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Memory Leak With Custom Object Detection Model Tensorflow #10685

Open anupamja-kore opened 2 years ago

anupamja-kore commented 2 years ago

I am biggner in tensorflow. I used transfer learning machanism and create custom object detection model using "ssd_resnet101_v1_fpn_keras" pre-trained model.

I follow the below documentation for custom traning:

https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html

I observed one issue while I used it for detection it takes lot of RAM and not releasing it.

I am sharing you the code snippet where it took lot of RAM and not releasing it.

detect_fn = tf.saved_model.load(visa_icon_model)

visa_icon_detections = detect_fn(input_tensor

Memory profiler info:

301   1675.0 MiB    191.8 MiB           1           visa_icon_detections = detect_fn(input_tensor)

Note: I am facing issue with CPU memory not with GPU memory

As you can see, it's take 191.8 Mb RAM. It's not releasing it after competion the process.

I used gc.collect() and tf.keras.backend.clear_session() for releasing the memory.

Both is not working for me.

Please anyone can help me how can I solve this problem.

purvang3 commented 2 years ago

@tombstone @pkulzc @jch1 experiencing similar issue. I am using tensorflow 2.6. I am using tensorflow 1.14 trainined object detection model in backward compatibility mode. when I run inference on stream of images, over the period of time, cpu memory grows and after certain time, process crashes. when I use tensorflow 1.14 for inference, inference runs without any problem for days and month. Any suggestions?