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‎README.md

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*Our method can realize **arbitrary face swapping** on images and videos with **one single trained model**.*
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Training and test code are now available! [Colab demo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
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Training and test code are now available!
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[ <a href="https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/train.ipynb)
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We are working with our incoming paper SimSwap++, keeping expecting!
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@@ -26,6 +27,8 @@ If you find this project useful, please star it. It is the greatest appreciation
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## Top News <img width=8% src="./docs/img/new.gif"/>
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**`2022-04-21`**: For resource limited users, we provide the cropped VGGFace2-224 dataset [VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing).
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**`2022-04-20`**: Training scripts are now available. We highly recommend that you guys train the simswap model with our released high quality dataset [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
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**`2021-11-24`**: We have trained a beta version of ***SimSwap-HQ*** on [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) and open sourced the checkpoint of this model (if you think the Simswap 512 is cool, please star our [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repo). Please don’t forget to go to [Preparation](./docs/guidance/preparation.md) and [Inference for image or video face swapping](./docs/guidance/usage.md) to check the latest set up.
@@ -65,7 +68,7 @@ Download the dataset from [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ).
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The training script is slightly different from the original version, e.g., we replace the patch discriminator with the projected discriminator, which saves a lot of hardware overhead and achieves slightly better results.
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In order to ensure normal training, the batch size must be greater than 1.
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- Train 224 models with VGGFace2 224*224 [VGGFace2-224](https://github.com/NNNNAI/VGGFace2-HQ)
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- Train 224 models with VGGFace2 224*224 [VGGFace2-224 (10.8G)](https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing)
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```
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python train.py --name simswap224_test --batchSize 4 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep False
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```

‎train.ipynb

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"!nvidia-smi"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "J8WrNaQHuUGC",
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"outputId": "afffa0be-92b5-4133-b6d9-6c3e08c6de64"
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"id": "J8WrNaQHuUGC"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Thu Apr 21 16:07:35 2022 \n",
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"+-----------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
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"|-------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
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"| | | MIG M. |\n",
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"|===============================+======================+======================|\n",
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"| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n",
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"| N/A 67C P8 32W / 149W | 0MiB / 11441MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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" \n",
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"+-----------------------------------------------------------------------------+\n",
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"| Processes: |\n",
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"| GPU GI CI PID Type Process name GPU Memory |\n",
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"| ID ID Usage |\n",
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"|=============================================================================|\n",
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"| No running processes found |\n",
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"+-----------------------------------------------------------------------------+\n"
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]
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"!cd SimSwap && git pull"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "9jZWwt97uvIe",
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"outputId": "42a1bda8-3ca3-46af-fc82-d1af99ce15e1"
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"id": "9jZWwt97uvIe"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Cloning into 'SimSwap'...\n",
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"remote: Enumerating objects: 1017, done.\u001b[K\n",
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"remote: Counting objects: 100% (16/16), done.\u001b[K\n",
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"remote: Compressing objects: 100% (13/13), done.\u001b[K\n",
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"remote: Total 1017 (delta 5), reused 10 (delta 3), pack-reused 1001\u001b[K\n",
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"Receiving objects: 100% (1017/1017), 210.79 MiB | 14.80 MiB/s, done.\n",
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"Resolving deltas: 100% (510/510), done.\n",
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"Already up to date.\n"
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]
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"!wget -P SimSwap/arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "rwvbPhtOvZAL",
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"outputId": "ffa12208-d388-412d-e83b-c54864c4526e"
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"id": "rwvbPhtOvZAL"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (0.4)\n",
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"Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n",
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"Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.21.6)\n"
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]
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}
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]
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"outputs": []
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{
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"cell_type": "markdown",
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"source": [
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"#Download the Training Dataset\n",
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"We employ the cropped VGGFace2-224 dataset for this toy training demo.\n",
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"You can download the dataset from our google driver "
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"\n",
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"You can download the dataset from our google driver https://drive.google.com/file/d/19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc/view?usp=sharing\n",
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"\n",
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"***Please check the dataset in dir /content/TrainingData***\n",
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"\n",
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"***If dataset already exists in /content/TrainingData, please do not run blow scripts!***\n"
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],
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"metadata": {
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"id": "hleVtHIJ_QUK"
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{
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"cell_type": "code",
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"source": [
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"from google_drive_downloader import GoogleDriveDownloader as gdd\n",
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"gdd.download_file_from_google_drive(file_id='1iytA1n2z4go3uVCwE__vIKouTKyIDjEq',dest_path='/content/TrainingData/vggface2_crop_arcfacealign_224.tar',showsize=True)\n",
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"!tar -xzvf /content/TrainingData/vggface2_crop_arcfacealign_224.tar"
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"!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=19pWvdEHS-CEG6tW3PdxdtZ5QEymVjImc\" -O /content/TrainingData/vggface2_crop_arcfacealign_224.tar && rm -rf /tmp/cookies.txt\n",
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"%%cd /content/\n",
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"!tar -xzvf /content/TrainingData/vggface2_crop_arcfacealign_224.tar\n",
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"!rm /content/TrainingData/vggface2_crop_arcfacealign_224.tar"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gMVKEej59LX9",
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"outputId": "2e508c44-d006-4183-81d9-f9753d08dea7"
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"id": "h2tyjBl0Llxp"
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Downloading 1iytA1n2z4go3uVCwE__vIKouTKyIDjEq into /content/TrainingData/mnist.zip... \n",
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"0.0 B Done.\n"
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]
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}
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"outputs": []
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"cell_type": "markdown",
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"!python train.py --name simswap224_test --gpu_ids 0 --dataset /content/TrainingData/vggface2_crop_arcfacealign_224 --Gdeep False"
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "XCxHa4oW507s",
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"outputId": "c84c52d9-0b36-4932-925d-1ae38a3f7bb0"
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"id": "XCxHa4oW507s"
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/SimSwap\n",
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" arcface_model\t predict.py\n",
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" cog.yaml\t README.md\n",
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" crop_224\t 'SimSwap colab.ipynb'\n",
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" data\t\t simswaplogo\n",
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" demo_file\t test_one_image.py\n",
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" docs\t\t test_video_swapmulti.py\n",
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" download-weights.sh test_video_swap_multispecific.py\n",
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" insightface_func test_video_swapsingle.py\n",
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" LICENSE\t test_video_swapspecific.py\n",
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" models\t\t test_wholeimage_swapmulti.py\n",
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" MultiSpecific.ipynb test_wholeimage_swap_multispecific.py\n",
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" options\t test_wholeimage_swapsingle.py\n",
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" output\t\t test_wholeimage_swapspecific.py\n",
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" parsing_model\t train.py\n",
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" pg_modules\t util\n",
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"------------ Options -------------\n",
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"Arc_path: arcface_model/arcface_checkpoint.tar\n",
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"Gdeep: False\n",
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"batchSize: 2\n",
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"beta1: 0.0\n",
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"checkpoints_dir: ./checkpoints\n",
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"continue_train: False\n",
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"dataset: /path/to/VGGFace2\n",
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"gpu_ids: 0\n",
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"isTrain: True\n",
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"lambda_feat: 10.0\n",
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"lambda_id: 30.0\n",
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"lambda_rec: 10.0\n",
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"load_pretrain: checkpoints\n",
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"log_frep: 200\n",
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"lr: 0.0004\n",
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"model_freq: 10000\n",
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"name: simswap\n",
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"niter: 10000\n",
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"niter_decay: 10000\n",
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"phase: train\n",
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"sample_freq: 1000\n",
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"tag: simswap\n",
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"total_step: 1000000\n",
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"train_simswap: True\n",
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"use_tensorboard: False\n",
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"which_epoch: 800000\n",
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"-------------- End ----------------\n",
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"GPU used : 0\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.parallel.data_parallel.DataParallel' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.activation.PReLU' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.pooling.MaxPool2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.container.Sequential' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.pooling.AdaptiveAvgPool2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.activation.Sigmoid' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.dropout.Dropout' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"/usr/local/lib/python3.7/dist-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm1d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
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" warnings.warn(msg, SourceChangeWarning)\n",
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"Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth\" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_lite0-0aa007d2.pth\n",
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"processing Swapping dataset images...\n",
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"Finished preprocessing the Swapping dataset, total dirs number: 0...\n",
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"Traceback (most recent call last):\n",
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" File \"train.py\", line 163, in <module>\n",
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" train_loader = GetLoader(opt.dataset,opt.batchSize,8,1234)\n",
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" File \"/content/SimSwap/data/data_loader_Swapping.py\", line 119, in GetLoader\n",
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" drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)\n",
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" File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 268, in __init__\n",
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" sampler = RandomSampler(dataset, generator=generator)\n",
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" File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/sampler.py\", line 103, in __init__\n",
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" \"value, but got num_samples={}\".format(self.num_samples))\n",
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"ValueError: num_samples should be a positive integer value, but got num_samples=0\n"
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]
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}
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]
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"outputs": []
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}
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]
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}

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