From 495ffc4777522e40941753e3b1b79c02f84b25b4 Mon Sep 17 00:00:00 2001
From: Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com>
Date: Fri, 17 Jan 2025 11:00:30 +0000
Subject: Add files via upload

---
 r_basicsr/data/ffhq_dataset.py | 80 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 r_basicsr/data/ffhq_dataset.py

(limited to 'r_basicsr/data/ffhq_dataset.py')

diff --git a/r_basicsr/data/ffhq_dataset.py b/r_basicsr/data/ffhq_dataset.py
new file mode 100644
index 0000000..03dc72c
--- /dev/null
+++ b/r_basicsr/data/ffhq_dataset.py
@@ -0,0 +1,80 @@
+import random
+import time
+from os import path as osp
+from torch.utils import data as data
+from torchvision.transforms.functional import normalize
+
+from r_basicsr.data.transforms import augment
+from r_basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
+from r_basicsr.utils.registry import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register()
+class FFHQDataset(data.Dataset):
+    """FFHQ dataset for StyleGAN.
+
+    Args:
+        opt (dict): Config for train datasets. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            io_backend (dict): IO backend type and other kwarg.
+            mean (list | tuple): Image mean.
+            std (list | tuple): Image std.
+            use_hflip (bool): Whether to horizontally flip.
+
+    """
+
+    def __init__(self, opt):
+        super(FFHQDataset, self).__init__()
+        self.opt = opt
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+
+        self.gt_folder = opt['dataroot_gt']
+        self.mean = opt['mean']
+        self.std = opt['std']
+
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.io_backend_opt['db_paths'] = self.gt_folder
+            if not self.gt_folder.endswith('.lmdb'):
+                raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
+            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
+                self.paths = [line.split('.')[0] for line in fin]
+        else:
+            # FFHQ has 70000 images in total
+            self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)]
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        # load gt image
+        gt_path = self.paths[index]
+        # avoid errors caused by high latency in reading files
+        retry = 3
+        while retry > 0:
+            try:
+                img_bytes = self.file_client.get(gt_path)
+            except Exception as e:
+                logger = get_root_logger()
+                logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}')
+                # change another file to read
+                index = random.randint(0, self.__len__())
+                gt_path = self.paths[index]
+                time.sleep(1)  # sleep 1s for occasional server congestion
+            else:
+                break
+            finally:
+                retry -= 1
+        img_gt = imfrombytes(img_bytes, float32=True)
+
+        # random horizontal flip
+        img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False)
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True)
+        # normalize
+        normalize(img_gt, self.mean, self.std, inplace=True)
+        return {'gt': img_gt, 'gt_path': gt_path}
+
+    def __len__(self):
+        return len(self.paths)
-- 
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