Open LSK0821 opened 1 year ago
LSK0821
commented
1 year ago My apologies. I tried to specify the batch size by adding .batch(batch_size=32) "/home/liusikang/cord/superpoint/superpoint/datasets/synthetic_shapes.py", line 191, in _get_data (filenames[split_name]['images'], filenames[split_name]['points'])).batch(batch_size=32), still no solve. So I removed batch(batch_size=32) and it still showed IndexError: list index out of range.
rpautrat
commented
1 year ago Hi, why are you using such an old version of Tensorflow? I suggest you to upgrade, e.g. to 1.12 as in https://github.com/rpautrat/SuperPoint/issues/173#issue-730896838
vrahnos3
commented
1 year ago Hello, thanks for the great job,
I followed the steps of #173
I have a problem with loss nan after extracting all syntetic shapes. Any ideas?
Here is my error message:
/home/panosvrach/anaconda3/envs/superpoint/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/panosvrach/anaconda3/envs/superpoint/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/panosvrach/anaconda3/envs/superpoint/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/panosvrach/anaconda3/envs/superpoint/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/panosvrach/anaconda3/envs/superpoint/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/panosvrach/anaconda3/envs/superpoint/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
[02/17/2023 16:50:21 INFO] Running command TRAIN
[02/17/2023 16:50:21 INFO] Number of GPUs detected: 1
[02/17/2023 16:50:23 INFO] Extracting archive for primitive draw_lines.
[02/17/2023 16:50:26 INFO] Extracting archive for primitive draw_polygon.
[02/17/2023 16:50:29 INFO] Extracting archive for primitive draw_multiple_polygons.
[02/17/2023 16:50:32 INFO] Extracting archive for primitive draw_ellipses.
[02/17/2023 16:50:35 INFO] Extracting archive for primitive draw_star.
[02/17/2023 16:50:38 INFO] Extracting archive for primitive draw_checkerboard.
[02/17/2023 16:50:41 INFO] Extracting archive for primitive draw_stripes.
[02/17/2023 16:50:43 INFO] Extracting archive for primitive draw_cube.
[02/17/2023 16:50:46 INFO] Extracting archive for primitive gaussian_noise.
[02/17/2023 16:50:50 INFO] Caching data, fist access will take some time.
[02/17/2023 16:50:51 INFO] Caching data, fist access will take some time.
[02/17/2023 16:50:51 INFO] Caching data, fist access will take some time.
2023-02-17 16:50:51.923674: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2023-02-17 16:50:52.270209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: NVIDIA GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.785
pciBusID: 0000:3b:00.0
totalMemory: 23.70GiB freeMemory: 23.43GiB
2023-02-17 16:50:52.270250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2023-02-17 16:55:07.976783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-02-17 16:55:07.976819: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2023-02-17 16:55:07.976825: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2023-02-17 16:55:07.976957: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2023-02-17 16:55:07.976989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22711 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:3b:00.0, compute capability: 8.6)
[02/17/2023 16:55:08 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:08 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:08 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:08 INFO] Scale of 0 disables regularizer.
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[02/17/2023 16:55:08 INFO] Scale of 0 disables regularizer.
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[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
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[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
[02/17/2023 16:55:09 INFO] Scale of 0 disables regularizer.
2023-02-17 16:55:09.704916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2023-02-17 16:55:09.704969: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-02-17 16:55:09.704973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2023-02-17 16:55:09.704977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2023-02-17 16:55:09.705063: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22711 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:3b:00.0, compute capability: 8.6)
[02/17/2023 16:55:14 INFO] Start training
[02/17/2023 17:14:51 INFO] Iter 0: loss 4.1759, precision 0.0005, recall 0.0482
/home/panosvrach/Desktop/SuperPoint/superpoint/models/base_model.py:387: RuntimeWarning: Mean of empty slice
metrics = {m: np.nanmean(metrics[m], axis=0) for m in metrics}
[02/17/2023 17:15:10 INFO] Iter 1000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:15:30 INFO] Iter 2000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:15:49 INFO] Iter 3000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:16:08 INFO] Iter 4000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:16:27 INFO] Iter 5000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:16:47 INFO] Iter 6000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:17:06 INFO] Iter 7000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:17:25 INFO] Iter 8000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:17:44 INFO] Iter 9000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:18:03 INFO] Iter 10000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:18:23 INFO] Iter 11000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:18:42 INFO] Iter 12000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:19:01 INFO] Iter 13000: loss nan, precision nan, recall 0.0000
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[02/17/2023 17:19:39 INFO] Iter 15000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:19:58 INFO] Iter 16000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:20:17 INFO] Iter 17000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:20:37 INFO] Iter 18000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:20:56 INFO] Iter 19000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:21:15 INFO] Iter 20000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:21:34 INFO] Iter 21000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:21:53 INFO] Iter 22000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:22:13 INFO] Iter 23000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:22:32 INFO] Iter 24000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:22:51 INFO] Iter 25000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:23:10 INFO] Iter 26000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:23:30 INFO] Iter 27000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:23:49 INFO] Iter 28000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:24:08 INFO] Iter 29000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:24:27 INFO] Iter 30000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:24:46 INFO] Iter 31000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:25:05 INFO] Iter 32000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:25:25 INFO] Iter 33000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:25:44 INFO] Iter 34000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:26:03 INFO] Iter 35000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:26:22 INFO] Iter 36000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:26:41 INFO] Iter 37000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:27:01 INFO] Iter 38000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:27:20 INFO] Iter 39000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:27:39 INFO] Iter 40000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:27:58 INFO] Iter 41000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:28:18 INFO] Iter 42000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:28:37 INFO] Iter 43000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:28:56 INFO] Iter 44000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:29:15 INFO] Iter 45000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:29:34 INFO] Iter 46000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:29:54 INFO] Iter 47000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:30:13 INFO] Iter 48000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:30:32 INFO] Iter 49000: loss nan, precision nan, recall 0.0000
[02/17/2023 17:30:47 INFO] Training finished
[02/17/2023 17:30:47 INFO] Saving checkpoint for iteration #50000
2023-02-17 17:30:47.452008: W tensorflow/core/kernels/data/cache_dataset_ops.cc:770] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the datasetwill be discarded. This can happen if you have an input pipeline similar to dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead.
rpautrat
commented
1 year ago Hi, this seems to be very related to this issue: https://github.com/rpautrat/SuperPoint/issues/189
In this other case, it was due to an incompatibility of ampere technology (SM_86) and old cuda + tf versions. Could it be the same for you?
vrahnos3
commented
1 year ago I use cuda 11.4 so probably its an incompatibility of cuda-tf version. I will try the pytorch implementation as mentioned in #189 otherwise I will try to downgrade cuda version. Thanks a lot for your fast reply.
1z2213
commented
1 year ago Dear author， Hello！ Hi, I followed your instructions of https://github.com/rpautrat/SuperPoint/issues/173 and now I am trying to run the 1st step. python experiment.py train configs/magic-point_shapes.yaml magic-point_synth. I have the same problem with loss nan after extracting all syntetic shapes. I desperately need your help. Looking forward to your reply. Here are the instructions and all the information displayed when I run the code. sunlab@sunlab-ThinkStation-P520:~$ source ~/anaconda3/bin/activate (base) sunlab@sunlab-ThinkStation-P520:~$ conda create -n linenv python=3.6.3 Collecting package metadata (current_repodata.json): done Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: done
==> WARNING: A newer version of conda exists. <== current version: 4.10.1 latest version: 23.5.0
Please update conda by running
$ conda update -n base -c defaults condaenvironment location: /home/sunlab/anaconda3/envs/linenv
added / updated specs:
The following packages will be downloaded:
package | build
---------------------------|-----------------
pip-21.2.2 | py36h06a4308_0 1.8 MB defaults
python-3.6.3 | h6c0c0dc_5 25.5 MB defaults
setuptools-58.0.4 | py36h06a4308_0 788 KB defaults
------------------------------------------------------------
Total: 28.1 MBThe following NEW packages will be INSTALLED:
_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu ca-certificates pkgs/main/linux-64::ca-certificates-2023.05.30-h06a4308_0 certifi pkgs/main/linux-64::certifi-2021.5.30-py36h06a4308_0 libedit pkgs/main/linux-64::libedit-3.1.20221030-h5eee18b_0 libffi pkgs/main/linux-64::libffi-3.2.1-hf484d3e_1007 libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 ncurses pkgs/main/linux-64::ncurses-6.4-h6a678d5_0 openssl pkgs/main/linux-64::openssl-1.0.2u-h7b6447c_0 pip pkgs/main/linux-64::pip-21.2.2-py36h06a4308_0 python pkgs/main/linux-64::python-3.6.3-h6c0c0dc_5 readline pkgs/main/linux-64::readline-7.0-h7b6447c_5 setuptools pkgs/main/linux-64::setuptools-58.0.4-py36h06a4308_0 sqlite pkgs/main/linux-64::sqlite-3.33.0-h62c20be_0 tk pkgs/main/linux-64::tk-8.6.12-h1ccaba5_0 wheel pkgs/main/noarch::wheel-0.37.1-pyhd3eb1b0_0 xz pkgs/main/linux-64::xz-5.4.2-h5eee18b_0 zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_0
Proceed ([y]/n)? y
Downloading and Extracting Packages python-3.6.3 | 25.5 MB | ##################################### | 100% setuptools-58.0.4 | 788 KB | ##################################### | 100% pip-21.2.2 | 1.8 MB | ##################################### | 100% Preparing transaction: done Verifying transaction: done Executing transaction: done #
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(base) sunlab@sunlab-ThinkStation-P520:~$ conda activate linenv (linenv) sunlab@sunlab-ThinkStation-P520:~$ conda install tensorflow-gpu=1.13 Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Collecting package metadata (repodata.json): done Solving environment: done
==> WARNING: A newer version of conda exists. <== current version: 4.10.1 latest version: 23.5.0
Please update conda by running
$ conda update -n base -c defaults condaenvironment location: /home/sunlab/anaconda3/envs/linenv
added / updated specs:
The following NEW packages will be INSTALLED:
_tflow_select pkgs/main/linux-64::_tflow_select-2.1.0-gpu absl-py pkgs/main/noarch::absl-py-0.15.0-pyhd3eb1b0_0 astor pkgs/main/linux-64::astor-0.8.1-py36h06a4308_0 blas pkgs/main/linux-64::blas-1.0-mkl c-ares pkgs/main/linux-64::c-ares-1.19.0-h5eee18b_0 cudatoolkit pkgs/main/linux-64::cudatoolkit-10.0.130-0 cudnn pkgs/main/linux-64::cudnn-7.6.5-cuda10.0_0 cupti pkgs/main/linux-64::cupti-10.0.130-0 dataclasses pkgs/main/noarch::dataclasses-0.8-pyh4f3eec9_6 gast pkgs/main/noarch::gast-0.5.3-pyhd3eb1b0_0 grpcio pkgs/main/linux-64::grpcio-1.14.1-py36h9ba97e2_0 h5py pkgs/main/linux-64::h5py-2.10.0-py36hd6299e0_1 hdf5 pkgs/main/linux-64::hdf5-1.10.6-hb1b8bf9_0 intel-openmp pkgs/main/linux-64::intel-openmp-2022.1.0-h9e868ea_3769 keras-applications pkgs/main/noarch::keras-applications-1.0.8-py_1 keras-preprocessi~ pkgs/main/noarch::keras-preprocessing-1.1.2-pyhd3eb1b0_0 libgfortran-ng pkgs/main/linux-64::libgfortran-ng-7.5.0-ha8ba4b0_17 libgfortran4 pkgs/main/linux-64::libgfortran4-7.5.0-ha8ba4b0_17 libprotobuf pkgs/main/linux-64::libprotobuf-3.17.2-h4ff587b_1 markdown pkgs/main/linux-64::markdown-3.1.1-py36_0 mkl pkgs/main/linux-64::mkl-2020.2-256 mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py36he8ac12f_0 mkl_fft pkgs/main/linux-64::mkl_fft-1.3.0-py36h54f3939_0 mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py36h0573a6f_0 mock pkgs/main/noarch::mock-4.0.3-pyhd3eb1b0_0 numpy pkgs/main/linux-64::numpy-1.19.2-py36h54aff64_0 numpy-base pkgs/main/linux-64::numpy-base-1.19.2-py36hfa32c7d_0 protobuf pkgs/main/linux-64::protobuf-3.17.2-py36h295c915_0 scipy pkgs/main/linux-64::scipy-1.5.2-py36h0b6359f_0 six pkgs/main/noarch::six-1.16.0-pyhd3eb1b0_1 tensorboard pkgs/main/linux-64::tensorboard-1.13.1-py36hf484d3e_0 tensorflow pkgs/main/linux-64::tensorflow-1.13.1-gpu_py36h3991807_0 tensorflow-base pkgs/main/linux-64::tensorflow-base-1.13.1-gpu_py36h8d69cac_0 tensorflow-estima~ pkgs/main/noarch::tensorflow-estimator-1.13.0-py_0 tensorflow-gpu pkgs/main/linux-64::tensorflow-gpu-1.13.1-h0d30ee6_0 termcolor pkgs/main/linux-64::termcolor-1.1.0-py36h06a4308_1 werkzeug pkgs/main/noarch::werkzeug-2.0.3-pyhd3eb1b0_0
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(linenv) sunlab@sunlab-ThinkStation-P520:~$ cd superpoint/SuperPoint-master
(linenv) sunlab@sunlab-ThinkStation-P520:~/superpoint/SuperPoint-master$ make install
pip3 install -r requirements.txt
Requirement already satisfied: numpy in /home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages (from -r requirements.txt (line 1)) (1.19.2)
Requirement already satisfied: scipy in /home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.5.2)
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pip3 install -e .
Obtaining file:///home/sunlab/superpoint/SuperPoint-master
Installing collected packages: superpoint
Running setup.py develop for superpoint
Successfully installed superpoint-0.0
sh setup.sh
Path of the directory where datasets are stored and read: /home/sunlab//superpoint/SuperPoint-master/DATA_DIR
Path of the directory where experiments data (logs, checkpoints, configs) are written: /home/sunlab//superpoint/SuperPoint-master/EXPER_DIR
(linenv) sunlab@sunlab-ThinkStation-P520:~/superpoint/SuperPoint-master$ cd ./superpoint
(linenv) sunlab@sunlab-ThinkStation-P520:~/superpoint/SuperPoint-master/superpoint$ export TMPDIR=/tmp/
(linenv) sunlab@sunlab-ThinkStation-P520:~/superpoint/SuperPoint-master/superpoint$ export TF_FORCE_GPU_ALLOW_GROWTH=true
(linenv) sunlab@sunlab-ThinkStation-P520:~/superpoint/SuperPoint-master/superpoint$ python experiment.py train configs/magic-point_shapes.yaml magic-point_synth/home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
[07/06/2023 23:37:58 INFO] Running command TRAIN
Traceback (most recent call last):
File "experiment.py", line 160, in tf.py_functions can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
[07/06/2023 23:38:45 INFO] Caching data, fist access will take some time.
[07/06/2023 23:38:45 WARNING] From /home/sunlab/anaconda3/envs/linenv/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py:423: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
[07/06/2023 23:38:45 WARNING] From /home/sunlab/superpoint/SuperPoint-master/superpoint/models/homographies.py:218: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
[07/06/2023 23:38:45 WARNING] From /home/sunlab/superpoint/SuperPoint-master/superpoint/models/homographies.py:277: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
[07/06/2023 23:38:46 INFO] Caching data, fist access will take some time.
[07/06/2023 23:38:46 INFO] Caching data, fist access will take some time.
2023-07-06 23:38:46.500189: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2023-07-06 23:38:46.688675: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3600000000 Hz
2023-07-06 23:38:46.701881: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x557760fccac0 executing computations on platform Host. Devices:
2023-07-06 23:38:46.702032: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead.
(linenv) sunlab@sunlab-ThinkStation-P520:~/superpoint/SuperPoint-master/superpoint$ python experiment.py train configs/magic-point_shapes.yaml magic-point_synth
00ohnim
commented
1 year ago I ran into this problem,too,but I don't know how to solve it.
1z2213
commented
1 year ago Add heading textAdd bold text, <Ctrl+b>Add italic text, <Ctrl+i> Add a quote, <Ctrl+Shift+.>Add code, <Ctrl+e>Add a link, <Ctrl+k> Add a bulleted list, <Ctrl+Shift+8>Add a numbered list, <Ctrl+Shift+7>Add a task list, <Ctrl+Shift+l> Directly mention a user or team Reference an issue, pull request, or discussion Add saved reply Slash commands It seems like a lot of people experienced this problem, but those who experienced it don't seem to have a solution for it.
rpautrat
commented
1 year ago Hi, this might be due to the warning about the cache returned by tensorflow at the end of your output. Could you try training without caching the data, i.e. by setting 'cache_in_memory' to false in superpoint/configs/magic-point_shapes.yaml?
Alternatively, you could also add the field 'on-the-fly' in a new line after 'cache_in_memory' and set it to true. This would generate the shapes on the fly instead of pre-generating them, and could be a good way to check if the error is caused by caching issues.
1z2213
commented
1 year ago Hi, this might be due to the warning about the cache returned by tensorflow at the end of your output. Could you try training without caching the data, i.e. by setting 'cache_in_memory' to false in
superpoint/configs/magic-point_shapes.yaml?Alternatively, you could also add the field 'on-the-fly' in a new line after 'cache_in_memory' and set it to true. This would generate the shapes on the fly instead of pre-generating them, and could be a good way to check if the error is caused by caching issues.
I apologize for responding to your message so late, I tried your method but it didn't solve my problem. This proves that the error is not caused by caching issues.
Dear Author, Hello! I am using TF1.2 and python3.6 under linux system.Having an out-of-index problem while executing step 1 to extract gaussian_noise. I changed line 184 in the file /superpoint/superpoint/synthetic_shapes.py to tf.contrib.data.Dataset.map_parallel = lambda self, fn: self.map( fn, num_parallel_calls=config['num_parallel_calls']), because I see that tf1.4 and below use data with contrib, the other parts have not been modified, but look at the content of the error report from this sentence into. The following is the content of the console error reporting：
(test) liusikang@4029GP-TRT:~/cord/superpoint/superpoint$ python experiment.py train configs/magic-point_shapes.yaml magic-point_synth /home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:458: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint8 = np.dtype([("qint8", np.int8, 1)]) /home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:459: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) /home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:460: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint16 = np.dtype([("qint16", np.int16, 1)]) /home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:461: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) /home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:462: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint32 = np.dtype([("qint32", np.int32, 1)]) /home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:465: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. np_resource = np.dtype([("resource", np.ubyte, 1)]) experiment.py:155: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. config = yaml.load(f) [12/05/2022 10:17:55 INFO] Running command TRAIN [12/05/2022 10:17:55 INFO] Number of GPUs detected: 1 INFO:tensorflow:Extracting archive for primitive draw_lines. [12/05/2022 10:17:58 INFO] Extracting archive for primitive draw_lines. INFO:tensorflow:Extracting archive for primitive draw_polygon. [12/05/2022 10:18:02 INFO] Extracting archive for primitive draw_polygon. INFO:tensorflow:Extracting archive for primitive draw_multiple_polygons. [12/05/2022 10:18:05 INFO] Extracting archive for primitive draw_multiple_polygons. INFO:tensorflow:Extracting archive for primitive draw_ellipses. [12/05/2022 10:18:08 INFO] Extracting archive for primitive draw_ellipses. INFO:tensorflow:Extracting archive for primitive draw_star. [12/05/2022 10:18:11 INFO] Extracting archive for primitive draw_star. INFO:tensorflow:Extracting archive for primitive draw_checkerboard. [12/05/2022 10:18:14 INFO] Extracting archive for primitive draw_checkerboard. INFO:tensorflow:Extracting archive for primitive draw_stripes. [12/05/2022 10:18:17 INFO] Extracting archive for primitive draw_stripes. INFO:tensorflow:Extracting archive for primitive draw_cube. [12/05/2022 10:18:20 INFO] Extracting archive for primitive draw_cube. INFO:tensorflow:Extracting archive for primitive gaussian_noise. [12/05/2022 10:18:23 INFO] Extracting archive for primitive gaussian_noise. Traceback (most recent call last): File "experiment.py", line 162, in
args.func(config, output_dir, args)
File "experiment.py", line 99, in _cli_train
train(config, config['train_iter'], output_dir, pretrained_dir)
File "experiment.py", line 22, in train
with _init_graph(config) as net:
File "/home/liusikang/anaconda3/envs/test/lib/python3.6/contextlib.py", line 82, in enter
return next(self.gen)
File "experiment.py", line 73, in _init_graph
dataset = get_dataset(config['data']['name'])(config['data'])
File "/home/liusikang/cord/superpoint/superpoint/datasets/base_dataset.py", line 108, in init
self.tf_splits[n] = self._get_data(self.dataset, n, self.config)
File "/home/liusikang/cord/superpoint/superpoint/datasets/synthetic_shapes.py", line 191, in _get_data
(filenames[split_name]['images'], filenames[split_name]['points'])).batch(batch_size=32)
File "/home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/dataset_ops.py", line 473, in from_tensor_slices
return TensorSliceDataset(tensors)
File "/home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/dataset_ops.py", line 896, in init
batch_dim = flat_tensors[0].get_shape()[0]
File "/home/liusikang/anaconda3/envs/test/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py", line 500, in getitem
return self._dims[key]
IndexError: list index out of range
Looking forward to your reply!