Converting the Tensorflow saved_model in to a keras model '_UserObject' object has no attribute 'summary'

[Matteo-Castrignano]

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Description

hi, i need to convert the yolo model in a keras model in order to change the tensor type to rum some experiment using a posit160 library. But i have this error "'_UserObject' object has no attribute 'summary' ". i have not idea how to chance the model in order to manage the weights, the input and the output type

Use case

model_path = "yolov5/yolov5s_saved_model"
imported = tf.saved_model.load(model_path)
imported.summary()

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @Matteo-Castrignano, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[RoudyES]

Yolov5 exports the model to a TensorFlow savedModel rather than a Keras model. TensorFlow models don't have the High-Level calls such as fit() and summary(). You need to look into ways to convert from TF models to Keras models or maybe export Yolov5 to ONNX and try to convert that back to Keras.

[glenn-jocher]

@Matteo-Castrignano @RoudyES set keras=True on SavedModel export.

yolov5/export.py

Lines 275 to 286 in 5774a15

	def export_saved_model(model,
	im,
	file,
	dynamic,
	tf_nms=False,
	agnostic_nms=False,
	topk_per_class=100,
	topk_all=100,
	iou_thres=0.45,
	conf_thres=0.25,
	keras=False,
	prefix=colorstr('TensorFlow SavedModel:')):

[Matteo-Castrignano]

@Matteo-Castrignano @RoudyES set keras=True on SavedModel export.

yolov5/export.py

Lines 275 to 286 in 5774a15

	def export_saved_model(model,
	im,
	file,
	dynamic,
	tf_nms=False,
	agnostic_nms=False,
	topk_per_class=100,
	topk_all=100,
	iou_thres=0.45,
	conf_thres=0.25,
	keras=False,
	prefix=colorstr('TensorFlow SavedModel:')):

Do you know how to do it from linux terminal?
So, i mean how can i specify the parameters Keras = true in the export command? I have already tried it but i have found the command to do this

[RoudyES]

@Matteo-Castrignano @RoudyES set keras=True on SavedModel export.

yolov5/export.py

Lines 275 to 286 in 5774a15

	def export_saved_model(model,
	im,
	file,
	dynamic,
	tf_nms=False,
	agnostic_nms=False,
	topk_per_class=100,
	topk_all=100,
	iou_thres=0.45,
	conf_thres=0.25,
	keras=False,
	prefix=colorstr('TensorFlow SavedModel:')):

Oh I didn't know that's a thing! Thank you for pointing it out!

[glenn-jocher]

@Matteo-Castrignano good news 😃! Your original issue may now be fixed ✅ in PR #7921. Now you can use the --keras flag when exporting TF models:

python export.py --include saved_model --keras

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!