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Unable to run model builder test for Object Detection API #64152

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smtc7648 opened this issue Mar 21, 2024 · 4 comments
Open

Unable to run model builder test for Object Detection API #64152

smtc7648 opened this issue Mar 21, 2024 · 4 comments
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@SuryanarayanaY
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type:build/install Build and install issues

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@smtc7648
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Issue type

Build/Install

Have you reproduced the bug with TensorFlow Nightly?

No

Source

source

TensorFlow version

v2.16.1-0-g5bc9d26649c 2.16.1

Custom code

No

OS platform and distribution

Mac OS Sonoma 14.4

Mobile device

No response

Python version

3.10

Bazel version

No response

GCC/compiler version

No response

CUDA/cuDNN version

No response

GPU model and memory

No response

Current behavior?

While running the below script
python3 models/research/object_detection/builders/model_builder_tf2_test.py

I'm getting following error:
Traceback (most recent call last):
File "/Users/smitparmar/Desktop/ML/custom-object-detection/models/research/object_detection/builders/model_builder_tf2_test.py", line 24, in
from object_detection.builders import model_builder
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/object_detection/builders/model_builder.py", line 26, in
from object_detection.builders import hyperparams_builder
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/object_detection/builders/hyperparams_builder.py", line 27, in
from object_detection.core import freezable_sync_batch_norm
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/object_detection/core/freezable_sync_batch_norm.py", line 20, in
class FreezableSyncBatchNorm(tf.keras.layers.experimental.SyncBatchNormalization
AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'

Standalone code to reproduce the issue

https://drive.google.com/file/d/18z2bj-xRlXT2J6QvplLiYv2uoLei_1bX/view?usp=sharing

Relevant log output

This output is from google colab and above one is from my local machine Apple MacBook Pro with M1 Pro. (Tensowflow Metal is also installed)

2024-03-21 11:07:15.432006: 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.
2024-03-21 11:07:16.927430: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Traceback (most recent call last):
  File "/content/models/research/object_detection/builders/model_builder_tf2_test.py", line 24, in <module>
    from object_detection.builders import model_builder
  File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/model_builder.py", line 26, in <module>
    from object_detection.builders import hyperparams_builder
  File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/hyperparams_builder.py", line 27, in <module>
    from object_detection.core import freezable_sync_batch_norm
  File "/usr/local/lib/python3.10/dist-packages/object_detection/core/freezable_sync_batch_norm.py", line 20, in <module>
    class FreezableSyncBatchNorm(tf.keras.layers.experimental.SyncBatchNormalization
AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'
@google-ml-butler google-ml-butler bot added the type:build/install Build and install issues label Mar 21, 2024
@NBCBM
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NBCBM commented Mar 21, 2024

import tensorflow as tf
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.applications import EfficientNetB0

Example usage of BatchNormalization

inputs = tf.keras.Input(shape=(32, 32, 3))
x = EfficientNetB0(include_top=False, input_tensor=inputs)
x = BatchNormalization()(x.output)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

Rest of your code

@smtc7648
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smtc7648 commented Mar 23, 2024 •
edited
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import tensorflow as tf
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.applications import EfficientNetB0

Example usage of BatchNormalization

inputs = tf.keras.Input(shape=(32, 32, 3))
x = EfficientNetB0(include_top=False, input_tensor=inputs)
x = BatchNormalization()(x.output)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

Rest of your code

Thanks for the response.
Could you please check the link mentioned above to reproduce the issue?

@mark-nicepants
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Following this tutorial i also ran into the same problem.
https://blog.roboflow.com/train-a-tensorflow2-object-detection-model/

It points to this Colab:
https://colab.research.google.com/drive/1sLqFKVV94wm-lglFq_0kGo2ciM0kecWD

And after a couple of steps (not Roboflow related part of the setup). It crashes on this step:

image

@smtc7648
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Hey @mark-nicepants, Thanks for your response. I'll surely try out your suggestion.
However, I was able to get some success and will share the link shortly.

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@smtc7648 @mark-nicepants @SuryanarayanaY @NBCBM