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

Go to '...'
Click on '....'
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See error

Installation was conda package

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2.75, "output_stride": 2, "loss_weight": 1.0, "offset_refinement": false }, "centered_instance": null, "multi_instance": null, "multi_class_bottomup": null, "multi_class_topdown": null }, "base_checkpoint": null }, "optimization": { "preload_data": true, "augmentation_config": { "rotate": true, "rotation_min_angle": -15.0, "rotation_max_angle": 15.0, "translate": false, "translate_min": -5, "translate_max": 5, "scale": false, "scale_min": 0.9, "scale_max": 1.1, "uniform_noise": false, "uniform_noise_min_val": 0.0, "uniform_noise_max_val": 10.0, "gaussian_noise": false, "gaussian_noise_mean": 5.0, "gaussian_noise_stddev": 1.0, "contrast": false, "contrast_min_gamma": 0.5, "contrast_max_gamma": 2.0, "brightness": false, "brightness_min_val": 0.0, "brightness_max_val": 10.0, "random_crop": false, "random_crop_height": 256, "random_crop_width": 256, "random_flip": false, "flip_horizontal": false }, "online_shuffling": true, "shuffle_buffer_size": 128, "prefetch": true, "batch_size": 4, "batches_per_epoch": 200, "min_batches_per_epoch": 200, "val_batches_per_epoch": 10, "min_val_batches_per_epoch": 10, "epochs": 200, "optimizer": "adam", "initial_learning_rate": 0.0001, "learning_rate_schedule": { "reduce_on_plateau": true, "reduction_factor": 0.5, "plateau_min_delta": 1e-06, "plateau_patience": 5, "plateau_cooldown": 3, "min_learning_rate": 1e-08 }, "hard_keypoint_mining": { "online_mining": false, "hard_to_easy_ratio": 2.0, "min_hard_keypoints": 2, "max_hard_keypoints": null, "loss_scale": 5.0 }, "early_stopping": { "stop_training_on_plateau": true, "plateau_min_delta": 1e-08, "plateau_patience": 20 } }, "outputs": { "save_outputs": true, "run_name": null, "run_name_prefix": "LBNcohort1_SIBody231024", "run_name_suffix": null, "runs_folder": "C:/Users/spike/Desktop/sleap/SLEAP Projects\models", "tags": [ "" ], "save_visualizations": true, "delete_viz_images": true, "zip_outputs": false, "log_to_csv": true, "checkpointing": { "initial_model": false, "best_model": true, "every_epoch": false, "latest_model": false, "final_model": false }, "tensorboard": { "write_logs": false, "loss_frequency": "epoch", "architecture_graph": false, "profile_graph": false, "visualizations": true }, "zmq": { "subscribe_to_controller": true, "controller_address": "tcp://127.0.0.1:9000", "controller_polling_timeout": 10, "publish_updates": true, "publish_address": "tcp://127.0.0.1:9001" } }, "name": "", "description": "", "sleap_version": "1.3.3", "filename": "C:/Users/spike/Desktop/sleap/SLEAP Projects\models\LBNcohort1_SIBody231024241023_102341.centroid.n=651\training_config.json" } { "data": { "labels": { "training_labels": "C:/Users/spike/Desktop/sleap/SLEAP Projects/SI_Cohort1_body.slp", "validation_labels": null, "validation_fraction": 0.1, "test_labels": null, "split_by_inds": false, "training_inds": [ 277, 530, 143, 611, 40, 22, 402, 616, 174, 561, 448, 610, 154, 475, 558, 123, 191, 41, 384, 458, 483, 605, 603, 241, 393, 120, 540, 127, 15, 200, 296, 107, 7, 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null, "search_path_hints": [ "", "", "", "", "", "", "" ], "skeletons": [] }, "preprocessing": { "ensure_rgb": false, "ensure_grayscale": false, "imagenet_mode": null, "input_scaling": 1.0, "pad_to_stride": 16, "resize_and_pad_to_target": true, "target_height": 1080, "target_width": 1080 }, "instance_cropping": { "center_on_part": null, "crop_size": 272, "crop_size_detection_padding": 16 } }, "model": { "backbone": { "leap": null, "unet": { "stem_stride": null, "max_stride": 16, "output_stride": 2, "filters": 64, "filters_rate": 2.0, "middle_block": true, "up_interpolate": false, "stacks": 1 }, "hourglass": null, "resnet": null, "pretrained_encoder": null }, "heads": { "single_instance": null, "centroid": null, "centered_instance": null, "multi_instance": null, "multi_class_bottomup": null, "multi_class_topdown": { "confmaps": { "anchor_part": null, "part_names": [ "nose1", "neck1", "earL1", "earR1", "forelegL1", "forelegR1", "tailstart1", "hindlegL1", "hindlegR1", "tail1", "tailend1" ], "sigma": 5.0, "output_stride": 2, "loss_weight": 1.0, "offset_refinement": false }, "class_vectors": { "classes": [ "1", "2" ], "num_fc_layers": 3, "num_fc_units": 64, "global_pool": true, "output_stride": 16, "loss_weight": 1.0 } } }, "base_checkpoint": null }, "optimization": { "preload_data": true, "augmentation_config": { "rotate": false, "rotation_min_angle": -180.0, "rotation_max_angle": 180.0, "translate": false, "translate_min": -5, "translate_max": 5, "scale": false, "scale_min": 0.9, "scale_max": 1.1, "uniform_noise": false, "uniform_noise_min_val": 0.0, "uniform_noise_max_val": 10.0, "gaussian_noise": false, "gaussian_noise_mean": 5.0, "gaussian_noise_stddev": 1.0, "contrast": false, "contrast_min_gamma": 0.5, "contrast_max_gamma": 2.0, "brightness": false, "brightness_min_val": 0.0, "brightness_max_val": 10.0, "random_crop": false, "random_crop_height": 256, "random_crop_width": 256, "random_flip": false, "flip_horizontal": false }, "online_shuffling": true, "shuffle_buffer_size": 128, "prefetch": true, "batch_size": 8, "batches_per_epoch": 200, "min_batches_per_epoch": 200, "val_batches_per_epoch": 10, "min_val_batches_per_epoch": 10, "epochs": 100, "optimizer": "adam", "initial_learning_rate": 0.0001, "learning_rate_schedule": { "reduce_on_plateau": true, "reduction_factor": 0.5, "plateau_min_delta": 1e-06, "plateau_patience": 5, "plateau_cooldown": 3, "min_learning_rate": 1e-08 }, "hard_keypoint_mining": { "online_mining": false, "hard_to_easy_ratio": 2.0, "min_hard_keypoints": 2, "max_hard_keypoints": null, "loss_scale": 5.0 }, "early_stopping": { "stop_training_on_plateau": true, "plateau_min_delta": 1e-06, "plateau_patience": 10 } }, "outputs": { "save_outputs": true, "run_name": null, "run_name_prefix": "LBNcohort1_SIBody231024", "run_name_suffix": null, "runs_folder": "C:/Users/spike/Desktop/sleap/SLEAP Projects\models", "tags": [ "" ], "save_visualizations": true, "delete_viz_images": true, "zip_outputs": false, "log_to_csv": true, "checkpointing": { "initial_model": false, "best_model": true, "every_epoch": false, "latest_model": false, "final_model": false }, "tensorboard": { "write_logs": false, "loss_frequency": "epoch", "architecture_graph": false, "profile_graph": false, "visualizations": true }, "zmq": { "subscribe_to_controller": true, "controller_address": "tcp://127.0.0.1:9000", "controller_polling_timeout": 10, "publish_updates": true, "publish_address": "tcp://127.0.0.1:9001" } }, "name": "", "description": "", "sleap_version": "1.3.3", "filename": "C:/Users/spike/Desktop/sleap/SLEAP Projects\models\LBNcohort1_SIBody231024241023_111429.multi_class_topdown.n=651\training_config.json" }

talmolab / sleap