Training error in multi-animal top-down-model

[pinjuu]

Bug description

When I try to train the model this error occurs:

File "C:\Users\spike.conda\envs\sleap\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\spike.conda\envs\sleap\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1129, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:

File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\keras\engine\training.py", line 1621, in predict_function  *
    return step_function(self, iterator)
File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\keras\engine\training.py", line 1611, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\keras\engine\training.py", line 1604, in run_step  **
    outputs = model.predict_step(data)
File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\keras\engine\training.py", line 1572, in predict_step
    return self(x, training=False)
File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None

TypeError: Exception encountered when calling layer "top_down_multi_class_inference_model" (type TopDownMultiClassInferenceModel).

in user code:

    File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\sleap\nn\inference.py", line 4102, in call  *
        crop_output = self.centroid_crop(example)
    File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler  **
        raise e.with_traceback(filtered_tb) from None

    TypeError: Exception encountered when calling layer "centroid_crop_ground_truth" (type CentroidCropGroundTruth).

    in user code:

        File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\sleap\nn\inference.py", line 772, in call  *
            crops = sleap.nn.peak_finding.crop_bboxes(full_imgs, bboxes, crop_sample_inds)
        File "C:\Users\spike\.conda\envs\sleap\lib\site-packages\sleap\nn\peak_finding.py", line 173, in crop_bboxes  *
            image_height = tf.shape(images)[1]

        TypeError: Failed to convert elements of tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:2", shape=(None, 1), dtype=uint8), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:1", shape=(None,), dtype=int64)), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:0", shape=(5,), dtype=int64)) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.


    Call arguments received:
      • example_gt={'image': 'tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:2", shape=(None, 1), dtype=uint8), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:1", shape=(None,), dtype=int64)), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'raw_image_size': 'tf.Tensor(shape=(4, 3), dtype=int32)', 'example_ind': 'tf.Tensor(shape=(4, 1), dtype=int64)', 'video_ind': 'tf.Tensor(shape=(4, 1), dtype=int32)', 'frame_ind': 'tf.Tensor(shape=(4, 1), dtype=int64)', 'scale': 'tf.Tensor(shape=(4, 2), dtype=float32)', 'instances': 'tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("RaggedFromVariant_2/RaggedTensorFromVariant:2", shape=(None, 2), dtype=float32), row_splits=Tensor("RaggedFromVariant_2/RaggedTensorFromVariant:1", shape=(None,), dtype=int64)), row_splits=Tensor("RaggedFromVariant_2/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'skeleton_inds': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant_3/RaggedTensorFromVariant:1", shape=(None,), dtype=int32), row_splits=Tensor("RaggedFromVariant_3/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'track_inds': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant_4/RaggedTensorFromVariant:1", shape=(None,), dtype=int32), row_splits=Tensor("RaggedFromVariant_4/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'n_tracks': 'tf.Tensor(shape=(4, 1), dtype=int32)', 'centroids': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant/RaggedTensorFromVariant:1", shape=(None, 2), dtype=float32), row_splits=Tensor("RaggedFromVariant/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))'}


Call arguments received:
  • example={'image': 'tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:2", shape=(None, 1), dtype=uint8), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:1", shape=(None,), dtype=int64)), row_splits=Tensor("RaggedFromVariant_1/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'raw_image_size': 'tf.Tensor(shape=(4, 3), dtype=int32)', 'example_ind': 'tf.Tensor(shape=(4, 1), dtype=int64)', 'video_ind': 'tf.Tensor(shape=(4, 1), dtype=int32)', 'frame_ind': 'tf.Tensor(shape=(4, 1), dtype=int64)', 'scale': 'tf.Tensor(shape=(4, 2), dtype=float32)', 'instances': 'tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("RaggedFromVariant_2/RaggedTensorFromVariant:2", shape=(None, 2), dtype=float32), row_splits=Tensor("RaggedFromVariant_2/RaggedTensorFromVariant:1", shape=(None,), dtype=int64)), row_splits=Tensor("RaggedFromVariant_2/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'skeleton_inds': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant_3/RaggedTensorFromVariant:1", shape=(None,), dtype=int32), row_splits=Tensor("RaggedFromVariant_3/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'track_inds': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant_4/RaggedTensorFromVariant:1", shape=(None,), dtype=int32), row_splits=Tensor("RaggedFromVariant_4/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))', 'n_tracks': 'tf.Tensor(shape=(4, 1), dtype=int32)', 'centroids': 'tf.RaggedTensor(values=Tensor("RaggedFromVariant/RaggedTensorFromVariant:1", shape=(None, 2), dtype=float32), row_splits=Tensor("RaggedFromVariant/RaggedTensorFromVariant:0", shape=(5,), dtype=int64))'}

Expected behaviour

Succesfuly train the model

Actual behaviour

Training does not complete due to the error

Your personal set up

SLEAP v1.3.3

Environment packages

# paste output of `pip freeze` or `conda list` here

Logs

# paste relevant logs here, if any

Screenshots

How to reproduce

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4. See error

[eberrigan]

Hi @pinjuu,

I will just need some more information from you.

How did you install SLEAP?

Please provide the command you are using to get this error.

Thanks!

Elizabeth

[pinjuu]

Installation was conda package

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"outputs.run_name_prefix": "LBNcohort1_SIBody231024",
"outputs.runs_folder": "C:/Users/spike/Desktop/sleap/SLEAP Projects\models",
"outputs.tags": "",
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"outputs.checkpointing.final_model": false,
"outputs.tensorboard.write_logs": false,
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[eberrigan]

It looks like your skeletons is an empty list. Are you able to open this project in the GUI and take a peek at the skeleton and labels?

[pinjuu]

I have labeled 651 frames in the project. Also I have trained this model before and it worked.