download.pytorch.org/whl/cu124/torchaudio-2.5.1%2Bcu124-cp311-cp311-linux_x86_64.whl" }
torch = { url = "https://download.pytorch.org/whl/cu124/torch-2.5.1%2Bcu124-cp311-cp311-linux_x86_64.whl" }
torchvision = { url = "https://download.pytorch.org/whl/cu124/torchvision-0.20.1%2Bcu124-cp311-cp311-linux_x86_64.whl" }

The versions of these packages (besides matplotlib and Jupyter) are pinned to their values in Colab. Next, run uv sync, then source .venv/bin/activate. Finally, copy and paste the following code blocks into a Jupyter notebook (taken from the tutorial here):

import os

os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "1.0"

import keras
import numpy as np
import matplotlib.pyplot as plt

image_url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg"
image_path = keras.utils.get_file(origin=image_url)
image = keras.utils.load_img(image_path)
plt.imshow(image)

import keras_hub

image_classifier = keras_hub.models.ImageClassifier.from_preset(
    "resnet_50_imagenet",
    activation="softmax",
)
batch = np.array([image])
image_classifier.preprocessor.image_size = (224, 224)
preds = image_classifier.predict(batch)
preds.shape

The final cell results in the following error:

TypeError: <class 'keras_hub.src.models.resnet.resnet_image_classifier.ResNetImageClassifier'> could not be deserialized properly. Please ensure that components that are Python object instances (layers, models, etc.) returned by `get_config()` are explicitly deserialized in the model's `from_config()` method.

config={'module': 'keras_hub.src.models.resnet.resnet_image_classifier', 'class_name': 'ResNetImageClassifier', 'config': {'backbone': {'module': 'keras_hub.src.models.resnet.resnet_backbone', 'class_name': 'ResNetBackbone', 'config': {'name': 'res_net_backbone', 'trainable': True, 'input_conv_filters': [64], 'input_conv_kernel_sizes': [7], 'stackwise_num_filters': [64, 128, 256, 512], 'stackwise_num_blocks': [3, 4, 6, 3], 'stackwise_num_strides': [1, 2, 2, 2], 'block_type': 'bottleneck_block', 'use_pre_activation': False, 'image_shape': [None, None, 3], 'dtype': None}, 'registered_name': 'keras_hub>ResNetBackbone'}, 'preprocessor': {'module': 'keras_hub.src.models.resnet.resnet_image_classifier_preprocessor', 'class_name': 'ResNetImageClassifierPreprocessor', 'config': {'name': 'res_net_image_classifier_preprocessor', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'image_converter': {'module': 'keras_hub.src.models.resnet.resnet_image_converter', 'class_name': 'ResNetImageConverter', 'config': {'name': 'res_net_image_converter', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'image_size': [224, 224], 'scale': [0.017124753831663668, 0.01750700280112045, 0.017429193899782133], 'offset': [-2.1179039301310043, -2.0357142857142856, -1.8044444444444445], 'interpolation': 'bicubic', 'crop_to_aspect_ratio': True}, 'registered_name': 'keras_hub>ResNetImageConverter'}, 'config_file': 'preprocessor.json'}, 'registered_name': 'keras_hub>ResNetImageClassifierPreprocessor'}, 'name': 'res_net_image_classifier', 'num_classes': 1000, 'pooling': 'avg', 'activation': 'softmax', 'dropout': 0.0}, 'registered_name': 'keras_hub>ResNetImageClassifier'}.

Exception encountered: <class 'keras_hub.src.models.resnet.resnet_backbone.ResNetBackbone'> could not be deserialized properly. Please ensure that components that are Python object instances (layers, models, etc.) returned by `get_config()` are explicitly deserialized in the model's `from_config()` method.

config={'module': 'keras_hub.src.models.resnet.resnet_backbone', 'class_name': 'ResNetBackbone', 'config': {'name': 'res_net_backbone', 'trainable': True, 'input_conv_filters': [64], 'input_conv_kernel_sizes': [7], 'stackwise_num_filters': [64, 128, 256, 512], 'stackwise_num_blocks': [3, 4, 6, 3], 'stackwise_num_strides': [1, 2, 2, 2], 'block_type': 'bottleneck_block', 'use_pre_activation': False, 'image_shape': [None, None, 3], 'dtype': None}, 'registered_name': 'keras_hub>ResNetBackbone'}.

Exception encountered: unsupported operand type(s) for %: 'NoneType' and 'int'

Expected behavior

I expect the last cell to produce the output

 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step
(1, 1000)

from the tutorial page.

Additional context

The tutorial works on Colab, but not locally; I tried this both on Linux with CUDA GPUs and on my M1 MacBook Pro. Neither worked.

Would you like to help us fix it? I don't believe I have the expertise or time to fix it, unfortunately.