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20 hours ago •
talmolab
talmolab
Different input image size - "ValueError: all input arrays must have the same shape"
I have video data with different image sizes that I recorded using different cameras. I was wondering how sleap deals with that. I suspect the input image size is the problem since I get the "ValueError: all input arrays must have the same shape" error.
An example image is below. It's pretty large in size (1300x900) and I have other images that are slightly smaller in size (1100x800).
Thank you! Mehmet
Hey Mehmet,
SLEAP should handle this at training time by padding to the size of the largest video.
At what stage do you get this error? When exporting a training package or when training or?
If you don't mind, you could send this specific project file (.slp) to [email protected]. We're going to be a little delayed with responses this and next week but we'll check it out as soon as we get a chance. Thanks!
Thanks Talmo! It also happens when I try to export it as well as when training.
I have sent the .slp file your way. Appreciate the help!
Best, Mehmet
Hi Talmo,
I was wondering if you had a chance to look at this! Thanks so much again!
Best, Mehmet
Hi @mfkeles Sorry for the late response here we're a bit swamped. Let me try to reproduce the issue and get back to you. I found the file you sent and will let you know soon if I need more info.
Thanks
Hi @mfkeles Can you please email a self-contained folder (with images included) and precise steps to reproduce this bug? I have tried reproducing from the labels file alone (by using sample images of variable sizes) but could not (both exporting and training worked fine).
Thanks
Hey @ariematsliah-princeton thanks for the help again!
I think I found the problem. Most of the labels that I imported from DLC labels were fine but there was a set that had labels outside image for some reason. The individual points were occluded ones so they wouldn't even show up when using DLC. When I took out that set of images, I was able to export it. However, I ran into a different issue. This might be related to the fact that my training dataset is composed of images with different dimensions. I am not sure but I was using google colab to train and I got the following error. Let me know if this is something that I can fix! I would be happy to send you the package. Thanks so much !
TRACEBACK
2021-05-24 21:00:17.239067: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
INFO:sleap.nn.training:Versions:
SLEAP: 1.1.3
TensorFlow: 2.3.1
Numpy: 1.18.5
Python: 3.7.10
OS: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
INFO:sleap.nn.training:Training labels file: colab.pkg.slp
INFO:sleap.nn.training:Training profile: single_instance.json
INFO:sleap.nn.training:
INFO:sleap.nn.training:Arguments:
INFO:sleap.nn.training:{
"training_job_path": "single_instance.json",
"labels_path": "colab.pkg.slp",
"video_paths": "",
"val_labels": null,
"test_labels": null,
"tensorboard": false,
"save_viz": false,
"zmq": false,
"run_name": "",
"prefix": "",
"suffix": ""
}
INFO:sleap.nn.training:
INFO:sleap.nn.training:Training job:
INFO:sleap.nn.training:{
"data": {
"labels": {
"training_labels": null,
"validation_labels": null,
"validation_fraction": 0.1,
"test_labels": null,
"split_by_inds": false,
"training_inds": null,
"validation_inds": null,
"test_inds": null,
"search_path_hints": [],
"skeletons": []
},
"preprocessing": {
"ensure_rgb": false,
"ensure_grayscale": true,
"imagenet_mode": null,
"input_scaling": 0.75,
"pad_to_stride": null,
"resize_and_pad_to_target": true,
"target_height": null,
"target_width": null
},
"instance_cropping": {
"center_on_part": null,
"crop_size": null,
"crop_size_detection_padding": 16
}
},
"model": {
"backbone": {
"leap": null,
"unet": {
"stem_stride": null,
"max_stride": 64,
"output_stride": 4,
"filters": 32,
"filters_rate": 1.5,
"middle_block": true,
"up_interpolate": true,
"stacks": 1
},
"hourglass": null,
"resnet": null,
"pretrained_encoder": null
},
"heads": {
"single_instance": {
"part_names": null,
"sigma": 5.0,
"output_stride": 4,
"offset_refinement": false
},
"centroid": null,
"centered_instance": null,
"multi_instance": 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": true
},
"online_shuffling": true,
"shuffle_buffer_size": 128,
"prefetch": true,
"batch_size": 4,
"batches_per_epoch": null,
"min_batches_per_epoch": 200,
"val_batches_per_epoch": null,
"min_val_batches_per_epoch": 10,
"epochs": 200,
"optimizer": "adam",
"initial_learning_rate": 0.0001,
"learning_rate_schedule": {
"reduce_on_plateau": true,
"reduction_factor": 0.5,
"plateau_min_delta": 1e-06,
"plateau_patience": 5,
"plateau_cooldown": 3,
"min_learning_rate": 1e-08
},
"hard_keypoint_mining": {
"online_mining": false,
"hard_to_easy_ratio": 2.0,
"min_hard_keypoints": 2,
"max_hard_keypoints": null,
"loss_scale": 5.0
},
"early_stopping": {
"stop_training_on_plateau": true,
"plateau_min_delta": 1e-06,
"plateau_patience": 10
}
},
"outputs": {
"save_outputs": true,
"run_name": "210524_165811",
"run_name_prefix": "",
"run_name_suffix": ".single_instance",
"runs_folder": "",
"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": false,
"controller_address": "tcp://127.0.0.1:9000",
"controller_polling_timeout": 10,
"publish_updates": false,
"publish_address": "tcp://127.0.0.1:9001"
}
},
"name": "",
"description": "",
"sleap_version": "1.1.3",
"filename": "single_instance.json"
}
INFO:sleap.nn.training:
INFO:sleap.nn.training:System:
2021-05-24 21:00:18.660443: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2021-05-24 21:00:18.674797: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.675396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2021-05-24 21:00:18.675447: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-05-24 21:00:18.677002: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-05-24 21:00:18.678829: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-05-24 21:00:18.679166: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-05-24 21:00:18.681123: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-05-24 21:00:18.682031: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-05-24 21:00:18.686351: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-05-24 21:00:18.686490: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.687110: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.687665: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
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: colab.pkg.slp
INFO:sleap.nn.training:Creating training and validation splits from validation fraction: 0.1
INFO:sleap.nn.training: Splits: Training = 362 / Validation = 40.
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...
2021-05-24 21:00:18.897594: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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.
2021-05-24 21:00:18.902155: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2000170000 Hz
2021-05-24 21:00:18.902427: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55807278c8c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-05-24 21:00:18.902462: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-05-24 21:00:18.995227: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.996108: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55807278ca80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-05-24 21:00:18.996143: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2021-05-24 21:00:18.996384: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.996925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2021-05-24 21:00:18.997007: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-05-24 21:00:18.997054: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-05-24 21:00:18.997081: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-05-24 21:00:18.997108: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-05-24 21:00:18.997132: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-05-24 21:00:18.997155: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-05-24 21:00:18.997180: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-05-24 21:00:18.997280: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.997855: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:18.998383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-05-24 21:00:18.998464: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-05-24 21:00:19.436736: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-05-24 21:00:19.436792: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0
2021-05-24 21:00:19.436804: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N
2021-05-24 21:00:19.437030: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:19.437705: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-05-24 21:00:19.438263: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14958 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
INFO:sleap.nn.training:Loaded test example. [2.252s]
INFO:sleap.nn.training: Input shape: (704, 1024, 1)
INFO:sleap.nn.training:Created Keras model.
INFO:sleap.nn.training: Backbone: UNet(stacks=1, filters=32, filters_rate=1.5, kernel_size=3, stem_kernel_size=7, convs_per_block=2, stem_blocks=0, down_blocks=6, middle_block=True, up_blocks=4, up_interpolate=True, block_contraction=False)
INFO:sleap.nn.training: Max stride: 64
INFO:sleap.nn.training: Parameters: 6,786,842
INFO:sleap.nn.training: Heads:
INFO:sleap.nn.training: [0] = SingleInstanceConfmapsHead(part_names=['a6', 'a5', 'atip', 'headtip', 'head_r', 'head_l', 'thor_ant', 'thor_post', 't1_r_tip', 'joint1_r', 'joint1_rmid', 'joint1_rtop', 't1_l_tip', 'joint1_l', 'joint1_lmid', 'joint1_ltop', 't2_r_tip', 'joint2_r', 'joint2_rmid', 'joint2_rtop', 't2_l_tip', 'joint2_l', 'joint2_lmid', 'joint2_ltop', 't3_r_tip', 'joint3_r', 'joint_3rmid', 'joint3_rtop', 't3_l_tip', 'joint3_l', 'joint_3lmid', 'joint3_ltop', 'halt_r', 'halt_l', 'prob'], sigma=5.0, output_stride=4, loss_weight=1.0)
INFO:sleap.nn.training: Outputs:
INFO:sleap.nn.training: [0] = Tensor("SingleInstanceConfmapsHead_0/BiasAdd:0", shape=(None, 176, 256, 35), dtype=float32)
INFO:sleap.nn.training:Setting up data pipelines...
INFO:sleap.nn.training:Training set: n = 362
INFO:sleap.nn.training:Validation set: n = 40
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-06, plateau_patience=10)
INFO:sleap.nn.training:Setting up outputs...
INFO:sleap.nn.training:Created run path: 210524_165811.single_instance
INFO:sleap.nn.training:Setting up visualization...
Unable to use Qt backend for matplotlib. This probably means Qt is running headless.
INFO:sleap.nn.training:Finished trainer set up. [3.7s]
INFO:sleap.nn.training:Creating tf.data.Datasets for training data generation...
INFO:sleap.nn.training:Finished creating training datasets. [10.8s]
INFO:sleap.nn.training:Starting training loop...
Epoch 1/200
2021-05-24 21:00:35.289608: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-05-24 21:00:36.371513: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0530s vs `on_train_batch_end` time: 0.1285s). Check your callbacks.
Traceback (most recent call last):
File "/usr/local/bin/sleap-train", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.7/dist-packages/sleap/nn/training.py", line 1582, in main
trainer.train()
File "/usr/local/lib/python3.7/dist-packages/sleap/nn/training.py", line 892, in train
verbose=2,
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py", line 1137, in fit
callbacks.on_epoch_end(epoch, epoch_logs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/callbacks.py", line 416, in on_epoch_end
callback.on_epoch_end(epoch, numpy_logs)
File "/usr/local/lib/python3.7/dist-packages/sleap/nn/callbacks.py", line 280, in on_epoch_end
figure = self.plot_fn()
File "/usr/local/lib/python3.7/dist-packages/sleap/nn/training.py", line 1061, in <lambda>
viz_fn=lambda: visualize_example(next(training_viz_ds_iter)),
File "/usr/local/lib/python3.7/dist-packages/sleap/nn/training.py", line 1042, in visualize_example
preds = inference_layer(tf.expand_dims(img, axis=0))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/sleap/nn/inference.py", line 1006, in call
preds = self.keras_model(imgs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py", line 386, in call
inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/merge.py", line 183, in call
return self._merge_function(inputs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/merge.py", line 522, in _merge_function
return K.concatenate(inputs, axis=self.axis)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 2881, in concatenate
return array_ops.concat([to_dense(x) for x in tensors], axis)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1654, in concat
return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1207, in concat_v2
_ops.raise_from_not_ok_status(e, name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py", line 6843, in raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,17,24,243] vs. shape[1] = [1,18,24,364] [Op:ConcatV2] name: concat
Do you mind sharing the exported package or other means to reproduce the same error?
here you go: https://drive.google.com/drive/folders/13qldYfJ_k8mP9QN8fzw_AlZi0h86NvHW?usp=sharing
Hi @ariematsliah-princeton I was wondering if you had a chance to look at this! Thanks so much!
Best, -M
Hey @mfkeles,
@ariematsliah-princeton is out of the office for a few more days, but we'll try to get back to you as soon as possible. Sorry about the delay!!
Hi @mfkeles Just wanted to update you that I'm still debugging this issue. Will update once root cause is found. Best
Closing this issue due to inactivity but please feel free to comment again if you're still having issues and we'll reopen it.
Hi @talmo,
I got the same error as mentioned here. These are my steps:
- I import a dataset from DLC with crop and uncrop images.
- I then try to export with "training job package" and I get the error.
Did you guys found any solution to this issue?
Thanks!
MY INFO + TERMINAL OUTPUT
Software versions:
SLEAP: 1.2.6
TensorFlow: 2.8.0
Numpy: 1.21.5
Python: 3.7.5
OS: Darwin-21.1.0-x86_64-i386-64bit
Happy SLEAPing! :)
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/gui/learning/dialog.py", line 684, in export_package
suggested=include_suggestions,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/gui/commands.py", line 1250, in export_dataset_gui
progress_callback=update_progress,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/dataset.py", line 1971, in save_file
write(filename, labels, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/format/main.py", line 162, in write
return disp.write(filename, source_object, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/format/dispatch.py", line 78, in write
return adaptor.write(filename, source_object, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/format/hdf5.py", line 253, in write
progress_callback=progress_callback,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/dataset.py", line 2258, in save_frame_data_hdf5
frame_numbers=frame_nums,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/video.py", line 1413, in to_hdf5
frame_data = self.get_frames(frame_numbers)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sleap/io/video.py", line 1101, in get_frames
return np.stack([self.get_frame(idx) for idx in idxs], axis=0)
File "<__array_function__ internals>", line 6, in stack
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/shape_base.py", line 426, in stack
raise ValueError('all input arrays must have the same shape')
ValueError: all input arrays must have the same shape
Hi @cosimogonnelli,
We did not get a fix yet. Can you share your data with [email protected] please.
Thanks, Liezl
I am closing this issue again due to inactivity. If you experience this issue please comment and I will reopen it. Also, we are having trouble recreating the issue, so data for a minimal working example to [email protected] would be much appreciated. Thanks!