Fixed mistakenly commited inference.py

inference.py

@@ -3,73 +3,21 @@
 from torchvision.transforms.functional import to_tensor
 from torchvision.utils import save_image
 import os
-import cv2
 from torch.utils.data import DataLoader
 from torch_datasets.image_folder_dataset import ImageFolderDataset
 from tqdm import tqdm
-import numpy as np
 import argparse
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils import img2tensor, tensor2img
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from realesrgan.utils import RealESRGANer
-
 
 torch.set_float32_matmul_precision('high')
 torch.set_grad_enabled(False)
 
 
-
-realesrgan_folder = "checkpoints"
-os.makedirs(realesrgan_folder, exist_ok=True)
-realesr_model_path = f"{realesrgan_folder}/RealESRGAN_x4plus.pth"
-if not os.path.exists(realesr_model_path):
-    os.system(
-        f"wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -O {realesrgan_folder}/RealESRGAN_x4plus.pth"
-    )
-
-
-def set_realesrgan():
-    use_half = False
-    if torch.cuda.is_available():  # set False in CPU/MPS mode
-        no_half_gpu_list = ["1650", "1660"]  # set False for GPUs that don't support f16
-        if not True in [
-            gpu in torch.cuda.get_device_name(0) for gpu in no_half_gpu_list
-        ]:
-            use_half = True
-
-    model = RRDBNet(
-        num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2,
-    )
-    upsampler = RealESRGANer(
-        scale=2,
-        model_path="https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth",
-        model=model,
-        tile=400,
-        tile_pad=40,
-        pre_pad=0,
-        half=use_half,
-    )
-    return upsampler
-
-
-upsampler = set_realesrgan()
-def resize(img, size):
-    # From https://github.com/sczhou/CodeFormer/blob/master/facelib/utils/face_restoration_helper.py
-    h, w = img.shape[0:2]
-    scale = size / min(h, w)
-    h, w = int(h * scale), int(w * scale)
-    interp = cv2.INTER_AREA if scale < 1 else cv2.INTER_LINEAR
-    return cv2.resize(img, (w, h), interpolation=interp)
-
 def main(args):
-    torch.manual_seed(args.seed)
-    np.random.seed(args.seed)
-
     def identity(x):
         return x, None
     ds = ImageFolderDataset(args.lq_data_path, degradation=identity, transform=to_tensor)
     dl = DataLoader(ds, batch_size=args.batch_size, shuffle=False, drop_last=False, num_workers=0)
+
     output_path = os.path.join(args.output_dir, 'restored_images')
     os.makedirs(output_path, exist_ok=True)
 
@@ -128,7 +76,5 @@ def identity(x):
                         help='Number of flow steps to use for inference.')
     parser.add_argument('--batch_size', type=int, required=False, default=64,
                         help='Batch size for inference.')
-    parser.add_argument('--seed', type=int, required=False, default=0,
-                        help='The input random seed.')
 
     main(parser.parse_args())