- Chainer (Neural network framework)
- I'm using master version on GitHub, so
git clone https://github.com/pfnet/chainer.gitcd chainer; python setup.py install
- and then, if you want to train networks with GPU,
pip install chainer-cuda-deps
- I'm using master version on GitHub, so
- progressbar2
pip install progressbar2- NOTE: it's not
progressbar!
bash scripts/downloader.sh
python scripts/flic_dataset.py
python scripts/lsp_dataset.py
This script downloads FLIC-full dataset (http://vision.grasp.upenn.edu/cgi-bin/index.php?n=VideoLearning.FLIC) and perform cropping regions of human and save poses as numpy files into FLIC-full directory.
- MPII Human Pose Dataset
-
- test images don't have any annotations
- so we split trining imges into training/test joint set
- each joint set has
nohup python scripts/train.py \
--model models/AlexNet.py \
--gpu 0 \
--epoch 1000 \
--batchsize 128 \
--prefix AlexNet_LCN_AdaGrad_lr-0.0005 \
--snapshot 10 \
--datadir data/FLIC-full \
--channel 3 \
--flip True \
--size 220 \
--crop_pad_inf 1.5 \
--crop_pad_sup 2.0 \
--shift 5 \
--lcn True \
--joint_num 7 \
> AlexNet_LCN_AdaGrad_lr-0.0005.log 2>&1 &
See the help messages with --help option for details.
- Go to result dir of a model
python ../../scripts/draw_filters.py
python scripts/predict_flic.py \
--model results/AlexNet_2015/AlexNet.py \
--param results/AlexNet_2015/AlexNet_epoch_400.chainermodel \
--datadir data/FLIC-full
--gpu 0 \
--batchsize 128 \
--mode test
python scripts/predict_flic.py \
--model results/AlexNet_2015/AlexNet.py \
--param results/AlexNet_2015/AlexNet_epoch_450.chainermodel \
--mode tile \
--n_imgs 25
cd results/AlexNet_2015
bash ../../scripts/create_anime.sh test_450_tiled_pred.jpg test_450_tiled_label.jpg test_450.gif