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Multiple detection and tf.function retracing #82

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pasqLisena opened this issue Mar 9, 2020 · 3 comments
Closed

Multiple detection and tf.function retracing #82

pasqLisena opened this issue Mar 9, 2020 · 3 comments

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@pasqLisena
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Hello,
I am using MTCNN to extract faces from a big set of images (basically the training set of a face classification system).

My code is more or less like this:

detector = MTCNN()
for img, label, path in zip(data, labels, paths):
        bounding_boxes = detector.detect_faces(img)
        ...

After a few iterations, I receive plenty of warning like this:

W0309 18:16:40.960232 123145478152192 def_function.py:586] 5 out of the last 5 calls to <function _make_execution_function..distributed_function at 0x15b3a78c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.

What can I do?

@manoharsaialapati
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Same Here. Any Help??

@alexkyllo
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This happens when you call the constructor MTCNN() inside a loop (or within a function that you're repeatedly calling). If you move the call to the constructor outside of the loop, the warning will go away.

@DAlkemade
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I only call the constructor once, and this still happens. Anyone have a solution? Converting the input to a tf.Tensor works sometimes, but breaks the function in other cases.

ipazc pushed a commit that referenced this issue Oct 7, 2024
…tch processing support

- Completely refactored the MTCNN implementation following best coding practices.
- Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22.
- Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency.
- Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110.
- Added batch processing support to enhance performance for multiple input images. Fixes #9, #71.
- Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116.
- Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117.
- Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23.
- Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72.
- Configured PyLint support to ensure code quality and style adherence.
- Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity.
- Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102.
- Added a comprehensive training guide for the model. Fixes #35, #39.
- Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73.
- Configured GitHub Actions for continuous integration and delivery (CI/CD).
- Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128.
- Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95.
- Added comprehensive unit tests for increased reliability and coverage.
@ipazc ipazc mentioned this issue Oct 8, 2024
ipazc pushed a commit that referenced this issue Oct 8, 2024
…tch processing support

- Completely refactored the MTCNN implementation following best coding practices.
- Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22.
- Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency.
- Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110.
- Added batch processing support to enhance performance for multiple input images. Fixes #9, #71.
- Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116.
- Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117.
- Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23.
- Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72.
- Configured PyLint support to ensure code quality and style adherence.
- Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity.
- Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102.
- Added a comprehensive training guide for the model. Fixes #35, #39.
- Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73.
- Configured GitHub Actions for continuous integration and delivery (CI/CD).
- Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128.
- Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95.
- Added comprehensive unit tests for increased reliability and coverage.
@ipazc ipazc closed this as completed Oct 8, 2024
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