Delete predict.py

predict.py

@@ -1,83 +0,0 @@
-import cog
-import tempfile
-from pathlib import Path
-import argparse
-import shutil
-import os
-import glob
-import torch
-from skimage import img_as_ubyte
-from PIL import Image
-from model.SRMNet import SRMNet
-from main_test_SRMNet import save_img, setup
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-
-class Predictor(cog.Predictor):
-    def setup(self):
-        model_dir = 'experiments/pretrained_models/AWGN_denoising_SRMNet.pth'
-
-        parser = argparse.ArgumentParser(description='Demo Image Denoising')
-        parser.add_argument('--input_dir', default='./test/', type=str, help='Input images')
-        parser.add_argument('--result_dir', default='./result/', type=str, help='Directory for results')
-        parser.add_argument('--weights',
-                            default='./checkpoints/SRMNet_real_denoise/models/model_bestPSNR.pth', type=str,
-                            help='Path to weights')
-
-        self.args = parser.parse_args()
-
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    @cog.input("image", type=Path, help="input image")
-
-    def predict(self, image):
-
-        # set input folder
-        input_dir = 'input_cog_temp'
-        os.makedirs(input_dir, exist_ok=True)
-        input_path = os.path.join(input_dir, os.path.basename(image))
-        shutil.copy(str(image), input_path)
-
-        # Load corresponding models architecture and weights
-        model = SRMNet()
-        model.eval()
-        model = model.to(self.device)
-
-        folder, save_dir = setup(self.args)
-        os.makedirs(save_dir, exist_ok=True)
-
-        out_path = Path(tempfile.mkdtemp()) / "out.png"
-        mul = 16
-        for file_ in sorted(glob.glob(os.path.join(folder, '*.PNG'))):
-            img = Image.open(file_).convert('RGB')
-            input_ = TF.to_tensor(img).unsqueeze(0).cuda()
-
-            # Pad the input if not_multiple_of 8
-            h, w = input_.shape[2], input_.shape[3]
-            H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
-            padh = H - h if h % mul != 0 else 0
-            padw = W - w if w % mul != 0 else 0
-            input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-            with torch.no_grad():
-                restored = model(input_)
-                
-            restored = torch.clamp(restored, 0, 1)
-            restored = restored[:, :, :h, :w]
-            restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-            restored = img_as_ubyte(restored[0])
-
-        save_img(str(out_path), restored)
-        clean_folder(input_dir)
-        return out_path
-
-
-def clean_folder(folder):
-    for filename in os.listdir(folder):
-        file_path = os.path.join(folder, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)
-        except Exception as e:
-            print('Failed to delete %s. Reason: %s' % (file_path, e))