From 7cd16e7f59f07042eb51e90171fb53d9cdcd7ce5 Mon Sep 17 00:00:00 2001
From: innolab <123.hadikoubeissy@gmail.com>
Date: Thu, 2 Jul 2020 11:46:13 +0200
Subject: [PATCH 01/20] Adding support for the state-of-the-art Yolov4 darknet
training with cross compatibility for Yolov3 training. boosting performance
for CPU version by enabling multi-core CPU training. boosting performance for
GPU version by enabling Cudnn-Half for GPUs that support this feature.
---
README.md | 38 +-
.../darknet/yolo_default_cfgs/yolov3-tiny.cfg | 1 +
config/darknet/yolo_default_cfgs/yolov3.cfg | 1 +
.../yolo_default_weights/download_weights.sh | 6 +-
config/darknet/yolov4_default_cfgs/yolov4.cfg | 1161 +++++++++++++++++
.../darknet/yolov4_default_weights/README.md | 3 +
.../download_weights.sh | 11 +
config/train_config.json.template | 14 +-
config/train_config_schema.json | 66 +-
docker/Dockerfile | 9 +-
run_docker_linux_cpu.sh | 6 +-
run_docker_linux_gpu.sh | 6 +-
sample_dataset/train_config.json | 22 +-
scripts/install_dependencies.sh | 0
src/api.py | 2 +-
src/factory.py | 63 +-
src/main.py | 6 +-
src/model2config.json | 14 +-
src/train_darknet.py | 6 +-
src/train_darknetv4.py | 412 ++++++
src/train_utils.py | 83 ++
21 files changed, 1883 insertions(+), 47 deletions(-)
create mode 100644 config/darknet/yolov4_default_cfgs/yolov4.cfg
create mode 100644 config/darknet/yolov4_default_weights/README.md
create mode 100755 config/darknet/yolov4_default_weights/download_weights.sh
mode change 100644 => 100755 run_docker_linux_cpu.sh
mode change 100644 => 100755 run_docker_linux_gpu.sh
mode change 100644 => 100755 scripts/install_dependencies.sh
create mode 100644 src/train_darknetv4.py
diff --git a/README.md b/README.md
index e0fbe44..34dbb33 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
-# YOLOv3 Training Automation API for Linux
-
-This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. Training with YOLOv3 has never been so easy.
+# YOLOv4 Training Automation API for Linux
+This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. Training with YOLOv4 has never been so easy.
+This repository has also cross compatibility with Yolov3 training
## Prerequisites
@@ -22,7 +22,7 @@ chmod +x scripts/install_docker.sh && source scripts/install_docker.sh
Prepare the docker image with all weights for GPU usage
```bash
-sudo docker build -f docker/Dockerfile -t darknet_yolo_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 --build-arg DOWNLOAD_ALL=1 .
+sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 --build-arg DOWNLOAD_ALL=1 .
```
@@ -38,20 +38,31 @@ The environment is dockerized to run on GPU or CPU.
For GPU, you need to build the image in the following way:
```bash
-sudo docker build -f docker/Dockerfile -t darknet_yolo_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 .
+sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 .
+```
+
+If you have a GPU: Volta, Xavier, Turing and higher
+
+```bash
+sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=1 --build-arg OPENCV=1 .
```
For CPU only, you can run the same command while setting GPU=0 CUDNN=0 and naming it darknet_yolo_cpu:1 for clarity.
```bash
-sudo docker build -f docker/Dockerfile -t darknet_yolo_cpu:1 --build-arg GPU=0 --build-arg CUDNN=0 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 .
+sudo docker build -f docker/Dockerfile -t darknet_yolov4_cpu:1 --build-arg GPU=0 --build-arg CUDNN=0 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 .
```
+If you want multi-core cpu training you can add OPENMP=1 to the build
+```bash
+sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=0 --build-arg CUDNN=0 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 --build-arg OPENMP=1 .
+```
## Preparing your dataset
We provided a `sample_dataset` to show how your data should be structured in order to start the training seemlesly.
The `train_config.json` file found in `sample_dataset` is a copy of the template `config/train_config.json.template` with needed modifications. The template can as well be copied as is while making sure to remove the '.template' from the name.
-You can also provide your own `train.txt` and `test.txt` to specify which images will be used for training and which ones are for testing. If not provided, the dataset will be split according to the `data/train_ratio` (by default 80% train 20% test)
+You can also provide your own `train.txt` and `test.txt` to specify which images will be used for training and which ones are for testing. If not provided, the dataset will be split according to the `data/train_ratio` (by default 80% train 20% test).
+If you are using **Yolov4** training please make sure to choose your `yolov4` instead of **yolov3** in `train_config.json` `model/model-name` **Yolov4** specific hyperparams ("mosaic","blur")
## Starting the training
@@ -174,7 +185,7 @@ Change your current working directory to be inside the repo. The following steps
yolov3.weights
```bash
-wget https://pjreddie.com/media/files/yolov3.weights -P config/darknet/yolo_default_weights
+wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov3.weights -P config/darknet/yolo_default_weights
```
yolov3-tiny.weights
@@ -189,6 +200,13 @@ darknet53.conv.74
wget https://pjreddie.com/media/files/darknet53.conv.74 -P config/darknet/yolo_default_weights
```
+ yolov4.weights
+
+```bash
+wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights -P config/darknet/yolov4_default_weights
+```
+
+
## Known Issues
@@ -198,6 +216,8 @@ Issue related to darknet itself can be filed in [the correct repo](https://githu
- If during training you see nan values for avg (loss) field - then training goes wrong, but if nan is in some other lines - then training goes well.
- If error Out of memory occurs then you should try increasing subdivisions to 16, 32 or 64 or have a smaller image size.
- If training finishes immediately without any error you should decrease batch size and subdivisions.
+- if training on multiple GPU produce nan try decreasing the learning rate to 0,00065
+
## Acknowledgements
@@ -208,3 +228,5 @@ Issue related to darknet itself can be filed in [the correct repo](https://githu
Lynn Nassif, Beirut, Lebanon
Nour Azzi, Beirut, Lebanon
+
+Hadi Koubeissy , Beirut, Lebanon
\ No newline at end of file
diff --git a/config/darknet/yolo_default_cfgs/yolov3-tiny.cfg b/config/darknet/yolo_default_cfgs/yolov3-tiny.cfg
index ae652be..1dff650 100644
--- a/config/darknet/yolo_default_cfgs/yolov3-tiny.cfg
+++ b/config/darknet/yolo_default_cfgs/yolov3-tiny.cfg
@@ -15,6 +15,7 @@ saturation = 1.5
exposure = 1.5
hue=.1
+letter_box=1
learning_rate=0.001
burn_in=1000
max_batches = 500200
diff --git a/config/darknet/yolo_default_cfgs/yolov3.cfg b/config/darknet/yolo_default_cfgs/yolov3.cfg
index 3793564..2537954 100644
--- a/config/darknet/yolo_default_cfgs/yolov3.cfg
+++ b/config/darknet/yolo_default_cfgs/yolov3.cfg
@@ -15,6 +15,7 @@ saturation=1.5
exposure=1.5
hue=.1
+letter_box=1
learning_rate=0.001
burn_in=1000
max_batches=500200
diff --git a/config/darknet/yolo_default_weights/download_weights.sh b/config/darknet/yolo_default_weights/download_weights.sh
index d85dbc4..12d015e 100644
--- a/config/darknet/yolo_default_weights/download_weights.sh
+++ b/config/darknet/yolo_default_weights/download_weights.sh
@@ -6,11 +6,11 @@ else
if test $1 -eq 1; then
echo "Downloading Weights"
echo "Downloading yolov3.weights"
- wget https://pjreddie.com/media/files/yolov3.weights -P $2
+ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov3.weights -P $2 -4
echo "Downloading yolov3-tiny.weights"
- wget https://pjreddie.com/media/files/yolov3-tiny.weights -P $2
+ wget https://pjreddie.com/media/files/yolov3-tiny.weights -P $2 -4
echo "Downloading darknet53.conv.74"
- wget https://pjreddie.com/media/files/darknet53.conv.74 -P $2
+ wget https://pjreddie.com/media/files/darknet53.conv.74 -P $2 -4
echo "All weights downloaded!"
fi
fi
diff --git a/config/darknet/yolov4_default_cfgs/yolov4.cfg b/config/darknet/yolov4_default_cfgs/yolov4.cfg
new file mode 100644
index 0000000..5fb2714
--- /dev/null
+++ b/config/darknet/yolov4_default_cfgs/yolov4.cfg
@@ -0,0 +1,1161 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=16
+width=608
+height=608
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500500
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+
+mosaic=0
+blur=1
+
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+stopbackward=800
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
diff --git a/config/darknet/yolov4_default_weights/README.md b/config/darknet/yolov4_default_weights/README.md
new file mode 100644
index 0000000..6584426
--- /dev/null
+++ b/config/darknet/yolov4_default_weights/README.md
@@ -0,0 +1,3 @@
+# Important
+
+This folder should include yolov4.weights which can be downloaded from [darknet repository](https://github.com/AlexeyAB/darknet). Please follow the repo README
\ No newline at end of file
diff --git a/config/darknet/yolov4_default_weights/download_weights.sh b/config/darknet/yolov4_default_weights/download_weights.sh
new file mode 100755
index 0000000..b314a7f
--- /dev/null
+++ b/config/darknet/yolov4_default_weights/download_weights.sh
@@ -0,0 +1,11 @@
+#!/bin/bash -x
+
+if test $1 -eq 0; then
+ echo "Weights of yolov4 will not be downloaded"
+else
+ if test $1 -eq 1; then
+ echo "Downloading Weights"
+ echo "Downloading yolov4.weights"
+ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights -4 -P $2
+ fi
+fi
diff --git a/config/train_config.json.template b/config/train_config.json.template
index f86015d..0861f2c 100644
--- a/config/train_config.json.template
+++ b/config/train_config.json.template
@@ -9,12 +9,12 @@
},
"model": {
"framework": "darknet",
- "model_name": "yolov3",
+ "model_name": "yolov4",
"custom_weights": {
"enable": false,
"name": "darknet53.conv.74"
},
- "generate_custom_anchors": true,
+ "generate_custom_anchors": false,
"batch_size": 2,
"max_batches": 120000,
"subdivisions": 1,
@@ -23,7 +23,15 @@
"angle": 0,
"saturation": 1.5,
"exposure": 1.5,
- "hue": 0.1
+ "hue": 0.1,
+ "yolov3_config": {
+ "learning_rate": 0.001
+ },
+ "yolov4_config":
+ { "learning_rate": 0.0013,
+ "mosaic": false,
+ "blur": false
+ }
},
"training": {
"gpus": [
diff --git a/config/train_config_schema.json b/config/train_config_schema.json
index 4445a1b..bcf8943 100644
--- a/config/train_config_schema.json
+++ b/config/train_config_schema.json
@@ -87,7 +87,8 @@
"examples": [
"yolov3",
"yolov3-tiny",
- "darknet53.conv.74"
+ "darknet53.conv.74",
+ "yolov4"
],
"pattern": "^(.*)$"
},
@@ -211,7 +212,68 @@
"examples": [
0.1
]
- }
+ },
+ "yolov3_config":
+ {"$id":"#/properties/model/properties/yolov3_config",
+ "type":"object",
+ "title": "yolov3 learning rate ",
+ "required": [
+ "learning_rate"
+ ],
+ "properties": {
+ "learning_rate": {
+ "$id": "#/properties/model/properties/yolov3_config/properties/learning_rate",
+ "type": "number",
+ "title": "The learning rate for yolov3 training ",
+ "default": 0.001,
+ "examples": [
+ 0.0013
+ ]
+ }
+ }
+
+ },
+ "yolov4_config":
+ {"$id":"#/properties/model/properties/yolov4_config",
+ "type":"object",
+ "title": "The yolov4 special configuration regarding data augmentation feature. those configuration will have no effect when using yolov3 training ",
+ "required": [
+ "learning_rate",
+ "mosaic",
+ "blur"
+ ],
+ "properties": {
+ "learning_rate": {
+ "$id": "#/properties/model/properties/yolov4_config/properties/learning_rate",
+ "type": "number",
+ "title": "The learning rate for yolov4",
+ "default": 0.0013,
+ "examples": [
+ 0.00261,
+ 0.001
+ ]
+ },
+ "mosaic": {
+ "$id": "#/properties/model/properties/yolov4_config/properties/mosaic",
+ "type": "boolean",
+ "title": "Enable mosaic augmentation technique used in yolov4 during training that will group multiple images into 1 image",
+ "default": false,
+ "examples": [
+ true
+ ]
+ },
+ "blur": {
+ "$id": "#/properties/model/properties/yolov4_config/properties/blur",
+ "type": "boolean",
+ "title": "Enable blur augmentation technique used in yolov4 during training that will blur images ",
+ "default": false,
+ "examples": [
+ true
+ ]
+ }
+ }
+
+ }
}
},
"training": {
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 123242c..390e5a2 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -8,7 +8,7 @@ ARG CUDNN=1
ARG CUDNN_HALF=0
ARG OPENCV=1
ARG DOWNLOAD_ALL=0
-
+ARG OPENMP=0
# To save you a headache
ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8
@@ -19,7 +19,7 @@ RUN apt-get update && \
apt-get upgrade -y && \
apt-get install -y clang-format wget apt-utils build-essential\
checkinstall cmake pkg-config yasm git vim curl\
- autoconf automake libtool libopencv-dev
+ autoconf automake libtool libopencv-dev build-essential
# Install python-dev and pip
@@ -36,7 +36,7 @@ WORKDIR /training
RUN git clone https://github.com/AlexeyAB/darknet.git
WORKDIR darknet
# Checkout the last working version in our case
-RUN git checkout 5a6afe96d3aa8aed19405577db7dba0ff173c848
+RUN git checkout tags/darknet_yolo_v4_pre
# Modify Makefile according to needed env variables
RUN \
@@ -48,6 +48,7 @@ RUN \
sed -i "s/CUDNN=0/CUDNN=${CUDNN}/" Makefile && \
sed -i "s/CUDNN_HALF=0/CUDNN_HALF=${CUDNN_HALF}/" Makefile && \
sed -i "s/OPENCV=0/OPENCV=${OPENCV}/" Makefile && \
+ sed -i "s/OPENMP=0/OPENMP=${OPENMP}/" Makefile && \
make
WORKDIR /training
@@ -58,6 +59,6 @@ COPY ./config ./config
# If ARG DOWNLOAD_ALL = 1, download 3 weights into config directly
RUN chmod u+x ./config/darknet/yolo_default_weights/download_weights.sh && ./config/darknet/yolo_default_weights/download_weights.sh ${DOWNLOAD_ALL} "/training/config/darknet/yolo_default_weights"
-
+RUN chmod u+x ./config/darknet/yolov4_default_weights/download_weights.sh && ./config/darknet/yolov4_default_weights/download_weights.sh ${DOWNLOAD_ALL} "/training/config/darknet/yolov4_default_weights"
# Default command
CMD ["python3", "main.py"]
diff --git a/run_docker_linux_cpu.sh b/run_docker_linux_cpu.sh
old mode 100644
new mode 100755
index a7a5f5a..1a38e23
--- a/run_docker_linux_cpu.sh
+++ b/run_docker_linux_cpu.sh
@@ -13,14 +13,14 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:6006"
+ ports="$ports -p $tensorboard_port:$tensorboard_port"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
web_ui_port=`jq .training.web_ui.port $configfile`
- ports="$ports -p $web_ui_port:8090"
+ ports="$ports -p $web_ui_port:$web_ui_port"
fi
- sudo docker run --rm --runtime=nvidia -it -e TRAIN_NAME=$container_name -e TRAIN_START_TIME="$(date '+%Y%m%d_%H:%M:%S')" $ports -v $folder_path:/training/assets -v $(pwd)/trainings:/training/custom_training -v /etc/timezone:/etc/timezone:ro -v /etc/localtime:/etc/localtime:ro --name $container_name darknet_yolo_cpu:1 ;
+ sudo docker run --rm --runtime=nvidia -it -e TRAIN_NAME=$container_name -e TRAIN_START_TIME="$(date '+%Y%m%d_%H:%M:%S')" $ports -v $folder_path:/training/assets -v $(pwd)/trainings:/training/custom_training -v /etc/timezone:/etc/timezone:ro -v /etc/localtime:/etc/localtime:ro --name $container_name darknet_yolov4_cpu:1 ;
else
echo "Error: Configuration file not found in the provided path"
fi
diff --git a/run_docker_linux_gpu.sh b/run_docker_linux_gpu.sh
old mode 100644
new mode 100755
index 30134f8..5871117
--- a/run_docker_linux_gpu.sh
+++ b/run_docker_linux_gpu.sh
@@ -13,14 +13,14 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:6006"
+ ports="$ports -p $tensorboard_port:$tensorboard_port"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
web_ui_port=`jq .training.web_ui.port $configfile`
- ports="$ports -p $web_ui_port:8090"
+ ports="$ports -p $web_ui_port:$web_ui_port"
fi
- sudo docker run --rm --runtime=nvidia -it -e TRAIN_NAME=$container_name -e TRAIN_START_TIME="$(date '+%Y%m%d_%H:%M:%S')" $ports -v $folder_path:/training/assets -v $(pwd)/trainings:/training/custom_training -v /etc/timezone:/etc/timezone:ro -v /etc/localtime:/etc/localtime:ro --name $container_name darknet_yolo_gpu:1 ;
+ sudo docker run --rm --runtime=nvidia -it -e TRAIN_NAME=$container_name -e TRAIN_START_TIME="$(date '+%Y%m%d_%H:%M:%S')" $ports -v $folder_path:/training/assets -v $(pwd)/trainings:/training/custom_training -v /etc/timezone:/etc/timezone:ro -v /etc/localtime:/etc/localtime:ro --name $container_name darknet_yolov4_gpu:1 ;
else
echo "Error: Configuration file not found in the provided path"
fi
diff --git a/sample_dataset/train_config.json b/sample_dataset/train_config.json
index 9c17894..cd450a4 100644
--- a/sample_dataset/train_config.json
+++ b/sample_dataset/train_config.json
@@ -9,21 +9,29 @@
},
"model": {
"framework": "darknet",
- "model_name": "yolov3",
+ "model_name": "yolov4",
"custom_weights": {
"enable": false,
"name": "darknet53.conv.74"
},
- "generate_custom_anchors": true,
- "batch_size": 2,
- "max_batches": 120000,
- "subdivisions": 1,
+ "generate_custom_anchors": false,
+ "batch_size": 64,
+ "max_batches": 10000,
+ "subdivisions": 32,
"train_image_width": 416,
"train_image_height": 416,
"angle": 0,
"saturation": 1.5,
"exposure": 1.5,
- "hue": 0.1
+ "hue": 0.1,
+ "yolov3_config": {
+ "learning_rate": 0.001
+ },
+ "yolov4_config":
+ { "learning_rate": 0.001,
+ "mosaic": true,
+ "blur": true
+ }
},
"training": {
"gpus": [
@@ -32,7 +40,7 @@
"calculate_map": true,
"custom_api": {
"enable": true,
- "port": 8000
+ "port": 8099
},
"dashboard": {
"enable": true,
diff --git a/scripts/install_dependencies.sh b/scripts/install_dependencies.sh
old mode 100644
new mode 100755
diff --git a/src/api.py b/src/api.py
index 56623e0..5d36fdd 100644
--- a/src/api.py
+++ b/src/api.py
@@ -20,7 +20,7 @@
app: FastAPI = FastAPI(
title="BMW InnovationLab YOLO Training Automation",
- description='API for Monitoring YOLO Training
Contact the developers:
Nour Azzi: https://github.com/nourazzii
Lynn Nassif:
BMW Innovation Lab: innovation-lab@bmw.de',
+ description='API for Monitoring YOLO Training
Contact the developers:
Nour Azzi: https://github.com/nourazzii
Lynn Nassif:
Hadi Koubeissy: https://github.com/hadikoub
BMW Innovation Lab: innovation-lab@bmw.de'',
docs_url="/",
version="3.1.0",
)
diff --git a/src/factory.py b/src/factory.py
index d9f9734..6ff49c5 100644
--- a/src/factory.py
+++ b/src/factory.py
@@ -1,5 +1,6 @@
from pathlib import Path
from train_darknet import *
+from train_darknetv4 import *
from exception_utils import *
@@ -25,6 +26,15 @@ def init_coach_from_config(self, train_config_path: str) -> Coach:
image_width: int = train_config.get("model").get("train_image_width", 416)
image_height: int = train_config.get("model").get("train_image_height", 416)
max_batches: int = train_config.get("model").get("max_batches", None)
+ learning_rate_yolov3 : float = train_config.get("model").get("yolov3_config").get("learning_rate", 0.001)
+
+
+ # modification by hadi to get yolov4 specific variable of data augmentation
+ learning_rate_yolov4 : float = train_config.get("model").get("yolov4_config").get("learning_rate",0.0013)
+ mosaic: bool = train_config.get("model").get("yolov4_config").get("mosaic", False)
+ blur: bool = train_config.get("model").get("yolov4_config").get("blur", False)
+ # end of modification
+
gpus: list = train_config.get("training").get("gpus", None)
custom_api_el = train_config.get("training").get(
"custom_api", {"enable": False, "port": 8000}
@@ -59,9 +69,9 @@ def init_coach_from_config(self, train_config_path: str) -> Coach:
if not max_batches:
max_batches = 2000 * len(classes)
- if str(framework).lower() == "darknet":
+ if str(framework).lower() == "darknet"and str(model_name).lower() == "yolov3":
generate_custom_anchors: bool = train_config.get("model").get(
- "generate_custom_anchors", True
+ "generate_custom_anchors", False
)
angle: int = train_config.get("model").get("angle", 0)
saturation: float = train_config.get("model").get("saturation", 1.5)
@@ -103,7 +113,56 @@ def init_coach_from_config(self, train_config_path: str) -> Coach:
web_ui_port=web_ui_port,
custom_weights=custom_weights,
custom_weights_name=custom_weights_name,
+ learning_rate_yolov3 = learning_rate_yolov3
+ )
+ elif str(framework).lower() == "darknet" and str(model_name).lower() == "yolov4":
+ generate_custom_anchors: bool = train_config.get("model").get(
+ "generate_custom_anchors", False
+ )
+ angle: int = train_config.get("model").get("angle", 0)
+ saturation: float = train_config.get("model").get("saturation", 1.5)
+ exposure: float = train_config.get("model").get("exposure", 1.5)
+ hue: float = train_config.get("model").get("hue", 0.1)
+ calculate_map: bool = train_config.get("training").get(
+ "calculate_map", True
+ )
+ web_ui_el = train_config.get("training").get(
+ "web_ui", {"enable": False, "port": 8090}
)
+ web_ui: bool = web_ui_el["enable"]
+ web_ui_port: int = web_ui_el["port"]
+
+ return DarknetCoachV4(
+ model_name=model_name,
+ name=name,
+ train_ratio=train_ratio,
+ classes=classes,
+ generate_custom_anchors=generate_custom_anchors,
+ batch_size=batch_size,
+ max_batches=max_batches,
+ subdivisions=subdivisions,
+ image_width=image_width,
+ image_height=image_height,
+ angle=angle,
+ saturation=saturation,
+ exposure=exposure,
+ hue=hue,
+ gpus=gpus,
+ calculate_map=calculate_map,
+ custom_api=custom_api,
+ custom_api_port=custom_api_port,
+ dashboard=dashboard,
+ dashboard_port=dashboard_port,
+ tensorboard=tensorboard,
+ tensorboard_port=tensorboard_port,
+ web_ui=web_ui,
+ web_ui_port=web_ui_port,
+ custom_weights=custom_weights,
+ custom_weights_name=custom_weights_name,
+ learning_rate_yolov4=learning_rate_yolov4,
+ mosaic=mosaic,
+ blur=blur,
+ )
else:
raise ConfigError(framework, "No such available framework")
diff --git a/src/main.py b/src/main.py
index 1f79062..163c13d 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,8 +1,6 @@
import factory
from train import Coach
-
+import sys
if __name__ == "__main__":
- coach: Coach = factory.CoachFactory().init_coach_from_config(
- "assets/train_config.json"
- )
+ coach: Coach = factory.CoachFactory().init_coach_from_config("assets/train_config.json")
coach.train()
diff --git a/src/model2config.json b/src/model2config.json
index 94d4114..215d09e 100644
--- a/src/model2config.json
+++ b/src/model2config.json
@@ -2,12 +2,16 @@
"darknet":
{
"yolov3": {
- "weights": "../training/config/darknet/yolo_default_weights/yolov3.weights",
- "config": "../training/config/darknet/yolo_default_cfgs/yolov3.cfg"
+ "weights": "./config/darknet/yolo_default_weights/yolov3.weights",
+ "config": "./config/darknet/yolo_default_cfgs/yolov3.cfg"
},
"yolov3-tiny": {
- "weights": "../training/config/darknet/yolo_default_weights/yolov3-tiny.weights",
- "config": "../training/config/darknet/yolo_default_cfgs/yolov3-tiny.cfg"
- }
+ "weights": "./config/darknet/yolo_default_weights/yolov3-tiny.weights",
+ "config": "./config/darknet/yolo_default_cfgs/yolov3-tiny.cfg"
+ },
+ "yolov4": {
+ "weights": "./config/darknet/yolov4_default_weights/yolov4.weights",
+ "config": "./config/darknet/yolov4_default_cfgs/yolov4.cfg"
+ }
}
}
diff --git a/src/train_darknet.py b/src/train_darknet.py
index e598441..0062180 100644
--- a/src/train_darknet.py
+++ b/src/train_darknet.py
@@ -26,6 +26,7 @@ def __init__(
hue: float = 0.1,
exposure: float = 1.5,
saturation: float = 1.5,
+ learning_rate_yolov3:float = 0.001,
calculate_map: bool = True,
web_ui: bool = False,
web_ui_port: int = 8090,
@@ -44,6 +45,7 @@ def __init__(
self._exposure: float = exposure
self._hue: float = hue
self._calculate_map: bool = calculate_map
+ self._learning_rate_yolov3 :float= learning_rate_yolov3
self._web_ui: bool = web_ui
self._web_ui_port: int = web_ui_port
@@ -116,6 +118,7 @@ def create_output_files(self):
self._logger.info(
"Copying {} file to needed location".format(weights_path.stem)
)
+
copy_weights_file(
source=weights_path,
destination=self._custom_weights_path,
@@ -283,6 +286,7 @@ def update_config(self):
saturation=self._saturation,
exposure=self._exposure,
hue=self._hue,
+ learning_rate_yolov3 =self._learning_rate_yolov3,
custom_anchors=self._custom_anchors,
)
)
@@ -302,7 +306,6 @@ def start_training(self):
"-clear",
"1",
]
-
if self._gpus:
arg: str = ",".join(str(gpu) for gpu in self._gpus)
command.append("-gpus")
@@ -344,7 +347,6 @@ def start_training(self):
subprocess.Popen(
["tensorboard", "--logdir", "runs"], stderr=DEVNULL, stdout=DEVNULL
)
-
# Create a rotating log
yolo_training_logger: logging.Logger = logging.getLogger("Rotating Log")
yolo_training_logger.setLevel(logging.INFO)
diff --git a/src/train_darknetv4.py b/src/train_darknetv4.py
new file mode 100644
index 0000000..f46a11a
--- /dev/null
+++ b/src/train_darknetv4.py
@@ -0,0 +1,412 @@
+import os
+import json
+import socket
+import shutil
+import logging
+import subprocess
+
+from train import Coach
+from pathlib import Path
+from yolo_utils import *
+from train_utils import *
+from PIL import Image, JpegImagePlugin
+from logging.handlers import RotatingFileHandler
+
+
+class DarknetCoachV4(Coach):
+
+ # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+ def __init__(self, model_name: str = "yolov4", generate_custom_anchors: bool = True, angle: int = 0,
+ hue: float = 0.1, exposure: float = 1.5, saturation: float = 1.5, calculate_map: bool = True,
+ web_ui: bool = False, web_ui_port: int = 8090, learning_rate_yolov3: float = 0.001,
+ mosaic: bool = False,
+ blur: bool = False, learning_rate_yolov4: float = 0.00261, *args,
+ **kwargs) -> None:
+ print("using yolo v4 version")
+ super().__init__(model_name=model_name, *args, **kwargs)
+ self._images_path: Path = self._working_dir / "assets/images"
+ self._labels_path: Path = self._working_dir / "assets/labels/yolo"
+ self._train_txt_path: Path = self._working_dir / "assets/train.txt"
+ self._test_txt_path: Path = self._working_dir / "assets/test.txt"
+ self._model2config_path: Path = self._working_dir / "model2config.json"
+ self._generate_custom_anchors: bool = generate_custom_anchors
+ self._angle: int = angle
+ self._saturation: float = saturation
+ self._exposure: float = exposure
+ self._hue: float = hue
+ self._calculate_map: bool = calculate_map
+ self._web_ui: bool = web_ui
+ self._web_ui_port: int = web_ui_port
+ self._learning_rate_yolov3: float = learning_rate_yolov3
+ self._learning_rate_yolov4: float = learning_rate_yolov4
+ self._mosaic: bool = mosaic
+ self._blur: bool = blur
+
+ # -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+ # TODO is_data_valid() return true false
+ def check_data(self):
+ self._logger.info("Checking if all data is valid")
+ weights_path: Path = None
+ if self._custom_weights:
+ weights_path = self._working_dir / "assets" / self._custom_weights_name
+
+ data_checkup(
+ framework="darknet",
+ model2config_path=self._model2config_path,
+ classes=self._classes,
+ images_path=self._images_path,
+ labels_path=self._labels_path,
+ model_name=self._model_name,
+ weights_path=weights_path,
+ extension=".txt",
+ )
+
+ # TODO parameters to where to create the files and how
+ # return something to know about failure or success
+ def create_output_files(self):
+ # Symlink images and labels folders to same 'dataset' folder
+ # since darknet requires them to be in the same directory
+ dataset_path: Path = self._working_dir / "dataset"
+ self._dataset_path: Path = Path(dataset_path)
+ if dataset_path.exists():
+ shutil.rmtree(dataset_path)
+
+ Path.mkdir(dataset_path)
+ self._logger.info("Loading all images")
+ for _ in tqdm(range(1)):
+ command = 'for f in {}; do ln -sf "$f" {}; done'.format(
+ str(self._images_path / "*"), str(dataset_path)
+ )
+ os.system(command)
+ self._logger.info("Loading all labels")
+ for _ in tqdm(range(1)):
+ command = 'for f in {}; do ln -sf "$f" {}; done'.format(
+ str(self._labels_path / "*"), str(dataset_path)
+ )
+ os.system(command)
+
+ # Create custom_folder ex: hello00_20190822:15:26:20
+ self._logger.info("Creating your training folder")
+
+ _custom_training_folder_path: Path = self._custom_training_dir / Path(
+ os.getenv('TRAIN_NAME') + "_" + os.getenv("TRAIN_START_TIME"))
+ if (os.path.exists(_custom_training_folder_path)):
+ shutil.rmtree(_custom_training_folder_path)
+ self._custom_training_folder_path: Path = _custom_training_folder_path
+ Path.mkdir(_custom_training_folder_path)
+
+ # Create config and weights folders
+ self._logger.info("Creating needed folders: weights and config")
+ self._custom_config_path: Path = self._custom_training_folder_path / "config"
+ self._custom_weights_path: Path = self._custom_training_folder_path / "weights"
+
+ Path.mkdir(self._custom_config_path)
+ Path.mkdir(self._custom_weights_path)
+
+ with open(self._model2config_path, "r", encoding="utf-8") as convertionReader:
+ self._model2config: dict = json.load(convertionReader)
+
+ if self._custom_weights:
+ weights_path: str = self._working_dir / "assets" / self._custom_weights_name
+ else:
+ weights_path: str = Path(
+ self._model2config.get("darknet").get(self._model_name).get("weights")
+ )
+
+ self._logger.info(
+ "Copying {} file to needed location".format(weights_path.stem)
+ )
+ copy_weights_file(
+ source=weights_path,
+ destination=self._custom_weights_path,
+ weights_name="initial.weights",
+ )
+
+ def split_train_test(self):
+ destination_train_txt_path: Path = self._custom_training_folder_path / "train.txt"
+ destination_test_txt_path: Path = self._custom_training_folder_path / "test.txt"
+ if self._train_txt_path.exists():
+ self._logger.info(
+ "Updating paths in train.txt and copying it to needed location"
+ )
+ with open(self._train_txt_path, "r", encoding="utf-8") as train_txt_file:
+ train_txt: str = "".join(train_txt_file.readlines())
+
+ with open(
+ destination_train_txt_path, "w", encoding="utf-8"
+ ) as train_txt_file:
+ train_txt_file.write(
+ update_label_file(
+ file_txt=train_txt, dataset_path=self._dataset_path,
+ )
+ )
+
+ if self._test_txt_path.exists():
+ self._logger.info(
+ "Updating paths in test.txt and copying it to needed location"
+ )
+ with open(self._test_txt_path, "r", encoding="utf-8") as test_txt_file:
+ test_txt: str = "".join(test_txt_file.readlines())
+
+ with open(
+ destination_test_txt_path, "w", encoding="utf-8"
+ ) as test_txt_file:
+ test_txt_file.write(
+ update_label_file(
+ file_txt=test_txt, dataset_path=self._dataset_path,
+ )
+ )
+
+ test_txt_path: Path = destination_test_txt_path
+
+ else:
+ test_txt_path: Path = None
+
+ else:
+ self._logger.info("train.txt and test.txt not found")
+ self._logger.info("Creating train.txt and test.txt in needed location")
+ train_images_list, test_images_list = split_train_test(
+ train_ratio=self._train_ratio,
+ classes_count=len(self._classes),
+ images_path=self._dataset_path,
+ labels_path=self._dataset_path,
+ extension=".txt",
+ )
+
+ with open(
+ self._custom_training_folder_path / "train.txt", "w+"
+ ) as train_file:
+ train_file.writelines("\n".join(train_images_list))
+
+ with open(
+ self._custom_training_folder_path / "test.txt", "w+"
+ ) as test_file:
+ test_file.writelines("\n".join(test_images_list))
+
+ test_txt_path: Path = destination_test_txt_path
+
+ self._train_txt_path = destination_train_txt_path
+ self._test_txt_path = test_txt_path
+
+ prediction_image: str = None
+ if test_txt_path:
+ self._logger.info("Choosing an image from test.txt for inference")
+ with open(test_txt_path, "r") as test_txt_file:
+ test_images_list: list = test_txt_file.readlines()
+ if test_images_list:
+ prediction_image = test_images_list[0].rstrip()
+ self._prediction_image_extension: str = Path(
+ prediction_image
+ ).suffix
+ draw_bb(
+ image=prediction_image,
+ classes=self._classes,
+ destination=self._working_dir / "ground_truth.jpg",
+ )
+
+ self._prediction_image: str = prediction_image
+
+ # Create obj.names and obj.data
+ def create_training_files(self):
+ self._logger.info("Creating .data file")
+ dot_data: str = "{}.data".format(self._name)
+ self._logger.info("Creating .names file")
+ dot_names: str = "{}.names".format(self._name)
+ _custom_dot_data_path: Path = self._custom_config_path / dot_data
+ _custom_dot_names_path: Path = self._custom_config_path / dot_names
+
+ with open(_custom_dot_names_path, "w", encoding="utf-8") as obj_names_file:
+ obj_names_file.write(create_obj_names(classes=self._classes))
+
+ with open(_custom_dot_data_path, "w", encoding="utf-8") as obj_data_file:
+ obj_data_file.write(
+ create_obj_data(
+ classes=self._classes,
+ train_txt_path=self._train_txt_path,
+ test_txt_path=self._test_txt_path,
+ dot_names_path=_custom_dot_names_path,
+ saved_weights_path=self._custom_weights_path,
+ )
+ )
+
+ self._custom_dot_data_path: Path = _custom_dot_data_path
+
+ def generate_anchors(self):
+ _darknet_dir: Path = self._working_dir / "darknet"
+ _darknet_exec: Path = _darknet_dir / "darknet"
+ self._darknet_exec: str = str(_darknet_exec)
+ self._custom_anchors: str = None
+
+ if self._generate_custom_anchors:
+ self._logger.info("Recalculating anchors")
+ command: list = [
+ self._darknet_exec,
+ "detector",
+ "calc_anchors",
+ self._custom_dot_data_path,
+ "-num_of_clusters",
+ str(9),
+ "-width",
+ str(self._image_width),
+ "-height",
+ str(self._image_height),
+ ]
+ for _ in tqdm(range(1)):
+ with open(os.devnull, "w") as DEVNULL:
+ subprocess.call(command, stdout=DEVNULL, stderr=DEVNULL)
+
+ anchors_path: Path = self._working_dir / "anchors.txt"
+ with open(anchors_path, "r") as anchors_file:
+ self._custom_anchors = anchors_file.read()
+
+ def update_config(self):
+ config_path: str = self._model2config.get("darknet").get(self._model_name).get(
+ "config"
+ )
+ with open(config_path, "r", encoding="utf-8") as default_yolo_cfg_file:
+ default_yolo_cfg: str = "".join(default_yolo_cfg_file.readlines())
+
+ saved_config_path: Path = self._custom_config_path / self._model_name
+ saved_config_path: str = "{}.cfg".format(str(saved_config_path))
+ self._logger.info("Modifying configuration file as needed")
+ with open(saved_config_path, "w", encoding="utf-8") as customize_cfg_file:
+ customize_cfg_file.write(
+ customize_yolo_cfg_v4(
+ yolo_cfg=default_yolo_cfg,
+ classes_nb=len(self._classes),
+ batch_size=self._batch_size,
+ subdivisions=self._subdivisons,
+ max_batches=self._max_batches,
+ image_width=self._image_width,
+ image_height=self._image_height,
+ angle=self._angle,
+ saturation=self._saturation,
+ exposure=self._exposure,
+ hue=self._hue,
+ custom_anchors=self._custom_anchors,
+ learning_rate_yolov4=self._learning_rate_yolov4,
+ mosaic=self._mosaic,
+ blur=self._blur
+ )
+ )
+
+ def start_training(self):
+ yolo_cfg_path: str = "{}.cfg".format(
+ str(self._custom_config_path / self._model_name)
+ )
+ command: list = [
+ self._darknet_exec,
+ "detector",
+ "train",
+ str(self._custom_dot_data_path),
+ yolo_cfg_path,
+ str(self._custom_weights_path / "initial.weights"),
+ "-dont_show",
+ "-clear",
+ "1",
+ ]
+
+ if self._gpus:
+ arg: str = ",".join(str(gpu) for gpu in self._gpus)
+ command.append("-gpus")
+ command.append(arg)
+
+ if self._web_ui:
+ self._logger.info(
+ "Running web_ui on port {}".format(str(self._web_ui_port))
+ )
+ command.append("-mjpeg_port")
+ command.append(str(self._web_ui_port))
+
+ if self._calculate_map:
+ command.append("-map")
+
+ if self._custom_api:
+ self._logger.info(
+ "Running YOLO API on port {}".format(str(self._custom_api_port))
+ )
+ with open(os.devnull, "w") as DEVNULL:
+ subprocess.Popen(
+ [
+ "uvicorn",
+ "api:app",
+ "--host",
+ "0.0.0.0",
+ "--port",
+ str(self._custom_api_port),
+ ],
+ stderr=DEVNULL,
+ stdout=DEVNULL,
+ )
+
+ if self._tensorboard:
+ self._logger.info(
+ "Running Tensorboard on port {}".format(str(self._tensorboard_port))
+ )
+ with open(os.devnull, "w") as DEVNULL:
+ subprocess.Popen(
+ ["tensorboard", "--logdir", "runs"], stderr=DEVNULL, stdout=DEVNULL
+ )
+
+ # Create a rotating log
+ yolo_training_logger: logging.Logger = logging.getLogger("Rotating Log")
+ yolo_training_logger.setLevel(logging.INFO)
+
+ # Add a rotating handler for yolo training output
+ yolo_log_path = self._custom_training_folder_path / "yolo_events.log"
+ yolo_handler: RotatingFileHandler = RotatingFileHandler(
+ yolo_log_path, maxBytes=51200, backupCount=1
+ )
+ yolo_training_logger.addHandler(yolo_handler)
+ Path.mkdir(self._working_dir / "data")
+ os.symlink(
+ self._working_dir / "darknet/data/labels", self._working_dir / "data/labels"
+ )
+
+ update_arg(self._tensorboard, self._working_dir)
+ self._logger.info("Starting YOLO training")
+ process: subprocess.Popen = subprocess.Popen(
+ command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
+ )
+ print(
+ "\nYou can now monitor the training using any of the provided means or by viewing the logs saved in the custom training folder\n"
+ )
+ pid_path = self._working_dir / "pid.txt"
+ with open(pid_path, "w+") as pid:
+ pid.write(str(process.pid))
+
+ last_weights_path = "{}/{}_last.weights".format(
+ str(self._custom_weights_path), self._model_name
+ )
+ labeled_prediction_image_path = "{}/predictions.jpg".format(
+ str(self._working_dir)
+ )
+
+ step: int = 0
+ while True:
+ line: bytes = process.stdout.readline()
+ if not line:
+ break
+ line_decoded: str = line.decode("utf-8")
+ yolo_training_logger.info(line_decoded)
+ process_output(line_decoded)
+ checkpoint: list = re.findall(
+ "Saving weights to .*_last.weights", line_decoded
+ )
+ if checkpoint:
+ if self._prediction_image:
+ get_prediction(
+ darknet_path=self._darknet_exec,
+ dot_data_path=str(self._custom_dot_data_path),
+ yolo_cfg_path=yolo_cfg_path,
+ weights_path=last_weights_path,
+ image=self._prediction_image,
+ )
+ if self._tensorboard:
+ img: JpegImagePlugin.JpegImageFile = Image.open(
+ labeled_prediction_image_path
+ )
+ img: np.ndarray = np.transpose(img, (2, 0, 1))
+ writer.add_image("predictions", img, step)
+ step += 1
diff --git a/src/train_utils.py b/src/train_utils.py
index 0805741..53d9227 100644
--- a/src/train_utils.py
+++ b/src/train_utils.py
@@ -106,6 +106,83 @@ def split_train_test(
return train_images_list, test_images_list
+def customize_yolo_cfg_v4(yolo_cfg: str, classes_nb: int, batch_size: int, subdivisions: int, max_batches: int,
+ image_width: int, image_height: int, angle: int, saturation: float, exposure: float,
+ hue: float,
+ custom_anchors: str, mosaic:bool, blur:bool, learning_rate_yolov4:float ) -> str:
+ custom_yolo_cfg: str = yolo_cfg
+ # Set classes
+ custom_yolo_cfg = re.sub(
+ r"(classes *= *)(.+)", r"\1 {}".format(classes_nb), custom_yolo_cfg
+ )
+ # Set batch_size
+ custom_yolo_cfg = re.sub(
+ r"(batch *= *)(.+)", r"\1 {}".format(batch_size), custom_yolo_cfg
+ )
+ # Set subdivisions
+ custom_yolo_cfg = re.sub(
+ r"(subdivisions *= *)(.+)", r"\1 {}".format(subdivisions), custom_yolo_cfg
+ )
+ # Set image_width
+ custom_yolo_cfg = re.sub(
+ r"(width *= *)(.+)", r"\1 {}".format(image_width), custom_yolo_cfg
+ )
+ # Set image_height
+ custom_yolo_cfg = re.sub(
+ r"(height *= *)(.+)", r"\1 {}".format(image_height), custom_yolo_cfg
+ )
+ # Set max_batches
+ custom_yolo_cfg = re.sub(
+ r"(max_batches *= *)(.+)", r"\1 {}".format(max_batches), custom_yolo_cfg
+ )
+ # Set steps
+ steps = [int(0.8 * max_batches), int(0.9 * max_batches)]
+ custom_yolo_cfg = re.sub(
+ r"(steps *= *)(.+)",
+ r"\1 {}".format("{},{}".format(steps[0], steps[1])),
+ custom_yolo_cfg,
+ )
+ # Set angle
+ custom_yolo_cfg = re.sub(
+ r"(angle *= *)(.+)", r"\1 {}".format(angle), custom_yolo_cfg
+ )
+ # Set saturation
+ custom_yolo_cfg = re.sub(
+ r"(saturation *= *)(.+)", r"\1 {}".format(saturation), custom_yolo_cfg
+ )
+ # Set exposure
+ custom_yolo_cfg = re.sub(
+ r"(exposure *= *)(.+)", r"\1 {}".format(exposure), custom_yolo_cfg
+ )
+ # Set hue
+ custom_yolo_cfg = re.sub(r"(hue *= *)(.+)", r"\1 {}".format(hue), custom_yolo_cfg)
+
+ #set learning rate
+ custom_yolo_cfg = re.sub(r"(learning_rate *= *)(.+)", r"\1 {}".format(learning_rate_yolov4), custom_yolo_cfg)
+ # set mosaic augmentation
+ custom_yolo_cfg = re.sub(r"(mosaic *= *)(.+)", r"\1 {}".format(int(mosaic)), custom_yolo_cfg)
+ # set blur augmentation
+ custom_yolo_cfg = re.sub(r"(blur *= *)(.+)", r"\1 {}".format(int(blur)), custom_yolo_cfg)
+
+ # Set custom_anchors
+ if custom_anchors:
+ custom_yolo_cfg = re.sub(
+ r"(anchors *= *)(.+)", r"\1 {}".format(custom_anchors), custom_yolo_cfg
+ )
+ # Set custom filters
+ all_cfg_lines: list = custom_yolo_cfg.split("\n")
+ yolo_indexes: list = [i for i, line in enumerate(all_cfg_lines) if "[yolo]" in line]
+ for yolo_layer in yolo_indexes:
+ for i, cfg_line in enumerate(reversed(all_cfg_lines[:yolo_layer])):
+ if "filters" in cfg_line:
+ all_cfg_lines[yolo_layer - i - 1] = re.sub(
+ r"(filters *= *)(.+)",
+ r"\1 {}".format((classes_nb + 5) * 3),
+ cfg_line,
+ )
+ break
+
+ return "\n".join(all_cfg_lines)
def customize_yolo_cfg(
yolo_cfg: str,
@@ -120,6 +197,7 @@ def customize_yolo_cfg(
exposure: float,
hue: float,
custom_anchors: str,
+ learning_rate_yolov3:float
) -> str:
custom_yolo_cfg: str = yolo_cfg
@@ -168,6 +246,11 @@ def customize_yolo_cfg(
)
# Set hue
custom_yolo_cfg = re.sub(r"(hue *= *)(.+)", r"\1 {}".format(hue), custom_yolo_cfg)
+
+ #set learning rate
+ custom_yolo_cfg = re.sub(
+ r"(learning_rate *= *)(.+)", r"\1 {}".format(learning_rate_yolov3), custom_yolo_cfg
+ )
# Set custom_anchors
if custom_anchors:
custom_yolo_cfg = re.sub(
From 47894431c057522b0447d3785162f777685c9a78 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Thu, 2 Jul 2020 11:48:07 +0200
Subject: [PATCH 02/20] Update README.md
---
README.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 34dbb33..4823ff5 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# YOLOv4 Training Automation API for Linux
+# YOLOv4-v3 Training Automation API for Linux
This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. Training with YOLOv4 has never been so easy.
This repository has also cross compatibility with Yolov3 training
@@ -229,4 +229,4 @@ Lynn Nassif, Beirut, Lebanon
Nour Azzi, Beirut, Lebanon
-Hadi Koubeissy , Beirut, Lebanon
\ No newline at end of file
+Hadi Koubeissy , Beirut, Lebanon
From 45a2b0f91ea63cd828706dc1c7e353635ef491cf Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Thu, 2 Jul 2020 11:58:52 +0200
Subject: [PATCH 03/20] Update Readme.md
Add documentation for BUILD behind Proxy
---
README.md | 6 +++++-
1 file changed, 5 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 4823ff5..55e951f 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,10 @@ If you have a GPU: Volta, Xavier, Turing and higher
```bash
sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=1 --build-arg OPENCV=1 .
```
-
+If you are behind proxy
+```bash
+sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=1 --build-arg OPENCV=1 --build-arg http_proxy='' --build-arg https_proxy='' .
+```
For CPU only, you can run the same command while setting GPU=0 CUDNN=0 and naming it darknet_yolo_cpu:1 for clarity.
```bash
@@ -57,6 +60,7 @@ If you want multi-core cpu training you can add OPENMP=1 to the build
```bash
sudo docker build -f docker/Dockerfile -t darknet_yolov4_gpu:1 --build-arg GPU=0 --build-arg CUDNN=0 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 --build-arg OPENMP=1 .
```
+
## Preparing your dataset
We provided a `sample_dataset` to show how your data should be structured in order to start the training seemlesly.
From e457ac320d342a11077e06f267305d532e5b3127 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Fri, 3 Jul 2020 13:17:42 +0200
Subject: [PATCH 04/20] Update api.py
---
src/api.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/api.py b/src/api.py
index 5d36fdd..ca4696b 100644
--- a/src/api.py
+++ b/src/api.py
@@ -20,7 +20,7 @@
app: FastAPI = FastAPI(
title="BMW InnovationLab YOLO Training Automation",
- description='API for Monitoring YOLO Training
Contact the developers:
Nour Azzi: https://github.com/nourazzii
Lynn Nassif:
Hadi Koubeissy: https://github.com/hadikoub
BMW Innovation Lab: innovation-lab@bmw.de'',
+ description='API for Monitoring YOLO Training
Contact the developers:
Nour Azzi: https://github.com/nourazzii
Lynn Nassif:
Hadi Koubeissy: https://github.com/hadikoub
BMW Innovation Lab: innovation-lab@bmw.de',
docs_url="/",
version="3.1.0",
)
From 1b0f37beb4e8efaf8b1f06618cc9ae82c0d14376 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Fri, 3 Jul 2020 15:22:27 +0200
Subject: [PATCH 05/20] Update train_darknetv4.py
fixing tensorboard running on custom port
---
src/train_darknetv4.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/train_darknetv4.py b/src/train_darknetv4.py
index f46a11a..69b2272 100644
--- a/src/train_darknetv4.py
+++ b/src/train_darknetv4.py
@@ -346,7 +346,7 @@ def start_training(self):
)
with open(os.devnull, "w") as DEVNULL:
subprocess.Popen(
- ["tensorboard", "--logdir", "runs"], stderr=DEVNULL, stdout=DEVNULL
+ ["tensorboard", "--logdir", "runs "," --port ", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
)
# Create a rotating log
From 4bf76b932fcc9ff92a730e6e2be274e2c06c5c4e Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Fri, 3 Jul 2020 15:23:53 +0200
Subject: [PATCH 06/20] Update train_darknet.py
fixing tensorboard custom port
---
src/train_darknet.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/train_darknet.py b/src/train_darknet.py
index 0062180..eb48516 100644
--- a/src/train_darknet.py
+++ b/src/train_darknet.py
@@ -345,7 +345,7 @@ def start_training(self):
)
with open(os.devnull, "w") as DEVNULL:
subprocess.Popen(
- ["tensorboard", "--logdir", "runs"], stderr=DEVNULL, stdout=DEVNULL
+ ["tensorboard", "--logdir ", " runs "," --port ", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
)
# Create a rotating log
yolo_training_logger: logging.Logger = logging.getLogger("Rotating Log")
From 5762adbca29c2b1f9c38feb9fcffb0780d11b321 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Mon, 6 Jul 2020 19:37:41 +0200
Subject: [PATCH 07/20] Update Dockerfile
---
docker/Dockerfile | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 390e5a2..543990d 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -36,7 +36,7 @@ WORKDIR /training
RUN git clone https://github.com/AlexeyAB/darknet.git
WORKDIR darknet
# Checkout the last working version in our case
-RUN git checkout tags/darknet_yolo_v4_pre
+RUN git checkout 00919eeb99f73484cf9f51b277a4e4ebacef70df
# Modify Makefile according to needed env variables
RUN \
From a01e20fc4c426076a76e16486d758768410b8793 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Wed, 8 Jul 2020 12:33:37 +0200
Subject: [PATCH 08/20] Update Dockerfile
---
docker/Dockerfile | 1 +
1 file changed, 1 insertion(+)
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 543990d..7cdeb78 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -35,6 +35,7 @@ WORKDIR /training
# Fetch Repo
RUN git clone https://github.com/AlexeyAB/darknet.git
WORKDIR darknet
+#Checkout version should be yolov4 when stable version is released
# Checkout the last working version in our case
RUN git checkout 00919eeb99f73484cf9f51b277a4e4ebacef70df
From 608bddc5f3670b22d99681abccadc28d011abac7 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Wed, 8 Jul 2020 13:58:05 +0200
Subject: [PATCH 09/20] Update Dockerfile
---
docker/Dockerfile | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 7cdeb78..6000258 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -36,8 +36,8 @@ WORKDIR /training
RUN git clone https://github.com/AlexeyAB/darknet.git
WORKDIR darknet
#Checkout version should be yolov4 when stable version is released
-# Checkout the last working version in our case
-RUN git checkout 00919eeb99f73484cf9f51b277a4e4ebacef70df
+# Checkout the last working version of Yolov4_pre
+RUN git checkout bef28445e57cd560fa3d0a24af98a562d289135b
# Modify Makefile according to needed env variables
RUN \
From 627fded56b36552ee98193d268c66732de5d4572 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Thu, 9 Jul 2020 11:58:28 +0200
Subject: [PATCH 10/20] Update run_docker_linux_gpu.sh
---
run_docker_linux_gpu.sh | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/run_docker_linux_gpu.sh b/run_docker_linux_gpu.sh
index 5871117..3e94ed4 100755
--- a/run_docker_linux_gpu.sh
+++ b/run_docker_linux_gpu.sh
@@ -13,7 +13,7 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:$tensorboard_port"
+ ports="$ports -p $tensorboard_port:6006"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
From 864019bd51d3ae462c753ed09adbf35d7f1600cd Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Thu, 9 Jul 2020 11:59:07 +0200
Subject: [PATCH 11/20] Update run_docker_linux_cpu.sh
---
run_docker_linux_cpu.sh | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/run_docker_linux_cpu.sh b/run_docker_linux_cpu.sh
index 1a38e23..3a16aa9 100755
--- a/run_docker_linux_cpu.sh
+++ b/run_docker_linux_cpu.sh
@@ -13,7 +13,7 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:$tensorboard_port"
+ ports="$ports -p $tensorboard_port:6006"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
From dc63e014f574531371052c211558f1b04add6e74 Mon Sep 17 00:00:00 2001
From: hadikoub <123.hadikoubeissy@gmail.com>
Date: Sun, 12 Jul 2020 19:14:22 +0200
Subject: [PATCH 12/20] Fixed Tensorboard "no data found"
---
run_docker_linux_cpu.sh | 2 +-
run_docker_linux_gpu.sh | 2 +-
src/train_darknet.py | 2 +-
src/train_darknetv4.py | 2 +-
4 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/run_docker_linux_cpu.sh b/run_docker_linux_cpu.sh
index 3a16aa9..1a38e23 100755
--- a/run_docker_linux_cpu.sh
+++ b/run_docker_linux_cpu.sh
@@ -13,7 +13,7 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:6006"
+ ports="$ports -p $tensorboard_port:$tensorboard_port"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
diff --git a/run_docker_linux_gpu.sh b/run_docker_linux_gpu.sh
index 3e94ed4..96814b8 100755
--- a/run_docker_linux_gpu.sh
+++ b/run_docker_linux_gpu.sh
@@ -13,7 +13,7 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:6006"
+ ports="$ports -p $tensorboard_port:$tensorboard"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
diff --git a/src/train_darknet.py b/src/train_darknet.py
index eb48516..b0a44e1 100644
--- a/src/train_darknet.py
+++ b/src/train_darknet.py
@@ -345,7 +345,7 @@ def start_training(self):
)
with open(os.devnull, "w") as DEVNULL:
subprocess.Popen(
- ["tensorboard", "--logdir ", " runs "," --port ", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
+ ["tensorboard", "--logdir", "./runs","--port", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
)
# Create a rotating log
yolo_training_logger: logging.Logger = logging.getLogger("Rotating Log")
diff --git a/src/train_darknetv4.py b/src/train_darknetv4.py
index 69b2272..4cad186 100644
--- a/src/train_darknetv4.py
+++ b/src/train_darknetv4.py
@@ -346,7 +346,7 @@ def start_training(self):
)
with open(os.devnull, "w") as DEVNULL:
subprocess.Popen(
- ["tensorboard", "--logdir", "runs "," --port ", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
+ ["tensorboard", "--logdir", "./runs"," --port", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
)
# Create a rotating log
From 608a3c8b8b01b927e224a6e7cd9cae69acc8cbc8 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Tue, 14 Jul 2020 14:02:17 +0200
Subject: [PATCH 13/20] Update run_docker_linux_gpu.sh
---
run_docker_linux_gpu.sh | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/run_docker_linux_gpu.sh b/run_docker_linux_gpu.sh
index 96814b8..5871117 100755
--- a/run_docker_linux_gpu.sh
+++ b/run_docker_linux_gpu.sh
@@ -13,7 +13,7 @@ if [ -f $configfile ]; then
tensorboard=`jq .training.tensorboard.enable $configfile`
if [ "$tensorboard" = "true" ]; then
tensorboard_port=`jq .training.tensorboard.port $configfile`
- ports="$ports -p $tensorboard_port:$tensorboard"
+ ports="$ports -p $tensorboard_port:$tensorboard_port"
fi
web_ui=`jq .training.web_ui.enable $configfile`
if [ "$web_ui" = "true" ]; then
From d5b6a7aa7394420a34a1d03574da6705b21d7657 Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Tue, 14 Jul 2020 15:09:09 +0200
Subject: [PATCH 14/20] Update train_darknetv4.py
---
src/train_darknetv4.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/train_darknetv4.py b/src/train_darknetv4.py
index 4cad186..ad7732f 100644
--- a/src/train_darknetv4.py
+++ b/src/train_darknetv4.py
@@ -346,7 +346,7 @@ def start_training(self):
)
with open(os.devnull, "w") as DEVNULL:
subprocess.Popen(
- ["tensorboard", "--logdir", "./runs"," --port", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
+ ["tensorboard", "--logdir", "./runs","--port", str(self._tensorboard_port)], stderr=DEVNULL, stdout=DEVNULL
)
# Create a rotating log
From 4c5f7677b6290112e07f9fbe381d61876e00046a Mon Sep 17 00:00:00 2001
From: Hadi Koubeissy <31547256+hadikoub@users.noreply.github.com>
Date: Tue, 14 Jul 2020 23:10:51 +0200
Subject: [PATCH 15/20] Add files via upload
---
gifs/2.gif | Bin 1836108 -> 6692675 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
diff --git a/gifs/2.gif b/gifs/2.gif
index fe9ab210b627f83d4353e7206dd63e95ddeb569a..41013ca90a0c51a73bb2f3aa6567b3cedae77985 100644
GIT binary patch
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