Merge BasicSR v1.2.0

.github/workflows/pylint.yml

@@ -1,6 +1,6 @@
 name: Python Lint
 
-on: [push]
+on: [push, pull_request]
 
 jobs:
   build:
@@ -25,5 +25,5 @@ jobs:
     - name: Lint
       run: |
         flake8 .
-        isort --check-only --diff basicsr/ options/ scripts/ tests/ setup.py
-        yapf -r -d basicsr/ options/ scripts/ tests/ setup.py
+        isort --check-only --diff basicsr/ options/ scripts/ tests/ inference/ setup.py
+        yapf -r -d basicsr/ options/ scripts/ tests/ inference/ setup.py

.gitignore

@@ -14,6 +14,7 @@ version.py
 # ignored files with suffix
 *.html
 *.png
+*.jpeg
 *.jpg
 *.gif
 *.pth

LICENSE/README.md

@@ -13,3 +13,5 @@ This BasicSR project is released under the Apache 2.0 license.
 1. NIQE metric: the codes are translated from the [official MATLAB codes](http://live.ece.utexas.edu/research/quality/niqe_release.zip)
 
     > A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", IEEE Signal Processing Letters, 2012.
+
+1. FID metric: the codes are modified from [pytorch-fid](https://github.com/mseitzer/pytorch-fid) and [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch).

README.md

@@ -7,32 +7,32 @@ Note that this version is not compatible with previous versions. If you want to
 
 [English](README.md) **|** [简体中文](README_CN.md)   [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR)
 
-:arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing)
+<a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
+:m: [Model Zoo](docs/ModelZoo.md) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing)
 :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) <br>
+:file_folder: [Datasets](docs/DatasetPreparation.md) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr)<br>
 :chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr) <br>
 :computer: [Commands for training and testing](docs/TrainTest.md) <br>
 :zap: [HOWTOs](#zap-howtos)
 
 ---
 
-BasicSR is an **open source** image and video super-resolution toolbox based on PyTorch (will extend to more restoration tasks in the future).<br>
+BasicSR (**Basic** **S**uper **R**estoration) is an open source **image and video restoration** toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, *etc*.<br>
 <sub>([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))</sub>
+<sub>([HandyView](https://github.com/xinntao/HandyView), [HandyFigure](https://github.com/xinntao/HandyFigure), [HandyCrawler](https://github.com/xinntao/HandyCrawler), [HandyWriting](https://github.com/xinntao/HandyWriting))</sub>
 
-## :sparkles: New Feature
+## :sparkles: New Features
 
-- Sep 8, 2020. Add **blind face restoration inference codes: [DFDNet](https://github.com/csxmli2016/DFDNet)**. Note that it is slightly different from the official testing codes.
-   > Blind Face Restoration via Deep Multi-scale Component Dictionaries <br>
-   > Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang <br>
-   > European Conference on Computer Vision (ECCV), 2020
+- Nov 29, 2020. Add **ESRGAN** and **DFDNet** [colab demo](colab).
+- Sep 8, 2020. Add **blind face restoration** inference codes: [DFDNet](https://github.com/csxmli2016/DFDNet).
 - Aug 27, 2020. Add **StyleGAN2 training and testing** codes: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch).
-   > Analyzing and Improving the Image Quality of StyleGAN <br>
-   > Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila <br>
-   > Computer Vision and Pattern Recognition (CVPR), 2020
 
 <details>
   <summary>More</summary>
 <ul>
-  <li>Aug 19, 2020. A brand-new BasicSR v1.0.0 online.</li>
+  <li> Sep 8, 2020. Add <b>blind face restoration</b> inference codes: <b>DFDNet</b>. <br> <i><font color="#DCDCDC">ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries</font></i> <br> <i><font color="#DCDCDC">Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang</font></i> </li>
+  <li> Aug 27, 2020. Add <b>StyleGAN2</b> training and testing codes. <br> <i><font color="#DCDCDC">CVPR20: Analyzing and Improving the Image Quality of StyleGAN</font></i> <br> <i><font color="#DCDCDC">Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila</font></i> </li>
+  <li>Aug 19, 2020. A <b>brand-new</b> BasicSR v1.0.0 online.</li>
 </ul>
 </details>
 
@@ -41,23 +41,68 @@ BasicSR is an **open source** image and video super-resolution toolbox based on
 We provides simple pipelines to train/test/inference models for quick start.
 These pipelines/commands cannot cover all the cases and more details are in the following sections.
 
-- [How to train StyleGAN2](docs/HOWTOs.md#How-to-train-StyleGAN2)
-- [How to test StyleGAN2](docs/HOWTOs.md#How-to-test-StyleGAN2)
-- [How to test DFDNet](docs/HOWTOs.md#How-to-test-DFDNet)
+| GAN |  |  |  | | |
+| :--- | :---:        |     :---:      | :--- | :---:        |     :---:      |
+| StyleGAN2   | [Train](docs/HOWTOs.md#How-to-train-StyleGAN2) | [Inference](docs/HOWTOs.md#How-to-inference-StyleGAN2) | | | |
+| **Face Restoration** |  |  |  | | |
+| DFDNet | - | [Inference](docs/HOWTOs.md#How-to-inference-DFDNet) | | | |
+| **Super Resolution** |  |  |  | | |
+| ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO*|
+| EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO*|
+| RCAN | *TODO* | *TODO* |  |  | |
+| EDVR | *TODO* | *TODO* | DUF | - | *TODO* |
+| BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* |
+| **Deblurring** |  |  |  | | |
+| DeblurGANv2 | - | *TODO* |  | | |
+| **Denoise** |  |  |  | | |
+| RIDNet | - | *TODO* | CBDNet | - | *TODO*|
 
 ## :wrench: Dependencies and Installation
 
 - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
 - [PyTorch >= 1.3](https://pytorch.org/)
 - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
 
-Please run the following commands in the **BasicSR root path** to install BasicSR:<br>
-(Make sure that your GCC version: gcc >= 5)
+1. Clone repo
 
-```bash
-pip install -r requirements.txt
-python setup.py develop
-```
+    ```bash
+    git clone https://github.com/xinntao/BasicSR.git
+    ```
+
+1. Install dependent packages
+
+    ```bash
+    cd BasicSR
+    pip install -r requirements.txt
+    ```
+
+1. Install BasicSR
+
+    Please run the following commands in the **BasicSR root path** to install BasicSR:<br>
+    (Make sure that your GCC version: gcc >= 5) <br>
+    If you do not need the cuda extensions: <br>
+    &emsp;[*dcn* for EDVR](basicsr/models/ops)<br>
+    &emsp;[*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/models/ops)<br>
+    please add `--no_cuda_ext` when installing
+
+    ```bash
+    python setup.py develop --no_cuda_ext
+    ```
+
+    If you use the EDVR and StyleGAN2 model, the above cuda extensions are necessary.
+
+    ```bash
+    python setup.py develop
+    ```
+
+    You may also want to specify the CUDA paths:
+
+      ```bash
+      CUDA_HOME=/usr/local/cuda \
+      CUDNN_INCLUDE_DIR=/usr/local/cuda \
+      CUDNN_LIB_DIR=/usr/local/cuda \
+      python setup.py develop
+      ```
 
 Note that BasicSR is only tested in Ubuntu, and may be not suitable for Windows. You may try [Windows WSL with CUDA supports](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) :-) (It is now only available for insider build with Fast ring).
 
@@ -76,7 +121,7 @@ Please see [project boards](https://github.com/xinntao/BasicSR/projects).
 - **Options/Configs**: Please refer to [Config.md](docs/Config.md).
 - **Logging**: Please refer to [Logging.md](docs/Logging.md).
 
-## :card_file_box: Model Zoo and Baselines
+## :european_castle: Model Zoo and Baselines
 
 - The descriptions of currently supported models are in [Models.md](docs/Models.md).
 - **Pre-trained models and log examples** are available in **[ModelZoo.md](docs/ModelZoo.md)**.
@@ -97,8 +142,25 @@ The figure below shows the overall framework. More descriptions for each compone
 
 ## :scroll: License and Acknowledgement
 
-This project is released under the Apache 2.0 license.
-More details about license and acknowledgement are in [LICENSE](LICENSE/README.md).
+This project is released under the Apache 2.0 license.<br>
+More details about **license** and **acknowledgement** are in [LICENSE](LICENSE/README.md).
+
+## :earth_asia: Citations
+
+If BasicSR helps your research or work, please consider citing BasicSR.<br>
+The following is a BibTeX reference. The BibTeX entry requires the `url` LaTeX package.
+
+``` latex
+@misc{wang2020basicsr,
+  author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
+                  Chao Dong and Chen Change Loy},
+  title =        {BasicSR},
+  howpublished = {\url{https://github.com/xinntao/BasicSR}},
+  year =         {2020}
+}
+```
+
+> Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR. https://github.com/xinntao/BasicSR, 2020.
 
 ## :e-mail: Contact
 

README_CN.md

@@ -8,56 +8,101 @@
 
 [English](README.md) **|** [简体中文](README_CN.md)   [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR)
 
-:arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)
+<a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
+:m: [模型库](docs/ModelZoo_CN.md) :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)
 :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing) <br>
+:file_folder: [数据](docs/DatasetPreparation_CN.md) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) <br>
 :chart_with_upwards_trend: [wandb的训练曲线](https://app.wandb.ai/xintao/basicsr) <br>
 :computer: [训练和测试的命令](docs/TrainTest_CN.md) <br>
 :zap: [HOWTOs](#zap-howtos)
 
 ---
 
-BasicSR 是一个基于 PyTorch 的**开源**图像视频超分辨率 (Super-Resolution) 工具箱 (之后会支持更多的 Restoration 任务).<br>
+BasicSR (**Basic** **S**uper **R**estoration) 是一个基于 PyTorch 的开源图像视频复原工具箱, 比如 超分辨率, 去噪, 去模糊, 去 JPEG 压缩噪声等.<br>
 <sub>([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))</sub>
+<sub>([HandyView](https://gitee.com/xinntao/HandyView), [HandyFigure](https://gitee.com/xinntao/HandyFigure), [HandyCrawler](https://gitee.com/xinntao/HandyCrawler), [HandyWriting](https://gitee.com/xinntao/HandyWriting))</sub>
 
 ## :sparkles: 新的特性
 
-- Sep 8, 2020. 添加 **盲人脸复原推理代码: [DFDNet](https://github.com/csxmli2016/DFDNet)**. 注意和官方代码有些微差异.
-   > Blind Face Restoration via Deep Multi-scale Component Dictionaries <br>
-   > Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang <br>
-   > European Conference on Computer Vision (ECCV), 2020
-- Aug 27, 2020. 添加 **StyleGAN2  训练和测试** 代码: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch).
-   > Analyzing and Improving the Image Quality of StyleGAN <br>
-   > Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila <br>
-   > Computer Vision and Pattern Recognition (CVPR), 2020
+- Nov 29, 2020. 添加 **ESRGAN** and **DFDNet** [colab demo](colab).
+- Sep 8, 2020. 添加 **盲人脸复原**测试代码: [DFDNet](https://github.com/csxmli2016/DFDNet).
+- Aug 27, 2020. 添加 **StyleGAN2 训练和测试** 代码: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch).
 
 <details>
   <summary>更多</summary>
 <ul>
-  <li>Aug 19, 2020. 全新的 BasicSR v1.0.0 上线.</li>
+  <li> Sep 8, 2020. 添加 <b>盲人脸复原</b> 测试代码: <b>DFDNet</b>. <br> <i><font color="#DCDCDC">ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries</font></i> <br> <i><font color="#DCDCDC">Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang</font></i> </li>
+  <li> Aug 27, 2020. 添加 <b>StyleGAN2</b> 训练和测试代码. <br> <i><font color="#DCDCDC">CVPR20: Analyzing and Improving the Image Quality of StyleGAN</font></i> <br> <i><font color="#DCDCDC">Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila</font></i> </li>
+  <li>Aug 19, 2020. <b>全新的</b> BasicSR v1.0.0 上线.</li>
 </ul>
 </details>
 
 ## :zap: HOWTOs
 
 我们提供了简单的流程来快速上手 训练/测试/推理 模型. 这些命令并不能涵盖所有用法, 更多的细节参见下面的部分.
 
-- [如何训练 StyleGAN2](docs/HOWTOs_CN.md#如何训练-StyleGAN2)
-- [如何测试 StyleGAN2](docs/HOWTOs_CN.md#如何测试-StyleGAN2)
-- [如何测试 DFDNet](docs/HOWTOs_CN.md#如何测试-DFDNet)
+| GAN |  |  |  | | |
+| :--- | :---:        |     :---:      | :--- | :---:        |     :---:      |
+| StyleGAN2   | [训练](docs/HOWTOs_CN.md#如何训练-StyleGAN2) | [测试](docs/HOWTOs_CN.md#如何测试-StyleGAN2) | | | |
+| **Face Restoration** |  |  |  | | |
+| DFDNet | - | [测试](docs/HOWTOs_CN.md#如何测试-DFDNet) | | | |
+| **Super Resolution** |  |  |  | | |
+| ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO*|
+| EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO*|
+| RCAN | *TODO* | *TODO* |  |  | |
+| EDVR | *TODO* | *TODO* | DUF | - | *TODO* |
+| BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* |
+| **Deblurring** |  |  |  | | |
+| DeblurGANv2 | - | *TODO* |  | | |
+| **Denoise** |  |  |  | | |
+| RIDNet | - | *TODO* | CBDNet | - | *TODO*|
 
 ## :wrench: 依赖和安装
 
 - Python >= 3.7 (推荐使用 [Anaconda](https://www.anaconda.com/download/#linux) 或 [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
 - [PyTorch >= 1.3](https://pytorch.org/)
 - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
 
-在BasicSR的**根目录**下运行以下命令:<br>
-(确保 GCC 版本: gcc >= 5)
+1. Clone repo
 
-```bash
-pip install -r requirements.txt
-python setup.py develop
-```
+    ```bash
+    git clone https://github.com/xinntao/BasicSR.git
+    ```
+
+1. 安装依赖包
+
+    ```bash
+    cd BasicSR
+    pip install -r requirements.txt
+    ```
+
+1. 安装 BasicSR
+
+    在BasicSR的**根目录**下运行以下命令:<br>
+    (确保 GCC 版本: gcc >= 5) <br>
+    如果你不需要以下 cuda 扩展算子: <br>
+    &emsp;[*dcn* for EDVR](basicsr/models/ops)<br>
+    &emsp;[*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/models/ops)<br>
+    在安装命令后添加 `--no_cuda_ext`
+
+    ```bash
+    python setup.py develop --no_cuda_ext
+    ```
+
+    如果使用 EDVR 和 StyleGAN2 模型, 则需要使用上面的 cuda 扩展算子.
+
+    ```bash
+    python setup.py develop
+    ```
+
+    你或许需要指定 CUDA 路径:
+
+      ```bash
+      CUDA_HOME=/usr/local/cuda \
+      CUDNN_INCLUDE_DIR=/usr/local/cuda \
+      CUDNN_LIB_DIR=/usr/local/cuda \
+      python setup.py develop
+      ```
 
 注意: BasicSR 仅在 Ubuntu 下进行测试，或许不支持Windows. 可以在Windows下尝试[支持CUDA的Windows WSL](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) :-) (目前只有Fast ring的预览版系统可以安装).
 
@@ -76,7 +121,7 @@ python setup.py develop
 - **Options/Configs**配置文件的说明, 参见 [Config_CN.md](docs/Config_CN.md).
 - **Logging**日志系统的说明, 参见 [Logging_CN.md](docs/Logging_CN.md).
 
-## :card_file_box: 模型库和基准
+## :european_castle: 模型库和基准
 
 - 目前支持的模型描述, 参见 [Models_CN.md](docs/Models_CN.md).
 - **预训练模型和log样例**, 参见 **[ModelZoo_CN.md](docs/ModelZoo_CN.md)**.
@@ -97,8 +142,25 @@ python setup.py develop
 
 ## :scroll: 许可
 
-本项目使用 Apache 2.0 license.
-更多细节参见 [LICENSE](LICENSE/README.md).
+本项目使用 Apache 2.0 license.<br>
+更多关于**许可**和**致谢**, 请参见 [LICENSE](LICENSE/README.md).
+
+## :earth_asia: 引用
+
+如果 BasicSR 对你有所帮助, 可以考虑引用BasicSR. <br>
+下面是一个 BibTex 引用条目, 它需要 `url` LaTeX package.
+
+``` latex
+@misc{wang2020basicsr,
+  author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and
+                  Chao Dong and Chen Change Loy},
+  title =        {BasicSR},
+  howpublished = {\url{https://github.com/xinntao/BasicSR}},
+  year =         {2020}
+}
+```
+
+> Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR. https://github.com/xinntao/BasicSR, 2020.
 
 ## :e-mail: 联系
 

VERSION

@@ -1 +1 @@
-1.1.1
+1.2.0

basicsr/data/__init__.py

@@ -1,23 +1,22 @@
 import importlib
-import mmcv
 import numpy as np
 import random
 import torch
 import torch.utils.data
 from functools import partial
-from mmcv.runner import get_dist_info
 from os import path as osp
 
 from basicsr.data.prefetch_dataloader import PrefetchDataLoader
-from basicsr.utils import get_root_logger
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.dist_util import get_dist_info
 
 __all__ = ['create_dataset', 'create_dataloader']
 
 # automatically scan and import dataset modules
 # scan all the files under the data folder with '_dataset' in file names
 data_folder = osp.dirname(osp.abspath(__file__))
 dataset_filenames = [
-    osp.splitext(osp.basename(v))[0] for v in mmcv.scandir(data_folder)
+    osp.splitext(osp.basename(v))[0] for v in scandir(data_folder)
     if v.endswith('_dataset.py')
 ]
 # import all the dataset modules
@@ -99,7 +98,7 @@ def create_dataloader(dataset,
             seed=seed) if seed is not None else None
     elif phase in ['val', 'test']:  # validation
         dataloader_args = dict(
-            dataset=dataset, batch_size=1, shuffle=False, num_workers=1)
+            dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
     else:
         raise ValueError(f'Wrong dataset phase: {phase}. '
                          "Supported ones are 'train', 'val' and 'test'.")