Create predict.py

predict.py

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+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))