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test_inr_liif_metasr_aliif.py
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test_inr_liif_metasr_aliif.py
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import argparse
import json
import os
import math
from functools import partial
import cv2
import numpy as np
import yaml
import torch
from einops import rearrange
from torch.utils.data import DataLoader
from tqdm import tqdm
import datasets
import models
import utils
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
def eval_psnr(loader, class_names, model,
data_norm=None, eval_type=None, save_fig=False,
scale_ratio=1, save_path=None, verbose=False, crop_border=4,
cal_metrics=True,
):
crop_border = int(crop_border) if crop_border else crop_border
print('crop border: ', crop_border)
model.eval()
if data_norm is None:
data_norm = {
'inp': {'sub': [0], 'div': [1]},
'gt': {'sub': [0], 'div': [1]}
}
t = data_norm['inp']
inp_sub = torch.FloatTensor(t['sub']).view(1, -1, 1, 1).to(device)
inp_div = torch.FloatTensor(t['div']).view(1, -1, 1, 1).to(device)
t = data_norm['gt']
gt_sub = torch.FloatTensor(t['sub']).view(1, 1, -1).to(device)
gt_div = torch.FloatTensor(t['div']).view(1, 1, -1).to(device)
if eval_type is None:
metric_fn = [utils.calculate_psnr_pt, utils.calculate_ssim_pt]
elif eval_type == 'psnr+ssim':
metric_fn = [utils.calculate_psnr_pt, utils.calculate_ssim_pt]
elif eval_type.startswith('div2k'):
scale = int(eval_type.split('-')[1])
metric_fn = partial(utils.calc_psnr, dataset='div2k', scale=scale)
elif eval_type.startswith('benchmark'):
scale = int(eval_type.split('-')[1])
metric_fn = partial(utils.calc_psnr, dataset='benchmark', scale=scale)
else:
raise NotImplementedError
val_res_psnr = utils.Averager(class_names)
val_res_ssim = utils.Averager(class_names)
pbar = tqdm(loader, leave=False, desc='val')
for batch in pbar:
for k, v in batch.items():
if torch.is_tensor(v):
batch[k] = v.to(device)
inp = (batch['inp'] - inp_sub) / inp_div
with torch.no_grad():
pred = model(inp, batch['coord'], batch['cell'])
pred = pred * gt_div + gt_sub
if eval_type is not None: # reshape for shaving-eval
ih, iw = batch['inp'].shape[-2:]
s = math.sqrt(batch['coord'].shape[1] / (ih * iw))
if s > 1:
shape = [batch['inp'].shape[0], round(ih * s), round(iw * s), 3]
else:
shape = [batch['inp'].shape[0], 32, batch['coord'].shape[1]//32, 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
batch['gt'] = batch['gt'].view(*shape) \
.permute(0, 3, 1, 2).contiguous()
if cal_metrics:
res_psnr = metric_fn[0](
pred,
batch['gt'],
crop_border=crop_border
)
res_ssim = metric_fn[1](
pred,
batch['gt'],
crop_border=crop_border
)
else:
res_psnr = torch.ones(len(pred))
res_ssim = torch.ones(len(pred))
file_names = batch.get('filename', None)
if file_names is not None and save_fig:
for idx in range(len(batch['inp'])):
ori_img = batch['inp'][idx].cpu().numpy() * 255
ori_img = np.clip(ori_img, a_min=0, a_max=255)
ori_img = ori_img.astype(np.uint8)
ori_img = rearrange(ori_img, 'C H W -> H W C')
pred_img = pred[idx].cpu().numpy() * 255
pred_img = np.clip(pred_img, a_min=0, a_max=255)
pred_img = pred_img.astype(np.uint8)
pred_img = rearrange(pred_img, 'C H W -> H W C')
gt_img = batch['gt'][idx].cpu().numpy() * 255
gt_img = np.clip(gt_img, a_min=0, a_max=255)
gt_img = gt_img.astype(np.uint8)
gt_img = rearrange(gt_img, 'C H W -> H W C')
psnr = res_psnr[idx].cpu().numpy()
ssim = res_ssim[idx].cpu().numpy()
ori_file_name = f'{save_path}/{file_names[idx]}_Ori.png'
cv2.imwrite(ori_file_name, ori_img)
pred_file_name = f'{save_path}/{file_names[idx]}_{scale_ratio}X_{psnr:.2f}_{ssim:.4f}.png'
cv2.imwrite(pred_file_name, pred_img)
gt_file_name = f'{save_path}/{file_names[idx]}_GT.png'
cv2.imwrite(gt_file_name, gt_img)
val_res_psnr.add(batch['class_name'], res_psnr)
val_res_ssim.add(batch['class_name'], res_ssim)
if verbose:
pbar.set_description(
'val psnr: {:.4f} ssim: {:.4f}'.format(val_res_psnr.item()['all'], val_res_ssim.item()['all']))
return val_res_psnr.item(), val_res_ssim.item()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='configs/test_INR_liif_metasr_aliif.yaml')
parser.add_argument('--model', default='pretrain/EDSR_UC_ALIIF.pth')
parser.add_argument('--scale_ratio', default=4, type=float)
parser.add_argument('--save_fig', default=True, type=bool)
parser.add_argument('--save_path', default='tmp', type=str)
parser.add_argument('--cal_metrics', default=True, type=bool)
parser.add_argument('--return_class_metrics', default=False, type=bool)
parser.add_argument('--dataset_name', default='UC', type=str)
args = parser.parse_args()
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
root_split_file = {
'UC':
{
'root_path': 'samples/UCMerced',
'split_file': 'samples/uc_split.json'
},
'AID':
{
'root_path': 'samples/AID',
'split_file': 'data_split/AID_split.json'
}
}
config['test_dataset']['dataset']['args']['root_path'] = root_split_file[args.dataset_name]['root_path']
config['test_dataset']['dataset']['args']['split_file'] = root_split_file[args.dataset_name]['split_file']
config['test_dataset']['wrapper']['args']['scale_ratio'] = args.scale_ratio
spec = config['test_dataset']
dataset = datasets.make(spec['dataset'])
dataset = datasets.make(spec['wrapper'], args={'dataset': dataset})
loader = DataLoader(dataset, batch_size=spec['batch_size'], num_workers=0, pin_memory=True, shuffle=False,
drop_last=False)
if not os.path.exists(args.model):
assert NameError
model_spec = torch.load(args.model)['model']
print(model_spec['args'])
model = models.make(model_spec, load_sd=True).to(device)
file_names = json.load(open(config['test_dataset']['dataset']['args']['split_file']))['test']
class_names = list(set([os.path.basename(os.path.dirname(x)) for x in file_names]))
crop_border = config['test_dataset']['wrapper']['args']['scale_ratio'] + 5
dataset_name = os.path.basename(config['test_dataset']['dataset']['args']['split_file']).split('_')[0].lower()
max_scale = {'uc': 5, 'aid': 12}
if args.scale_ratio > max_scale[dataset_name]:
crop_border = int((args.scale_ratio - max_scale[dataset_name]) / 2 * 48)
if args.save_fig:
os.makedirs(args.save_path, exist_ok=True)
res = eval_psnr(
loader, class_names, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
crop_border=crop_border,
verbose=True,
save_fig=args.save_fig,
scale_ratio=args.scale_ratio,
save_path=args.save_path,
cal_metrics=args.cal_metrics
)
if args.return_class_metrics:
keys = list(res[0].keys())
keys.sort()
print('psnr')
for k in keys:
print(f'{k}: {res[0][k]:0.2f}')
print('ssim')
for k in keys:
print(f'{k}: {res[1][k]:0.4f}')
print(f'psnr: {res[0]["all"]:0.2f}')
print(f'ssim: {res[1]["all"]:0.4f}')