GitLab

PiperOrigin-RevId: 402944074

- Added configurable arguments in CenterNet for maximum normalized coordinate (this might be useful for handling cases where the bounding box can only be presented out-of-bound with the direction-of-path point at the center in panorama rotation).

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datasets which contain different subset of keypoints.

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that maps from the keypoint names to their ids.

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using 5d tensors to avoid issue when converted to TfLite model.

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PiperOrigin-RevId: 394505975

CenterNet meta arch to avoid using scatter_nd and tf.cond.

Also, use static_or_dynamic_map_fn in the convert_strided_predictions_to_normalized_keypoints function to avoid introducing cycles in the graph that causes the TPU converter errors.

PiperOrigin-RevId: 394152521

runs much slower in the browser.

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PiperOrigin-RevId: 393480597

The order of the input tensors is incorrect and therefore the sample notebook does not run.

The sample code has the order of the input tensors as `boxes, classes, scores, num_detections`, but actually the order should be `scores, boxes,  num_detections, classes`

After correcting the indexes of the input tensor, the sample notebook runs.

PiperOrigin-RevId: 392061572

  * Replaced the deprecated `to_int/to_float` in `center_net_meta_arch.py` with `tf.cast()` (following the warning).
  * Added `max_norm_coord` argument to CenterNet source code to allow controlling the maximum coordinate value to be considered, to allow for the wrap-around special case for RxR data.

PiperOrigin-RevId: 391319766

PiperOrigin-RevId: 391079006

true_image_shape is not required and can be inferred by the prediction tensors.

PiperOrigin-RevId: 390421432

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PiperOrigin-RevId: 385224486

Made changes in `center_net_meta_arch.py` for the particular use case in Marky Landmark Detection, i.e.
1. Accessing an access-protected field in the model;
2. Traverse layers to set trainability.

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This allows the exported TF Lite MoveNet model to be able to run on device GPU
using OpenCL/OpenGL.

PiperOrigin-RevId: 381343791

over new parameters in the CenterNet model.

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