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[Bug] Error occurs during converting scanNet #82
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I have found the solution. It seems to be because there is corruption in the specified data, I re-downloaded the specified scene and this time it works. But when I execute
test.py, it still doesn't go through and reports the following error:Traceback (most recent call last): File "tools/test.py", line 157, in <module> main() File "tools/test.py", line 153, in main runner.test() File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1823, in test metrics = self.test_loop.run() # type: ignore File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/mmengine/runner/loops.py", line 463, in run self.run_iter(idx, data_batch) File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/mmengine/runner/loops.py", line 487, in run_iter outputs = self.runner.model.test_step(data_batch) File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/mmengine/model/base_model/base_model.py", line 145, in test_step return self._run_forward(data, mode='predict') # type: ignore File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/mmengine/model/base_model/base_model.py", line 361, in _run_forward results = self(**data, mode=mode) File "/root/miniconda3/envs/embodiedscan/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "/root/EmbodiedScan/embodiedscan/models/detectors/sparse_featfusion_grounder.py", line 681, in forward return self.predict(inputs, data_samples, **kwargs) File "/root/EmbodiedScan/embodiedscan/models/detectors/sparse_featfusion_grounder.py", line 538, in predict positive_maps = self.get_positive_map(tokenized, tokens_positive) File "/root/EmbodiedScan/embodiedscan/models/detectors/sparse_featfusion_grounder.py", line 632, in get_positive_map positive_map = self.create_positive_map(tokenized, tp, idx) File "/root/EmbodiedScan/embodiedscan/models/detectors/sparse_featfusion_grounder.py", line 595, in create_positive_map for (beg, end) in tok_list: TypeError: cannot unpack non-iterable int objectIn the grounder decoder file, change the following lines in predict function in sparse_featfusion_grounder L532-L533
to the following code
tokens_positive = [[[[0, 1]]] for _ in range(len(batch_data_samples))]Reacted by Wu WeifengThanks! It works!
Thank you a lot for pointing out this bug, we'll fix it in the next update!
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Prerequisite
Task
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Branch
main branch https://github.com/open-mmlab/mmdetection3d
Environment
System environment:
sys.platform: linux
Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 287746113
GPU 0: NVIDIA A100-PCIE-40GB
CUDA_HOME: /usr/local/cuda-11.3
NVCC: Cuda compilation tools, release 11.3, V11.3.58
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.11.0
PyTorch compiling details: PyTorch built with:
GCC 7.3
C++ Version: 201402
Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 11.3
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
CuDNN 8.2
Magma 2.5.2
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.12.0
OpenCV: 4.10.0
MMEngine: 0.10.5
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 287746113
Distributed launcher: none
Distributed training: False
GPU number: 1
Reproduces the problem - code sample
Reproduces the problem - command or script
Reproduces the problem - error message
Additional information
I can train fine, but when I run the script for test.py, it reports the error
FileNotFoundError: [Errno 2] No such file or directory: 'data/scannet/posed_images/scene0568_00/00000.jpg. . After checking, I found that there was no such image in that path (it seems that not all of them were converted during my first convert), so I re-ran thegenerate_image_scannet.pyscript. But it fails, with the error as described above. (Oddly enough, it works at my first run) I added the following two lines to the source code:which prints the following.
It seems to be a buffer shortage? I would like to know how to solve this problem. Also, since I have already converted most of the scannet data, is there any way to just specify the converted files to save time.
Thnak you for your intime help.