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https://github.com/iperov/DeepFaceLab.git
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DockerFile for Mac users to run DeepfaceLab with CPU Mode (#95)
* fix localization nullpointer exception * fix devicelib error line:61,remove e * support create docker from cpu dockerfile * support preview or not when train(resolve cannot connect to X server)
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iperov
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4
.gitignore
vendored
4
.gitignore
vendored
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@ -12,4 +12,6 @@
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!mathlib
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!models
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!nnlib
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!utils
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!utils
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!Dockerfile*
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!*.sh
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132
DockerCPU.md
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132
DockerCPU.md
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@ -0,0 +1,132 @@
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# For Mac Users
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If you just have a **MacBook**.DeepFaceLab **GPU** mode does not works. However,it can also works with **CPU** mode.Follow the Steps below will help you build the **DRE** (DeepFaceLab Runtime Environment) Easier.
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### 1. Open a new terminal and Clone DeepFaceLab with git
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```
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$ git git@github.com:iperov/DeepFaceLab.git
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```
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### 2. Change the directory to DeepFaceLab
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```
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$ cd DeepFaceLab
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```
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### 3. Install Docker
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[Docker Desktop for Mac](https://hub.docker.com/editions/community/docker-ce-desktop-mac)
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### 4. Build Docker Image For DeepFaceLab
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```
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$ docker build -t deepfacelab-cpu -f Dockerfile.cpu .
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```
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### 5. Mount DeepFaceLab volume and Run it
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```
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$ docker run -p 8888:8888 --hostname deepfacelab-cpu --name deepfacelab-cpu -v $PWD:/notebooks deepfacelab-cpu
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```
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PS: Because your current directory is `DeepFaceLab`,so `-v $PWD:/notebooks` means Mount `DeepFaceLab` volume to `notebooks` in **Docker**
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And then you will see the log below:
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```
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The Jupyter Notebook is running at:
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http://(deepfacelab-cpu or 127.0.0.1):8888/?token=your token
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```
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### 6. Open a new terminal to run DeepFaceLab in /notebooks
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```
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$ docker exec -it deepfacelab-cpu bash
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$ ls -A
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```
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### 7. Use jupyter in deepfacelab-cpu bash
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```
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$ jupyter notebook list
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```
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or just open it on your browser `http://127.0.0.1:8888/?token=your_token`
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PS: You can run python with jupyter.However,we just run our code in bash.It's simpler and clearer.Now the **DRE** (DeepFaceLab Runtime Environment) almost builded.
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### 8. Stop or Kill Docker Container
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```
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$ docker stop deepfacelab-cpu
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$ docker kill deepfacelab-cpu
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```
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### 9. Start Docker Container
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```
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# start docker container
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$ docker start deepfacelab-cpu
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# open bash to run deepfacelab
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$ docker exec -it deepfacelab-cpu bash
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```
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PS: `STEP 8` or `STEP 9` just show you the way to stop and start **DRE**.
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### 10. enjoy it
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```
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# make sure you current directory is `/notebooks`
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$ pwd
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# make sure all `DeepFaceLab` code is in current path `/notebooks`
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$ ls -a
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# read and write permission
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$ chmod +x cpu.sh
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# run `DeepFaceLab`
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$ ./cpu.sh
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```
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### Details with `DeepFaceLab`
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#### 1. Concepts
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In our Case,**Cage**'s Face is **SRC Face**,and **Trump**'s Face is **DST Face**.and finally we get the **Result** below.
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So,before you run `./cpu.sh`.You should be aware of this.
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#### 2. Use MTCNN(mt) to extract faces
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Do not use DLIB extractor in CPU mode
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#### 3. Best practice for SORT
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1) delete first unsorted aligned groups of images what you can to delete.
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2) use `hist`
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#### 4. Use `H64 model` to train and convert
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Only H64 model reasonable to train on home CPU.You can choice other model like **H128 (3GB+)** | **DF (5GB+)** and so on ,it depends entirely on your CPU performance.
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#### 5. execute the script below one by one
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```
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root@deepfacelab-cpu:/notebooks# ./cpu.sh
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1) clear workspace 7) data_dst sort by hist
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2) extract PNG from video data_src 8) train
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3) data_src extract faces 9) convert
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4) data_src sort 10) converted to mp4
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5) extract PNG from video data_dst 11) quit
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6) data_dst extract faces
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Please enter your choice:
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```
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#### 6. Put all videos in `workspace` directory
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```
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.
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├── data_dst
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├── data_src
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├── dst.mp4
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├── model
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└── src.mp4
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3 directories, 2 files
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```
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17
Dockerfile.cpu
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17
Dockerfile.cpu
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@ -0,0 +1,17 @@
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FROM tensorflow/tensorflow:latest-py3
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RUN apt-get update -qq -y \
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&& apt-get install -y libsm6 libxrender1 libxext-dev python3-tk\
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&& apt-get install -y ffmpeg \
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&& apt-get install -y wget \
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&& apt-get install -y vim \
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&& apt-get install -y git \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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COPY requirements-cpu-docker.txt /opt/
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RUN pip3 install cmake
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RUN pip3 --no-cache-dir install -r /opt/requirements-cpu-docker.txt && rm /opt/requirements-cpu-docker.txt
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WORKDIR "/notebooks"
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CMD ["/run_jupyter.sh", "--allow-root"]
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@ -176,6 +176,9 @@ Video tutorial: https://www.youtube.com/watch?v=K98nTNjXkq8
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Windows 10 consumes % of VRAM even if card unused for video output.
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### For Mac Users
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Check out [DockerCPU.md](DockerCPU.md) for more detailed instructions.
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### **Problem of the year**:
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algorithm of overlaying neural face onto video face located in ConverterMasked.py.
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71
cpu.sh
Executable file
71
cpu.sh
Executable file
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@ -0,0 +1,71 @@
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#!/bin/bash
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INTERNAL_DIR=`pwd`
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WORKSPACE=$INTERNAL_DIR/workspace
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PYTHON=`which python`
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PS3="Please enter your choice: "
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options=("clear workspace" "extract PNG from video data_src" "data_src extract faces" "data_src sort" "extract PNG from video data_dst" "data_dst extract faces" "data_dst sort by hist" "train" "convert" "converted to mp4" "quit")
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select opt in "${options[@]}"
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do
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case $opt in
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"clear workspace" )
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echo -n "Clean up workspace? [Y/n] "; read workspace_ans
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if [ "$workspace_ans" == "Y" ] || [ "$workspace_ans" == "y" ]; then
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rm -rf $WORKSPACE
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mkdir -p $WORKSPACE/data_src/aligned
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mkdir -p $WORKSPACE/data_dst/aligned
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mkdir -p $WORKSPACE/model
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echo "Workspace has been successfully cleaned!"
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fi
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;;
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"extract PNG from video data_src" )
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echo -n "File name: "; read filename
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echo -n "FPS: "; read fps
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if [ -z "$fps" ]; then fps="25"; fi
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ffmpeg -i $WORKSPACE/$filename -r $fps $WORKSPACE/data_src/%04d.png -loglevel error
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;;
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"data_src extract faces" )
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echo -n "Detector? [mt | manual] "; read detector
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$PYTHON $INTERNAL_DIR/main.py extract --input-dir $WORKSPACE/data_src --output-dir $WORKSPACE/data_src/aligned --detector $detector --debug --cpu-only
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;;
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"data_src sort" )
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echo -n "Sort by? [blur | brightness | face-yaw | hue | hist | hist-blur | hist-dissim] "; read sort_method
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$PYTHON $INTERNAL_DIR/main.py sort --input-dir $WORKSPACE/data_src/aligned --by $sort_method
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;;
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"extract PNG from video data_dst" )
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echo -n "File name: "; read filename
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echo -n "FPS: "; read fps
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if [ -z "$fps" ]; then fps="25"; fi
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ffmpeg -i $WORKSPACE/$filename -r $fps $WORKSPACE/data_dst/%04d.png -loglevel error
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;;
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"data_dst extract faces" )
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echo -n "Detector? [mt | manual] "; read detector
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$PYTHON $INTERNAL_DIR/main.py extract --input-dir $WORKSPACE/data_dst --output-dir $WORKSPACE/data_dst/aligned --detector $detector --debug --cpu-only
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;;
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"data_dst sort by hist" )
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$PYTHON $INTERNAL_DIR/main.py sort --input-dir $WORKSPACE/data_dst/aligned --by hist
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;;
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"train" )
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echo -n "Model? [ H64 (2GB+) | H128 (3GB+) | DF (5GB+) | LIAEF128 (5GB+) | LIAEF128YAW (5GB+) | MIAEF128 (5GB+) | AVATAR (4GB+) ] "; read model
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echo -n "Show Preview? [Y/n] "; read preview
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if [ "$preview" == "Y" ] || [ "$preview" == "y" ]; then preview="--preview"; else preview=""; fi
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$PYTHON $INTERNAL_DIR/main.py train --training-data-src-dir $WORKSPACE/data_src/aligned --training-data-dst-dir $WORKSPACE/data_dst/aligned --model-dir $WORKSPACE/model --model $model --cpu-only $preview
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;;
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"convert" )
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echo -n "Model? [ H64 (2GB+) | H128 (3GB+) | DF (5GB+) | LIAEF128 (5GB+) | LIAEF128YAW (5GB+) | MIAEF128 (5GB+) | AVATAR(4GB+) ] "; read model
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$PYTHON $INTERNAL_DIR/main.py convert --input-dir $WORKSPACE/data_dst --output-dir $WORKSPACE/data_dst/merged --aligned-dir $WORKSPACE/data_dst/aligned --model-dir $WORKSPACE/model --model $model --ask-for-params --cpu-only
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;;
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"converted to mp4" )
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echo -n "File name of destination video: "; read filename
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echo -n "FPS: "; read fps
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if [ -z "$fps" ]; then fps="25"; fi
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ffmpeg -y -i $WORKSPACE/$filename -r $fps -i "$WORKSPACE/data_dst/merged/%04d.png" -map 0:a? -map 1:v -r $fps -c:v libx264 -b:v 8M -pix_fmt yuv420p -c:a aac -strict -2 -b:a 192k -ar 48000 "$WORKSPACE/result.mp4" -loglevel error
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;;
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"quit" )
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break
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;;
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*)
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echo "Invalid choice!"
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;;
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esac
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done
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BIN
doc/merged-face.jpg
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BIN
doc/merged-face.jpg
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Binary file not shown.
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After Width: | Height: | Size: 14 KiB |
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import locale
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system_locale = locale.getdefaultlocale()[0]
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system_language = system_locale[0:2]
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# system_locale may be nil
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system_language = system_locale[0:2] if system_locale is not None else "en"
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windows_font_name_map = {
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'en' : 'cour',
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6
main.py
6
main.py
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@ -80,6 +80,7 @@ if __name__ == "__main__":
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model_path=arguments.model_dir,
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model_name=arguments.model_name,
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debug = arguments.debug,
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preview = arguments.preview,
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#**options
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batch_size = arguments.batch_size,
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write_preview_history = arguments.write_preview_history,
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@ -106,8 +107,9 @@ if __name__ == "__main__":
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train_parser.add_argument('--force-best-gpu-idx', type=int, dest="force_best_gpu_idx", default=-1, help="Force to choose this GPU idx as best(worst).")
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train_parser.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="MultiGPU option. It will select only same best(worst) GPU models.")
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train_parser.add_argument('--force-gpu-idxs', type=str, dest="force_gpu_idxs", default=None, help="Override final GPU idxs. Example: 0,1,2.")
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train_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
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train_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
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train_parser.add_argument('--preview', action="store_true",dest="preview", default=False, help="Show preview.")
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train_parser.set_defaults (func=process_train)
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def process_convert(arguments):
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@ -277,13 +277,14 @@ def previewThread (input_queue, output_queue):
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cv2.destroyAllWindows()
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def main (training_data_src_dir, training_data_dst_dir, model_path, model_name, **in_options):
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print ("Running trainer.\r\n")
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def main (training_data_src_dir, training_data_dst_dir, model_path, model_name,preview, **in_options):
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print ("Running trainer(preview=%s).\r\n" % (preview))
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output_queue = queue.Queue()
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input_queue = queue.Queue()
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import threading
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thread = threading.Thread(target=trainerThread, args=(output_queue, input_queue, training_data_src_dir, training_data_dst_dir, model_path, model_name), kwargs=in_options )
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thread.start()
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previewThread (input_queue, output_queue)
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if preview:
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previewThread (input_queue, output_queue)
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@ -58,7 +58,7 @@ class devicelib:
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try:
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nvmlInit()
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nvmlShutdown()
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except e:
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except:
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return False
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return True
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9
requirements-cpu-docker.txt
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9
requirements-cpu-docker.txt
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pathlib==1.0.1
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scandir==1.6
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h5py==2.7.1
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Keras==2.2.4
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opencv-python==3.4.0.12
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scikit-image
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dlib==19.10.0
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tqdm
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git+https://www.github.com/keras-team/keras-contrib.git
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