dataset/:
dataset_generator.py eval_dataset __init__.py __pycache__ training_dataset
edge_tpu_inference_results/: # saves the EdgeTPU ImageNet model scheduling results, generated by RESPECT inference
densenet121.txt densenet169.txt densenet201.txt mobilenet_1.00_224.txt mobilenetv2_1.00_224.txt resnet101.txt resnet101v2.txt resnet152.txt resnet152v2.txt
nets/: # pointer-network design
attention_model.py __init__.py pointer_network_originalbatch.py pointer_network_singleTraining.py __pycache__
problems/: # scheduling cost function in toposort
__init__.py op pctsp __pycache__ toposort
RESPECT_Eval_ImageNet_Models/: # evaluation dataset (post-embedding)
densenet121.pt densenet201.pt inception_v3.pt mobilenetv2_1.00_224.pt resnet101v2.pt resnet152v2.pt resnet50v2.pt vgg19.pt
densenet169.pt inception_resnet_v2.pt mobilenet_1.00_224.pt resnet101.pt resnet152.pt resnet50.pt vgg16.pt xception.pt
saved_model/: # pre-trained model for re-producing the inference results; load for inference
run_20210827T155340Note:
- checking CUDA device (we set CUDA_VISIBLE_DEVICES=0)
- checking python env (we use conda env; see details of the env setups below)
bash RESPECT_scheduling_ImageNet.sh- results are written in edge_tpu_inference_results
- pre-trained model loaded from saved_model
[*] Loading data from saved_model/run_20210827T155340/epoch-298.pt
Batch inference runtime test over 64 (0.0357 per scheduling)
2.2832441329956055
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.28s/it]
Validation count of misMatch: 311.58
Validation mean of recall_accuracy: 0.50
Validation max of recall_accuracy: 0.53
Validation min of recall_accuracy: 0.45
Evaluating baseline model on the evaluation dataset
0/1 [00:00<?, ?it/s]torch.Size([1, 347, 15]) torch.Size([64, 347, 15])cudatoolkit 10.2.89 hfd86e86_1
cycler 0.10.0 py_2 conda-forge
dbus 1.13.18 hb2f20db_0
decorator 4.4.2 py_0 conda-forge
expat 2.4.1 h9c3ff4c_0 conda-forge
ffmpeg 4.3 hf484d3e_0 pytorch
fontconfig 2.13.1 he4413a7_1000 conda-forge
freetype 2.10.4 h5ab3b9f_0
glib 2.69.0 h5202010_0
gmp 6.2.1 h2531618_2
gnutls 3.6.15 he1e5248_0
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 h28cd5cc_2
icu 58.2 hf484d3e_1000 conda-forge
intel-openmp 2021.2.0 h06a4308_610
joblib 1.1.0 pyhd8ed1ab_0 conda-forge
jpeg 9b h024ee3a_2
kiwisolver 1.3.1 py38h1fd1430_1 conda-forge
lame 3.100 h7b6447c_0
lcms2 2.12 h3be6417_0
ld_impl_linux-64 2.35.1 h7274673_9
libblas 3.9.0 1_h6e990d7_netlib conda-forge
libcblas 3.9.0 3_h893e4fe_netlib conda-forge
libffi 3.3 he6710b0_2
libgcc-ng 11.2.0 h1d223b6_11 conda-forge
libgfortran-ng 7.5.0 h14aa051_19 conda-forge
libgfortran4 7.5.0 h14aa051_19 conda-forge
libgomp 11.2.0 h1d223b6_11 conda-forge
libiconv 1.15 h63c8f33_5
libidn2 2.3.1 h27cfd23_0
liblapack 3.9.0 3_h893e4fe_netlib conda-forge
libpng 1.6.37 hbc83047_0
libstdcxx-ng 11.2.0 he4da1e4_11 conda-forge
libtasn1 4.16.0 h27cfd23_0
libtiff 4.2.0 h85742a9_0
libunistring 0.9.10 h27cfd23_0
libuuid 2.32.1 h7f98852_1000 conda-forge
libuv 1.40.0 h7b6447c_0
libwebp-base 1.2.0 h27cfd23_0
libxcb 1.13 h7f98852_1003 conda-forge
libxml2 2.9.9 h13577e0_2 conda-forge
lz4-c 1.9.3 h2531618_0
matplotlib 3.4.2 py38h578d9bd_0 conda-forge
matplotlib-base 3.4.2 py38hcc49a3a_0 conda-forge
mkl 2020.2 256
mkl-service 2.3.0 py38h1e0a361_2 conda-forge
mkl_fft 1.2.0 py38hab2c0dc_1 conda-forge
mkl_random 1.2.0 py38hc5bc63f_1 conda-forge
munch 2.5.0 pypi_0 pypi
ncurses 6.2 he6710b0_1
nettle 3.7.3 hbbd107a_1
networkx 2.6.1 pyhd8ed1ab_1 conda-forge
ninja 1.10.2 hff7bd54_1
numpy 1.20.3 py38h9894fe3_0 conda-forge
numpy-base 1.18.5 py38hde5b4d6_0
olefile 0.46 py_0
openh264 2.1.0 hd408876_0
openssl 1.1.1l h7f98852_0 conda-forge
pandas 1.3.0 py38h1abd341_0 conda-forge
pcre 8.45 h9c3ff4c_0 conda-forge
pillow 8.2.0 py38he98fc37_0
pip 21.1.3 py38h06a4308_0
pretrainedmodels 0.7.4 pypi_0 pypi
protobuf 3.17.3 pypi_0 pypi
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
pyqt 5.9.2 py38h05f1152_4
python 3.8.10 h12debd9_8
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.8 2_cp38 conda-forge
pytorch 1.9.0 py3.8_cuda10.2_cudnn7.6.5_0 pytorch
pytz 2021.1 pyhd8ed1ab_0 conda-forge
qt 5.9.7 h5867ecd_1
readline 8.1 h27cfd23_0
scikit-learn 1.0.1 py38hacb3eff_1 conda-forge
scipy 1.5.3 py38h828c644_0 conda-forge
setuptools 52.0.0 py38h06a4308_0
sip 4.19.13 py38he6710b0_0
six 1.16.0 pyhd3eb1b0_0
sqlite 3.36.0 hc218d9a_0
tensorboard-logger 0.1.0 pypi_0 pypi
threadpoolctl 3.0.0 pyh8a188c0_0 conda-forge
tk 8.6.10 hbc83047_0
torchaudio 0.9.0 py38 pytorch
torchvision 0.10.0 py38_cu102 pytorch
tornado 6.1 py38h497a2fe_1
tqdm 4.61.2 pyhd8ed1ab_1 conda-forge
typing_extensions 3.10.0.0 pyh06a4308_0
tzdata 2021a h52ac0ba_0
wheel 0.36.2 pyhd3eb1b0_0
xz 5.2.5 h7b6447c_0
zlib 1.2.11 h7b6447c_3
zstd 1.4.9 haebb681_0