I try to use testscript.py to test DLC environment is work or not.
But is show some error and stuck at ”Starting training….”.
The following is what I do:
- A win11 computer with i7-12700 and RTX3060
- Install Anaconda
- Install CUDA 11.2 and cuDNN 8.1
- Create environment by using offical DEEPLABCUT.yaml file
- Enter environment and pip install Torch
- Check if TF can use GPU
- Run testscripy.py and stuck at "Starting training…"
Please help me solve this problem.
I think that may cause by some outdated packages.
The packages in environment:
# packages in environment at C:Appanaconda3envsDEEPLABCUT:
#
# Name Version Build Channel
absl-py 1.2.0 pypi_0 pypi
aom 3.4.0 h0e60522_1 conda-forge
argon2-cffi 21.3.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py38h294d835_2 conda-forge
asttokens 2.0.8 pyhd8ed1ab_0 conda-forge
astunparse 1.6.3 pypi_0 pypi
attrs 22.1.0 pyh71513ae_1 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
beautifulsoup4 4.11.1 pyha770c72_0 conda-forge
bleach 5.0.1 pyhd8ed1ab_0 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
ca-certificates 2022.6.15.1 h5b45459_0 conda-forge
cachetools 5.2.0 pypi_0 pypi
certifi 2022.6.15.1 pypi_0 pypi
cffi 1.15.1 py38hd8c33c5_0 conda-forge
charset-normalizer 2.1.1 pypi_0 pypi
colorama 0.4.5 pyhd8ed1ab_0 conda-forge
cycler 0.11.0 pypi_0 pypi
debugpy 1.6.3 py38h885f38d_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
deeplabcut 2.2.2 pypi_0 pypi
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
executing 1.0.0 pyhd8ed1ab_0 conda-forge
expat 2.4.8 h39d44d4_0 conda-forge
ffmpeg 5.1.1 gpl_h7b28927_101 conda-forge
filterpy 1.4.5 pypi_0 pypi
flatbuffers 2.0.7 pypi_0 pypi
flit-core 3.7.1 pyhd8ed1ab_0 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.14.0 hce3cb01_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.37.1 pypi_0 pypi
freetype 2.12.1 h546665d_0 conda-forge
gast 0.4.0 pypi_0 pypi
gettext 0.19.8.1 ha2e2712_1008 conda-forge
glib 2.72.1 h7755175_0 conda-forge
glib-tools 2.72.1 h7755175_0 conda-forge
google-auth 2.11.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.48.1 pypi_0 pypi
gst-plugins-base 1.20.3 h001b923_1 conda-forge
gstreamer 1.20.3 h6b5321d_1 conda-forge
h5py 3.7.0 pypi_0 pypi
icu 70.1 h0e60522_0 conda-forge
idna 3.3 pypi_0 pypi
imageio 2.21.2 pypi_0 pypi
imgaug 0.4.0 pypi_0 pypi
importlib-metadata 4.11.4 py38haa244fe_0 conda-forge
importlib_resources 5.9.0 pyhd8ed1ab_0 conda-forge
ipykernel 6.15.2 pyh025b116_0 conda-forge
ipython 8.5.0 pyh08f2357_1 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
ipywidgets 8.0.2 pyhd8ed1ab_1 conda-forge
jedi 0.18.1 pyhd8ed1ab_2 conda-forge
jinja2 3.1.2 pyhd8ed1ab_1 conda-forge
joblib 1.1.0 pypi_0 pypi
jpeg 9e h8ffe710_2 conda-forge
jsonschema 4.16.0 pyhd8ed1ab_0 conda-forge
jupyter 1.0.0 py38haa244fe_7 conda-forge
jupyter_client 7.3.5 pyhd8ed1ab_0 conda-forge
jupyter_console 6.4.4 pyhd8ed1ab_0 conda-forge
jupyter_core 4.11.1 py38haa244fe_0 conda-forge
jupyterlab_pygments 0.2.2 pyhd8ed1ab_0 conda-forge
jupyterlab_widgets 3.0.3 pyhd8ed1ab_0 conda-forge
keras 2.10.0 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.4.4 pypi_0 pypi
krb5 1.19.3 h1176d77_0 conda-forge
libclang 14.0.6 pypi_0 pypi
libclang13 14.0.6 default_h77d9078_0 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libglib 2.72.1 h3be07f2_0 conda-forge
libiconv 1.16 he774522_0 conda-forge
libogg 1.3.4 h8ffe710_1 conda-forge
libpng 1.6.37 h1d00b33_4 conda-forge
libsodium 1.0.18 h8d14728_1 conda-forge
libsqlite 3.39.3 hcfcfb64_0 conda-forge
libvorbis 1.3.7 h0e60522_0 conda-forge
libxml2 2.9.14 hf5bbc77_4 conda-forge
libxslt 1.1.35 h34f844d_0 conda-forge
libzlib 1.2.12 h8ffe710_2 conda-forge
llvmlite 0.39.1 pypi_0 pypi
lxml 4.9.1 py38h294d835_0 conda-forge
markdown 3.4.1 pypi_0 pypi
markupsafe 2.1.1 py38h294d835_1 conda-forge
matplotlib 3.5.3 pypi_0 pypi
matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge
mistune 2.0.4 pyhd8ed1ab_0 conda-forge
msgpack 1.0.4 pypi_0 pypi
msgpack-numpy 0.4.8 pypi_0 pypi
nb_conda 2.2.1 win_6 conda-forge
nb_conda_kernels 2.3.1 py38haa244fe_1 conda-forge
nbclient 0.6.8 pyhd8ed1ab_0 conda-forge
nbconvert 7.0.0 pyhd8ed1ab_0 conda-forge
nbconvert-core 7.0.0 pyhd8ed1ab_0 conda-forge
nbconvert-pandoc 7.0.0 pyhd8ed1ab_0 conda-forge
nbformat 5.4.0 pyhd8ed1ab_0 conda-forge
nest-asyncio 1.5.5 pyhd8ed1ab_0 conda-forge
networkx 2.8.6 pypi_0 pypi
notebook 6.4.12 pyha770c72_0 conda-forge
numba 0.56.2 pypi_0 pypi
numexpr 2.8.3 pypi_0 pypi
numpy 1.23.3 pypi_0 pypi
oauthlib 3.2.1 pypi_0 pypi
opencv-python 4.6.0.66 pypi_0 pypi
openh264 2.3.0 h0e60522_0 conda-forge
openssl 1.1.1q h8ffe710_0 conda-forge
opt-einsum 3.3.0 pypi_0 pypi
packaging 21.3 pyhd8ed1ab_0 conda-forge
pandas 1.4.4 pypi_0 pypi
pandoc 2.19.2 h57928b3_0 conda-forge
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
parso 0.8.3 pyhd8ed1ab_0 conda-forge
patsy 0.5.2 pypi_0 pypi
pcre 8.45 h0e60522_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 9.2.0 pypi_0 pypi
pip 22.2.2 pyhd8ed1ab_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_0 conda-forge
ply 3.11 py_1 conda-forge
prometheus_client 0.14.1 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.31 pyha770c72_0 conda-forge
prompt_toolkit 3.0.31 hd8ed1ab_0 conda-forge
protobuf 3.19.4 pypi_0 pypi
psutil 5.9.2 py38h91455d4_0 conda-forge
pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pygments 2.13.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge
pyqt 5.15.7 py38h75e37d8_0 conda-forge
pyqt5-sip 12.11.0 py38h885f38d_0 conda-forge
pyrsistent 0.18.1 py38h294d835_1 conda-forge
python 3.8.13 h9a09f29_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.16.1 pyhd8ed1ab_0 conda-forge
python_abi 3.8 2_cp38 conda-forge
pytz 2022.2.1 pypi_0 pypi
pywavelets 1.3.0 pypi_0 pypi
pywin32 303 py38h294d835_0 conda-forge
pywinpty 2.0.7 py38hd3f51b4_0 conda-forge
pyyaml 6.0 pypi_0 pypi
pyzmq 23.2.1 py38h09162b1_0 conda-forge
qt-main 5.15.6 hf0cf448_0 conda-forge
qtconsole 5.3.2 pyhd8ed1ab_0 conda-forge
qtconsole-base 5.3.2 pyha770c72_0 conda-forge
qtpy 2.2.0 pyhd8ed1ab_0 conda-forge
requests 2.28.1 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rsa 4.9 pypi_0 pypi
ruamel-yaml 0.17.21 pypi_0 pypi
ruamel-yaml-clib 0.2.6 pypi_0 pypi
scikit-image 0.19.3 pypi_0 pypi
scikit-learn 1.1.2 pypi_0 pypi
scipy 1.9.1 pypi_0 pypi
send2trash 1.8.0 pyhd8ed1ab_0 conda-forge
setuptools 59.8.0 pypi_0 pypi
shapely 1.8.4 pypi_0 pypi
sip 6.6.2 py38h885f38d_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
soupsieve 2.3.2.post1 pyhd8ed1ab_0 conda-forge
sqlite 3.39.3 hcfcfb64_0 conda-forge
stack_data 0.5.0 pyhd8ed1ab_0 conda-forge
statsmodels 0.13.2 pypi_0 pypi
svt-av1 1.2.1 h0e60522_0 conda-forge
tables 3.7.0 pypi_0 pypi
tabulate 0.8.10 pypi_0 pypi
tensorboard 2.10.0 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorflow 2.10.0 pypi_0 pypi
tensorflow-estimator 2.10.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.27.0 pypi_0 pypi
tensorpack 0.11 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
terminado 0.15.0 py38haa244fe_0 conda-forge
tf-slim 1.1.0 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tifffile 2022.8.12 pypi_0 pypi
tinycss2 1.1.1 pyhd8ed1ab_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
toml 0.10.2 pyhd8ed1ab_0 conda-forge
torch 1.12.1 pypi_0 pypi
tornado 6.2 py38h294d835_0 conda-forge
tqdm 4.64.1 pypi_0 pypi
traitlets 5.3.0 pyhd8ed1ab_0 conda-forge
typing_extensions 4.3.0 pyha770c72_0 conda-forge
ucrt 10.0.20348.0 h57928b3_0 conda-forge
urllib3 1.26.12 pypi_0 pypi
vc 14.2 hb210afc_7 conda-forge
vs2015_runtime 14.29.30139 h890b9b1_7 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
werkzeug 2.2.2 pypi_0 pypi
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
widgetsnbextension 4.0.3 pyhd8ed1ab_0 conda-forge
winpty 0.4.3 4 conda-forge
wrapt 1.14.1 pypi_0 pypi
wxpython 4.0.7.post2 pypi_0 pypi
x264 1!164.3095 h8ffe710_2 conda-forge
x265 3.5 h2d74725_3 conda-forge
xz 5.2.6 h8d14728_0 conda-forge
zeromq 4.3.4 h0e60522_1 conda-forge
zipp 3.8.1 pyhd8ed1ab_0 conda-forge
zstd 1.5.2 h7755175_4 conda-forge
I sure TF can use my GPU by this:
(DEEPLABCUT) C:Windowssystem32>python
Python 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 05:59:45) [MSC v.1929 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.python.client import device_lib
>>> print(device_lib.list_local_devices())
2022-09-11 20:41:50.137638: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-11 20:41:50.481875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 9616 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 200595950863773239
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 10083106816
locality {
bus_id: 1
links {
}
}
incarnation: 14570387183940456862
physical_device_desc: "device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6"
xla_global_id: 416903419
]
Terminal stuck at "Starting training…"
(DEEPLABCUT) C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamples>python testscript.py
Loading DLC 2.2.2...
Imported DLC!
On Windows/OSX tensorpack is not tested by default.
CREATING PROJECT
Created "C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamplesTEST-Alex-2022-09-11videos"
Created "C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamplesTEST-Alex-2022-09-11labeled-data"
Created "C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamplesTEST-Alex-2022-09-11training-datasets"
Created "C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamplesTEST-Alex-2022-09-11dlc-models"
Copying the videos
C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamplesTEST-Alex-2022-09-11videosreachingvideo1.avi
Generated "C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamplesTEST-Alex-2022-09-11config.yaml"
A new project with name TEST-Alex-2022-09-11 is created at C:worksDLCDLC_scriptDeepLabCut-masterDeepLabCut-masterexamples and a configurable file (config.yaml) is stored there. Change the parameters in this file to adapt to your project's needs.
Once you have changed the configuration file, use the function 'extract_frames' to select frames for labeling.
. [OPTIONAL] Use the function 'add_new_videos' to add new videos to your project (at any stage).
EXTRACTING FRAMES
Config file read successfully.
Extracting frames based on kmeans ...
Kmeans-quantization based extracting of frames from 0.0 seconds to 8.53 seconds.
Extracting and downsampling... 256 frames from the video.
256it [00:01, 214.77it/s]
Kmeans clustering ... (this might take a while)
Frames were successfully extracted, for the videos listed in the config.yaml file.
You can now label the frames using the function 'label_frames' (Note, you should label frames extracted from diverse videos (and many videos; we do not recommend training on single videos!)).
CREATING-SOME LABELS FOR THE FRAMES
Plot labels...
Creating images with labels by Alex.
100%|█████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 6.01it/s]
If all the labels are ok, then use the function 'create_training_dataset' to create the training dataset!
CREATING TRAININGSET
Downloading a ImageNet-pretrained model from https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b0.tar.gz....
The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!
CHANGING training parameters to end quickly!
TRAIN
Selecting single-animal trainer
Config:
{'all_joints': [[0], [1], [2], [3]],
'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'],
'alpha_r': 0.02,
'apply_prob': 0.5,
'batch_size': 1,
'contrast': {'clahe': True,
'claheratio': 0.1,
'histeq': True,
'histeqratio': 0.1},
'convolution': {'edge': False,
'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]},
'embossratio': 0.1,
'sharpen': False,
'sharpenratio': 0.3},
'crop_pad': 0,
'cropratio': 0.4,
'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_TESTSep11\TEST_Alex80shuffle1.mat',
'dataset_type': 'default',
'decay_steps': 30000,
'deterministic': False,
'display_iters': 2,
'fg_fraction': 0.25,
'global_scale': 0.8,
'init_weights': 'C:\App\anaconda3\envs\DEEPLABCUT\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\efficientnet-b0\model.ckpt',
'intermediate_supervision': False,
'intermediate_supervision_layer': 12,
'location_refinement': True,
'locref_huber_loss': True,
'locref_loss_weight': 0.05,
'locref_stdev': 7.2801,
'log_dir': 'log',
'lr_init': 0.0005,
'max_input_size': 1500,
'mean_pixel': [123.68, 116.779, 103.939],
'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_TESTSep11\Documentation_data-TEST_80shuffle1.pickle',
'min_input_size': 64,
'mirror': False,
'multi_stage': False,
'multi_step': [[0.001, 5]],
'net_type': 'efficientnet-b0',
'num_joints': 4,
'optimizer': 'sgd',
'pairwise_huber_loss': False,
'pairwise_predict': False,
'partaffinityfield_predict': False,
'pos_dist_thresh': 17,
'project_path': 'C:\works\DLC\DLC_script\DeepLabCut-master\DeepLabCut-master\examples\TEST-Alex-2022-09-11',
'regularize': False,
'rotation': 25,
'rotratio': 0.4,
'save_iters': 5,
'scale_jitter_lo': 0.5,
'scale_jitter_up': 1.25,
'scoremap_dir': 'test',
'shuffle': True,
'snapshot_prefix': 'C:\works\DLC\DLC_script\DeepLabCut-master\DeepLabCut-master\examples\TEST-Alex-2022-09-11\dlc-models\iteration-0\TESTSep11-trainset80shuffle1\train\snapshot',
'stride': 8.0,
'weigh_negatives': False,
'weigh_only_present_joints': False,
'weigh_part_predictions': False,
'weight_decay': 0.0001}
Batch Size is 1
C:Appanaconda3envsDEEPLABCUTlibsite-packagestensorflowpythonkerasenginebase_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
warnings.warn('`layer.apply` is deprecated and '
2022-09-11 20:18:35.578698: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-11 20:18:35.897924: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2022-09-11 20:18:35.898044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9616 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6
Loading ImageNet-pretrained efficientnet-b0
2022-09-11 20:18:36.209153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9616 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6
Switching to cosine decay schedule with adam!
Exception in thread Thread-2:
Traceback (most recent call last):
File "C:Appanaconda3envsDEEPLABCUTlibthreading.py", line 932, in _bootstrap_inner
self.run()
File "C:Appanaconda3envsDEEPLABCUTlibthreading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "C:Appanaconda3envsDEEPLABCUTlibsite-packagesdeeplabcutpose_estimation_tensorflowcoretrain.py", line 81, in load_and_enqueue
batch_np = dataset.next_batch()
File "C:Appanaconda3envsDEEPLABCUTlibsite-packagesdeeplabcutpose_estimation_tensorflowdatasetspose_imgaug.py", line 404, in next_batch
scmap_update = self.get_scmap_update(
File "C:Appanaconda3envsDEEPLABCUTlibsite-packagesdeeplabcutpose_estimation_tensorflowdatasetspose_imgaug.py", line 361, in get_scmap_update
) = self.compute_target_part_scoremap_numpy(
File "C:Appanaconda3envsDEEPLABCUTlibsite-packagesdeeplabcutpose_estimation_tensorflowdatasetspose_imgaug.py", line 498, in compute_target_part_scoremap_numpy
j_x = np.asscalar(joint_pt[0])
File "C:Appanaconda3envsDEEPLABCUTlibsite-packagesnumpy__init__.py", line 311, in __getattr__
raise AttributeError("module {!r} has no attribute "
AttributeError: module 'numpy' has no attribute 'asscalar'
2022-09-11 20:18:38.298353: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
Training parameter:
{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': 'C:\works\DLC\DLC_script\DeepLabCut-master\DeepLabCut-master\examples\TEST-Alex-2022-09-11\dlc-models\iteration-0\TESTSep11-trainset80shuffle1\train\snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'adam', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'default', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3]], 'all_joints_names': ['bodypart1', 'bodypart2', 'bodypart3', 'objectA'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'contrast': {'clahe': True, 'claheratio': 0.1, 'histeq': True, 'histeqratio': 0.1, 'gamma': False, 'sigmoid': False, 'log': False, 'linear': False}, 'convolution': {'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]}, 'embossratio': 0.1, 'sharpen': False, 'sharpenratio': 0.3}, 'cropratio': 0.4, 'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_TESTSep11\TEST_Alex80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 2, 'init_weights': 'C:\App\anaconda3\envs\DEEPLABCUT\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\efficientnet-b0\model.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_TESTSep11\Documentation_data-TEST_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.001, 5]], 'net_type': 'efficientnet-b0', 'num_joints': 4, 'pos_dist_thresh': 17, 'project_path': 'C:\works\DLC\DLC_script\DeepLabCut-master\DeepLabCut-master\examples\TEST-Alex-2022-09-11', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 5, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}, 'use_batch_norm': False, 'use_drop_out': False}
Starting training....
2
Answers
I suspect two issues here:
np.asscalarisn’t found. Your numpy version is1.23.3, butnp.asscalaris deprecated since1.16. Maybe try downgrading (pip install numpy==1.15/conda install numpy==1.15) and see if the error persists.Edit: I just checked the config file supplied by DLC and verified that no numpy version is specified. You should probably downgrade to a version <
1.16sincenp.asscalaris used.(Update)
This is now fixed in the repository (PR #1982). If working from a
git cloneversion of the DeepLabCut repository, then run agit pullto grab the latest version. Otherwise, they’ll probably drop a release soon with the fix integrated.(Original Answer)
The
numpy.asscalar()method was finally removed in NumPy 1.23 (see Release Notes) after being deprecated since v1.16. I added an Issue to the repository. Unless you want to send in a Pull Request to fix it, downgrade the Numpy to 1.22 or below.Consider using Mamba
BTW, no one should be waiting for slow solves anymore – Mamba has been stable for a long time and solved this issue. Once installed, just use the word
mambainstead ofcondafor most commands.Edit the YAML
Alternatively, edit the YAML to include the upper bound on
numpy:and recreate the environment from the updated YAML.