TypeError: tuple indices must be integers or slices, not tuple
hamza90ec opened this issue · comments
I am using jetson nano tx1.
can you please debug this error
here is my full terminal status
[TensorRT] WARNING: Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[ WARN:0] global /home/hamza/opencv/modules/videoio/src/cap_gstreamer.cpp (933) open OpenCV | GStreamer warning: Cannot query video position: status=0, value=-1, duration=-1
[ WARN:0] global /home/hamza/opencv/modules/videoio/src/cap_gstreamer.cpp (1184) setProperty OpenCV | GStreamer warning: GStreamer: unhandled property
2021-07-03 18:43:26.535555: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-07-03 18:43:42.980931: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-07-03 18:43:43.216263: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:43.252280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1742] Found device 0 with properties:
pciBusID: 0000:00:00.0 name: NVIDIA Tegra X1 computeCapability: 5.3
coreClock: 0.9216GHz coreCount: 1 deviceMemorySize: 3.87GiB deviceMemoryBandwidth: 194.55MiB/s
2021-07-03 18:43:43.252747: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-07-03 18:43:43.266841: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2021-07-03 18:43:43.267047: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-07-03 18:43:43.267235: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-07-03 18:43:43.267471: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-07-03 18:43:43.267641: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2021-07-03 18:43:43.277523: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-07-03 18:43:43.277755: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:43.277983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:43.292821: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1884] Adding visible gpu devices: 0
2021-07-03 18:43:44.054252: W tensorflow/core/platform/profile_utils/cpu_utils.cc:108] Failed to find bogomips or clock in /proc/cpuinfo; cannot determine CPU frequency
2021-07-03 18:43:44.090574: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ae52e50 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-07-03 18:43:44.090673: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-07-03 18:43:44.202240: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:44.202702: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ae71050 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-07-03 18:43:44.202767: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA Tegra X1, Compute Capability 5.3
2021-07-03 18:43:44.242034: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:44.242247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1742] Found device 0 with properties:
pciBusID: 0000:00:00.0 name: NVIDIA Tegra X1 computeCapability: 5.3
coreClock: 0.9216GHz coreCount: 1 deviceMemorySize: 3.87GiB deviceMemoryBandwidth: 194.55MiB/s
2021-07-03 18:43:44.242830: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-07-03 18:43:44.242999: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2021-07-03 18:43:44.243104: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-07-03 18:43:44.243232: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-07-03 18:43:44.243363: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-07-03 18:43:44.243472: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2021-07-03 18:43:44.243576: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-07-03 18:43:44.243784: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:44.244020: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:43:44.244120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1884] Adding visible gpu devices: 0
2021-07-03 18:44:09.863146: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1283] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-03 18:44:10.010804: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1289] 0
2021-07-03 18:44:10.010977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1302] 0: N
2021-07-03 18:44:10.386921: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:44:10.505899: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1046] ARM64 does not support NUMA - returning NUMA node zero
2021-07-03 18:44:10.606799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1428] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 92 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3)
WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
Traceback (most recent call last):
File "iva.py", line 275, in
matched, unmatched_trackers, unmatched_detections = tracker_match(track_boxes, [b[0] for b in bboxes])
File "iva.py", line 129, in tracker_match
if(d not in matched_idx[:,1]):
TypeError: tuple indices must be integers or slices, not tuple
Having the same issue
has anybody solved this yet?
Hi @smellslikeml do you have an idea what might have caused this?
We haven't reproduced this one but suggest printing the matched_idx array just before line 129.
We'll try updating the tracker matching logic
Hi @smellslikeml ,
just some background info, i am running the iva.py script on my laptop, on a video of a single person doing squats repeatedly for 3 reps. The model i used is the original resnet_baseline_att_224x224_A_epoch_249_trt.pth and a trained classifier.sav with images of a single person doing squats and standing (2 classes). I modified the code a little (changes shown below) to load the classifier.sav instead of the LSTM model which was by default in the script.
if RUNSECONDARY:
import tensorflow as tf
import pickle
import importlib
config = importlib.import_module('config.conf')
# secondary_model = tf.keras.models.load_model('models/lstm_spin_squat.h5')
secondary_model = pickle.load(open(config.classifier_model, 'rb'))
window = 1
pose_vec_dim = 36
motion_dict = {0: 'standing', 1: 'squats'}
For the issue mentioned by @hamza90ec and also the screenshot i uploaded above, i believe it has something that has got to do with the first frame for any video/live feed. As you can see from the screenshot that even though there is a detection of 1 person in the frame (which is correct, only 1 person in the frame), but the tracker did not take into account this first frame not added to the "trackers" list.
I made some changes to the iva.py code for a quick fix around this but i still faced an error when the code runs up till extracting unmatched trackers and detections after the linear_sum_assignment from IOU_mat. The screenshot below shows the error after making the changes, and as you can see the first frame objects detected are initialized as tracks and appended to trackers for the next iteration to continue tracking.
The code below is what i have changed in iva.py to get this error.
iva_modified.txt
Summary of changes to code:
line 297 - 304: Added a flag for the first frame detections
line 134 - 137: Some print statements for macthed_idx (output from linear_assignment() of IOU_mat)
line 16: changed import of from sklearn.utils.linear_assignment import linear_assignment to from scipy.optimize import linear_sum_assignment as linear_assignment as the sklearn library is deprecated.
I was reading through the documentation for scipy.optimize.linear_sum_assignment(), the output is the row and column indexes for the minimum cost for IOU_mat. However, it is combined into one by stating the variable matched_idx. I don't quite understand why you did this. I added in some commented statements to print out the row and col indexes from the output of the linear_assignment() just to visualize the output.
Do you have an idea of what might be wrong with the code/or what we did wrong? @smellslikeml
Thanks!
I see, alright i understand now.
Thanks and hope to hear from you soon. I will continue to try maybe debug this as well, if i find a fix will post it here!
Cheers!