Support PyPI

.github/workflows/publish-pip.yml

@@ -0,0 +1,30 @@
+name: PyPI Publish
+
+on: push
+
+jobs:
+  build-n-publish:
+    runs-on: ubuntu-latest
+    if: startsWith(github.event.ref, 'refs/tags')
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.8
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch (cpu)
+        run: pip install torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Build for distribution
+        # remove bdist_wheel for pip installation with compiling cuda extensions
+        run: python setup.py sdist
+      - name: Publish distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          password: ${{ secrets.PYPI_API_TOKEN }}

.github/workflows/pylint.yml

@@ -26,5 +26,5 @@ jobs:
     - name: Lint
       run: |
         flake8 .
-        isort --check-only --diff data/ models/ inference_realesrgan.py
-        yapf -r -d data/ models/ inference_realesrgan.py
+        isort --check-only --diff realesrgan/ scripts/ inference_realesrgan.py setup.py
+        yapf -r -d realesrgan/ scripts/ inference_realesrgan.py setup.py

.gitignore

@@ -1,3 +1,12 @@
+# ignored folders
+datasets/*
+experiments/*
+results/*
+tb_logger/*
+wandb/*
+tmp/*
+
+version.py
 .vscode
 
 # Byte-compiled / optimized / DLL files

MANIFEST.in

@@ -0,0 +1,8 @@
+include assets/*
+include inputs/*
+include scripts/*.py
+include inference_realesrgan.py
+include VERSION
+include LICENSE
+include requirements.txt
+include realesrgan/weights/README.md

README.md

@@ -97,6 +97,7 @@ This executable file is based on the wonderful [Tencent/ncnn](https://github.com
     # We use BasicSR for both training and inference
     pip install basicsr
     pip install -r requirements.txt
+    python setup.py develop
     ```
 
 ## :zap: Quick Inference

Training.md

@@ -44,7 +44,7 @@ DF2K_HR_sub/000001_s003.png
         name: DF2K+OST
         type: RealESRGANDataset
         dataroot_gt: datasets/DF2K  # modify to the root path of your folder
-        meta_info: data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # modify to your own generate meta info txt
+        meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # modify to your own generate meta info txt
         io_backend:
             type: disk
     ```
@@ -76,12 +76,12 @@ DF2K_HR_sub/000001_s003.png
 1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
     ```bash
     CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
     ```
 1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
     ```bash
     CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
     ```
 
 ## Train Real-ESRGAN
@@ -91,10 +91,10 @@ DF2K_HR_sub/000001_s003.png
 1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
     ```bash
     CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
     ```
 1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
     ```bash
     CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
     ```

VERSION

@@ -0,0 +1 @@
+0.2.0

inference_realesrgan.py

@@ -1,12 +1,9 @@
 import argparse
 import cv2
 import glob
-import math
-import numpy as np
 import os
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from torch.nn import functional as F
+
+from realesrgan import RealESRGANer
 
 
 def main():
@@ -53,203 +50,17 @@ def main():
         imgname, extension = os.path.splitext(os.path.basename(path))
         print('Testing', idx, imgname)
 
-        # ------------------------------ read image ------------------------------ #
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32)
-        if np.max(img) > 255:  # 16-bit image
-            max_range = 65535
-            print('\tInput is a 16-bit image')
-        else:
-            max_range = 255
-        img = img / max_range
-        if len(img.shape) == 2:  # gray image
-            img_mode = 'L'
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-        elif img.shape[2] == 4:  # RGBA image with alpha channel
-            img_mode = 'RGBA'
-            alpha = img[:, :, 3]
-            img = img[:, :, 0:3]
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            if args.alpha_upsampler == 'realesrgan':
-                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
-        else:
-            img_mode = 'RGB'
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-        # ------------------- process image (without the alpha channel) ------------------- #
-        upsampler.pre_process(img)
-        if args.tile:
-            upsampler.tile_process()
-        else:
-            upsampler.process()
-        output_img = upsampler.post_process()
-        output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
-        if img_mode == 'L':
-            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
-
-        # ------------------- process the alpha channel if necessary ------------------- #
-        if img_mode == 'RGBA':
-            if args.alpha_upsampler == 'realesrgan':
-                upsampler.pre_process(alpha)
-                if args.tile:
-                    upsampler.tile_process()
-                else:
-                    upsampler.process()
-                output_alpha = upsampler.post_process()
-                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
-                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
-            else:
-                h, w = alpha.shape[0:2]
-                output_alpha = cv2.resize(alpha, (w * args.scale, h * args.scale), interpolation=cv2.INTER_LINEAR)
-
-            # merge the alpha channel
-            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
-            output_img[:, :, 3] = output_alpha
-
-        # ------------------------------ save image ------------------------------ #
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        output, img_mode = upsampler.enhance(img)
         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}')
-        if max_range == 65535:  # 16-bit image
-            output = (output_img * 65535.0).round().astype(np.uint16)
-        else:
-            output = (output_img * 255.0).round().astype(np.uint8)
         cv2.imwrite(save_path, output)
 
 
-class RealESRGANer():
-
-    def __init__(self, scale, model_path, tile=0, tile_pad=10, pre_pad=10, half=False):
-        self.scale = scale
-        self.tile_size = tile
-        self.tile_pad = tile_pad
-        self.pre_pad = pre_pad
-        self.mod_scale = None
-        self.half = half
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        model.load_state_dict(loadnet[keyname], strict=True)
-        model.eval()
-        self.model = model.to(self.device)
-        if self.half:
-            self.model = self.model.half()
-
-    def pre_process(self, img):
-        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
-        self.img = img.unsqueeze(0).to(self.device)
-        if self.half:
-            self.img = self.img.half()
-
-        # pre_pad
-        if self.pre_pad != 0:
-            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad
-        if self.scale == 2:
-            self.mod_scale = 2
-        elif self.scale == 1:
-            self.mod_scale = 4
-        if self.mod_scale is not None:
-            self.mod_pad_h, self.mod_pad_w = 0, 0
-            _, _, h, w = self.img.size()
-            if (h % self.mod_scale != 0):
-                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
-            if (w % self.mod_scale != 0):
-                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
-            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
-    def process(self):
-        try:
-            # 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
-        """
-        batch, channel, height, width = self.img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        self.output = self.img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.model(input_tile)
-                except Exception as error:
-                    print('Error', error)
-                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                self.output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-
-    def post_process(self):
-        # remove extra pad
-        if self.mod_scale is not None:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
-        # remove prepad
-        if self.pre_pad != 0:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
-        return self.output
-
-
 if __name__ == '__main__':
     main()

options/train_realesrgan_x4plus.yml

@@ -39,7 +39,7 @@ datasets:
     name: DF2K+OST
     type: RealESRGANDataset
     dataroot_gt: datasets/DF2K
-    meta_info: data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    meta_info: realesrgan/data/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/model/net_g_1000000.pth
+  pretrain_network_g: experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/models/net_g_1000000.pth
   param_key_g: params_ema
   strict_load_g: true
   resume_state: ~

options/train_realesrnet_x4plus.yml

@@ -36,7 +36,7 @@ datasets:
     name: DF2K+OST
     type: RealESRGANDataset
     dataroot_gt: datasets/DF2K
-    meta_info: data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
+    meta_info: realesrgan/data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
     io_backend:
       type: disk
 

realesrgan/__init__.py

@@ -0,0 +1,6 @@
+# flake8: noqa
+from .archs import *
+from .data import *
+from .models import *
+from .utils import *
+from .version import __gitsha__, __version__

archs/__init__.py → realesrgan/archs/__init__.py

@@ -7,4 +7,4 @@
 arch_folder = osp.dirname(osp.abspath(__file__))
 arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
 # import all the arch modules
-_arch_modules = [importlib.import_module(f'archs.{file_name}') for file_name in arch_filenames]
+_arch_modules = [importlib.import_module(f'realesrgan.archs.{file_name}') for file_name in arch_filenames]

data/__init__.py → realesrgan/data/__init__.py

@@ -7,4 +7,4 @@
 data_folder = osp.dirname(osp.abspath(__file__))
 dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
 # import all the dataset modules
-_dataset_modules = [importlib.import_module(f'data.{file_name}') for file_name in dataset_filenames]
+_dataset_modules = [importlib.import_module(f'realesrgan.data.{file_name}') for file_name in dataset_filenames]

models/__init__.py → realesrgan/models/__init__.py

@@ -7,4 +7,4 @@
 model_folder = osp.dirname(osp.abspath(__file__))
 model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
 # import all the model modules
-_model_modules = [importlib.import_module(f'models.{file_name}') for file_name in model_filenames]
+_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]

realesrgan/train.py

@@ -0,0 +1,11 @@
+# flake8: noqa
+import os.path as osp
+from basicsr.train import train_pipeline
+
+import realesrgan.archs
+import realesrgan.data
+import realesrgan.models
+
+if __name__ == '__main__':
+    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
+    train_pipeline(root_path)