@StarSky
2019-02-15T07:27:06.000000Z
字数 2054
阅读 1013
FTW
工作日记
PyTorch v0.4
torch==0.4.0 torchvision
conda install ffmpeg=2.8.6
pip install lmdb ipdb
matplotlib
easydict==1.6
numpy==1.13.1
opencv_python==3.2.0.7
ImportError: libavcodec.so.56: cannot open shared object file: No such file or directory
solve:
reinstall ffmpeg version(2.8.6)
https://github.com/jabelone/OpenCV-for-Pi/issues/10
test for data
env:source /data/saber/soft/pytorch_py3.6
python3 crnn_main.py --trainroot /export/gpudata/saber/lmdb_capcha_million --valroot /export/gpudata/saber/lmdb_capcha_million_test --cuda (if cuda available)
python3 crnn_main.py --trainroot /export/gpudata/saber/capcha_img/lmdb_capcha_eduinfo_croped --valroot /export/gpudata/saber/capcha_img/lmdb_capcha_eduinfo_val_croped --cuda
test trained model
python test.py --images_dir '/data/saber/soft/crnn_chinese_characters_rec-master/to_lmdb/char_test_chinese/'
train on GPU & test on CPU
https://blog.csdn.net/dcrmg/article/details/79503978
solved refer:
python setup.py install
https://github.com/SeanNaren/warp-ctc/issues/76
from warpctc_pytorch import CTCLoss
https://github.com/SeanNaren/warp-ctc/pull/31
export WARP_CTC_PATH=/data/saber/soft/warp-ctc-pytorch_bindings/build
tensorboardX
https://github.com/lanpa/tensorboardX
imgW, imgH = 80, 32 || imgW, imgH = 160, 32
(train) the wide been setted to 100
in the paper, it depends...
(test) sepecially, remember to resize the image constant scale before recognize ( height=32 ) , make sure image wide >= imgW.
generate tfrecords file:
python tools/write_text_features.py --dataset_dir /data/saber/soft/CRNN_Tensorflow-master/data --save_dir /data/saber/soft/CRNN_Tensorflow-master/data --charset_dir /data/saber/soft/CRNN_Tensorflow-master/data/char_dict
train:
python tools/train_shadownet.py --dataset_dir /data/saber/soft/CRNN_Tensorflow-master/data
test single img
python tools/demo_shadownet.py --image_path /export/gpudata/saber/capcha_img/hebei_tax_for_label/9p8_869.jpg --weights_path model/shadownet/shadownet_2018-11-20-10-48-37.ckpt-33
special gpu
CUDA_VISIBLE_DEVICES=1 python ***.py