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talmolab
Crashing during inference on Apple M2 Max
sleap crashes during inference of entire video (even short video) but runs inference on a small number of suggested frames. Samples during training and these small inference batches look good. Inference starts and the count of inferred frames increases (then sometimes the speed of inference appears to double shortly before a crash). The popup says there is probably more info in the command line (posted below).
Expected behaviour
Inference running normally (oddly it did run once all the way through a movie that was 180000 frames). If relevant - this happens both when tracking a simple movie with a single animal or when tracking about 5 individuals. Training runs normally.
Actual behaviour
Command line outputs for training and inference below.
Your personal set up
I installed using the mamba instructions for apple silicon (M2 Max). I use conda activate sleap to load the environment.
-
OS: macOS Sonoma 14.2.1
-
Version(s):
- SLEAP installation method (listed here):
- [ ] Conda from package
- [ ] Conda from source
- [ ] pip package
- [ X] Apple Silicon Macs
Environment packages
conda list
# packages in environment at /Users/ugne/mambaforge3/envs/sleap:
#
# Name Version Build Channel
abseil-cpp 20211102.0 he4e09e4_3 conda-forge
absl-py 1.4.0 pypi_0 pypi
aiohttp 3.9.1 py39h17cfd9d_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
aom 3.5.0 h7ea286d_0 conda-forge
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge
attrs 23.1.0 pyh71513ae_1 conda-forge
blinker 1.7.0 pyhd8ed1ab_0 conda-forge
blosc 1.21.5 hc338f07_0 conda-forge
brotli 1.0.9 h1a8c8d9_9 conda-forge
brotli-bin 1.0.9 h1a8c8d9_9 conda-forge
brotli-python 1.0.9 py39h23fbdae_9 conda-forge
brunsli 0.1 h9f76cd9_0 conda-forge
bzip2 1.0.8 h93a5062_5 conda-forge
c-ares 1.24.0 h93a5062_0 conda-forge
c-blosc2 2.11.3 h8eb3132_0 conda-forge
ca-certificates 2023.11.17 hf0a4a13_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 5.3.1 pypi_0 pypi
cairo 1.16.0 h73a0509_1014 conda-forge
cattrs 1.1.1 pyhd8ed1ab_0 conda-forge
certifi 2023.11.17 pyhd8ed1ab_0 conda-forge
cffi 1.16.0 py39he153c15_0 conda-forge
cfitsio 4.2.0 h2f961c4_0 conda-forge
charls 2.3.4 hbdafb3b_0 conda-forge
charset-normalizer 3.2.0 pypi_0 pypi
click 8.1.7 unix_pyh707e725_0 conda-forge
contourpy 1.2.0 py39he9de807_0 conda-forge
cryptography 39.0.0 py39haa0b8cc_0 conda-forge
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
dav1d 1.2.1 hb547adb_0 conda-forge
efficientnet 1.0.0 pypi_0 pypi
expat 2.5.0 hb7217d7_1 conda-forge
ffmpeg 4.4.2 gpl_hf318d42_112 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 h77eed37_1 conda-forge
fontconfig 2.14.2 h82840c6_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.47.0 py39h17cfd9d_0 conda-forge
freetype 2.12.1 hadb7bae_2 conda-forge
frozenlist 1.4.1 py39h17cfd9d_0 conda-forge
gast 0.4.0 pyh9f0ad1d_0 conda-forge
geos 3.12.1 h965bd2d_0 conda-forge
gettext 0.21.1 h0186832_0 conda-forge
giflib 5.2.1 h1a8c8d9_3 conda-forge
glib 2.78.3 h9e231a4_0 conda-forge
glib-tools 2.78.3 h9e231a4_0 conda-forge
gmp 6.3.0 h965bd2d_0 conda-forge
gnutls 3.7.9 hd26332c_0 conda-forge
google-auth 2.23.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pyhd8ed1ab_0 conda-forge
google-pasta 0.2.0 pyh8c360ce_0 conda-forge
graphite2 1.3.13 h9f76cd9_1001 conda-forge
grpc-cpp 1.46.4 hcaf9be7_3 conda-forge
grpcio 1.58.0 pypi_0 pypi
gst-plugins-base 1.22.8 h09b4b5e_0 conda-forge
gstreamer 1.22.8 h551c6ff_0 conda-forge
h5py 3.8.0 nompi_py39hc9149d8_100 conda-forge
harfbuzz 5.3.0 hddbc195_0 conda-forge
hdf5 1.12.2 nompi_h55deafc_101 conda-forge
hdmf 3.9.0 pypi_0 pypi
icu 70.1 h6b3803e_0 conda-forge
idna 3.4 pypi_0 pypi
image-classifiers 1.0.0 pypi_0 pypi
imagecodecs 2022.9.26 py39hd7f743f_4 conda-forge
imageio 2.33.1 pyh8c1a49c_0 conda-forge
imgaug 0.4.0 pyhd8ed1ab_1 conda-forge
imgstore 0.2.9 pypi_0 pypi
importlib-metadata 7.0.0 pyha770c72_0 conda-forge
importlib-resources 6.1.1 pyhd8ed1ab_0 conda-forge
importlib_resources 6.1.1 pyhd8ed1ab_0 conda-forge
jasper 2.0.33 hc3cd1e9_1 conda-forge
joblib 1.3.2 pyhd8ed1ab_0 conda-forge
jpeg 9e h1a8c8d9_3 conda-forge
jsmin 3.0.1 pyhd8ed1ab_0 conda-forge
jsonpickle 1.2 py_0 conda-forge
jsonschema 4.19.0 pypi_0 pypi
jsonschema-specifications 2023.7.1 pypi_0 pypi
jxrlib 1.1 h27ca646_2 conda-forge
keras 2.9.0 pyhd8ed1ab_0 conda-forge
keras-applications 1.0.8 pypi_0 pypi
keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge
kiwisolver 1.4.5 py39hbd775c9_1 conda-forge
krb5 1.20.1 h127bd45_0 conda-forge
lame 3.100 h1a8c8d9_1003 conda-forge
lazy_loader 0.3 pyhd8ed1ab_0 conda-forge
lcms2 2.14 h8193b64_0 conda-forge
lerc 4.0.0 h9a09cb3_0 conda-forge
libabseil 20211102.0 cxx17_h28b99d4_3 conda-forge
libaec 1.1.2 h13dd4ca_1 conda-forge
libavif 0.11.1 h9f83d30_2 conda-forge
libblas 3.9.0 20_osxarm64_openblas conda-forge
libbrotlicommon 1.0.9 h1a8c8d9_9 conda-forge
libbrotlidec 1.0.9 h1a8c8d9_9 conda-forge
libbrotlienc 1.0.9 h1a8c8d9_9 conda-forge
libcblas 3.9.0 20_osxarm64_openblas conda-forge
libclang 16.0.6 pypi_0 pypi
libclang13 14.0.6 default_hc7183e1_1 conda-forge
libcurl 7.87.0 hbe9bab4_0 conda-forge
libcxx 16.0.6 h4653b0c_0 conda-forge
libdeflate 1.14 h1a8c8d9_0 conda-forge
libedit 3.1.20191231 hc8eb9b7_2 conda-forge
libev 4.33 h93a5062_2 conda-forge
libexpat 2.5.0 hb7217d7_1 conda-forge
libffi 3.4.2 h3422bc3_5 conda-forge
libgfortran 5.0.0 13_2_0_hd922786_1 conda-forge
libgfortran5 13.2.0 hf226fd6_1 conda-forge
libglib 2.78.3 hb438215_0 conda-forge
libiconv 1.17 h0d3ecfb_2 conda-forge
libidn2 2.3.4 h1a8c8d9_0 conda-forge
liblapack 3.9.0 20_osxarm64_openblas conda-forge
liblapacke 3.9.0 20_osxarm64_openblas conda-forge
libllvm14 14.0.6 hd1a9a77_4 conda-forge
libnghttp2 1.51.0 hd184df1_0 conda-forge
libogg 1.3.4 h27ca646_1 conda-forge
libopenblas 0.3.25 openmp_h6c19121_0 conda-forge
libopencv 4.6.0 py39he1c1adf_3 conda-forge
libopus 1.3.1 h27ca646_1 conda-forge
libpng 1.6.39 h76d750c_0 conda-forge
libpq 15.1 hbce9e56_3 conda-forge
libprotobuf 3.20.3 hb5ab8b9_0 conda-forge
libsodium 1.0.18 h27ca646_1 conda-forge
libsqlite 3.44.2 h091b4b1_0 conda-forge
libssh2 1.10.0 hb80f160_3 conda-forge
libtasn1 4.19.0 h1a8c8d9_0 conda-forge
libtiff 4.4.0 heb92581_5 conda-forge
libunistring 0.9.10 h3422bc3_0 conda-forge
libvorbis 1.3.7 h9f76cd9_0 conda-forge
libvpx 1.11.0 hc470f4d_3 conda-forge
libwebp-base 1.3.2 hb547adb_0 conda-forge
libxcb 1.13 h9b22ae9_1004 conda-forge
libxml2 2.10.3 h67585b2_4 conda-forge
libxslt 1.1.37 h1bd8bc4_0 conda-forge
libzlib 1.2.13 h53f4e23_5 conda-forge
libzopfli 1.0.3 h9f76cd9_0 conda-forge
llvm-openmp 17.0.6 hcd81f8e_0 conda-forge
lz4-c 1.9.4 hb7217d7_0 conda-forge
markdown 3.4.4 pypi_0 pypi
markdown-it-py 3.0.0 pyhd8ed1ab_0 conda-forge
markupsafe 2.1.3 py39h0f82c59_1 conda-forge
matplotlib-base 3.8.2 py39h1a09f3e_0 conda-forge
mdurl 0.1.0 pyhd8ed1ab_0 conda-forge
multidict 6.0.4 py39h02fc5c5_1 conda-forge
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
mysql-common 8.0.32 hab468bb_0 conda-forge
mysql-libs 8.0.32 hea58576_0 conda-forge
ncurses 6.4 h463b476_2 conda-forge
ndx-pose 0.1.1 pypi_0 pypi
nettle 3.9.1 h40ed0f5_0 conda-forge
networkx 3.2.1 pyhd8ed1ab_0 conda-forge
nixio 1.5.3 pypi_0 pypi
nspr 4.35 hb7217d7_0 conda-forge
nss 3.96 h5ce2875_0 conda-forge
numpy 1.22.4 py39h7df2422_0 conda-forge
oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge
opencv 4.6.0 py39hdf13c20_3 conda-forge
openh264 2.3.1 hb7217d7_2 conda-forge
openjpeg 2.5.0 h5d4e404_1 conda-forge
openssl 1.1.1w h53f4e23_0 conda-forge
opt_einsum 3.3.0 pyhc1e730c_2 conda-forge
p11-kit 0.24.1 h29577a5_0 conda-forge
packaging 23.2 pyhd8ed1ab_0 conda-forge
pandas 2.1.4 py39hf8cecc8_0 conda-forge
patsy 0.5.4 pyhd8ed1ab_0 conda-forge
pcre2 10.42 h26f9a81_0 conda-forge
pillow 9.2.0 py39h139752e_3 conda-forge
pip 23.3.2 pyhd8ed1ab_0 conda-forge
pixman 0.42.2 h13dd4ca_0 conda-forge
protobuf 3.19.6 pypi_0 pypi
psutil 5.9.7 py39h17cfd9d_0 conda-forge
pthread-stubs 0.4 h27ca646_1001 conda-forge
py-opencv 4.6.0 py39hfa6204d_3 conda-forge
pyasn1 0.5.0 pypi_0 pypi
pyasn1-modules 0.3.0 pyhd8ed1ab_0 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pygments 2.17.2 pyhd8ed1ab_0 conda-forge
pyjwt 2.8.0 pyhd8ed1ab_0 conda-forge
pykalman 0.9.5 py_1 conda-forge
pynwb 2.5.0 pypi_0 pypi
pyopenssl 23.2.0 pyhd8ed1ab_1 conda-forge
pyparsing 3.1.1 pyhd8ed1ab_0 conda-forge
pyside2 5.15.8 py39h0adaba8_2 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.9.15 h2d96c93_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-flatbuffers 1.12 pyhd8ed1ab_1 conda-forge
python-rapidjson 1.14 py39hf3050f2_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python_abi 3.9 4_cp39 conda-forge
pytz 2023.3.post1 pyhd8ed1ab_0 conda-forge
pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge
pywavelets 1.4.1 py39hf4a74a7_1 conda-forge
pyyaml 6.0.1 py39h0f82c59_1 conda-forge
pyzmq 25.1.2 py39he1e2164_0 conda-forge
qimage2ndarray 1.10.0 pypi_0 pypi
qt-main 5.15.8 hfe8d25c_6 conda-forge
qtpy 2.4.1 pyhd8ed1ab_0 conda-forge
re2 2022.06.01 h9a09cb3_1 conda-forge
readline 8.2 h92ec313_1 conda-forge
referencing 0.30.2 pypi_0 pypi
requests 2.31.0 pyhd8ed1ab_0 conda-forge
requests-oauthlib 1.3.1 pyhd8ed1ab_0 conda-forge
rich 13.7.0 pyhd8ed1ab_0 conda-forge
rpds-py 0.10.3 pypi_0 pypi
rsa 4.9 pyhd8ed1ab_0 conda-forge
ruamel-yaml 0.17.32 pypi_0 pypi
ruamel-yaml-clib 0.2.7 pypi_0 pypi
scikit-image 0.22.0 py39hf8cecc8_2 conda-forge
scikit-learn 1.0 py39h12ba089_1 conda-forge
scikit-video 1.1.11 pyh24bf2e0_0 conda-forge
scipy 1.9.0 py39h14896cb_0 conda-forge
seaborn 0.13.0 hd8ed1ab_0 conda-forge
seaborn-base 0.13.0 pyhd8ed1ab_0 conda-forge
segmentation-models 1.0.1 pypi_0 pypi
setuptools 68.2.2 pyhd8ed1ab_0 conda-forge
shapely 2.0.2 py39ha70ab96_1 conda-forge
six 1.15.0 pypi_0 pypi
sleap 1.3.3 pypi_0 pypi
snappy 1.1.10 h17c5cce_0 conda-forge
sqlite 3.44.2 hf2abe2d_0 conda-forge
statsmodels 0.14.1 py39h373d45f_0 conda-forge
svt-av1 1.4.1 h7ea286d_0 conda-forge
tensorboard 2.9.1 pypi_0 pypi
tensorboard-data-server 0.6.1 py39haa0b8cc_4 conda-forge
tensorboard-plugin-wit 1.8.1 pyhd8ed1ab_0 conda-forge
tensorflow 2.9.1 cpu_py39h2839aeb_0 conda-forge
tensorflow-base 2.9.1 cpu_py39ha1ad4ae_0 conda-forge
tensorflow-estimator 2.9.1 cpu_py39h7b621ec_0 conda-forge
tensorflow-hub 0.12.0 pyhca92ed8_0 conda-forge
tensorflow-macos 2.9.2 pypi_0 pypi
tensorflow-metal 0.5.0 pypi_0 pypi
termcolor 2.3.0 pyhd8ed1ab_0 conda-forge
threadpoolctl 3.2.0 pyha21a80b_0 conda-forge
tifffile 2022.10.10 pyhd8ed1ab_0 conda-forge
tk 8.6.13 h5083fa2_1 conda-forge
typing-extensions 4.9.0 hd8ed1ab_0 conda-forge
typing_extensions 4.9.0 pyha770c72_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
tzlocal 5.0.1 pypi_0 pypi
unicodedata2 15.1.0 py39h0f82c59_0 conda-forge
urllib3 1.26.16 pypi_0 pypi
werkzeug 2.3.7 pypi_0 pypi
wheel 0.42.0 pyhd8ed1ab_0 conda-forge
wrapt 1.15.0 pypi_0 pypi
x264 1!164.3095 h57fd34a_2 conda-forge
x265 3.5 hbc6ce65_3 conda-forge
xorg-libxau 1.0.11 hb547adb_0 conda-forge
xorg-libxdmcp 1.1.3 h27ca646_0 conda-forge
xz 5.2.6 h57fd34a_0 conda-forge
yaml 0.2.5 h3422bc3_2 conda-forge
yarl 1.9.3 py39h17cfd9d_0 conda-forge
zeromq 4.3.5 h965bd2d_0 conda-forge
zfp 1.0.1 ha8f4885_0 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 h53f4e23_5 conda-forge
zlib-ng 2.0.7 h1a8c8d9_0 conda-forge
zstd 1.5.5 h4f39d0f_0 conda-forge
Logs
Command line call:
sleap-track /Users/ugne/Dropbox/pregnant/labels.v001_pupx1.slp --only-suggested-frames -m /Users/ugne/Dropbox/pregnant/models/231223_213850.centroid.n=8 -m /Users/ugne/Dropbox/pregnant/models/231223_214910.centered_instance.n=8 --max_instances 1 -o /Users/ugne/Dropbox/pregnant/predictions/labels.v001_pupx1.slp.231223_220359.predictions.slp --verbosity json --no-empty-frames
Started inference at: 2023-12-23 22:04:03.074597
Args:
{
│ 'data_path': '/Users/ugne/Dropbox/pregnant/labels.v001_pupx1.slp',
│ 'models': [
│ │ '/Users/ugne/Dropbox/pregnant/models/231223_213850.centroid.n=8',
│ │ '/Users/ugne/Dropbox/pregnant/models/231223_214910.centered_instance.n=8'
│ ],
│ 'frames': '',
│ 'only_labeled_frames': False,
│ 'only_suggested_frames': True,
│ 'output': '/Users/ugne/Dropbox/pregnant/predictions/labels.v001_pupx1.slp.231223_220359.predictions.slp',
2023-12-23 22:04:03.684157: 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.
2023-12-23 22:04:03.684311: 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: <undefined>)
│ '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,
│ 'open_in_gui': False,
│ 'peak_threshold': 0.2,
│ 'max_instances': 1,
│ 'tracking.tracker': None,
│ 'tracking.max_tracking': None,
│ 'tracking.max_tracks': None,
2023-12-23 22:04:04.592476: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
│ 'tracking.target_instance_count': None,
│ 'tracking.pre_cull_to_target': None,
│ '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:Failed to query GPU memory from nvidia-smi. Defaulting to first GPU.
Metal device set to: Apple M2 Max
2023-12-23 22:04:06.796696: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2023-12-23 22:04:06.874764: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -47 } dim { size: -48 } dim { size: -49 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -50 } dim { size: -51 } dim { size: 1 } } }
2023-12-23 22:04:06.875581: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_UINT8 } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_UINT8 shape { dim { size: 4 } dim { size: 1200 } dim { size: 1920 } dim { size: 3 } } } inputs { dtype: DT_FLOAT shape { dim { size: -16 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -16 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -16 } dim { size: -63 } dim { size: -64 } dim { size: 3 } } }
2023-12-23 22:04:06.878681: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -131 } dim { size: -132 } dim { size: -133 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -22 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: -135 } dim { size: -136 } dim { size: 1 } } }
loc("mps_select"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/0032d1ee-80fd-11ee-8227-6aecfccc70fe/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":294:0)): error: 'anec.gain_offset_control' op result #0 must be 4D/5D memref of 16-bit float or 8-bit signed integer or 8-bit unsigned integer values, but got 'memref<4x3x1x1xi1>'
Versions:
SLEAP: 1.3.3
TensorFlow: 2.9.2
Numpy: 1.22.4
Python: 3.9.15
OS: macOS-14.2.1-arm64-arm-64bit
System:
GPUs: 1/1 available
Device: /physical_device:GPU:0
Available: True
2023-12-23 22:04:08.017111: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
Initalized: False
Memory growth: True
2023-12-23 22:04:08.096753: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -58 } dim { size: -59 } dim { size: -60 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -61 } dim { size: -62 } dim { size: 1 } } }
2023-12-23 22:04:08.097681: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_UINT8 } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_UINT8 shape { dim { size: -25 } dim { size: 1200 } dim { size: 1920 } dim { size: 3 } } } inputs { dtype: DT_FLOAT shape { dim { size: -16 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -16 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -16 } dim { size: -74 } dim { size: -75 } dim { size: 3 } } }
2023-12-23 22:04:08.100799: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -142 } dim { size: -143 } dim { size: -144 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -22 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: -146 } dim { size: -147 } dim { size: 1 } } }
loc("mps_select"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/0032d1ee-80fd-11ee-8227-6aecfccc70fe/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":294:0)): error: 'anec.gain_offset_control' op result #0 must be 4D/5D memref of 16-bit float or 8-bit signed integer or 8-bit unsigned integer values, but got 'memref<1x3x1x1xi1>'
Finished inference at: 2023-12-23 22:04:08.754709
Total runtime: 5.680121898651123 secs
Predicted frames: 13/13
Provenance:
{
│ 'model_paths': [
│ │ '/Users/ugne/Dropbox/pregnant/models/231223_213850.centroid.n=8/training_config.json',
Process return code: 0
Using already trained model for centroid: /Users/ugne/Dropbox/pregnant/models/231223_213850.centroid.n=8/training_config.json
Using already trained model for centered_instance: /Users/ugne/Dropbox/pregnant/models/231223_214910.centered_instance.n=8/training_config.json
Command line call:
sleap-track /Users/ugne/Dropbox/pregnant/labels.v001_pupx1.slp --video.index 0 --frames 0,-14999 -m /Users/ugne/Dropbox/pregnant/models/231223_213850.centroid.n=8/training_config.json -m /Users/ugne/Dropbox/pregnant/models/231223_214910.centered_instance.n=8/training_config.json --tracking.tracker none --max_instances 1 -o /Users/ugne/Dropbox/pregnant/predictions/labels.v001_pupx1.slp.231223_220450.predictions.slp --verbosity json --no-empty-frames
Started inference at: 2023-12-23 22:04:54.576367
Args:
{
│ 'data_path': '/Users/ugne/Dropbox/pregnant/labels.v001_pupx1.slp',
│ 'models': [
│ │ '/Users/ugne/Dropbox/pregnant/models/231223_213850.centroid.n=8/training_config.json',
│ │ '/Users/ugne/Dropbox/pregnant/models/231223_214910.centered_instance.n=8/training_config.json'
│ ],
│ 'frames': '0,-14999',
│ 'only_labeled_frames': False,
│ 'only_suggested_frames': False,
│ 'output': '/Users/ugne/Dropbox/pregnant/predictions/labels.v001_pupx1.slp.231223_220450.predictions.slp',
2023-12-23 22:04:55.185517: 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.
2023-12-23 22:04:55.185676: 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: <undefined>)
│ 'no_empty_frames': True,
│ 'verbosity': 'json',
│ 'video.dataset': None,
│ 'video.input_format': 'channels_last',
│ 'video.index': '0',
│ 'cpu': False,
│ 'first_gpu': False,
│ 'last_gpu': False,
│ 'gpu': 'auto',
│ 'max_edge_length_ratio': 0.25,
│ 'dist_penalty_weight': 1.0,
│ 'batch_size': 4,
│ 'open_in_gui': False,
│ 'peak_threshold': 0.2,
│ 'max_instances': 1,
│ 'tracking.tracker': 'none',
│ 'tracking.max_tracking': None,
│ 'tracking.max_tracks': None,
2023-12-23 22:04:56.094568: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
│ 'tracking.target_instance_count': None,
│ 'tracking.pre_cull_to_target': None,
│ '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:Failed to query GPU memory from nvidia-smi. Defaulting to first GPU.
Metal device set to: Apple M2 Max
2023-12-23 22:04:57.975830: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2023-12-23 22:04:58.054578: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -47 } dim { size: -48 } dim { size: -49 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -50 } dim { size: -51 } dim { size: 1 } } }
2023-12-23 22:04:58.055451: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_UINT8 } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_UINT8 shape { dim { size: 4 } dim { size: 1200 } dim { size: 1920 } dim { size: 3 } } } inputs { dtype: DT_FLOAT shape { dim { size: -16 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -16 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -16 } dim { size: -63 } dim { size: -64 } dim { size: 3 } } }
2023-12-23 22:04:58.058697: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -131 } dim { size: -132 } dim { size: -133 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -22 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" model: "0" num_cores: 12 environment { key: "cpu_instruction_set" value: "ARM NEON" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 16384 l2_cache_size: 524288 l3_cache_size: 524288 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -22 } dim { size: -135 } dim { size: -136 } dim { size: 1 } } }
loc("mps_select"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/0032d1ee-80fd-11ee-8227-6aecfccc70fe/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":294:0)): error: 'anec.gain_offset_control' op result #0 must be 4D/5D memref of 16-bit float or 8-bit signed integer or 8-bit unsigned integer values, but got 'memref<4x3x1x1xi1>'
Versions:
SLEAP: 1.3.3
TensorFlow: 2.9.2
Numpy: 1.22.4
Python: 3.9.15
OS: macOS-14.2.1-arm64-arm-64bit
System:
GPUs: 1/1 available
Device: /physical_device:GPU:0
Available: True
Initalized: False
Memory growth: True
Traceback (most recent call last):
File "/Users/ugne/mambaforge3/envs/sleap/bin/sleap-track", line 33, in <module>
sys.exit(load_entry_point('sleap==1.3.3', 'console_scripts', 'sleap-track')())
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 5424, in main
labels_pr = predictor.predict(provider)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 526, in predict
self._make_labeled_frames_from_generator(generator, data)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2633, in _make_labeled_frames_from_generator
for ex in generator:
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 457, in _predict_generator
ex = process_batch(ex)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 399, in process_batch
preds = self.inference_model.predict_on_batch(ex, numpy=True)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 1069, in predict_on_batch
outs = super().predict_on_batch(data, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 2230, in predict_on_batch
outputs = self.predict_function(iterator)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:
Detected at node 'top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear' defined at (most recent call last):
File "/Users/ugne/mambaforge3/envs/sleap/bin/sleap-track", line 33, in <module>
sys.exit(load_entry_point('sleap==1.3.3', 'console_scripts', 'sleap-track')())
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 5424, in main
labels_pr = predictor.predict(provider)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 526, in predict
self._make_labeled_frames_from_generator(generator, data)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2633, in _make_labeled_frames_from_generator
for ex in generator:
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 457, in _predict_generator
ex = process_batch(ex)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 399, in process_batch
preds = self.inference_model.predict_on_batch(ex, numpy=True)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 1069, in predict_on_batch
outs = super().predict_on_batch(data, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 2230, in predict_on_batch
outputs = self.predict_function(iterator)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1845, in predict_function
return step_function(self, iterator)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1834, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1823, in run_step
outputs = model.predict_step(data)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1791, in predict_step
return self(x, training=False)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2256, in call
if isinstance(self.instance_peaks, FindInstancePeaksGroundTruth):
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2265, in call
peaks_output = self.instance_peaks(crop_output)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2088, in call
out = self.keras_model(crops)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/functional.py", line 458, in call
return self._run_internal_graph(
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/functional.py", line 596, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/layers/reshaping/up_sampling2d.py", line 129, in call
return backend.resize_images(
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/backend.py", line 3432, in resize_images
x = tf.image.resize(x, new_shape, method=interpolations[interpolation])
Node: 'top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear'
Detected at node 'top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear' defined at (most recent call last):
File "/Users/ugne/mambaforge3/envs/sleap/bin/sleap-track", line 33, in <module>
sys.exit(load_entry_point('sleap==1.3.3', 'console_scripts', 'sleap-track')())
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 5424, in main
labels_pr = predictor.predict(provider)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 526, in predict
self._make_labeled_frames_from_generator(generator, data)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2633, in _make_labeled_frames_from_generator
for ex in generator:
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 457, in _predict_generator
ex = process_batch(ex)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 399, in process_batch
preds = self.inference_model.predict_on_batch(ex, numpy=True)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 1069, in predict_on_batch
outs = super().predict_on_batch(data, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 2230, in predict_on_batch
outputs = self.predict_function(iterator)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1845, in predict_function
return step_function(self, iterator)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1834, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1823, in run_step
outputs = model.predict_step(data)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 1791, in predict_step
return self(x, training=False)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2256, in call
if isinstance(self.instance_peaks, FindInstancePeaksGroundTruth):
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2265, in call
peaks_output = self.instance_peaks(crop_output)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/sleap/nn/inference.py", line 2088, in call
out = self.keras_model(crops)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/training.py", line 490, in __call__
return super().__call__(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/functional.py", line 458, in call
return self._run_internal_graph(
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/functional.py", line 596, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/engine/base_layer.py", line 1014, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/layers/reshaping/up_sampling2d.py", line 129, in call
return backend.resize_images(
File "/Users/ugne/mambaforge3/envs/sleap/lib/python3.9/site-packages/keras/backend.py", line 3432, in resize_images
x = tf.image.resize(x, new_shape, method=interpolations[interpolation])
Node: 'top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear'
2 root error(s) found.
(0) INTERNAL: Missing 0-th output from {{node top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear}}
[[top_down_inference_model/find_instance_peaks/PartitionedCall/cond/else/_82/cond/add_1/_402]]
(1) INTERNAL: Missing 0-th output from {{node top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear}}
0 successful operations.
0 derived errors ignored. [Op:__inference_predict_function_4953]
systemMemory: 64.00 GB
maxCacheSize: 24.00 GB
Process return code: 1
Screenshots
How to reproduce
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- Click on '....'
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- See error
Hi @uklibaite,
It looks like this is the main error:
Node: 'top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear'
2 root error(s) found.
(0) INTERNAL: Missing 0-th output from {{node top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear}}
[[top_down_inference_model/find_instance_peaks/PartitionedCall/cond/else/_82/cond/add_1/_402]]
(1) INTERNAL: Missing 0-th output from {{node top_down_inference_model/find_instance_peaks/model/stack0_dec0_s16_to_s8_interp_bilinear/resize/ResizeBilinear}}
This is currently a bug in the Mac TensorFlow working differently than Windows/Linux in how it handles empty tensors.
We have an issue tracking this (https://github.com/talmolab/sleap/issues/1100#issuecomment-1756560300) and an in-progress PR to fix it (#1547).
We'll keep you updated once the fix is ready for testing!
Talmo
