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DeepLab2

Requirements

DeepLab2 depends on the following libraries:

Installation

Git Clone the Project

Clone the google-research/deeplab2 repository.

mkdir ${YOUR_PROJECT_NAME}
cd ${YOUR_PROJECT_NAME}
git clone https://github.com/google-research/deeplab2.git

Install TensorFlow via PIP

# Install tensorflow 2.6 as an example.
# This should come with compatible numpy package.
pip install tensorflow==2.6 keras==2.6

NOTE: You should find the right Tensorflow version according to your own configuration at https://www.tensorflow.org/install/source#tested_build_configurations. You also need to choose the right cuda version as listed on the page if you want to run with GPU.

Install Protobuf

Below is a quick-to-start command line to install protobuf in Linux:

sudo apt-get install protobuf-compiler

Alternatively, you can also download the package from web on other platforms. Please refer to https://github.com/protocolbuffers/protobuf for more details about installation.

Other Libraries

The remaining libraries can be installed via pip:

# Pillow
pip install pillow
# matplotlib
pip install matplotlib
# Cython
pip install cython

Install Orbit

Orbit is a flexible, lightweight library designed to make it easy to write custom training loops in TensorFlow 2. We used Orbit in our train/eval loops. You need to download the code below:

cd ${YOUR_PROJECT_NAME}
git clone https://github.com/tensorflow/models.git

Install pycocotools

We also use pycocotools for instance segmentation evaluation. Below is the installation guide:

cd ${YOUR_PROJECT_NAME}
git clone https://github.com/cocodataset/cocoapi.git

# Compile cocoapi
cd ${YOUR_PROJECT_NAME}/cocoapi/PythonAPI
make
cd ${YOUR_PROJECT_NAME}

Compilation

The following instructions are running from ${YOUR_PROJECT_NAME} directory:

cd ${YOUR_PROJECT_NAME}

Add Libraries to PYTHONPATH

When running locally, ${YOUR_PROJECT_NAME} directory should be appended to PYTHONPATH. This can be done by running the following command:

# From ${YOUR_PROJECT_NAME}:

# deeplab2
export PYTHONPATH=$PYTHONPATH:`pwd`
# orbit
export PYTHONPATH=$PYTHONPATH:${PATH_TO_MODELS}
# pycocotools
export PYTHONPATH=$PYTHONPATH:${PATH_TO_cocoapi_PythonAPI}

If you clone models(for Orbit) and cocoapi under ${YOUR_PROJECT_NAME}, here is an example:

export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/models:`pwd`/cocoapi/PythonAPI

Compile Protocol Buffers

In DeepLab2, we define protocol buffers to configure training and evaluation variants (see proto definition). However, protobuf needs to be compiled beforehand into a python recognizable format. To compile protobuf, run:

# `${PATH_TO_PROTOC}` is the directory where the `protoc` binary locates.
${PATH_TO_PROTOC} deeplab2/*.proto --python_out=.

# Alternatively, if protobuf compiler is globally accessible, you can simply run:
protoc deeplab2/*.proto --python_out=.

(Optional) Compile Custom Ops

We implemented efficient merging operation to merge semantic and instance maps for fast inference. You can follow the guide below to compile the provided efficient merging operation in c++ under the folder tensorflow_ops.

The script is mostly from Compile the op using your system compiler in the official tensorflow guide to create custom ops. Please refer to Create an op for more details.

TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
OP_NAME='deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op'

# CPU
g++ -std=c++14 -shared \
${OP_NAME}.cc ${OP_NAME}_kernel.cc -o ${OP_NAME}.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2

# GPU support (https://www.tensorflow.org/guide/create_op#compiling_the_kernel_for_the_gpu_device)
nvcc -std=c++14 -c -o ${OP_NAME}_kernel.cu.o ${OP_NAME}_kernel.cu.cc \
  ${TF_CFLAGS[@]} -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC --expt-relaxed-constexpr

g++ -std=c++14 -shared -o ${OP_NAME}.so ${OP_NAME}.cc ${OP_NAME}_kernel.cc \
  ${OP_NAME}_kernel.cu.o ${TF_CFLAGS[@]} -fPIC -lcudart ${TF_LFLAGS[@]}

To test if the compilation is done successfully, you can run:

python deeplab2/tensorflow_ops/python/kernel_tests/merge_semantic_and_instance_maps_op_test.py

Optionally, you could set merge_semantic_and_instance_with_tf_op to false in the config file to skip provided efficient merging operation and use the slower pure TF functions instead. See deeplab2/configs/cityscaspes/panoptic_deeplab/resnet50_os32_merge_with_pure_tf_func.textproto as an example.

Test the Configuration

You can test if you have successfully installed and configured DeepLab2 by running the following commands (requires compilation of custom ops):

# Model training test (test for custom ops, protobuf)
python deeplab2/model/deeplab_test.py

# Model evaluator test (test for other packages such as orbit, cocoapi, etc)
python deeplab2/trainer/evaluator_test.py

Quick All-in-One Script for Compilation (Linux Only)

We also provide a shell script to help you quickly compile and test everything mentioned above for Linux users:

# CPU
deeplab2/compile.sh

# GPU
deeplab2/compile.sh gpu

Troubleshooting

Q1: Can I use conda instead of pip?

A1: We experienced several dependency issues with the most recent conda package. We therefore do not provide support for installing deeplab2 via conda at this stage.

Q2: How can I specify a specific nvcc to use a specific gcc version?

A2: At the moment, tensorflow requires a gcc version < 9. If your default compiler has a higher version, the path to a different gcc needs to be set to compile the custom GPU op. Please check that either gcc-7 or gcc-8 are installed.

The compiler can then be set as follows:

# Assuming gcc-7 is installed in /usr/bin (can be verified by which gcc-7)

nvcc -std=c++14 -c -o ${OP_NAME}_kernel.cu.o ${OP_NAME}_kernel.cu.cc \
${TF_CFLAGS[@]} -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -ccbin=/usr/bin/g++-7 \
--expt-relaxed-constexpr

g++-7 -std=c++14 -shared -o ${OP_NAME}.so ${OP_NAME}.cc ${OP_NAME}_kernel.cc \
${OP_NAME}_kernel.cu.o ${TF_CFLAGS[@]} -fPIC -lcudart ${TF_LFLAGS[@]}

Q3: I got the following errors while compiling the efficient merging operation:

fatal error: third_party/gpus/cuda/include/cuda_fp16.h: No such file or directory

A3: It sounds like that CUDA headers are not linked. To resolve this issue, you need to tell tensorflow where to find the CUDA headers:

  1. Find the CUDA installation directory ${CUDA_DIR} which contains the include folder (For example, ~/CUDA/gpus/cuda_11_0).
  2. Go to the directory where tensorflow package is installed. (You can find it via pip show tensorflow.)
  3. Then cd to tensorflow/include/third_party/gpus/. (If it doesn't exist, create one.)
  4. Symlink your CUDA include directory here:
ln -s ${CUDA_DIR} ./cuda

There have been similar issues and solutions discussed here: tensorflow/tensorflow#31912 (comment)