conda create -n DLC python=3.8                
conda activate DLC                                
pip install --upgrade --force-reinstall 'deeplabcut[gui,tf,modelzoo]'
conda config --append channels conda-forge
conda install cudatoolkit=11.2 (https://www.tensorflow.org/install/source#gpu)
conda install cudnn=8.1
conda install ffmpeg==4.2.2

(DLC) PS C:\Users\user\Desktop\DeepLabCut-main> python testscript_cli.py
Loading DLC 2.3.3...
Imported DLC!
['C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\Reaching-Mackenzie-2018-08-30\\videos\\reachingvideo1.avi']
On Windows/OSX tensorpack is not tested by default.
CREATING PROJECT
Created "C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\videos"
Created "C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\labeled-data"
Created "C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\training-datasets"
Created "C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\dlc-models"
Copying the videos
C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\videos\reachingvideo1.avi
Generated "C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\config.yaml"

A new project with name Testcore-Mackenzie-2023-04-11 is created at C:\Users\user\Desktop\DeepLabCut-main and a configurable file (config.yaml) is stored there. Change the parameters in this file to adapt to your project's needs.
 Once you have changed the configuration file, use the function 'extract_frames' to select frames for labeling.
. [OPTIONAL] Use the function 'add_new_videos' to add new videos to your project (at any stage).
EXTRACTING FRAMES
Config file read successfully.
Extracting frames based on kmeans ...
Kmeans-quantization based extracting of frames from 0.0  seconds to 8.53  seconds.
Extracting and downsampling... 256  frames from the video.
256it [00:01, 164.30it/s]
Kmeans clustering ... (this might take a while)
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 3 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
Frames were successfully extracted, for the videos listed in the config.yaml file.

You can now label the frames using the function 'label_frames' (Note, you should label frames extracted from diverse videos (and many videos; we do not recommend training on single videos!)).
CREATING SOME LABELS FOR THE FRAMES
Plot labels...
Creating images with labels by Mackenzie.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  4.57it/s]
If all the labels are ok, then use the function 'create_training_dataset' to create the training dataset!
CREATING TRAININGSET
Downloading a ImageNet-pretrained model from http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz....
The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!
CHANGING training parameters to end quickly!
TRAIN
Selecting single-animal trainer
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'alpha_r': 0.02,
 'apply_prob': 0.5,
 'batch_size': 1,
 'contrast': {'clahe': True,
              'claheratio': 0.1,
              'histeq': True,
              'histeqratio': 0.1},
 'convolution': {'edge': False,
                 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]},
                 'embossratio': 0.1,
                 'sharpen': False,
                 'sharpenratio': 0.3},
 'crop_pad': 0,
 'cropratio': 0.4,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Testcore_Mackenzie80shuffle1.mat',
 'dataset_type': 'default',
 'decay_steps': 30000,
 'deterministic': False,
 'display_iters': 2,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\resnet_v1_50.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 0.05,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'lr_init': 0.0005,
 'max_input_size': 1500,
 'mean_pixel': [123.68, 116.779, 103.939],
 'metadataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Documentation_data-Testcore_80shuffle1.pickle',
 'min_input_size': 64,
 'mirror': False,
 'multi_stage': False,
 'multi_step': [[0.001, 3]],
 'net_type': 'resnet_50',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': False,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'pos_dist_thresh': 17,
 'project_path': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11',
 'regularize': False,
 'rotation': 25,
 'rotratio': 0.4,
 'save_iters': 3,
 'scale_jitter_lo': 0.5,
 'scale_jitter_up': 1.25,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11\\dlc-models\\iteration-0\\TestcoreApr11-trainset80shuffle1\\train\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Batch Size is 1
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:05:44.486532: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-11 16:05:44.877456: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2023-04-11 16:05:44.877724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Loading ImageNet-pretrained resnet_50
2023-04-11 16:05:45.317876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
2023-04-11 16:05:46.269875: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
Training parameter:
{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11\\dlc-models\\iteration-0\\TestcoreApr11-trainset80shuffle1\\train\\snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'default', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3]], 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'contrast': {'clahe': True, 'claheratio': 0.1, 'histeq': True, 'histeqratio': 0.1, 'gamma': False, 'sigmoid': False, 'log': False, 'linear': False}, 'convolution': {'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]}, 'embossratio': 0.1, 'sharpen': False, 'sharpenratio': 0.3}, 'cropratio': 0.4, 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Testcore_Mackenzie80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 2, 'init_weights': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\resnet_v1_50.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'metadataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Documentation_data-Testcore_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.001, 3]], 'net_type': 'resnet_50', 'num_joints': 4, 'pos_dist_thresh': 17, 'project_path': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 3, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}
Starting training....
2023-04-11 16:05:50.417576: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100
2023-04-11 16:05:51.721011: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
iteration: 2 loss: 1.1650 lr: 0.001
2023-04-11 16:05:57.360177: W tensorflow/core/kernels/queue_base.cc:277] _0_fifo_queue: Skipping cancelled enqueue attempt with queue not closed
Exception in thread Thread-2:
Traceback (most recent call last):
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1378, in _do_call
    return fn(*args)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1361, in _run_fn
    return self._call_tf_sessionrun(options, feed_dict, fetch_list,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1454, in _call_tf_sessionrun
    return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled
         [[{{node fifo_queue_enqueue}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\user\anaconda3\envs\DLC\lib\threading.py", line 932, in _bootstrap_inner
    self.run()
  File "C:\Users\user\anaconda3\envs\DLC\lib\threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\core\train.py", line 85, in load_and_enqueue
    sess.run(enqueue_op, feed_dict=food)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 968, in run
    result = self._run(None, fetches, feed_dict, options_ptr,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1191, in _run
    results = self._do_run(handle, final_targets, final_fetches,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1371, in _do_run
    return self._do_call(_run_fn, feeds, fetches, targets, options,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1397, in _do_call
    raise type(e)(node_def, op, message)  # pylint: disable=no-value-for-parameter
tensorflow.python.framework.errors_impl.CancelledError: Graph execution error:

Detected at node 'fifo_queue_enqueue' defined at (most recent call last):
    File "testscript_cli.py", line 145, in <module>
      dlc.train_network(path_config_file)
    File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\training.py", line 212, in train_network
      train(
    File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\core\train.py", line 171, in train
      batch, enqueue_op, placeholders = setup_preloading(batch_spec)
    File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\core\train.py", line 71, in setup_preloading
      enqueue_op = q.enqueue(placeholders_list)
Node: 'fifo_queue_enqueue'
Enqueue operation was cancelled
         [[{{node fifo_queue_enqueue}}]]

Original stack trace for 'fifo_queue_enqueue':
  File "testscript_cli.py", line 145, in <module>
    dlc.train_network(path_config_file)
  File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\training.py", line 212, in train_network
    train(
  File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\core\train.py", line 171, in train
    batch, enqueue_op, placeholders = setup_preloading(batch_spec)
  File "C:\Users\user\Desktop\DeepLabCut-main\deeplabcut\pose_estimation_tensorflow\core\train.py", line 71, in setup_preloading
    enqueue_op = q.enqueue(placeholders_list)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\ops\data_flow_ops.py", line 346, in enqueue
    return gen_data_flow_ops.queue_enqueue_v2(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\ops\gen_data_flow_ops.py", line 4062, in queue_enqueue_v2
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 797, in _apply_op_helper
    op = g._create_op_internal(op_type_name, inputs, dtypes=None,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\framework\ops.py", line 3800, in _create_op_internal
    ret = Operation(

The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.
EVALUATE
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'batch_size': 1,
 'crop_pad': 0,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Testcore_Mackenzie80shuffle1.mat',
 'dataset_type': 'imgaug',
 'deterministic': False,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\resnet_v1_50.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 1.0,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'mean_pixel': [123.68, 116.779, 103.939],
 'mirror': False,
 'net_type': 'resnet_50',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': True,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'regularize': False,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11\\dlc-models\\iteration-0\\TestcoreApr11-trainset80shuffle1\\test\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Running  DLC_resnet50_TestcoreApr11shuffle1_3  with # of training iterations: 3
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:06:01.998741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Running evaluation ...
5it [00:00,  5.56it/s]
Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-3
Results for 3  training iterations: 80 1 train error: 383.81 pixels. Test error: 453.36  pixels.
With pcutoff of 0.01  train error: 383.81 pixels. Test error: 453.36 pixels
Thereby, the errors are given by the average distances between the labels by DLC and the scorer.
Plotting...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  4.10it/s]
The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.
Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.
Use the function 'analyze_video' to make predictions on new videos.
Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)
C:\Users\user\Desktop\DeepLabCut-main\Testcore-Mackenzie-2023-04-11\videos\reachingvideo1.avi
Export model...
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'alpha_r': 0.02,
 'apply_prob': 0.5,
 'batch_size': 1,
 'contrast': {'clahe': True,
              'claheratio': 0.1,
              'histeq': True,
              'histeqratio': 0.1},
 'convolution': {'edge': False,
                 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]},
                 'embossratio': 0.1,
                 'sharpen': False,
                 'sharpenratio': 0.3},
 'crop_pad': 0,
 'cropratio': 0.4,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Testcore_Mackenzie80shuffle1.mat',
 'dataset_type': 'default',
 'decay_steps': 30000,
 'deterministic': False,
 'display_iters': 2,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\resnet_v1_50.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 0.05,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'lr_init': 0.0005,
 'max_input_size': 1500,
 'mean_pixel': [123.68, 116.779, 103.939],
 'metadataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TestcoreApr11\\Documentation_data-Testcore_80shuffle1.pickle',
 'min_input_size': 64,
 'mirror': False,
 'multi_stage': False,
 'multi_step': [[0.001, 3]],
 'net_type': 'resnet_50',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': False,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'pos_dist_thresh': 17,
 'project_path': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11',
 'regularize': False,
 'rotation': 25,
 'rotratio': 0.4,
 'save_iters': 3,
 'scale_jitter_lo': 0.5,
 'scale_jitter_up': 1.25,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\Testcore-Mackenzie-2023-04-11\\dlc-models\\iteration-0\\TestcoreApr11-trainset80shuffle1\\train\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:06:07.482592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
ALL DONE!!! - default/imgaug cases of DLCcore training and evaluation are functional (no extract outlier or refinement tested).
(DLC) PS C:\Users\user\Desktop\DeepLabCut-main> cd C:\Users\user\Desktop\DeepLabCut-main\examples
(DLC) PS C:\Users\user\Desktop\DeepLabCut-main\examples> python testscript.py
Loading DLC 2.3.3...
Imported DLC!
On Windows/OSX tensorpack is not tested by default.
CREATING PROJECT
Created "C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos"
Created "C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\labeled-data"
Created "C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\training-datasets"
Created "C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\dlc-models"
Copying the videos
C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1.avi
Generated "C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\config.yaml"

A new project with name TEST-Alex-2023-04-11 is created at C:\Users\user\Desktop\DeepLabCut-main\examples and a configurable file (config.yaml) is stored there. Change the parameters in this file to adapt to your project's needs.
 Once you have changed the configuration file, use the function 'extract_frames' to select frames for labeling.
. [OPTIONAL] Use the function 'add_new_videos' to add new videos to your project (at any stage).
EXTRACTING FRAMES
Config file read successfully.
Extracting frames based on kmeans ...
Kmeans-quantization based extracting of frames from 0.0  seconds to 8.53  seconds.
Extracting and downsampling... 256  frames from the video.
256it [00:01, 177.05it/s]
Kmeans clustering ... (this might take a while)
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 3 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
Frames were successfully extracted, for the videos listed in the config.yaml file.

You can now label the frames using the function 'label_frames' (Note, you should label frames extracted from diverse videos (and many videos; we do not recommend training on single videos!)).
CREATING-SOME LABELS FOR THE FRAMES
Plot labels...
Creating images with labels by Alex.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  4.62it/s]
If all the labels are ok, then use the function 'create_training_dataset' to create the training dataset!
CREATING TRAININGSET
The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!
CHANGING training parameters to end quickly!
TRAIN
Selecting single-animal trainer
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'alpha_r': 0.02,
 'apply_prob': 0.5,
 'batch_size': 1,
 'contrast': {'clahe': True,
              'claheratio': 0.1,
              'histeq': True,
              'histeqratio': 0.1},
 'convolution': {'edge': False,
                 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]},
                 'embossratio': 0.1,
                 'sharpen': False,
                 'sharpenratio': 0.3},
 'crop_pad': 0,
 'cropratio': 0.4,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\TEST_Alex80shuffle1.mat',
 'dataset_type': 'default',
 'decay_steps': 30000,
 'deterministic': False,
 'display_iters': 2,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\anaconda3\\envs\\DLC\\lib\\site-packages\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\mobilenet_v2_0.35_224.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 0.05,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'lr_init': 0.0005,
 'max_input_size': 1500,
 'mean_pixel': [123.68, 116.779, 103.939],
 'metadataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\Documentation_data-TEST_80shuffle1.pickle',
 'min_input_size': 64,
 'mirror': False,
 'multi_stage': False,
 'multi_step': [[0.001, 5]],
 'net_type': 'mobilenet_v2_0.35',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': False,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'pos_dist_thresh': 17,
 'project_path': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11',
 'regularize': False,
 'rotation': 25,
 'rotratio': 0.4,
 'save_iters': 5,
 'scale_jitter_lo': 0.5,
 'scale_jitter_up': 1.25,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11\\dlc-models\\iteration-0\\TESTApr11-trainset80shuffle1\\train\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Batch Size is 1
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:07:37.717086: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-11 16:07:38.137864: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2023-04-11 16:07:38.137987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Loading ImageNet-pretrained mobilenet_v2_0.35
2023-04-11 16:07:38.576534: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
2023-04-11 16:07:39.524578: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
Training parameter:
{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11\\dlc-models\\iteration-0\\TESTApr11-trainset80shuffle1\\train\\snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'default', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3]], 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'contrast': {'clahe': True, 'claheratio': 0.1, 'histeq': True, 'histeqratio': 0.1, 'gamma': False, 'sigmoid': False, 'log': False, 'linear': False}, 'convolution': {'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]}, 'embossratio': 0.1, 'sharpen': False, 'sharpenratio': 0.3}, 'cropratio': 0.4, 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\TEST_Alex80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 2, 'init_weights': 'C:\\Users\\user\\anaconda3\\envs\\DLC\\lib\\site-packages\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\mobilenet_v2_0.35_224.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'metadataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\Documentation_data-TEST_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.001, 5]], 'net_type': 'mobilenet_v2_0.35', 'num_joints': 4, 'pos_dist_thresh': 17, 'project_path': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 5, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}
Starting training....
2023-04-11 16:07:43.700166: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8100
2023-04-11 16:07:45.101920: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
iteration: 2 loss: 1.0918 lr: 0.001
iteration: 4 loss: 0.7055 lr: 0.001
2023-04-11 16:07:46.359480: W tensorflow/core/kernels/queue_base.cc:277] _0_fifo_queue: Skipping cancelled enqueue attempt with queue not closed
Exception in thread Thread-2:
Traceback (most recent call last):
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1378, in _do_call
    return fn(*args)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1361, in _run_fn
    return self._call_tf_sessionrun(options, feed_dict, fetch_list,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1454, in _call_tf_sessionrun
    return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled
         [[{{node fifo_queue_enqueue}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\user\anaconda3\envs\DLC\lib\threading.py", line 932, in _bootstrap_inner
    self.run()
  File "C:\Users\user\anaconda3\envs\DLC\lib\threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\core\train.py", line 85, in load_and_enqueue
    sess.run(enqueue_op, feed_dict=food)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 968, in run
    result = self._run(None, fetches, feed_dict, options_ptr,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1191, in _run
    results = self._do_run(handle, final_targets, final_fetches,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1371, in _do_run
    return self._do_call(_run_fn, feeds, fetches, targets, options,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\client\session.py", line 1397, in _do_call
    raise type(e)(node_def, op, message)  # pylint: disable=no-value-for-parameter
tensorflow.python.framework.errors_impl.CancelledError: Graph execution error:

Detected at node 'fifo_queue_enqueue' defined at (most recent call last):
    File "testscript.py", line 177, in <module>
      deeplabcut.train_network(path_config_file)
    File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\training.py", line 212, in train_network
      train(
    File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\core\train.py", line 171, in train
      batch, enqueue_op, placeholders = setup_preloading(batch_spec)
    File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\core\train.py", line 71, in setup_preloading
      enqueue_op = q.enqueue(placeholders_list)
Node: 'fifo_queue_enqueue'
Enqueue operation was cancelled
         [[{{node fifo_queue_enqueue}}]]

Original stack trace for 'fifo_queue_enqueue':
  File "testscript.py", line 177, in <module>
    deeplabcut.train_network(path_config_file)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\training.py", line 212, in train_network
    train(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\core\train.py", line 171, in train
    batch, enqueue_op, placeholders = setup_preloading(batch_spec)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\pose_estimation_tensorflow\core\train.py", line 71, in setup_preloading
    enqueue_op = q.enqueue(placeholders_list)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\ops\data_flow_ops.py", line 346, in enqueue
    return gen_data_flow_ops.queue_enqueue_v2(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\ops\gen_data_flow_ops.py", line 4062, in queue_enqueue_v2
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 797, in _apply_op_helper
    op = g._create_op_internal(op_type_name, inputs, dtypes=None,
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\framework\ops.py", line 3800, in _create_op_internal
    ret = Operation(

The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.
EVALUATE
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'batch_size': 1,
 'crop_pad': 0,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\TEST_Alex80shuffle1.mat',
 'dataset_type': 'imgaug',
 'deterministic': False,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\anaconda3\\envs\\DLC\\lib\\site-packages\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\mobilenet_v2_0.35_224.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 1.0,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'mean_pixel': [123.68, 116.779, 103.939],
 'mirror': False,
 'net_type': 'mobilenet_v2_0.35',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': True,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'regularize': False,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11\\dlc-models\\iteration-0\\TESTApr11-trainset80shuffle1\\test\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Running  DLC_mobnet_35_TESTApr11shuffle1_5  with # of training iterations: 5
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:07:51.405861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Running evaluation ...
5it [00:00,  6.96it/s]
Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-5
Results for 5  training iterations: 80 1 train error: 321.32 pixels. Test error: 444.8  pixels.
With pcutoff of 0.01  train error: 321.32 pixels. Test error: 444.8 pixels
Thereby, the errors are given by the average distances between the labels by DLC and the scorer.
Plotting...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  4.22it/s]
The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.
Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.
Use the function 'analyze_video' to make predictions on new videos.
Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)
CUT SHORT VIDEO AND ANALYZE (with dynamic cropping!)
ffmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
  built with gcc 9.2.1 (GCC) 20200122
  configuration: --disable-static --enable-shared --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
Input #0, avi, from 'C:\Users\user\Desktop\DeepLabCut-main\examples\Reaching-Mackenzie-2018-08-30\videos\reachingvideo1.avi':
  Duration: 00:00:08.53, start: 0.000000, bitrate: 12642 kb/s
    Stream #0:0: Video: mjpeg (Baseline) (MJPG / 0x47504A4D), yuvj420p(pc, bt470bg/unknown/unknown), 832x747 [SAR 1:1 DAR 832:747], 12682 kb/s, 30 fps, 30 tbr, 30 tbn, 30 tbc
    Metadata:
      title           : ImageJ AVI
Stream mapping:
  Stream #0:0 -> #0:0 (mjpeg (native) -> mpeg4 (native))
Press [q] to stop, [?] for help
[swscaler @ 0000019f4be01740] deprecated pixel format used, make sure you did set range correctly
Output #0, avi, to 'C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi':
  Metadata:
    ISFT            : Lavf58.29.100
    Stream #0:0: Video: mpeg4 (FMP4 / 0x34504D46), yuv420p, 832x747 [SAR 1:1 DAR 832:747], q=2-31, 200 kb/s, 30 fps, 30 tbn, 30 tbc
    Metadata:
      title           : ImageJ AVI
      encoder         : Lavc58.54.100 mpeg4
    Side data:
      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1
frame=   30 fps=0.0 q=31.0 Lsize=     236kB time=00:00:01.00 bitrate=1933.4kbits/s speed=10.1x
video:230kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.772651%
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'batch_size': 1,
 'crop_pad': 0,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\TEST_Alex80shuffle1.mat',
 'dataset_type': 'imgaug',
 'deterministic': False,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\anaconda3\\envs\\DLC\\lib\\site-packages\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\mobilenet_v2_0.35_224.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 1.0,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'mean_pixel': [123.68, 116.779, 103.939],
 'mirror': False,
 'net_type': 'mobilenet_v2_0.35',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': True,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'regularize': False,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11\\dlc-models\\iteration-0\\TESTApr11-trainset80shuffle1\\test\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Using snapshot-5 for model C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\dlc-models\iteration-0\TESTApr11-trainset80shuffle1
Starting analysis in dynamic cropping mode with parameters: (True, 0.1, 5)
Switching batchsize to 1, num_outputs (per animal) to 1 and TFGPUinference to False (all these features are not supported in this mode).
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:07:56.981875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Starting to analyze %  C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi
Loading  C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi
Duration of video [s]:  1.0 , recorded with  30.0 fps!
Overall # of frames:  30  found with (before cropping) frame dimensions:  832 747
Starting to extract posture
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:01<00:00, 24.66it/s]
Saving results in C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos...
Saving csv poses!
The videos are analyzed. Now your research can truly start!
 You can create labeled videos with 'create_labeled_video'
If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function 'extract_outlier_frames' to extract a few representative outlier frames.
analyze again...
Config:
{'all_joints': [[0], [1], [2], [3]],
 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
 'batch_size': 1,
 'crop_pad': 0,
 'dataset': 'training-datasets\\iteration-0\\UnaugmentedDataSet_TESTApr11\\TEST_Alex80shuffle1.mat',
 'dataset_type': 'imgaug',
 'deterministic': False,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\\Users\\user\\anaconda3\\envs\\DLC\\lib\\site-packages\\deeplabcut\\pose_estimation_tensorflow\\models\\pretrained\\mobilenet_v2_0.35_224.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 1.0,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'mean_pixel': [123.68, 116.779, 103.939],
 'mirror': False,
 'net_type': 'mobilenet_v2_0.35',
 'num_joints': 4,
 'optimizer': 'sgd',
 'pairwise_huber_loss': True,
 'pairwise_predict': False,
 'partaffinityfield_predict': False,
 'regularize': False,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\\Users\\user\\Desktop\\DeepLabCut-main\\examples\\TEST-Alex-2023-04-11\\dlc-models\\iteration-0\\TESTApr11-trainset80shuffle1\\test\\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Using snapshot-5 for model C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\dlc-models\iteration-0\TESTApr11-trainset80shuffle1
C:\Users\user\anaconda3\envs\DLC\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  warnings.warn('`layer.apply` is deprecated and '
2023-04-11 16:08:01.322515: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Starting to analyze %  C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi
The videos are analyzed. Now your research can truly start!
 You can create labeled videos with 'create_labeled_video'
If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function 'extract_outlier_frames' to extract a few representative outlier frames.
CREATE VIDEO
Starting to process video: C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi
Loading C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi and data.
Duration of video [s]: 1.0, recorded with 30.0 fps!
Overall # of frames: 30 with cropped frame dimensions: 832 747
Generating frames and creating video.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:06<00:00,  4.70it/s]
Labeled video C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1shortDLC_mobnet_35_TESTApr11shuffle1_5_labeled.mp4 successfully created.
Making plots
Loading  C:\Users\user\Desktop\DeepLabCut-main\examples\TEST-Alex-2023-04-11\videos\reachingvideo1short.avi and data.
Plots created! Please check the directory "plot-poses" within the video directory
EXTRACT OUTLIERS
Traceback (most recent call last):
  File "testscript.py", line 233, in <module>
    deeplabcut.extract_outlier_frames(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\deeplabcut\refine_training_dataset\outlier_frames.py", line 408, in extract_outlier_frames
    sum_ = temp_dt.sum(axis=1, level=1)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\core\generic.py", line 11519, in sum
    return NDFrame.sum(self, axis, skipna, numeric_only, min_count, **kwargs)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\core\generic.py", line 11287, in sum
    return self._min_count_stat_function(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\core\generic.py", line 11259, in _min_count_stat_function
    nv.validate_sum((), kwargs)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\compat\numpy\function.py", line 82, in __call__
    validate_args_and_kwargs(
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\util\_validators.py", line 221, in validate_args_and_kwargs
    validate_kwargs(fname, kwargs, compat_args)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\util\_validators.py", line 162, in validate_kwargs
    _check_for_invalid_keys(fname, kwargs, compat_args)
  File "C:\Users\user\anaconda3\envs\DLC\lib\site-packages\pandas\util\_validators.py", line 136, in _check_for_invalid_keys
    raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'")
TypeError: sum() got an unexpected keyword argument 'level'