add finetune_realesrgan_x4plus, version 0.2.2.1

* Create gradiodemo.py

* Update requirements.txt

* Update gradiodemo.py

* Update requirements.txt

* Update requirements.txt

* Update README.md

* Update README.md

* Delete gradiodemo.py

* Update requirements.txt

.github/workflows/no-response.yml

@@ -0,0 +1,34 @@
+name: No Response
+
+# Modified from: https://raw.githubusercontent.com/github/docs/main/.github/workflows/no-response.yaml
+
+# **What it does**: Closes issues that don't have enough information to be
+#                   actionable.
+# **Why we have it**: To remove the need for maintainers to remember to check
+#                     back on issues periodically to see if contributors have
+#                     responded.
+# **Who does it impact**: Everyone that works on docs or docs-internal.
+
+on:
+  issue_comment:
+    types: [created]
+
+  schedule:
+    # Schedule for five minutes after the hour every hour
+    - cron: '5 * * * *'
+
+jobs:
+  noResponse:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: lee-dohm/no-response@v0.5.0
+        with:
+          token: ${{ github.token }}
+          closeComment: >
+            This issue has been automatically closed because there has been no response
+            to our request for more information from the original author. With only the
+            information that is currently in the issue, we don't have enough information
+            to take action. Please reach out if you have or find the answers we need so
+            that we can investigate further.
+            If you still have questions, please improve your description and re-open it.
+            Thanks :-)

.gitignore

@@ -5,9 +5,9 @@ results/*
 tb_logger/*
 wandb/*
 tmp/*
+realesrgan/weights/*
 
 version.py
-.vscode
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

.vscode/settings.json

@@ -0,0 +1,19 @@
+{
+    "files.trimTrailingWhitespace": true,
+    "editor.wordWrap": "on",
+    "editor.rulers": [
+        80,
+        120
+    ],
+    "editor.renderWhitespace": "all",
+    "editor.renderControlCharacters": true,
+    "python.formatting.provider": "yapf",
+    "python.formatting.yapfArgs": [
+        "--style",
+        "{BASED_ON_STYLE = pep8, BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true, SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true, COLUMN_LIMIT = 120}"
+    ],
+    "python.linting.flake8Enabled": true,
+    "python.linting.flake8Args": [
+        "max-line-length=120"
+    ],
+}

FAQ.md

@@ -0,0 +1,9 @@
+# FAQ
+
+1. **What is the difference of `--netscale` and `outscale`?**
+
+A: TODO.
+
+1. **How to select models?**
+
+A: TODO.

README.md

@@ -1,21 +1,32 @@
 # Real-ESRGAN
 
 [![download](https://img.shields.io/github/downloads/xinntao/Real-ESRGAN/total.svg)](https://github.com/xinntao/Real-ESRGAN/releases)
+[![PyPI](https://img.shields.io/pypi/v/realesrgan)](https://pypi.org/project/realesrgan/)
 [![Open issue](https://isitmaintained.com/badge/open/xinntao/Real-ESRGAN.svg)](https://github.com/xinntao/Real-ESRGAN/issues)
 [![LICENSE](https://img.shields.io/github/license/xinntao/Real-ESRGAN.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
 [![python lint](https://github.com/xinntao/Real-ESRGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
+[![Publish-pip](https://github.com/xinntao/Real-ESRGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
 
 1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN <a href="https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>.
-2. [Portable Windows/Linux/MacOS executable files for Intel/AMD/Nvidia GPU](https://github.com/xinntao/Real-ESRGAN/releases). You can find more information [here](#Portable-executable-files).
+2. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#Portable-executable-files).
 
 Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
 We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
 
-:triangular_flag_on_post: **Updates**
+:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md).
 
+:triangular_flag_on_post: **Updates**
+- :white_check_mark: Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
+- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN).
+- :white_check_mark: Support arbitrary scale with `--outscale` (It actually further resizes outputs with `LANCZOS4`). Add *RealESRGAN_x2plus.pth* model.
 - :white_check_mark: [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
 - :white_check_mark: The training codes have been released. A detailed guide can be found in [Training.md](Training.md).
 
+If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo. Thanks:blush: <br>
+Other recommended projects: &emsp; :arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN) &emsp; :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR) &emsp; :arrow_forward: [facexlib](https://github.com/xinntao/facexlib)
+
+---
+
 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
 
 > [[Paper](https://arxiv.org/abs/2107.10833)] &emsp; [Project Page] &emsp; [Demo] <br>
@@ -41,7 +52,7 @@ Here is a TODO list in the near future:
 
 - [ ] optimize for human faces
 - [ ] optimize for texts
-- [ ] optimize for animation images
+- [ ] optimize for anime images [in progress]
 - [ ] support more scales
 - [ ] support controllable restoration strength
 
@@ -52,7 +63,7 @@ If you have some images that Real-ESRGAN could not well restored, please also op
 
 ### Portable executable files
 
-You can download **Windows/Linux/MacOS executable files for Intel/AMD/Nvidia GPU** from https://github.com/xinntao/Real-ESRGAN/releases
+You can download [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.
 
 This executable file is **portable** and includes all the binaries and models required. No CUDA or PyTorch environment is needed.<br>
 
@@ -96,6 +107,9 @@ This executable file is based on the wonderful [Tencent/ncnn](https://github.com
     # Install basicsr - https://github.com/xinntao/BasicSR
     # We use BasicSR for both training and inference
     pip install basicsr
+    # facexlib and gfpgan are for face enhancement
+    pip install facexlib
+    pip install gfpgan
     pip install -r requirements.txt
     python setup.py develop
     ```
@@ -113,7 +127,7 @@ wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_
 Inference!
 
 ```bash
-python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs
+python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs --face_enhance
 ```
 
 Results are in the `results` folder
@@ -122,6 +136,7 @@ Results are in the `results` folder
 
 - [RealESRGAN-x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
 - [RealESRNet-x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
+- [RealESRGAN-x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth)
 - [official ESRGAN-x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)
 
 ## :computer: Training

Training.md

@@ -1,7 +1,7 @@
 # :computer: How to Train Real-ESRGAN
 
 The training codes have been released. <br>
-Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report issues and I will also retrain the models.
+Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report bugs/issues.
 
 ## Overview
 

VERSION

@@ -1 +1 @@
-0.2.0
+0.2.2.1

inference_realesrgan.py

@@ -15,11 +15,13 @@ def main():
         default='experiments/pretrained_models/RealESRGAN_x4plus.pth',
         help='Path to the pre-trained model')
     parser.add_argument('--output', type=str, default='results', help='Output folder')
-    parser.add_argument('--scale', type=int, default=4, help='Upsample scale factor')
+    parser.add_argument('--netscale', type=int, default=4, help='Upsample scale factor of the network')
+    parser.add_argument('--outscale', type=float, default=4, help='The final upsampling scale of the image')
     parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
-    parser.add_argument('--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
+    parser.add_argument('--tile', type=int, default=800, help='Tile size, 0 for no tile during testing')
     parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
     parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
+    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
     parser.add_argument('--half', action='store_true', help='Use half precision during inference')
     parser.add_argument(
         '--alpha_upsampler',
@@ -34,13 +36,23 @@ def main():
     args = parser.parse_args()
 
     upsampler = RealESRGANer(
-        scale=args.scale,
+        scale=args.netscale,
         model_path=args.model_path,
         tile=args.tile,
         tile_pad=args.tile_pad,
         pre_pad=args.pre_pad,
         half=args.half)
+
+    if args.face_enhance:
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
+            upscale=args.outscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)
     os.makedirs(args.output, exist_ok=True)
+
     if os.path.isfile(args.input):
         paths = [args.input]
     else:
@@ -51,15 +63,36 @@ def main():
         print('Testing', idx, imgname)
 
         img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-        output, img_mode = upsampler.enhance(img)
+        if len(img.shape) == 3 and img.shape[2] == 4:
-        if args.ext == 'auto':
+            img_mode = 'RGBA'
-            extension = extension[1:]
         else:
-            extension = args.ext
+            img_mode = None
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+
-            extension = 'png'
+        h, w = img.shape[0:2]
-        save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
+        if max(h, w) > 1000 and args.netscale == 4:
-        cv2.imwrite(save_path, output)
+            import warnings
+            warnings.warn('The input image is large, try X2 model for better performace.')
+        if max(h, w) < 500 and args.netscale == 2:
+            import warnings
+            warnings.warn('The input image is small, try X4 model for better performace.')
+
+        try:
+            if args.face_enhance:
+                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+            else:
+                output, _ = upsampler.enhance(img, outscale=args.outscale)
+        except Exception as error:
+            print('Error', error)
+            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+        else:
+            if args.ext == 'auto':
+                extension = extension[1:]
+            else:
+                extension = args.ext
+            if img_mode == 'RGBA':  # RGBA images should be saved in png format
+                extension = 'png'
+            save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
+            cv2.imwrite(save_path, output)
 
 
 if __name__ == '__main__':

options/finetune_realesrgan_x4plus.yml

@@ -0,0 +1,189 @@
+# general settings
+name: finetune_RealESRGANx4plus_400k_B12G4
+model_type: RealESRGANModel
+scale: 4
+num_gpu: 4
+manual_seed: 0
+
+# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
+# USM the ground-truth
+l1_gt_usm: True
+percep_gt_usm: True
+gan_gt_usm: False
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 0.5
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 0.4
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 0.8
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 0.5
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 0.4
+jpeg_range2: [30, 95]
+
+gt_size: 256
+queue_size: 180
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DF2K+OST
+    type: RealESRGANDataset
+    dataroot_gt: datasets/DF2K
+    meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    io_backend:
+      type: disk
+
+    blur_kernel_size: 21
+    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob: 0.1
+    blur_sigma: [0.2, 3]
+    betag_range: [0.5, 4]
+    betap_range: [1, 2]
+
+    blur_kernel_size2: 21
+    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob2: 0.1
+    blur_sigma2: [0.2, 1.5]
+    betag_range2: [0.5, 4]
+    betap_range2: [1, 2]
+
+    final_sinc_prob: 0.8
+
+    gt_size: 256
+    use_hflip: True
+    use_rot: False
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 5
+    batch_size_per_gpu: 12
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  # Uncomment these for validation
+  # val:
+  #   name: validation
+  #   type: PairedImageDataset
+  #   dataroot_gt: path_to_gt
+  #   dataroot_lq: path_to_lq
+  #   io_backend:
+  #     type: disk
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 23
+  num_grow_ch: 32
+
+
+network_d:
+  type: UNetDiscriminatorSN
+  num_in_ch: 3
+  num_feat: 64
+  skip_connection: True
+
+# path
+path:
+  # use the pre-trained Real-ESRNet model
+  pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
+  param_key_g: params_ema
+  strict_load_g: true
+  pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
+  param_key_d: params
+  strict_load_d: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+  optim_d:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [400000]
+    gamma: 0.5
+
+  total_iter: 400000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+  # perceptual loss (content and style losses)
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      # before relu
+      'conv1_2': 0.1
+      'conv2_2': 0.1
+      'conv3_4': 1
+      'conv4_4': 1
+      'conv5_4': 1
+    vgg_type: vgg19
+    use_input_norm: true
+    perceptual_weight: !!float 1.0
+    style_weight: 0
+    range_norm: false
+    criterion: l1
+  # gan loss
+  gan_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 1e-1
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+
+# Uncomment these for validation
+# validation settings
+# val:
+#   val_freq: !!float 5e3
+#   save_img: True
+
+#   metrics:
+#     psnr: # metric name, can be arbitrary
+#       type: calculate_psnr
+#       crop_border: 4
+#       test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/train_realesrgan_x2plus.yml

@@ -0,0 +1,187 @@
+# general settings
+name: train_RealESRGANx2plus_400k_B12G4
+model_type: RealESRGANModel
+scale: 2
+num_gpu: 4
+manual_seed: 0
+
+# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
+# USM the ground-truth
+l1_gt_usm: True
+percep_gt_usm: True
+gan_gt_usm: False
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 0.5
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 0.4
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 0.8
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 0.5
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 0.4
+jpeg_range2: [30, 95]
+
+gt_size: 256
+queue_size: 180
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DF2K+OST
+    type: RealESRGANDataset
+    dataroot_gt: datasets/DF2K
+    meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    io_backend:
+      type: disk
+
+    blur_kernel_size: 21
+    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob: 0.1
+    blur_sigma: [0.2, 3]
+    betag_range: [0.5, 4]
+    betap_range: [1, 2]
+
+    blur_kernel_size2: 21
+    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob2: 0.1
+    blur_sigma2: [0.2, 1.5]
+    betag_range2: [0.5, 4]
+    betap_range2: [1, 2]
+
+    final_sinc_prob: 0.8
+
+    gt_size: 256
+    use_hflip: True
+    use_rot: False
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 5
+    batch_size_per_gpu: 12
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  # Uncomment these for validation
+  # val:
+  #   name: validation
+  #   type: PairedImageDataset
+  #   dataroot_gt: path_to_gt
+  #   dataroot_lq: path_to_lq
+  #   io_backend:
+  #     type: disk
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 23
+  num_grow_ch: 32
+  scale: 2
+
+
+network_d:
+  type: UNetDiscriminatorSN
+  num_in_ch: 3
+  num_feat: 64
+  skip_connection: True
+
+# path
+path:
+  # use the pre-trained Real-ESRNet model
+  pretrain_network_g: experiments/pretrained_models/RealESRNet_x2plus.pth
+  param_key_g: params_ema
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+  optim_d:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [400000]
+    gamma: 0.5
+
+  total_iter: 400000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+  # perceptual loss (content and style losses)
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      # before relu
+      'conv1_2': 0.1
+      'conv2_2': 0.1
+      'conv3_4': 1
+      'conv4_4': 1
+      'conv5_4': 1
+    vgg_type: vgg19
+    use_input_norm: true
+    perceptual_weight: !!float 1.0
+    style_weight: 0
+    range_norm: false
+    criterion: l1
+  # gan loss
+  gan_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 1e-1
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+
+# Uncomment these for validation
+# validation settings
+# val:
+#   val_freq: !!float 5e3
+#   save_img: True
+
+#   metrics:
+#     psnr: # metric name, can be arbitrary
+#       type: calculate_psnr
+#       crop_border: 4
+#       test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/train_realesrgan_x4plus.yml

@@ -1,5 +1,5 @@
 # general settings
-name: train_RealESRGANx4plus_400k_B12G4_fromRealESRNet
+name: train_RealESRGANx4plus_400k_B12G4
 model_type: RealESRGANModel
 scale: 4
 num_gpu: 4
@@ -39,7 +39,7 @@ datasets:
     name: DF2K+OST
     type: RealESRGANDataset
     dataroot_gt: datasets/DF2K
-    meta_info: realesrgan/data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
     io_backend:
       type: disk
 
@@ -100,7 +100,7 @@ network_d:
 # path
 path:
   # use the pre-trained Real-ESRNet model
-  pretrain_network_g: experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/models/net_g_1000000.pth
+  pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
   param_key_g: params_ema
   strict_load_g: true
   resume_state: ~

options/train_realesrnet_x2plus.yml

@@ -0,0 +1,145 @@
+# general settings
+name: train_RealESRNetx2plus_1000k_B12G4
+model_type: RealESRNetModel
+scale: 2
+num_gpu: 4
+manual_seed: 0
+
+# ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
+gt_usm: True  # USM the ground-truth
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 0.5
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 0.4
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 0.8
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 0.5
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 0.4
+jpeg_range2: [30, 95]
+
+gt_size: 256
+queue_size: 180
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DF2K+OST
+    type: RealESRGANDataset
+    dataroot_gt: datasets/DF2K
+    meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    io_backend:
+      type: disk
+
+    blur_kernel_size: 21
+    kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob: 0.1
+    blur_sigma: [0.2, 3]
+    betag_range: [0.5, 4]
+    betap_range: [1, 2]
+
+    blur_kernel_size2: 21
+    kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+    kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+    sinc_prob2: 0.1
+    blur_sigma2: [0.2, 1.5]
+    betag_range2: [0.5, 4]
+    betap_range2: [1, 2]
+
+    final_sinc_prob: 0.8
+
+    gt_size: 256
+    use_hflip: True
+    use_rot: False
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 5
+    batch_size_per_gpu: 12
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  # Uncomment these for validation
+  # val:
+  #   name: validation
+  #   type: PairedImageDataset
+  #   dataroot_gt: path_to_gt
+  #   dataroot_lq: path_to_lq
+  #   io_backend:
+  #     type: disk
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 23
+  num_grow_ch: 32
+  scale: 2
+
+# path
+path:
+  pretrain_network_g: experiments/pretrained_models/RealESRGAN_x4plus.pth
+  param_key_g: params_ema
+  strict_load_g: False
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 2e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [1000000]
+    gamma: 0.5
+
+  total_iter: 1000000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+# Uncomment these for validation
+# validation settings
+# val:
+#   val_freq: !!float 5e3
+#   save_img: True
+
+#   metrics:
+#     psnr: # metric name, can be arbitrary
+#       type: calculate_psnr
+#       crop_border: 4
+#       test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500

options/train_realesrnet_x4plus.yml

@@ -1,5 +1,5 @@
 # general settings
-name: train_RealESRNetx4plus_1000k_B12G4_fromESRGAN
+name: train_RealESRNetx4plus_1000k_B12G4
 model_type: RealESRNetModel
 scale: 4
 num_gpu: 4
@@ -36,7 +36,7 @@ datasets:
     name: DF2K+OST
     type: RealESRGANDataset
     dataroot_gt: datasets/DF2K
-    meta_info: realesrgan/data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    meta_info: datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
     io_backend:
       type: disk
 

realesrgan/utils.py

@@ -63,12 +63,7 @@ class RealESRGANer():
             self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
 
     def process(self):
-        try:
+        self.output = self.model(self.img)
-            # inference
-            with torch.no_grad():
-                self.output = self.model(self.img)
-        except Exception as error:
-            print('Error', error)
 
     def tile_process(self):
         """Modified from: https://github.com/ata4/esrgan-launcher
@@ -143,10 +138,12 @@ class RealESRGANer():
             self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
         return self.output
 
-    def enhance(self, img, tile=False, alpha_upsampler='realesrgan'):
+    @torch.no_grad()
+    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
+        h_input, w_input = img.shape[0:2]
         # img: numpy
         img = img.astype(np.float32)
-        if np.max(img) > 255:  # 16-bit image
+        if np.max(img) > 256:  # 16-bit image
             max_range = 65535
             print('\tInput is a 16-bit image')
         else:
@@ -203,6 +200,14 @@ class RealESRGANer():
             output = (output_img * 65535.0).round().astype(np.uint16)
         else:
             output = (output_img * 255.0).round().astype(np.uint8)
+
+        if outscale is not None and outscale != float(self.scale):
+            output = cv2.resize(
+                output, (
+                    int(w_input * outscale),
+                    int(h_input * outscale),
+                ), interpolation=cv2.INTER_LANCZOS4)
+
         return output, img_mode
 
 

requirements.txt

@@ -1,4 +1,7 @@
-basicsr
+basicsr>=1.3.3.11
+facexlib>=0.2.0.3
+gfpgan>=0.2.1
 numpy
 opencv-python
+Pillow
 torch>=1.7

scripts/generate_meta_info.py

@@ -0,0 +1,40 @@
+import argparse
+import glob
+import os
+
+
+def main(args):
+    txt_file = open(args.meta_info, 'w')
+    for folder, root in zip(args.input, args.root):
+        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
+        for img_path in img_paths:
+            img_name = os.path.relpath(img_path, root)
+            print(img_name)
+            txt_file.write(f'{img_name}\n')
+
+
+if __name__ == '__main__':
+    """Generate meta info (txt file) for only Ground-Truth images.
+
+    It can also generate meta info from several folders into one txt file.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input',
+        nargs='+',
+        default=['datasets/DF2K/DF2K_HR', 'datasets/DF2K/DF2K_multiscale'],
+        help='Input folder, can be a list')
+    parser.add_argument(
+        '--root',
+        nargs='+',
+        default=['datasets/DF2K', 'datasets/DF2K'],
+        help='Folder root, should have the length as input folders')
+    parser.add_argument(
+        '--meta_info',
+        type=str,
+        default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt',
+        help='txt path for meta info')
+    args = parser.parse_args()
+    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
+                                               f'{len(args.input)} and {len(args.root)}.')
+    main(args)

scripts/generate_multiscale_DF2K.py

@@ -0,0 +1,46 @@
+import argparse
+import glob
+import os
+from PIL import Image
+
+
+def main(args):
+
+    # For DF2K, we consider the following three scales,
+    # and the smallest image whose shortest edge is 400
+    scale_list = [0.75, 0.5, 1 / 3]
+    shortest_edge = 400
+
+    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
+    for path in path_list:
+        print(path)
+        basename = os.path.splitext(os.path.basename(path))[0]
+
+        img = Image.open(path)
+        width, height = img.size
+        for idx, scale in enumerate(scale_list):
+            print(f'\t{scale:.2f}')
+            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
+            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
+
+        # save the smallest image which the shortest edge is 400
+        if width < height:
+            ratio = height / width
+            width = shortest_edge
+            height = int(width * ratio)
+        else:
+            ratio = width / height
+            height = shortest_edge
+            width = int(height * ratio)
+        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
+        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
+    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
+    args = parser.parse_args()
+
+    os.makedirs(args.output, exist_ok=True)
+    main(args)

setup.cfg

@@ -17,6 +17,6 @@ line_length = 120
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = realesrgan
-known_third_party = basicsr,cv2,numpy,torch
+known_third_party = PIL,basicsr,cv2,numpy,torch
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY