Create demo_gradio.py

demo_gradio.py

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+## Restormer: Efficient Transformer for High-Resolution Image Restoration
+## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
+## https://arxiv.org/abs/2111.09881
+
+
+import torch
+import torch.nn.functional as F
+import os
+from runpy import run_path
+from skimage import img_as_ubyte
+import cv2
+from tqdm import tqdm
+import argparse
+
+parser = argparse.ArgumentParser(description='Test Restormer on your own images')
+parser.add_argument('--input_path', default='./demo/degraded/', type=str, help='Directory of input images or path of single image')
+parser.add_argument('--result_dir', default='./demo/restored/', type=str, help='Directory for restored results')
+parser.add_argument('--task', required=True, type=str, help='Task to run', choices=['Motion_Deblurring',
+                                                                                    'Single_Image_Defocus_Deblurring',
+                                                                                    'Deraining',
+                                                                                    'Real_Denoising',
+                                                                                    'Gaussian_Gray_Denoising',
+                                                                                    'Gaussian_Color_Denoising'])
+
+args = parser.parse_args()
+
+
+def get_weights_and_parameters(task, parameters):
+    if task == 'Motion_Deblurring':
+        weights = os.path.join('Motion_Deblurring', 'pretrained_models', 'motion_deblurring.pth')
+    elif task == 'Single_Image_Defocus_Deblurring':
+        weights = os.path.join('Defocus_Deblurring', 'pretrained_models', 'single_image_defocus_deblurring.pth')
+    elif task == 'Deraining':
+        weights = os.path.join('Deraining', 'pretrained_models', 'deraining.pth')
+    elif task == 'Real_Denoising':
+        weights = os.path.join('Denoising', 'pretrained_models', 'real_denoising.pth')
+        parameters['LayerNorm_type'] =  'BiasFree'
+    return weights, parameters
+
+task    = args.task
+out_dir = os.path.join(args.result_dir, task)
+
+os.makedirs(out_dir, exist_ok=True)
+
+# Get model weights and parameters
+parameters = {'inp_channels':3, 'out_channels':3, 'dim':48, 'num_blocks':[4,6,6,8], 'num_refinement_blocks':4, 'heads':[1,2,4,8], 'ffn_expansion_factor':2.66, 'bias':False, 'LayerNorm_type':'WithBias', 'dual_pixel_task':False}
+weights, parameters = get_weights_and_parameters(task, parameters)
+
+load_arch = run_path(os.path.join('basicsr', 'models', 'archs', 'restormer_arch.py'))
+model = load_arch['Restormer'](**parameters)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+# device = torch.device('cpu')
+
+model = model.to(device)
+checkpoint = torch.load(weights)
+model.load_state_dict(checkpoint['params'])
+
+model.eval()
+
+
+img_multiple_of = 8
+
+
+with torch.inference_mode():
+
+    img = cv2.cvtColor(cv2.imread(args.input_path), cv2.COLOR_BGR2RGB)
+
+    input_ = torch.from_numpy(img).float().div(255.).permute(2,0,1).unsqueeze(0).to(device)
+
+    # Pad the input if not_multiple_of 8
+    h,w = input_.shape[2], input_.shape[3]
+    H,W = ((h+img_multiple_of)//img_multiple_of)*img_multiple_of, ((w+img_multiple_of)//img_multiple_of)*img_multiple_of
+    padh = H-h if h%img_multiple_of!=0 else 0
+    padw = W-w if w%img_multiple_of!=0 else 0
+    input_ = F.pad(input_, (0,padw,0,padh), 'reflect')
+    
+    restored = torch.clamp(model(input_),0,1)
+    
+    # Unpad the output
+    restored = img_as_ubyte(restored[:,:,:h,:w].permute(0, 2, 3, 1).cpu().detach().numpy()[0])
+
+    cv2.imwrite(os.path.join(out_dir, os.path.split(args.input_path)[-1]),cv2.cvtColor(restored, cv2.COLOR_RGB2BGR))