import argparse import subprocess from tqdm import tqdm import numpy as np import torch from torch.utils.data import DataLoader from utils.dataset_utils_CDD import DerainDehazeDataset from utils.val_utils import AverageMeter, compute_psnr_ssim from utils.image_io import save_image_tensor from text_net.model import AirNet device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') def test_Derain_Dehaze(opt, net, dataset, task="derain"): output_path = opt.output_path + task + '/' subprocess.check_output(['mkdir', '-p', output_path]) # dataset.set_dataset(task) testloader = DataLoader(dataset, batch_size=1, pin_memory=True, shuffle=False, num_workers=0) print(len(testloader)) with torch.no_grad(): for ([degraded_name], degradation, degrad_patch, clean_patch, text_prompt) in tqdm(testloader): degrad_patch, clean_patch = degrad_patch.to(device), clean_patch.to(device) restored = net(x_query=degrad_patch, x_key=degrad_patch, text_prompt = text_prompt) return save_image_tensor(restored) def infer(text_prompt = "", img=None): parser = argparse.ArgumentParser() # Input Parameters parser.add_argument('--cuda', type=int, default=0) parser.add_argument('--derain_path', type=str, default="data/Test_prompting/", help='save path of test raining images') parser.add_argument('--output_path', type=str, default="output/demo11", help='output save path') parser.add_argument('--ckpt_path', type=str, default="ckpt/epoch_287.pth", help='checkpoint save path') # parser.add_argument('--text_prompt', type=str, default="derain") opt = parser.parse_args() # opt.text_prompt = text_prompt np.random.seed(0) torch.manual_seed(0) opt.batch_size = 7 ckpt_path = opt.ckpt_path derain_set = DerainDehazeDataset(opt, img=img, text_prompt = text_prompt) # Make network net = AirNet(opt).to(device) net.eval() net.load_state_dict(torch.load(ckpt_path, map_location=device)) restored = test_Derain_Dehaze(opt, net, derain_set, task="derain") return restored