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import torch.nn as nn |
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import torch.nn.functional as F |
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from torch.autograd import Variable |
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import torch |
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import numpy as np |
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import os, time, random |
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import argparse |
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from torch.utils.data import Dataset, DataLoader |
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from PIL import Image as PILImage |
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from model.model import InvISPNet |
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from dataset.FiveK_dataset import FiveKDatasetTest |
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from config.config import get_arguments |
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from utils.JPEG import DiffJPEG |
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from utils.commons import denorm, preprocess_test_patch |
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from tqdm import tqdm |
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os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp') |
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os.environ['CUDA_VISIBLE_DEVICES'] = str(np.argmax([int(x.split()[2]) for x in open('tmp', 'r').readlines()])) |
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os.system('rm tmp') |
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DiffJPEG = DiffJPEG(differentiable=True, quality=90).cuda() |
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parser = get_arguments() |
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parser.add_argument("--ckpt", type=str, help="Checkpoint path.") |
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parser.add_argument("--out_path", type=str, default="./exps/", help="Path to save results. ") |
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parser.add_argument("--split_to_patch", dest='split_to_patch', action='store_true', help="Test on patch. ") |
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args = parser.parse_args() |
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print("Parsed arguments: {}".format(args)) |
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ckpt_name = args.ckpt.split("/")[-1].split(".")[0] |
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if args.split_to_patch: |
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os.makedirs(args.out_path+"%s/results_metric_%s/"%(args.task, ckpt_name), exist_ok=True) |
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out_path = args.out_path+"%s/results_metric_%s/"%(args.task, ckpt_name) |
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else: |
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os.makedirs(args.out_path+"%s/results_%s/"%(args.task, ckpt_name), exist_ok=True) |
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out_path = args.out_path+"%s/results_%s/"%(args.task, ckpt_name) |
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def main(args): |
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net = InvISPNet(channel_in=3, channel_out=3, block_num=8) |
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device = torch.device("cuda:0") |
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net.to(device) |
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net.eval() |
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if os.path.isfile(args.ckpt): |
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net.load_state_dict(torch.load(args.ckpt), strict=False) |
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print("[INFO] Loaded checkpoint: {}".format(args.ckpt)) |
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print("[INFO] Start data load and preprocessing") |
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RAWDataset = FiveKDatasetTest(opt=args) |
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dataloader = DataLoader(RAWDataset, batch_size=1, shuffle=False, num_workers=0, drop_last=True) |
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print("[INFO] Start test...") |
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for i_batch, sample_batched in enumerate(tqdm(dataloader)): |
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step_time = time.time() |
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input, target_rgb, target_raw = sample_batched['input_raw'].to(device), sample_batched['target_rgb'].to(device), \ |
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sample_batched['target_raw'].to(device) |
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file_name = sample_batched['file_name'][0] |
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if args.split_to_patch: |
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input_list, target_rgb_list, target_raw_list = preprocess_test_patch(input, target_rgb, target_raw) |
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else: |
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input_list, target_rgb_list, target_raw_list = [input[:,:,::2,::2]], [target_rgb[:,:,::2,::2]], [target_raw[:,:,::2,::2]] |
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for i_patch in range(len(input_list)): |
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input_patch = input_list[i_patch] |
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target_rgb_patch = target_rgb_list[i_patch] |
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target_raw_patch = target_raw_list[i_patch] |
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with torch.no_grad(): |
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reconstruct_rgb = net(input_patch) |
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reconstruct_rgb = torch.clamp(reconstruct_rgb, 0, 1) |
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pred_rgb = reconstruct_rgb.detach().permute(0,2,3,1) |
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target_rgb_patch = target_rgb_patch.permute(0,2,3,1) |
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pred_rgb = denorm(pred_rgb, 255) |
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target_rgb_patch = denorm(target_rgb_patch, 255) |
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pred_rgb = pred_rgb.cpu().numpy() |
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target_rgb_patch = target_rgb_patch.cpu().numpy().astype(np.float32) |
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pred = PILImage.fromarray(np.uint8(pred_rgb[0,:,:,:])) |
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tar_pred = PILImage.fromarray(np.hstack((np.uint8(target_rgb_patch[0,:,:,:]), np.uint8(pred_rgb[0,:,:,:])))) |
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tar = PILImage.fromarray(np.uint8(target_rgb_patch[0,:,:,:])) |
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pred.save(out_path+"pred_%s_%05d.jpg"%(file_name, i_patch), quality=90, subsampling=1) |
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tar.save(out_path+"tar_%s_%05d.jpg"%(file_name, i_patch), quality=90, subsampling=1) |
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tar_pred.save(out_path+"gt_pred_%s_%05d.jpg"%(file_name, i_patch), quality=90, subsampling=1) |
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del reconstruct_rgb |
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if __name__ == '__main__': |
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torch.set_num_threads(4) |
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main(args) |
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