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Deci-AI / super-gradients

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
https://www.supergradients.com
Apache License 2.0
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yolo nas pose demo/colab is broken #2019

Open yuvraj108c opened 5 months ago

yuvraj108c commented 5 months ago
  1. The demo from readme doesn't work, prediction is None
  2. The colab also doesn't work: UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fd2db2bb420>

🐛 Describe the bug


import super_gradients

yolo_nas = super_gradients.training.models.get("yolo_nas_pose_l", pretrained_weights="coco_pose").cuda()
model_predictions  = yolo_nas.predict("https://deci-pretrained-models.s3.amazonaws.com/sample_images/beatles-abbeyroad.jpg", conf=0.5).show()

prediction = model_predictions[0].prediction # One prediction per image - Here we work with 1 image, so we get the first.

bboxes = prediction.bboxes_xyxy # [Num Instances, 4] List of predicted bounding boxes for each object 
poses  = prediction.poses       # [Num Instances, Num Joints, 3] list of predicted joints for each detected object (x,y, confidence)
scores = prediction.scores      # [Num Instances] - Confidence value for each predicted instance

Versions

Collecting environment information... PyTorch version: 2.3.1+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.19.0-1010-nvidia-lowlatency-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe Nvidia driver version: 550.90.07 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 32 On-line CPU(s) list: 0-31 Vendor ID: AuthenticAMD Model name: AMD EPYC 9124 16-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 1 Frequency boost: enabled CPU max MHz: 3711.9141 CPU min MHz: 1500.0000 BogoMIPS: 6000.01 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d Virtualization: AMD-V L1d cache: 512 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 16 MiB (16 instances) L3 cache: 64 MiB (4 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-31 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.23.0 [pip3] onnx==1.15.0 [pip3] onnxruntime==1.15.0 [pip3] onnxsim==0.4.36 [pip3] torch==2.3.1 [pip3] torchmetrics==0.8.0 [pip3] torchvision==0.18.1 [pip3] triton==2.3.1 [conda] No relevant packages

stulee0707 commented 3 months ago

DId you use "!pip install super-gradients" I had this problem before，but i used again this code.