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import numpy as np
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import os
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import argparse
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from tqdm import tqdm
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import torch.nn as nn
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import torch
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import torch.nn.functional as F
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import utils
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from natsort import natsorted
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from glob import glob
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from basicsr.models.archs.restormer_arch import Restormer
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from skimage import img_as_ubyte
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from pdb import set_trace as stx
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parser = argparse.ArgumentParser(description='Image Deraining using Restormer')
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parser.add_argument('--input_dir', default='./Datasets/', type=str, help='Directory of validation images')
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parser.add_argument('--result_dir', default='./results/', type=str, help='Directory for results')
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parser.add_argument('--weights', default='./pretrained_models/deraining.pth', type=str, help='Path to weights')
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args = parser.parse_args()
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yaml_file = 'Options/Deraining_Restormer.yml'
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import yaml
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try:
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from yaml import CLoader as Loader
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except ImportError:
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from yaml import Loader
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x = yaml.load(open(yaml_file, mode='r'), Loader=Loader)
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s = x['network_g'].pop('type')
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model_restoration = Restormer(**x['network_g'])
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checkpoint = torch.load(args.weights)
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model_restoration.load_state_dict(checkpoint['params'])
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print("===>Testing using weights: ",args.weights)
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model_restoration.cuda()
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model_restoration = nn.DataParallel(model_restoration)
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model_restoration.eval()
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factor = 8
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datasets = ['Rain100L', 'Rain100H', 'Test100', 'Test1200', 'Test2800']
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for dataset in datasets:
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result_dir = os.path.join(args.result_dir, dataset)
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os.makedirs(result_dir, exist_ok=True)
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inp_dir = os.path.join(args.input_dir, 'test', dataset, 'input')
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files = natsorted(glob(os.path.join(inp_dir, '*.png')) + glob(os.path.join(inp_dir, '*.jpg')))
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with torch.no_grad():
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for file_ in tqdm(files):
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torch.cuda.ipc_collect()
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torch.cuda.empty_cache()
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img = np.float32(utils.load_img(file_))/255.
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img = torch.from_numpy(img).permute(2,0,1)
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input_ = img.unsqueeze(0).cuda()
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h,w = input_.shape[2], input_.shape[3]
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H,W = ((h+factor)//factor)*factor, ((w+factor)//factor)*factor
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padh = H-h if h%factor!=0 else 0
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padw = W-w if w%factor!=0 else 0
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input_ = F.pad(input_, (0,padw,0,padh), 'reflect')
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restored = model_restoration(input_)
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restored = restored[:,:,:h,:w]
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restored = torch.clamp(restored,0,1).cpu().detach().permute(0, 2, 3, 1).squeeze(0).numpy()
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utils.save_img((os.path.join(result_dir, os.path.splitext(os.path.split(file_)[-1])[0]+'.png')), img_as_ubyte(restored))
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