Retinaface

This repo is build on top of https://github.com/biubug6/Pytorch_Retinaface

Differences

Train loop moved to Pytorch Lightning

IT added a set of functionality:

• Distributed training
• fp16
• Syncronized BatchNorm
• Support for various loggers like W&B or Neptune.ml

Hyperparameters are defined in the config file

Hyperparameters that were scattered across the code moved to the config at retinadace/config

Augmentations => Albumentations

Color that were manually implemented replaced by the Albumentations library.

Todo:

• Horizontal Flip is not implemented in Albumentations
• Spatial transforms like rotations or transpose are not implemented yet.

Color transforms defined in the config.

Added mAP calculation for validation

In order to track the progress, mAP metric is calculated on validation.

Installation

pip install -U retinaface_pytorch

Example inference

import cv2
from retinaface.pre_trained_models import get_model

image = <numpy array with shape (height, width, 3)>

model = get_model("resnet50_2020-07-20", max_size=2048)
model.eval()
annotation = model.predict_jsons(image)

• Jupyter notebook with the example:
• Jupyter notebook with the example on how to combine face detector with mask detector:

Data Preparation

The pipeline expects labels in the format:

[
  {
    "file_name": "0--Parade/0_Parade_marchingband_1_849.jpg",
    "annotations": [
      {
        "bbox": [
          449,
          330,
          571,
          720
        ],
        "landmarks": [
          [
            488.906,
            373.643
          ],
          [
            542.089,
            376.442
          ],
          [
            515.031,
            412.83
          ],
          [
            485.174,
            425.893
          ],
          [
            538.357,
            431.491
          ]
        ]
      }
    ]
  },

You can convert the default labels of the WiderFaces to the json of the propper format with this script.

Training

Install dependencies

pip install -r requirements.txt
pip install -r requirements_dev.txt

Define config

Example configs could be found at retinaface/configs

Define environmental variables

export TRAIN_IMAGE_PATH=<path to train images>
export VAL_IMAGE_PATH=<path to validation images>
export TRAIN_LABEL_PATH=<path to train annotations>
export VAL_LABEL_PATH=<path to validation annotations>

Run training script

python retinaface/train.py -h
usage: train.py [-h] -c CONFIG_PATH

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG_PATH, --config_path CONFIG_PATH
                        Path to the config.

Inference

python retinaface/inference.py -h
usage: inference.py [-h] -i INPUT_PATH -c CONFIG_PATH -o OUTPUT_PATH [-v]
                    [-g NUM_GPUS] [-m MAX_SIZE] [-b BATCH_SIZE]
                    [-j NUM_WORKERS]
                    [--confidence_threshold CONFIDENCE_THRESHOLD]
                    [--nms_threshold NMS_THRESHOLD] -w WEIGHT_PATH
                    [--keep_top_k KEEP_TOP_K] [--world_size WORLD_SIZE]
                    [--local_rank LOCAL_RANK] [--fp16]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT_PATH, --input_path INPUT_PATH
                        Path with images.
  -c CONFIG_PATH, --config_path CONFIG_PATH
                        Path to config.
  -o OUTPUT_PATH, --output_path OUTPUT_PATH
                        Path to save jsons.
  -v, --visualize       Visualize predictions
  -g NUM_GPUS, --num_gpus NUM_GPUS
                        The number of GPUs to use.
  -m MAX_SIZE, --max_size MAX_SIZE
                        Resize the largest side to this number
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        batch_size
  -j NUM_WORKERS, --num_workers NUM_WORKERS
                        num_workers
  --confidence_threshold CONFIDENCE_THRESHOLD
                        confidence_threshold
  --nms_threshold NMS_THRESHOLD
                        nms_threshold
  -w WEIGHT_PATH, --weight_path WEIGHT_PATH
                        Path to weights.
  --keep_top_k KEEP_TOP_K
                        keep_top_k
  --world_size WORLD_SIZE
                        number of nodes for distributed training
  --local_rank LOCAL_RANK
                        node rank for distributed training
  --fp16                Use fp6

python -m torch.distributed.launch --nproc_per_node=<num_gpus> retinaface/inference.py <parameters>

• Weights for the model with config.
• Weights for the model with config.

Web app

https://retinaface.herokuapp.com/

Code for the web app: https://github.com/ternaus/retinaface_demo

Converting to ONNX

The inference could be sped up on CPU by converting the model to ONNX.

Ex: python -m converters.to_onnx -m 1280 -o retinaface1280.onnx