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Description
This PR is a hotfix to add further constraints to
attrs(<22.2.0) andopencv(<=4.8.1 # [linux]).Items
Manual Installation Tests
Linux
GUI Training
 Terminal: Training
```bash Resetting monitor window. Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/viz/validation.*.png Start training centroid... ['sleap-train', '/tmp/tmpd1et2_6d/240903_105333_training_job.json', '/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp', '--zmq', '--save_viz'] INFO:sleap.nn.training:Versions: SLEAP: 1.3.4 TensorFlow: 2.7.0 Numpy: 1.21.6 Python: 3.7.12 OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid INFO:sleap.nn.training:Training labels file: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Training profile: /tmp/tmpd1et2_6d/240903_105333_training_job.json INFO:sleap.nn.training: INFO:sleap.nn.training:Arguments: INFO:sleap.nn.training:{ "training_job_path": "/tmp/tmpd1et2_6d/240903_105333_training_job.json", "labels_path": "/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp", "video_paths": [ "" ], "val_labels": null, "test_labels": null, "base_checkpoint": null, "tensorboard": false, "save_viz": true, "zmq": true, "run_name": "", "prefix": "", "suffix": "", "cpu": false, "first_gpu": false, "last_gpu": false, "gpu": "auto" } INFO:sleap.nn.training: INFO:sleap.nn.training:Training job: INFO:sleap.nn.training:{ "data": { "labels": { "training_labels": "/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp", "validation_labels": null, "validation_fraction": 0.1, "test_labels": null, "split_by_inds": false, "training_inds": [ 99, 13, 36, 84, 5, 29, 16, 11, 53, 88, 9, 66, 82, 79, 78, 77, 91, 50, 35, 37, 75, 26, 69, 31, 62, 8, 97, 32, 67, 59, 85, 2, 83, 87, 1, 12, 42, 21, 65, 68, 48, 90, 95, 25, 49, 4, 63, 89, 98, 73, 86, 60, 94, 24, 47, 93, 80, 0, 17, 51, 30, 23, 38, 71, 18, 39, 44, 81, 10, 20, 57, 6, 15, 64, 61, 22, 56, 54, 46, 27, 72, 14, 55, 41, 70, 45, 74, 76, 92, 34 ], "validation_inds": [ 43, 96, 7, 58, 28, 19, 40, 33, 3, 52 ], "test_inds": null, "search_path_hints": [ "", "", "", "", "", "" ], "skeletons": [] }, "preprocessing": { "ensure_rgb": false, "ensure_grayscale": false, "imagenet_mode": null, "input_scaling": 0.5, "pad_to_stride": 16, "resize_and_pad_to_target": true, "target_height": 1024, "target_width": 1024 }, "instance_cropping": { "center_on_part": "thorax", "crop_size": null, "crop_size_detection_padding": 16 } }, "model": { "backbone": { "leap": null, "unet": { "stem_stride": null, "max_stride": 16, "output_stride": 2, "filters": 16, "filters_rate": 2.0, "middle_block": true, "up_interpolate": true, "stacks": 1 }, "hourglass": null, "resnet": null, "pretrained_encoder": null }, "heads": { "single_instance": null, "centroid": { "anchor_part": "thorax", "sigma": 2.5, "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": -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": 4, "batches_per_epoch": 200, "min_batches_per_epoch": 200, "val_batches_per_epoch": 10, "min_val_batches_per_epoch": 10, "epochs": 2, "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": "240903_105332.centroid.n=100", "run_name_prefix": "", "run_name_suffix": "", "runs_folder": "/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/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": "/tmp/tmpd1et2_6d/240903_105333_training_job.json" } INFO:sleap.nn.training: 2024-09-03 10:53:34.958393: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:34.962997: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:34.963711: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero INFO:sleap.nn.training:Auto-selected GPU 0 with 23692 MiB of free memory. INFO:sleap.nn.training:Using GPU 0 for acceleration. INFO:sleap.nn.training:Disabled GPU memory pre-allocation. INFO:sleap.nn.training:System: GPUs: 1/1 available Device: /physical_device:GPU:0 Available: True Initalized: False Memory growth: True INFO:sleap.nn.training: INFO:sleap.nn.training:Initializing trainer... INFO:sleap.nn.training:Loading training labels from: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1 INFO:sleap.nn.training: Splits: Training = 90 / Validation = 10. INFO:sleap.nn.training:Setting up for training... INFO:sleap.nn.training:Setting up pipeline builders... INFO:sleap.nn.training:Setting up model... INFO:sleap.nn.training:Building test pipeline... 2024-09-03 10:53:35.790360: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-09-03 10:53:35.791013: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:35.791858: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:35.792617: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:36.063704: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:36.064484: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:36.065168: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:53:36.065830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21787 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6 INFO:sleap.nn.training:Loaded test example. [1.300s] INFO:sleap.nn.training: Input shape: (512, 512, 1) INFO:sleap.nn.training:Created Keras model. INFO:sleap.nn.training: Backbone: UNet(stacks=1, filters=16, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=3, up_interpolate=True, block_contraction=False) INFO:sleap.nn.training: Max stride: 16 INFO:sleap.nn.training: Parameters: 1,953,105 INFO:sleap.nn.training: Heads: INFO:sleap.nn.training: [0] = CentroidConfmapsHead(anchor_part='thorax', sigma=2.5, output_stride=2, loss_weight=1.0) INFO:sleap.nn.training: Outputs: INFO:sleap.nn.training: [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 256, 256, 1), dtype=tf.float32, name=None), name='CentroidConfmapsHead/BiasAdd:0', description="created by layer 'CentroidConfmapsHead'") INFO:sleap.nn.training:Training from scratch INFO:sleap.nn.training:Setting up data pipelines... INFO:sleap.nn.training:Training set: n = 90 INFO:sleap.nn.training:Validation set: n = 10 INFO:sleap.nn.training:Setting up optimization... INFO:sleap.nn.training: Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-06, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08) INFO:sleap.nn.training: Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=20) INFO:sleap.nn.training:Setting up outputs... INFO:sleap.nn.callbacks:Training controller subscribed to: tcp://127.0.0.1:9000 (topic: ) INFO:sleap.nn.training: ZMQ controller subcribed to: tcp://127.0.0.1:9000 INFO:sleap.nn.callbacks:Progress reporter publishing on: tcp://127.0.0.1:9001 for: not_set INFO:sleap.nn.training: ZMQ progress reporter publish on: tcp://127.0.0.1:9001 INFO:sleap.nn.training:Created run path: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100 INFO:sleap.nn.training:Setting up visualization... INFO:sleap.nn.training:Finished trainer set up. [3.2s] INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation... INFO:sleap.nn.training:Finished creating training datasets. [3.5s] INFO:sleap.nn.training:Starting training loop... Epoch 1/2 2024-09-03 10:53:43.671648: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0073s vs `on_train_batch_end` time: 0.0272s). Check your callbacks. 2024-09-03 10:53:58.686686: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 200/200 - 17s - loss: 2.1690e-04 - val_loss: 1.0114e-04 - lr: 1.0000e-04 - 17s/epoch - 84ms/step Epoch 2/2 Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/viz/validation.*.png 200/200 - 9s - loss: 6.7886e-05 - val_loss: 4.3949e-05 - lr: 1.0000e-04 - 9s/epoch - 45ms/step Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/viz/validation.*.png INFO:sleap.nn.training:Finished training loop. [0.4 min] INFO:sleap.nn.training:Deleting visualization directory: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/viz INFO:sleap.nn.training:Saving evaluation metrics to model folder... Predicting... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% ETA: 0:00:00 34.9 FPS INFO:sleap.nn.evals:Saved predictions: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/labels_pr.train.slp INFO:sleap.nn.evals:Saved metrics: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/metrics.train.npz INFO:sleap.nn.evals:OKS mAP: 0.737803 Predicting... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% ETA: 0:00:00 7.3 FPS INFO:sleap.nn.evals:Saved predictions: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/labels_pr.val.slp INFO:sleap.nn.evals:Saved metrics: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/metrics.val.npz INFO:sleap.nn.evals:OKS mAP: 0.633989 INFO:sleap.nn.callbacks:Closing the reporter controller/context. INFO:sleap.nn.callbacks:Closing the training controller socket/context. Run Path: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100 Finished training centroid. Resetting monitor window. Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/viz/validation.*.png Start training centered_instance... ['sleap-train', '/tmp/tmpnk568sb3/240903_105416_training_job.json', '/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp', '--zmq', '--save_viz'] INFO:sleap.nn.training:Versions: SLEAP: 1.3.4 TensorFlow: 2.7.0 Numpy: 1.21.6 Python: 3.7.12 OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid INFO:sleap.nn.training:Training labels file: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Training profile: /tmp/tmpnk568sb3/240903_105416_training_job.json INFO:sleap.nn.training: INFO:sleap.nn.training:Arguments: INFO:sleap.nn.training:{ "training_job_path": "/tmp/tmpnk568sb3/240903_105416_training_job.json", "labels_path": "/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp", "video_paths": [ "" ], "val_labels": null, "test_labels": null, "base_checkpoint": null, "tensorboard": false, "save_viz": true, "zmq": true, "run_name": "", "prefix": "", "suffix": "", "cpu": false, "first_gpu": false, "last_gpu": false, "gpu": "auto" } INFO:sleap.nn.training: INFO:sleap.nn.training:Training job: INFO:sleap.nn.training:{ "data": { "labels": { "training_labels": "/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp", "validation_labels": null, "validation_fraction": 0.1, "test_labels": null, "split_by_inds": false, "training_inds": [ 66, 96, 58, 4, 65, 61, 85, 28, 31, 35, 90, 60, 16, 27, 55, 13, 9, 80, 87, 6, 75, 70, 95, 56, 34, 82, 69, 76, 89, 18, 39, 77, 51, 33, 24, 32, 81, 7, 53, 20, 36, 67, 54, 12, 10, 19, 30, 86, 44, 71, 0, 73, 40, 83, 2, 14, 38, 74, 94, 68, 98, 17, 49, 57, 84, 78, 72, 26, 93, 92, 5, 3, 47, 1, 88, 37, 45, 99, 46, 63, 62, 23, 8, 42, 50, 48, 91, 64, 79, 43 ], "validation_inds": [ 15, 11, 25, 21, 22, 29, 41, 52, 97, 59 ], "test_inds": null, "search_path_hints": [ "", "", "", "", "", "" ], "skeletons": [] }, "preprocessing": { "ensure_rgb": false, "ensure_grayscale": false, "imagenet_mode": null, "input_scaling": 1.0, "pad_to_stride": 1, "resize_and_pad_to_target": true, "target_height": 1024, "target_width": 1024 }, "instance_cropping": { "center_on_part": "thorax", "crop_size": 144, "crop_size_detection_padding": 16 } }, "model": { "backbone": { "leap": null, "unet": { "stem_stride": null, "max_stride": 16, "output_stride": 4, "filters": 24, "filters_rate": 2.0, "middle_block": true, "up_interpolate": true, "stacks": 1 }, "hourglass": null, "resnet": null, "pretrained_encoder": null }, "heads": { "single_instance": null, "centroid": null, "centered_instance": { "anchor_part": "thorax", "part_names": [ "head", "thorax", "abdomen", "wingL", "wingR", "forelegL4", "forelegR4", "midlegL4", "midlegR4", "hindlegL4", "hindlegR4", "eyeL", "eyeR" ], "sigma": 2.5, "output_stride": 4, "loss_weight": 1.0, "offset_refinement": false }, "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": -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": 4, "batches_per_epoch": 200, "min_batches_per_epoch": 200, "val_batches_per_epoch": 10, "min_val_batches_per_epoch": 10, "epochs": 2, "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": 10 } }, "outputs": { "save_outputs": true, "run_name": "240903_105416.centered_instance.n=100", "run_name_prefix": "", "run_name_suffix": "", "runs_folder": "/home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/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": "/tmp/tmpnk568sb3/240903_105416_training_job.json" } INFO:sleap.nn.training: 2024-09-03 10:54:18.223901: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:18.227782: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:18.228513: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero INFO:sleap.nn.training:Auto-selected GPU 0 with 23719 MiB of free memory. INFO:sleap.nn.training:Using GPU 0 for acceleration. INFO:sleap.nn.training:Disabled GPU memory pre-allocation. INFO:sleap.nn.training:System: GPUs: 1/1 available Device: /physical_device:GPU:0 Available: True Initalized: False Memory growth: True INFO:sleap.nn.training: INFO:sleap.nn.training:Initializing trainer... INFO:sleap.nn.training:Loading training labels from: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1 INFO:sleap.nn.training: Splits: Training = 90 / Validation = 10. INFO:sleap.nn.training:Setting up for training... INFO:sleap.nn.training:Setting up pipeline builders... INFO:sleap.nn.training:Setting up model... INFO:sleap.nn.training:Building test pipeline... 2024-09-03 10:54:19.052567: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-09-03 10:54:19.053454: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:19.054528: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:19.055374: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:19.317241: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:19.317998: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:19.318680: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:54:19.319340: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21814 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:01:00.0, compute capability: 8.6 INFO:sleap.nn.training:Loaded test example. [1.644s] INFO:sleap.nn.training: Input shape: (144, 144, 1) INFO:sleap.nn.training:Created Keras model. INFO:sleap.nn.training: Backbone: UNet(stacks=1, filters=24, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=4, middle_block=True, up_blocks=2, up_interpolate=True, block_contraction=False) INFO:sleap.nn.training: Max stride: 16 INFO:sleap.nn.training: Parameters: 4,311,445 INFO:sleap.nn.training: Heads: INFO:sleap.nn.training: [0] = CenteredInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], anchor_part='thorax', sigma=2.5, output_stride=4, loss_weight=1.0) INFO:sleap.nn.training: Outputs: INFO:sleap.nn.training: [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 36, 36, 13), dtype=tf.float32, name=None), name='CenteredInstanceConfmapsHead/BiasAdd:0', description="created by layer 'CenteredInstanceConfmapsHead'") INFO:sleap.nn.training:Training from scratch INFO:sleap.nn.training:Setting up data pipelines... INFO:sleap.nn.training:Training set: n = 90 INFO:sleap.nn.training:Validation set: n = 10 INFO:sleap.nn.training:Setting up optimization... INFO:sleap.nn.training: Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-06, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08) INFO:sleap.nn.training: Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=10) INFO:sleap.nn.training:Setting up outputs... INFO:sleap.nn.callbacks:Training controller subscribed to: tcp://127.0.0.1:9000 (topic: ) INFO:sleap.nn.training: ZMQ controller subcribed to: tcp://127.0.0.1:9000 INFO:sleap.nn.callbacks:Progress reporter publishing on: tcp://127.0.0.1:9001 for: not_set INFO:sleap.nn.training: ZMQ progress reporter publish on: tcp://127.0.0.1:9001 INFO:sleap.nn.training:Created run path: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100 INFO:sleap.nn.training:Setting up visualization... INFO:sleap.nn.training:Finished trainer set up. [3.0s] INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation... INFO:sleap.nn.training:Finished creating training datasets. [4.2s] INFO:sleap.nn.training:Starting training loop... Epoch 1/2 2024-09-03 10:54:27.519675: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0093s vs `on_train_batch_end` time: 0.0115s). Check your callbacks. 2024-09-03 10:54:35.713655: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 200/200 - 10s - loss: 0.0110 - head: 0.0078 - thorax: 0.0070 - abdomen: 0.0109 - wingL: 0.0121 - wingR: 0.0125 - forelegL4: 0.0114 - forelegR4: 0.0109 - midlegL4: 0.0130 - midlegR4: 0.0131 - hindlegL4: 0.0131 - hindlegR4: 0.0132 - eyeL: 0.0093 - eyeR: 0.0092 - val_loss: 0.0088 - val_head: 0.0036 - val_thorax: 0.0037 - val_abdomen: 0.0073 - val_wingL: 0.0085 - val_wingR: 0.0105 - val_forelegL4: 0.0098 - val_forelegR4: 0.0095 - val_midlegL4: 0.0120 - val_midlegR4: 0.0129 - val_hindlegL4: 0.0123 - val_hindlegR4: 0.0124 - val_eyeL: 0.0057 - val_eyeR: 0.0068 - lr: 1.0000e-04 - 10s/epoch - 51ms/step Epoch 2/2 Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/viz/validation.*.png Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/viz/validation.*.png 200/200 - 6s - loss: 0.0073 - head: 0.0023 - thorax: 0.0033 - abdomen: 0.0060 - wingL: 0.0069 - wingR: 0.0082 - forelegL4: 0.0083 - forelegR4: 0.0082 - midlegL4: 0.0107 - midlegR4: 0.0114 - hindlegL4: 0.0117 - hindlegR4: 0.0117 - eyeL: 0.0033 - eyeR: 0.0033 - val_loss: 0.0064 - val_head: 0.0020 - val_thorax: 0.0030 - val_abdomen: 0.0051 - val_wingL: 0.0065 - val_wingR: 0.0072 - val_forelegL4: 0.0065 - val_forelegR4: 0.0070 - val_midlegL4: 0.0091 - val_midlegR4: 0.0105 - val_hindlegL4: 0.0105 - val_hindlegR4: 0.0105 - val_eyeL: 0.0028 - val_eyeR: 0.0030 - lr: 1.0000e-04 - 6s/epoch - 31ms/step INFO:sleap.nn.training:Finished training loop. [0.3 min] INFO:sleap.nn.training:Deleting visualization directory: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/viz INFO:sleap.nn.training:Saving evaluation metrics to model folder... Predicting... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0% ETA: -:--:-- ?Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105332.centroid.n=100/viz/validation.*.png Polling: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/viz/validation.*.png Predicting... ━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18% ETA: 0:00:01 210.8 FPS2024-09-03 10:54:44.962420: W tensorflow/core/data/root_dataset.cc:163] Optimization loop failed: CANCELLED: Operation was cancelled Predicting... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% ETA: 0:00:00 52.8 FPS INFO:sleap.nn.evals:Saved predictions: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/labels_pr.train.slp INFO:sleap.nn.evals:Saved metrics: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/metrics.train.npz INFO:sleap.nn.evals:OKS mAP: 0.013254 Predicting... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% ETA: 0:00:00 11.8 FPS INFO:sleap.nn.evals:Saved predictions: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/labels_pr.val.slp INFO:sleap.nn.evals:Saved metrics: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100/metrics.val.npz INFO:sleap.nn.evals:OKS mAP: 0.010202 INFO:sleap.nn.callbacks:Closing the reporter controller/context. INFO:sleap.nn.callbacks:Closing the training controller socket/context. Run Path: /home/talmolab/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/drosophila-melanogaster-courtship/models/240903_105416.centered_instance.n=100 Finished training centered_instance. ```Terminal: Opening GUI
```bash ``` Nothing.sleap-label
```bash (sleap_1.3) talmolab@talmolab-01-ubuntu:~$ sleap-label Saving config: /home/talmolab/.sleap/1.3.4/preferences.yaml 2024-09-03 10:45:48.326588: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:45:48.330951: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2024-09-03 10:45:48.331731: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero Software versions: SLEAP: 1.3.4 TensorFlow: 2.7.0 Numpy: 1.21.6 Python: 3.7.12 OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid Happy SLEAPing! :) ```mm create sleap
```bash talmolab@talmolab-01-ubuntu:~$ mm create -y -n -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.3.4 nvidia/noarch (check zst) Checked 0.1s nvidia/linux-64 (check zst) Checked 0.1s anaconda/noarch (check zst) Checked 0.1s anaconda/linux-64 (check zst) Checked 0.0s sleap/label/dev/noarch (check zst) Checked 0.2s sleap/label/dev/linux-64 (check zst) Checked 0.2s sleap/linux-64 (check zst) Checked 0.2s sleap/noarch (check zst) Checked 0.2s nvidia/noarch 14.7kB @ 291.8kB/s 0.1s nvidia/linux-64 210.3kB @ 2.7MB/s 0.1s sleap/label/dev/noarch 116.0 B @ 792.0 B/s 0.1s anaconda/linux-64 3.0MB @ 17.7MB/s 0.1s sleap/label/dev/linux-64 1.6kB @ 8.2kB/s 0.2s conda-forge/noarch 16.3MB @ 57.0MB/s 0.2s anaconda/noarch 402.7kB @ 1.3MB/s 0.1s sleap/linux-64 2.4kB @ 7.4kB/s 0.3s sleap/noarch 116.0 B @ 330.0 B/s 0.2s conda-forge/linux-64 37.5MB @ 51.7MB/s 0.7s error libmamba Could not solve for environment specs The following package could not be installed └─ conda-forge does not exist (perhaps a typo or a missing channel). critical libmamba Could not solve for environment specs talmolab@talmolab-01-ubuntu:~$ mm create -y -n -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.3.4 nvidia/linux-64 Using cache nvidia/noarch Using cache anaconda/linux-64 Using cache anaconda/noarch Using cache conda-forge/linux-64 Using cache conda-forge/noarch Using cache sleap/linux-64 No change sleap/label/dev/linux-64 No change sleap/label/dev/noarch No change sleap/noarch No change error libmamba Could not solve for environment specs The following package could not be installed └─ conda-forge does not exist (perhaps a typo or a missing channel). critical libmamba Could not solve for environment specs talmolab@talmolab-01-ubuntu:~$ mm create -y -n sleap_1.3.4 -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.3.4 conda-forge/linux-64 Using cache conda-forge/noarch Using cache nvidia/linux-64 Using cache nvidia/noarch Using cache anaconda/linux-64 Using cache anaconda/noarch Using cache sleap/label/dev/linux-64 No change sleap/label/dev/noarch No change sleap/noarch No change sleap/linux-64 No change Transaction Prefix: /home/talmolab/micromamba/envs/sleap_1.3.4 Updating specs: - sleap=1.3.4 Package Version Build Channel Size ────────────────────────────────────────────────────────────────────────────────────────── Install: ────────────────────────────────────────────────────────────────────────────────────────── + cuda-nvcc 11.3.58 h2467b9f_0 nvidia Cached + python_abi 3.7 4_cp37m conda-forge Cached + _libgcc_mutex 0.1 conda_forge conda-forge Cached + ld_impl_linux-64 2.40 hf3520f5_7 conda-forge 708kB + ca-certificates 2024.8.30 hbcca054_0 conda-forge 159kB + libgomp 14.1.0 h77fa898_1 conda-forge 460kB + _openmp_mutex 4.5 2_gnu conda-forge Cached + libgcc 14.1.0 h77fa898_1 conda-forge 846kB + libbrotlicommon 1.1.0 hb9d3cd8_2 conda-forge 69kB + libgfortran5 14.1.0 hc5f4f2c_1 conda-forge 1MB + libstdcxx 14.1.0 hc0a3c3a_1 conda-forge 4MB + libgcc-ng 14.1.0 h69a702a_1 conda-forge 52kB + libbrotlienc 1.1.0 hb9d3cd8_2 conda-forge 282kB + libbrotlidec 1.1.0 hb9d3cd8_2 conda-forge 33kB + libgfortran 14.1.0 h69a702a_1 conda-forge 52kB + libstdcxx-ng 14.1.0 h4852527_1 conda-forge 52kB + libev 4.33 hd590300_2 conda-forge 113kB + c-ares 1.33.1 heb4867d_0 conda-forge 183kB + libogg 1.3.5 h4ab18f5_0 conda-forge 206kB + yaml 0.2.5 h7f98852_2 conda-forge Cached + libsodium 1.0.18 h36c2ea0_1 conda-forge Cached + xorg-renderproto 0.11.1 h7f98852_1002 conda-forge Cached + xorg-xproto 7.0.31 h7f98852_1007 conda-forge Cached + xorg-kbproto 1.0.7 h7f98852_1002 conda-forge Cached + xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge Cached + libuuid 2.38.1 h0b41bf4_0 conda-forge Cached + xorg-libice 1.1.1 hd590300_0 conda-forge Cached + x264 1!161.3030 h7f98852_1 conda-forge Cached + lame 3.100 h166bdaf_1003 conda-forge Cached + nettle 3.6 he412f7d_0 conda-forge Cached + bzip2 1.0.8 h4bc722e_7 conda-forge 253kB + libopus 1.3.1 h7f98852_1 conda-forge Cached + alsa-lib 1.2.3.2 h166bdaf_0 conda-forge Cached + libgettextpo 0.22.5 he02047a_3 conda-forge 171kB + gettext-tools 0.22.5 he02047a_3 conda-forge 3MB + libwebp-base 1.4.0 hd590300_0 conda-forge 439kB + libdeflate 1.14 h166bdaf_0 conda-forge Cached + keyutils 1.6.1 h166bdaf_0 conda-forge Cached + xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge Cached + xorg-libxau 1.0.11 hd590300_0 conda-forge Cached + pthread-stubs 0.4 h36c2ea0_1001 conda-forge Cached + libiconv 1.17 hd590300_2 conda-forge 706kB + jpeg 9e h0b41bf4_3 conda-forge Cached + libexpat 2.6.2 h59595ed_0 conda-forge 74kB + xz 5.2.6 h166bdaf_0 conda-forge Cached + openssl 1.1.1w hd590300_0 conda-forge Cached + ncurses 6.5 he02047a_1 conda-forge 889kB + libzlib 1.2.13 h4ab18f5_6 conda-forge 62kB + libnsl 2.0.1 hd590300_0 conda-forge 33kB + libffi 3.4.2 h7f98852_5 conda-forge Cached + brotli-bin 1.1.0 hb9d3cd8_2 conda-forge 19kB + libgfortran-ng 14.1.0 h69a702a_1 conda-forge 52kB + geos 3.11.0 h27087fc_0 conda-forge Cached + pixman 0.43.2 h59595ed_0 conda-forge 387kB + gmp 6.3.0 hac33072_2 conda-forge 460kB + libasprintf 0.22.5 he8f35ee_3 conda-forge 43kB + graphite2 1.3.13 h59595ed_1003 conda-forge 97kB + lerc 4.0.0 h27087fc_0 conda-forge Cached + nspr 4.35 h27087fc_0 conda-forge Cached + icu 68.2 h9c3ff4c_0 conda-forge Cached + cudatoolkit 11.3.1 hb98b00a_13 conda-forge 633MB + libvorbis 1.3.7 h9c3ff4c_0 conda-forge Cached + zeromq 4.3.5 h59595ed_1 conda-forge 343kB + xorg-libsm 1.2.4 h7391055_0 conda-forge Cached + gnutls 3.6.13 h85f3911_1 conda-forge Cached + libgettextpo-devel 0.22.5 he02047a_3 conda-forge 37kB + libxcb 1.13 h7f98852_1004 conda-forge Cached + jasper 1.900.1 h07fcdf6_1006 conda-forge Cached + expat 2.6.2 h59595ed_0 conda-forge 138kB + mysql-common 8.0.32 h14678bc_0 conda-forge Cached + libevent 2.1.10 h9b69904_4 conda-forge Cached + libedit 3.1.20191231 he28a2e2_2 conda-forge Cached + readline 8.2 h8228510_1 conda-forge Cached + libssh2 1.10.0 haa6b8db_3 conda-forge Cached + libnghttp2 1.51.0 hdcd2b5c_0 conda-forge Cached + zstd 1.5.6 ha6fb4c9_0 conda-forge 555kB + libllvm11 11.1.0 he0ac6c6_5 conda-forge Cached + libpng 1.6.43 h2797004_0 conda-forge 288kB + pcre2 10.43 hcad00b1_0 conda-forge 951kB + zlib 1.2.13 h4ab18f5_6 conda-forge 93kB + tk 8.6.13 noxft_h4845f30_101 conda-forge 3MB + libsqlite 3.46.0 hde9e2c9_0 conda-forge 865kB + brotli 1.1.0 hb9d3cd8_2 conda-forge 19kB + fftw 3.3.10 nompi_hf1063bd_110 conda-forge 2MB + libopenblas 0.3.27 pthreads_hac2b453_1 conda-forge 6MB + libasprintf-devel 0.22.5 he8f35ee_3 conda-forge 34kB + cudnn 8.2.1.32 h86fa8c9_0 conda-forge Cached + xorg-libx11 1.8.4 h0b41bf4_0 conda-forge Cached + krb5 1.19.3 h3790be6_0 conda-forge Cached + libtiff 4.4.0 h82bc61c_5 conda-forge Cached + mysql-libs 8.0.32 h54cf53e_0 conda-forge Cached + libclang 11.1.0 default_ha53f305_1 conda-forge Cached + freetype 2.12.1 h267a509_2 conda-forge Cached + libglib 2.80.2 hf974151_0 conda-forge 4MB + openh264 2.1.1 h780b84a_0 conda-forge Cached + libxml2 2.9.12 h72842e0_0 conda-forge Cached + libprotobuf 3.16.0 h780b84a_0 conda-forge Cached + nss 3.100 hca3bf56_0 conda-forge 2MB + sqlite 3.46.0 h6d4b2fc_0 conda-forge 860kB + openblas 0.3.27 pthreads_h9eca1d5_1 conda-forge 6MB + libblas 3.9.0 23_linux64_openblas conda-forge 15kB + gettext 0.22.5 he02047a_3 conda-forge 479kB + xorg-libxext 1.3.4 h0b41bf4_2 conda-forge Cached + xorg-libxrender 0.9.10 h7f98852_1003 conda-forge Cached + libcurl 7.86.0 h7bff187_1 conda-forge Cached + libpq 13.8 hd77ab85_0 conda-forge Cached + openjpeg 2.5.0 h7d73246_1 conda-forge Cached + lcms2 2.14 h6ed2654_0 conda-forge Cached + fontconfig 2.14.2 h14ed4e7_0 conda-forge Cached + dbus 1.13.6 h5008d03_3 conda-forge Cached + ffmpeg 4.3.2 h37c90e5_3 conda-forge Cached + libxslt 1.1.33 h15afd5d_2 conda-forge Cached + libxkbcommon 1.0.3 he3ba5ed_0 conda-forge Cached + python 3.7.12 hb7a2778_100_cpython conda-forge Cached + blas 1.1 openblas conda-forge 1kB + libcblas 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sleap/label/dev 5MB Summary: Install: 195 packages Total download: 1GB ```Windows
GUI Training
 Terminal: Training
```bash Resetting monitor window. Polling: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\viz\validation.*.png Start training multi_instance... ['sleap-train', 'C:\\Users\\Liezl\\AppData\\Local\\Temp\\tmp65eu9h6c\\240831_110334_training_job.json', 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp', '--zmq', '--save_viz'] INFO:sleap.nn.training:Versions: SLEAP: 1.3.4 TensorFlow: 2.7.0 Numpy: 1.21.6 Python: 3.7.12 OS: Windows-10-10.0.22621-SP0 INFO:sleap.nn.training:Training labels file: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Training profile: C:\Users\Liezl\AppData\Local\Temp\tmp65eu9h6c\240831_110334_training_job.json INFO:sleap.nn.training: INFO:sleap.nn.training:Arguments: INFO:sleap.nn.training:{ "training_job_path": "C:\\Users\\Liezl\\AppData\\Local\\Temp\\tmp65eu9h6c\\240831_110334_training_job.json", "labels_path": "C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp", "video_paths": [ "" ], "val_labels": null, "test_labels": null, "base_checkpoint": null, "tensorboard": false, "save_viz": true, "zmq": true, "run_name": "", "prefix": "", "suffix": "", "cpu": false, "first_gpu": false, "last_gpu": false, "gpu": "auto" } INFO:sleap.nn.training: INFO:sleap.nn.training:Training job: INFO:sleap.nn.training:{ "data": { "labels": { "training_labels": "D:\\Users\\Liezl\\ProjectsData\\sleap-estimates-animal-poses\\datasets\\drosophila-melanogaster-courtship\\courtship_labels.slp", "validation_labels": null, "validation_fraction": 0.1, "test_labels": null, "split_by_inds": false, "training_inds": [ 7, 50, 29, 42, 73, 67, 89, 46, 17, 47, 11, 65, 16, 69, 13, 72, 93, 8, 23, 6, 9, 5, 97, 53, 91, 19, 100, 71, 31, 94, 36, 85, 18, 83, 3, 82, 21, 74, 1, 86, 81, 64, 51, 30, 54, 52, 58, 95, 92, 62, 48, 76, 27, 45, 15, 57, 79, 4, 56, 98, 84, 38, 78, 34, 90, 63, 87, 25, 44, 32, 2, 28, 55, 22, 61, 66, 33, 41, 14, 88, 26, 68, 12, 39, 60, 99, 24, 77, 43, 10, 80 ], "validation_inds": [ 37, 0, 96, 40, 49, 70, 75, 20, 35, 59 ], "test_inds": null, "search_path_hints": [ "" ], "skeletons": [] }, "preprocessing": { "ensure_rgb": false, "ensure_grayscale": false, "imagenet_mode": null, "input_scaling": 1.0, "pad_to_stride": 32, "resize_and_pad_to_target": true, "target_height": 1024, "target_width": 1024 }, "instance_cropping": { "center_on_part": null, "crop_size": null, "crop_size_detection_padding": 16 } }, "model": { "backbone": { "leap": null, "unet": { "stem_stride": null, "max_stride": 32, "output_stride": 4, "filters": 16, "filters_rate": 2.0, "middle_block": true, "up_interpolate": true, "stacks": 1 }, "hourglass": null, "resnet": null, "pretrained_encoder": null }, "heads": { "single_instance": null, "centroid": null, "centered_instance": null, "multi_instance": { "confmaps": { "part_names": [ "head", "thorax", "abdomen", "wingL", "wingR", "forelegL4", "forelegR4", "midlegL4", "midlegR4", "hindlegL4", "hindlegR4", "eyeL", "eyeR" ], "sigma": 2.5, "output_stride": 4, "loss_weight": 1.0, "offset_refinement": false }, "pafs": { "edges": [ [ "thorax", "head" ], [ "thorax", "abdomen" ], [ "thorax", "wingL" ], [ "thorax", "wingR" ], [ "thorax", "forelegL4" ], [ "thorax", "forelegR4" ], [ "thorax", "midlegL4" ], [ "thorax", "midlegR4" ], [ "thorax", "hindlegL4" ], [ "thorax", "hindlegR4" ], [ "head", "eyeL" ], [ "head", "eyeR" ] ], "sigma": 75.0, "output_stride": 8, "loss_weight": 1.0 } }, "multi_class_bottomup": null, "multi_class_topdown": null }, "base_checkpoint": null }, "optimization": { "preload_data": true, "augmentation_config": { "rotate": true, "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": 4, "batches_per_epoch": 200, "min_batches_per_epoch": 200, "val_batches_per_epoch": 10, "min_val_batches_per_epoch": 10, "epochs": 2, "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": 10 } }, "outputs": { "save_outputs": true, "run_name": "240831_110334.multi_instance.n=101", "run_name_prefix": "", "run_name_suffix": "", "runs_folder": "C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\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": "1.4.1a2", "filename": "C:\\Users\\Liezl\\AppData\\Local\\Temp\\tmp65eu9h6c\\240831_110334_training_job.json" } INFO:sleap.nn.training: INFO:sleap.nn.training:Auto-selected GPU 0 with 11675 MiB of free memory. INFO:sleap.nn.training:Using GPU 0 for acceleration. INFO:sleap.nn.training:Disabled GPU memory pre-allocation. INFO:sleap.nn.training:System: GPUs: 1/1 available Device: /physical_device:GPU:0 Available: True Initalized: False Memory growth: True INFO:sleap.nn.training: INFO:sleap.nn.training:Initializing trainer... INFO:sleap.nn.training:Loading training labels from: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1 INFO:sleap.nn.training: Splits: Training = 91 / Validation = 10. INFO:sleap.nn.training:Setting up for training... INFO:sleap.nn.training:Setting up pipeline builders... INFO:sleap.nn.training:Setting up model... INFO:sleap.nn.training:Building test pipeline... 2024-08-31 11:03:41.866195: I tensorflow/core/platform/cpu_feature_guard.cc:151] 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. 2024-08-31 11:03:42.671549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9599 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:02:00.0, compute capability: 8.6 INFO:sleap.nn.training:Loaded test example. [2.209s] INFO:sleap.nn.training: Input shape: (1024, 1024, 1) INFO:sleap.nn.training:Created Keras model. INFO:sleap.nn.training: Backbone: UNet(stacks=1, filters=16, filters_rate=2.0, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=5, middle_block=True, up_blocks=3, up_interpolate=True, block_contraction=False) INFO:sleap.nn.training: Max stride: 32 INFO:sleap.nn.training: Parameters: 7,819,861 INFO:sleap.nn.training: Heads: INFO:sleap.nn.training: [0] = MultiInstanceConfmapsHead(part_names=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], sigma=2.5, output_stride=4, loss_weight=1.0) INFO:sleap.nn.training: [1] = PartAffinityFieldsHead(edges=[('thorax', 'head'), ('thorax', 'abdomen'), ('thorax', 'wingL'), ('thorax', 'wingR'), ('thorax', 'forelegL4'), ('thorax', 'forelegR4'), ('thorax', 'midlegL4'), ('thorax', 'midlegR4'), ('thorax', 'hindlegL4'), ('thorax', 'hindlegR4'), ('head', 'eyeL'), ('head', 'eyeR')], sigma=75.0, output_stride=8, loss_weight=1.0) INFO:sleap.nn.training: Outputs: INFO:sleap.nn.training: [0] = KerasTensor(type_spec=TensorSpec(shape=(None, 256, 256, 13), dtype=tf.float32, name=None), name='MultiInstanceConfmapsHead/BiasAdd:0', description="created by layer 'MultiInstanceConfmapsHead'") INFO:sleap.nn.training: [1] = KerasTensor(type_spec=TensorSpec(shape=(None, 128, 128, 24), dtype=tf.float32, name=None), name='PartAffinityFieldsHead/BiasAdd:0', description="created by layer 'PartAffinityFieldsHead'") INFO:sleap.nn.training:Training from scratch INFO:sleap.nn.training:Setting up data pipelines... INFO:sleap.nn.training:Training set: n = 91 INFO:sleap.nn.training:Validation set: n = 10 INFO:sleap.nn.training:Setting up optimization... INFO:sleap.nn.training: Learning rate schedule: LearningRateScheduleConfig(reduce_on_plateau=True, reduction_factor=0.5, plateau_min_delta=1e-06, plateau_patience=5, plateau_cooldown=3, min_learning_rate=1e-08) INFO:sleap.nn.training: Early stopping: EarlyStoppingConfig(stop_training_on_plateau=True, plateau_min_delta=1e-08, plateau_patience=10) INFO:sleap.nn.training:Setting up outputs... INFO:sleap.nn.callbacks:Training controller subscribed to: tcp://127.0.0.1:9000 (topic: ) INFO:sleap.nn.training: ZMQ controller subcribed to: tcp://127.0.0.1:9000 INFO:sleap.nn.callbacks:Progress reporter publishing on: tcp://127.0.0.1:9001 for: not_set INFO:sleap.nn.training: ZMQ progress reporter publish on: tcp://127.0.0.1:9001 INFO:sleap.nn.training:Created run path: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101 INFO:sleap.nn.training:Setting up visualization... INFO:sleap.nn.training:Finished trainer set up. [5.1s] INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation... INFO:sleap.nn.training:Finished creating training datasets. [7.2s] INFO:sleap.nn.training:Starting training loop... Epoch 1/2 2024-08-31 11:03:57.202413: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0176s vs `on_train_batch_end` time: 0.1818s). Check your callbacks. 2024-08-31 11:05:17.899812: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 2024-08-31 11:05:20.997320: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.27GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2024-08-31 11:05:21.326703: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.27GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2024-08-31 11:05:21.392040: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.27GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 200/200 - 90s - loss: 0.0012 - MultiInstanceConfmapsHead_loss: 5.1135e-04 - PartAffinityFieldsHead_loss: 7.3340e-04 - val_loss: 0.0011 - val_MultiInstanceConfmapsHead_loss: 4.4563e-04 - val_PartAffinityFieldsHead_loss: 6.5197e-04 - lr: 1.0000e-04 - 90s/epoch - 450ms/step Epoch 2/2 Polling: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\viz\validation.*.png 200/200 - 58s - loss: 8.8963e-04 - MultiInstanceConfmapsHead_loss: 3.6029e-04 - PartAffinityFieldsHead_loss: 5.2933e-04 - val_loss: 6.1292e-04 - val_MultiInstanceConfmapsHead_loss: 2.5777e-04 - val_PartAffinityFieldsHead_loss: 3.5515e-04 - lr: 1.0000e-04 - 58s/epoch - 291ms/step Polling: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\viz\validation.*.png INFO:sleap.nn.training:Finished training loop. [2.5 min] INFO:sleap.nn.training:Deleting visualization directory: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\viz INFO:sleap.nn.training:Saving evaluation metrics to model folder... Predicting... ---------------------------------------- 100% ETA: 0:00:00 0.6 FPS INFO:sleap.nn.evals:Saved predictions: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\labels_pr.train.slp INFO:sleap.nn.evals:Saved metrics: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\metrics.train.npz INFO:sleap.nn.evals:OKS mAP: 0.000000 Predicting... ---------------------------------------- 100% ETA: 0:00:00 1.2 FPS INFO:sleap.nn.evals:Saved predictions: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\labels_pr.val.slp INFO:sleap.nn.evals:Saved metrics: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101\metrics.val.npz INFO:sleap.nn.evals:OKS mAP: 0.000000 INFO:sleap.nn.callbacks:Closing the reporter controller/context. INFO:sleap.nn.callbacks:Closing the training controller socket/context. Run Path: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101 Finished training multi_instance. Command line call: sleap-track C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp --only-suggested-frames -m C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\models\240831_110334.multi_instance.n=101 -o C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\predictions\courtship_labels.slp.240831_110650.predictions.slp --verbosity json --no-empty-frames Started inference at: 2024-08-31 11:06:56.316214 Args: { 'data_path': 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp', 'models': [ 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\models\\240831_110334.multi_instance.n=101' ], 'frames': '', 'only_labeled_frames': False, 'only_suggested_frames': True, 'output': 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\predictions\\courtship_labels.slp.240831_110650.predictions.slp', 'no_empty_frames': True, 'verbosity': 'json', 'video.dataset': None, 'video.input_format': 'channels_last', 'video.index': '', 'cpu': False, 'first_gpu': False, 'last_gpu': False, 'gpu': 'auto', 'max_edge_length_ratio': 0.25, 'dist_penalty_weight': 1.0, 'batch_size': 4, 2024-08-31 11:06:57.480056: I tensorflow/core/platform/cpu_feature_guard.cc:151] 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. 'open_in_gui': False, 'peak_threshold': 0.2, 'max_instances': None, 'tracking.tracker': None, 'tracking.max_tracking': None, 'tracking.max_tracks': None, 'tracking.target_instance_count': None, 'tracking.pre_cull_to_target': None, 2024-08-31 11:06:57.911416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9599 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:02:00.0, compute capability: 8.6 'tracking.pre_cull_iou_threshold': None, 'tracking.post_connect_single_breaks': None, 'tracking.clean_instance_count': None, 'tracking.clean_iou_threshold': None, 'tracking.similarity': None, 'tracking.match': None, 'tracking.robust': None, 'tracking.track_window': None, 'tracking.min_new_track_points': None, 'tracking.min_match_points': None, 'tracking.img_scale': None, 'tracking.of_window_size': None, 'tracking.of_max_levels': None, 'tracking.save_shifted_instances': None, 'tracking.kf_node_indices': None, 'tracking.kf_init_frame_count': None } INFO:sleap.nn.inference:Auto-selected GPU 0 with 11662 MiB of free memory. 2024-08-31 11:07:05.171639: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8201 Versions: SLEAP: 1.3.4 TensorFlow: 2.7.0 Numpy: 1.21.6 Python: 3.7.12 OS: Windows-10-10.0.22621-SP0 System: GPUs: 1/1 available Device: /physical_device:GPU:0 Available: True Initalized: False Memory growth: True Finished inference at: 2024-08-31 11:07:16.914447 Total runtime: 20.598233461380005 secs Predicted frames: 39/39 Provenance: { 'model_paths': [ 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\models\\240831_110334.multi_instance.n=101\\training_config.json' ], 'predictor': 'BottomUpPredictor', 'sleap_version': '1.3.4', 'platform': 'Windows-10-10.0.22621-SP0', 'command': 'C:\\Users\\Liezl\\miniconda3\\envs\\sleap_1.3.4\\Scripts\\sleap-track C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp --only-suggested-frames -m C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\models\\240831_110334.multi_instance.n=101 -o C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\predictions\\courtship_labels.slp.240831_110650.predictions.slp --verbosity json --no-empty-frames', 'data_path': 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship/courtship_labels.slp', 'output_path': 'C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\\predictions\\courtship_labels.slp.240831_110650.predictions.slp', 'total_elapsed': 20.598233461380005, 'start_timestamp': '2024-08-31 11:06:56.316214', 'finish_timestamp': '2024-08-31 11:07:16.914447' } Saved output: C:/Users/Liezl/Projects/sleap-estimates-animal-poses/datasets/drosophila-melanogaster-courtship\predictions\courtship_labels.slp.240831_110650.predictions.slp Process return code: 0 ```Terminal: Opening Training GUI
```bash unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' ```sleap-label
 ```bash (sleap_1.3.4) λ sleap-label Saving config: C:\Users\Liezl/.sleap/1.3.4/preferences.yaml Software versions: SLEAP: 1.3.4 TensorFlow: 2.7.0 Numpy: 1.21.6 Python: 3.7.12 OS: Windows-10-10.0.22621-SP0 Happy SLEAPing! :) ```mamba create sleap
```bash (base) λ mamba create -y -n sleap_1.3.4 -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.3.4 __ __ __ __ / \ / \ / \ / \ / \/ \/ \/ \ ███████████████/ /██/ /██/ /██/ /████████████████████████ / / \ / \ / \ / \ \____ / / \_/ \_/ \_/ \ o \__, / _/ \_____/ ` |/ ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗ ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗ ██╔████╔██║███████║██╔████╔██║██████╔╝███████║ ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║ ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝ mamba (1.4.2) supported by @QuantStack GitHub: https://github.com/mamba-org/mamba Twitter: https://twitter.com/QuantStack █████████████████████████████████████████████████████████████ Looking for: ['sleap=1.3.4'] sleap/label/dev/win-64 1.5kB @ 4.1kB/s 0.4s sleap/label/dev/noarch 135.0 B @ 243.0 B/s 0.2s nvidia/noarch 18.3kB @ 31.2kB/s 0.6s sleap/noarch No change anaconda/noarch No change nvidia/win-64 110.3kB @ 111.9kB/s 1.0s sleap/win-64 No change anaconda/win-64 3.2MB @ 2.0MB/s 1.2s conda-forge/noarch 18.8MB @ 6.6MB/s 3.5s conda-forge/win-64 28.2MB @ 6.2MB/s 5.6s Transaction Prefix: C:\Users\Liezl\miniconda3\envs\sleap_1.3.4 Updating specs: - sleap=1.3.4 Package Version Build Channel Size ------------------------------------------------------------------------------------------------------------ Install: ------------------------------------------------------------------------------------------------------------ + attrs 21.4.0 pyhd8ed1ab_0 conda-forge/noarch Cached + brotli 1.1.0 hcfcfb64_1 conda-forge/win-64 Cached + brotli-bin 1.1.0 hcfcfb64_1 conda-forge/win-64 Cached + ca-certificates 2024.8.30 h56e8100_0 conda-forge/win-64 159kB + cached-property 1.5.2 hd8ed1ab_1 conda-forge/noarch Cached + cached_property 1.5.2 pyha770c72_1 conda-forge/noarch Cached + cattrs 1.1.1 pyhd8ed1ab_0 conda-forge/noarch Cached + certifi 2024.7.4 pyhd8ed1ab_0 conda-forge/noarch Cached + cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge/noarch 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h2466b09_0 conda-forge/win-64 Cached + statsmodels 0.13.2 py37h3a130e4_0 conda-forge/win-64 Cached + tbb 2021.12.0 hc790b64_4 conda-forge/win-64 Cached + tensorflow 2.7.0 py37h5685391_4 sleap/label/dev/win-64 Cached + tensorflow-hub 0.12.0 pyhca92ed8_0 conda-forge/noarch Cached + threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge/noarch Cached + tifffile 2020.6.3 py_0 conda-forge/noarch Cached + tk 8.6.13 h5226925_1 conda-forge/win-64 Cached + toolz 0.12.1 pyhd8ed1ab_0 conda-forge/noarch Cached + typing-extensions 4.7.1 hd8ed1ab_0 conda-forge/noarch Cached + typing_extensions 4.7.1 pyha770c72_0 conda-forge/noarch Cached + ucrt 10.0.22621.0 h57928b3_0 conda-forge/win-64 Cached + unicodedata2 14.0.0 py37hcc03f2d_1 conda-forge/win-64 Cached + vc 14.3 h8a93ad2_20 conda-forge/win-64 Cached + vc14_runtime 14.40.33810 hcc2c482_20 conda-forge/win-64 Cached + vs2015_runtime 14.40.33810 h3bf8584_20 conda-forge/win-64 Cached + wheel 0.42.0 pyhd8ed1ab_0 conda-forge/noarch Cached + xorg-libxau 1.0.11 hcd874cb_0 conda-forge/win-64 Cached + xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge/win-64 Cached + xz 5.2.6 h8d14728_0 conda-forge/win-64 Cached + yaml 0.2.5 h8ffe710_2 conda-forge/win-64 Cached + zeromq 4.3.4 h0e60522_1 conda-forge/win-64 Cached + zipp 3.15.0 pyhd8ed1ab_0 conda-forge/noarch Cached + zlib 1.2.13 h2466b09_6 conda-forge/win-64 Cached + zstd 1.5.6 h0ea2cb4_0 conda-forge/win-64 Cached Summary: Install: 140 packages Total download: 39MB ```Mac
GUI Training
 Terminal: Opening Training GUI
```bash 2024-08-31 10:38:42.835 python[28887:4467799] +[CATransaction synchronize] called within transaction unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' unhashable type: 'dict' qt.qpa.fonts: Populating font family aliases took 123 ms. Replace uses of missing font family ".AppleSystemUIFont" with one that exists to avoid this cost. ```Terminal: Training
```bash Resetting monitor window. Polling: /Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/models/240831_104002.centroid.n=103/viz/validation.*.png Start training centroid... ['sleap-train', '/var/folders/64/rjln6zpx7tlgwf8cqgvhm7fr0000gn/T/tmpdf8ag4oh/240831_104002_training_job.json', '/Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/courtship_labels.slp', '--zmq', '--save_viz'] INFO:sleap.nn.training:Versions: SLEAP: 1.3.4 TensorFlow: 2.9.2 Numpy: 1.22.4 Python: 3.9.15 OS: macOS-13.5-arm64-arm-64bit INFO:sleap.nn.training:Training labels file: /Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Training profile: /var/folders/64/rjln6zpx7tlgwf8cqgvhm7fr0000gn/T/tmpdf8ag4oh/240831_104002_training_job.json INFO:sleap.nn.training: INFO:sleap.nn.training:Arguments: INFO:sleap.nn.training:{ "training_job_path": "/var/folders/64/rjln6zpx7tlgwf8cqgvhm7fr0000gn/T/tmpdf8ag4oh/240831_104002_training_job.json", "labels_path": "/Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/courtship_labels.slp", "video_paths": [ "" ], "val_labels": null, "test_labels": null, "base_checkpoint": null, "tensorboard": false, "save_viz": true, "zmq": true, "run_name": "", "prefix": "", "suffix": "", "cpu": false, "first_gpu": false, "last_gpu": false, "gpu": "auto" } INFO:sleap.nn.training: INFO:sleap.nn.training:Training job: INFO:sleap.nn.training:{ "data": { "labels": { "training_labels": "/Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/courtship_labels.slp", "validation_labels": null, "validation_fraction": 0.1, "test_labels": null, "split_by_inds": false, "training_inds": [ 12, 98, 101, 71, 2, 95, 86, 50, 63, 90, 87, 37, 58, 13, 43, 16, 40, 24, 82, 44, 67, 10, 26, 84, 49, 6, 48, 53, 100, 47, 61, 21, 15, 52, 75, 30, 59, 72, 56, 80, 5, 102, 94, 27, 69, 74, 38, 73, 29, 20, 33, 42, 81, 14, 62, 78, 70, 7, 64, 45, 77, 85, 3, 54, 51, 99, 39, 31, 46, 66, 93, 19, 83, 11, 0, 92, 25, 8, 60, 22, 28, 36, 1, 9, 55, 57, 17, 88, 65, 97, 96, 23, 41 ], "validation_inds": [ 76, 79, 4, 89, 34, 35, 32, 91, 68, 18 ], "test_inds": null, "search_path_hints": [ "", "", "", "", "" ], "skeletons": [] }, "preprocessing": { "ensure_rgb": false, "ensure_grayscale": false, "imagenet_mode": null, "input_scaling": 0.5, "pad_to_stride": 16, "resize_and_pad_to_target": true, "target_height": 1024, "target_width": 1024 }, "instance_cropping": { "center_on_part": "thorax", "crop_size": null, "crop_size_detection_padding": 16 } }, "model": { "backbone": { "leap": null, "unet": { "stem_stride": null, "max_stride": 16, "output_stride": 2, "filters": 16, "filters_rate": 2.0, "middle_block": true, "up_interpolate": true, "stacks": 1 }, "hourglass": null, "resnet": null, "pretrained_encoder": null }, "heads": { "single_instance": null, "centroid": { "anchor_part": "thorax", "sigma": 2.5, "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": -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": 4, "batches_per_epoch": 200, "min_batches_per_epoch": 200, "val_batches_per_epoch": 10, "min_val_batches_per_epoch": 10, "epochs": 2, "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": "240831_104002.centroid.n=103", "run_name_prefix": "", "run_name_suffix": "", "runs_folder": "/Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/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": "1.4.1a2", "filename": "/var/folders/64/rjln6zpx7tlgwf8cqgvhm7fr0000gn/T/tmpdf8ag4oh/240831_104002_training_job.json" } INFO:sleap.nn.training: INFO:sleap.nn.training:Failed to query GPU memory from nvidia-smi. Defaulting to first GPU. INFO:sleap.nn.training:Using GPU 0 for acceleration. INFO:sleap.nn.training:Disabled GPU memory pre-allocation. INFO:sleap.nn.training:System: GPUs: 1/1 available Device: /physical_device:GPU:0 Available: True Initalized: False Memory growth: True INFO:sleap.nn.training: INFO:sleap.nn.training:Initializing trainer... INFO:sleap.nn.training:Loading training labels from: /Users/liezlmaree/Projects/sleap-datasets/drosophila-melanogaster-courtship/courtship_labels.slp INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1 INFO:sleap.nn.training: Splits: Training = 93 / Validation = 10. INFO:sleap.nn.training:Setting up for training... INFO:sleap.nn.training:Setting up pipeline builders... INFO:sleap.nn.training:Setting up model... INFO:sleap.nn.training:Building test pipeline... Metal device set to: Apple M2 Pro systemMemory: 16.00 GB maxCacheSize: 5.33 GB 2024-08-31 10:40:07.306830: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2024-08-31 10:40:07.307008: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id:sleap-label
 ```bash (sleap_1.3) liezlmaree:~$sleap-label Saving config: /Users/liezlmaree/.sleap/1.3.4/preferences.yaml Restoring GUI state... Software versions: SLEAP: 1.3.4 TensorFlow: 2.9.2 Numpy: 1.22.4 Python: 3.9.15 OS: macOS-13.5-arm64-arm-64bit Happy SLEAPing! :) ```mm create sleap
```bash liezlmaree:~$mm create -y -n sleap_1.3.4 -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.3.4 nvidia/noarch (check zst) Checked 0.2s nvidia/osx-arm64 (check zst) Checked 0.3s sleap/osx-arm64 (check zst) Checked 1.0s sleap/label/dev/osx-arm64 (check zst) Checked 1.1s sleap/noarch (check zst) Checked 2.0s sleap/label/dev/noarch (check zst) Checked 2.8s nvidia/osx-arm64 No change nvidia/noarch 14.7kB @ 123.3kB/s 0.1s sleap/osx-arm64 No change sleap/label/dev/noarch 116.0 B @ 215.0 B/s 0.4s sleap/label/dev/osx-arm64 962.0 B @ 1.3kB/s 0.7s anaconda/noarch No change sleap/noarch No change anaconda/osx-arm64 No change conda-forge/osx-arm64 11.4MB @ 788.8kB/s 14.5s conda-forge/noarch 16.2MB @ 1.1MB/s 14.7s Transaction Prefix: /Users/liezlmaree/micromamba/envs/sleap_1.3.4 Updating specs: - sleap=1.3.4 warning libmamba Invalid package cache, file '/Users/liezlmaree/micromamba/pkgs/pyside2-5.15.8-py39h0adaba8_2/lib/python3.9/site-packages/shiboken2/files.dir/shibokensupport/signature/mapping.py' has incorrect size warning libmamba Extracted package cache '/Users/liezlmaree/micromamba/pkgs/sleap-1.3.4-py39_0' has invalid size warning libmamba Extracted package cache '/Users/liezlmaree/micromamba/pkgs/sleap-1.3.4-py39_0' has invalid SHA-256 checksum Package Version Build Channel Size ───────────────────────────────────────────────────────────────────────────────────────────────── Install: ───────────────────────────────────────────────────────────────────────────────────────────────── + bzip2 1.0.8 h99b78c6_7 conda-forge Cached + libffi 3.4.2 h3422bc3_5 conda-forge Cached + libzlib 1.3.1 hfb2fe0b_1 conda-forge Cached + libcxx 18.1.8 h3ed4263_6 conda-forge Cached + python_abi 3.9 5_cp39 conda-forge Cached + giflib 5.2.2 h93a5062_0 conda-forge Cached + jpeg 9e h1a8c8d9_3 conda-forge Cached + ncurses 6.5 h7bae524_1 conda-forge Cached 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py39h48c5dd5_0 conda-forge Cached + shapely 2.0.6 py39h2abb8a4_0 conda-forge Cached + pandas 2.2.2 py39h998126f_1 conda-forge Cached + h5py 3.8.0 nompi_py39hc9149d8_100 conda-forge Cached + libopencv 4.6.0 py39he1c1adf_3 conda-forge Cached + zstandard 0.22.0 py39h0b77d07_1 conda-forge Cached + cryptography 39.0.0 py39haa0b8cc_0 conda-forge Cached + gstreamer 1.22.9 h551c6ff_1 conda-forge Cached + matplotlib-base 3.8.4 py39h15359f4_2 conda-forge Cached + py-opencv 4.6.0 py39hfa6204d_3 conda-forge Cached + gst-plugins-base 1.22.9 h09b4b5e_1 conda-forge Cached + opencv 4.6.0 py39hdf13c20_3 conda-forge Cached + qt-main 5.15.8 hfe8d25c_6 conda-forge Cached + pyside2 5.15.8 py39h0adaba8_2 conda-forge Cached + aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge Cached + werkzeug 3.0.4 pyhd8ed1ab_0 conda-forge Cached + dask-core 2024.8.0 pyhd8ed1ab_0 conda-forge Cached + patsy 0.5.6 pyhd8ed1ab_0 conda-forge Cached + imageio 2.35.1 pyh12aca89_0 conda-forge Cached + opt_einsum 3.3.0 pyhc1e730c_2 conda-forge Cached + tifffile 2020.6.3 py_0 conda-forge Cached + urllib3 2.2.2 pyhd8ed1ab_1 conda-forge Cached + pyopenssl 23.2.0 pyhd8ed1ab_1 conda-forge Cached + oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge Cached + requests 2.32.3 pyhd8ed1ab_0 conda-forge Cached + requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge Cached + scipy 1.7.3 py39haa152ba_2 anaconda Cached + aiohttp 3.10.5 py39hfea33bf_0 conda-forge Cached + statsmodels 0.14.1 py39h373d45f_0 conda-forge Cached + scikit-learn 1.0 py39h12ba089_1 conda-forge Cached + scikit-image 0.19.3 py39hde7b980_2 conda-forge Cached + seaborn-base 0.13.2 pyhd8ed1ab_2 conda-forge Cached + pykalman 0.9.7 pyhd8ed1ab_0 conda-forge Cached + keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge Cached + scikit-video 1.1.11 pyh24bf2e0_0 conda-forge Cached + google-auth 2.34.0 pyhff2d567_0 conda-forge Cached + imgaug 0.4.0 pyhd8ed1ab_1 conda-forge Cached + seaborn 0.13.2 hd8ed1ab_2 conda-forge Cached + google-auth-oauthlib 0.4.6 pyhd8ed1ab_0 conda-forge Cached + tensorboard 2.9.0 pyhd8ed1ab_0 conda-forge Cached + tensorflow-base 2.9.1 cpu_py39ha1ad4ae_0 conda-forge Cached + tensorflow-estimator 2.9.1 cpu_py39h7b621ec_0 conda-forge Cached + tensorflow 2.9.1 cpu_py39h2839aeb_0 conda-forge Cached + tensorflow-hub 0.12.0 pyhca92ed8_0 conda-forge Cached + sleap 1.3.4 py39_0 sleap/label/dev 240MB Summary: Install: 253 packages Total download: 242MB ```⚠️ This build is branched off of main. In order to get the build runners working, we needed to make some changes (that were already made in develop). This will cause merge conflicts when merging develop into main. Anything in develop should override all files in main - even the
attrsandopencvconstraints added here as they will be more officially handled in develop before the next release. ⚠️ These merge conflicts from develop -> main will break the develop runners from runningPerhaps we release from this branch (not main) and delete this branch instead of merging into main? The source code would still be able to be accessed through the tags and the zipped source code in the release.
Types of changes
Does this address any currently open issues?
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Outside contributors checklist
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