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object_detection_guide.txt
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object_detection_guide.txt
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---- set up PC ----
OS: ubuntu 16.04 * during installation check both third party ... external ...
Graphic card: nvidia 1080
---- drivers ----
drivers: 384.130 * from ubuntu installation installation
* from settings->additional drivers -> 384.130
* from $ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt-get update
$ sudo apt-get install nvidia-384 nvidia-modprobe
---- basic tools ---- https://www.youtube.com/watch?v=rILtTjrecQc
$ sudo apt-get update
$ sudo apt-get --assume-yes upgrade
$ sudo apt-get --assume-yes install tmux build-essential gcc g++ make binutils
$ sudo apt-get --assume-yes install software-properties-common
---- CUDA ---- https://medium.com/repro-repo/install-cuda-and-cudnn-for-tensorflow-gpu-on-ubuntu-79306e4ac04e
CUDA: 8.0 GA2 RUNFILE * from https://developer.nvidia.com/cuda-toolkit-archive
$ chmod +x cuda_8.0.61_375.26_linux.run
$ ./cuda_8.0.61_375.26_linux.run --extract=$HOME
You should have unpacked three components:
1 - NVIDIA-Linux-x86_64-384.81.run (NVIDIA driver that we ignore)
2 - cuda-linux.9.0.176-22781540.run (CUDA 8.0 installer)
3 - cuda-samples.9.0.176-22781540-linux.run (CUDA 8.0 Samples).
$ sudo ./cuda-linux.9.0.176-22781540.run
You now have to accept the license by scrolling down to the bottom and enter “accept”. Next accept the defaults.
To verify our CUDA installation, install the sample tests by
$ sudo ./cuda-samples.9.0.176-22781540-linux.run
After the installation finishes, configure the runtime library.
$ sudo bash -c "echo /usr/local/cuda/lib64/ > /etc/ld.so.conf.d/cuda.conf"
$ sudo ldconfig
include nvcc in $PATH
$ sudo nano /etc/environment
add :/usr/local/cuda/bin (including the ":") at the end of the PATH="/blah:/blah/blah" string (inside the quotes)
$ reboot
Lets test
$ cd /usr/local/cuda-8.0/samples
$ sudo make
$ cd /usr/local/cuda/samples/bin/x86_64/linux/release
$ ./deviceQuery
Result = PASS
---- CUDNN ---- https://medium.com/repro-repo/install-cuda-and-cudnn-for-tensorflow-gpu-on-ubuntu-79306e4ac04e
CUDNN: 6.0 fron CUDA 8 * from https://developer.nvidia.com/rdp/cudnn-archive (mikimartin11@gmail, L _ _ _ _ _ _ _ _ _ _ !)
Download all 3 .deb files: the runtime library, the developer library, and the code samples library for Ubuntu 16.04
$ sudo dpkg -i libcudnn7_7.0.5.15–1+cuda9.0_amd64.deb (the runtime library) # 6
$ sudo dpkg -i libcudnn7-dev_7.0.5.15–1+cuda9.0_amd64.deb (the developer library) # 6
$ sudo dpkg -i libcudnn7-doc_7.0.5.15–1+cuda9.0_amd64.deb (the code samples) # 6
test CUDNN
- copy the code samples somewhere you have write access:
$ cp -r /usr/src/cudnn_samples_v6/ /home/miguel/Desktop/
$ cd /home/miguel/Desktop/cudnn_samples_v6/mnistCUDNN
$ make clean && make
$ ./mnistCUDNN
Test passed!
configure the CUDA and cuDNN library paths, put the following line in the end or your .bashrc file
# configure the CUDA and cuDNN library paths
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64"
---- ANACONDA ----
download anaconda (end of page): https://www.anaconda.com/products/individual#linux
install conda dependencies and anaconda (https://docs.anaconda.com/anaconda/install/linux/)
$ sudo apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6
$ bash ~/Downloads/Anaconda3-2020.02-Linux-x86_64.sh
you can deactivate the auto base environment on new shell
$ conda config --set auto_activate_base false
create conda env
conda create --name det35 python=3.5
conda create --name det36 python=3.6
conda activate det3x
---- TENSORFLOW ----
error when installing tensorflow 1.4 from conda. Installed by pip
pip install tensorflow-gpu==1.4
---- OBJECT DETECTION API ---- https://github.com/tensorflow/models/blob/a7c84b82b70f6f5fd3b523656680989a757aa0a8/research/object_detection/g3doc/installation.md
https://pythonprogramming.net/introduction-use-tensorflow-object-detection-api-tutorial/
clone git at commit a7c84b82b70f6f5fd3b523656680989a757aa0a8 (https://github.com/tensorflow/models/tree/a7c84b82b70f6f5fd3b523656680989a757aa0a8)
$ git clone https://github.com/tensorflow/models
$ cd https://github.com/tensorflow/models
$ git reset --hard a7c84b82b70f6f5fd3b523656680989a757aa0a8
install needed libraries
$ conda install pillow
$ conda install lxml
$ conda install jupyter
$ conda install matplotlib
$ conda install scipy=1.1.0
check protoc version with protoc --version, if not 3.4.0:
- https://github.com/protocolbuffers/protobuf/releases
- go to protoc 3.4.0 (maybe this link will go https://github.com/protocolbuffers/protobuf/releases?after=v3.6.0rc1)
- bottom -> assets -> download protoc-3.4.0-linux-x86_64.zip
- unzip protoc-3.4.0-linux-x86_64.zip
- install (https://gist.github.com/ryujaehun/991f5f1e8c1485dea72646877707f497):
$ cd protoc-3.4.0-linux-x86_64
$ sudo mv bin/* /usr/local/bin/
$ sudo mv include/* /usr/local/include/
- check protoc --version
from models/research:
protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
test API on jupyter notebook
$ cd object_detection
$ jupyter notebook
- cells -> run all (it should work on conda env pyth python 3.5, not on 3.6 missmatch something, idk, idc)
cute dogs found
---- DATA ---- https://pythonprogramming.net/custom-objects-tracking-tensorflow-object-detection-api-tutorial/
https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/
further instructions on adapt.txt
process: labelimg -> xml -> xml_to_csv.py -> csv -> generate_tfrecord.py -> train/test.record
model/research/object_detection/data/train.record
model/research/object_detection/data/test.record
model/research/object_detection/data/object_detection.pbtxt (label map)
model/research/object_detection/test_images
---- TRAIN ---- https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/
further instructions on adapt.txt
architecture: faster_rcnn_inception_resnet_v2_atrous_coco
from: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md
goes in: model/research/object_detection/architectures
configuration
from: faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28/pipeline.config
from: https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs
data augmentation: https://stackoverflow.com/questions/44906317/what-are-possible-values-for-data-augmentation-options-in-the-tensorflow-object * he tenido que quitar vertical_flip y rotation90
goes into: model/research/object_detection/training
python3 train.py --logtostderr --train_dir=RUNS/xxx --pipeline_config_path=training/xxx.config
---- INFERENCE ---- https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/
further instructions on adapt.txt
take my evaluate.py
frozen graph
python3 export_inference_graph.py --input_type image_tensor --pipeline_config_path training/xxx.config --trained_checkpoint_prefix RUNS/xxx/model.ckpt-xxx --output_directory RUNS/xxx/frozen
infer
python3 evaluate.py --inference_graph_dir=RUNS/xxx/frozen/ --print_thr 0.1 --test_dir test_images/ --out_dir results/xxx/ --id ""