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test_real.py
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test_real.py
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import argparse
import os
import math
from functools import partial
import yaml
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import datasets
import models
import utils
def batched_predict(model, inp, coord, cell, bsize):
with torch.no_grad():
model.inp = inp
model.gen_feat(inp)
n = coord.shape[1]
ql = 0
preds = []
while ql < n:
qr = min(ql + bsize, n)
pred = model.query_rgb(coord[:, ql: qr, :], cell[:, ql: qr, :])
preds.append(pred)
ql = qr
pred = torch.cat(preds, dim=1)
return pred
def eval_psnr(loader, model, data_norm=None, eval_type=None, eval_bsize=None, eval_scale=2,
verbose=False):
model.eval()
loader.dataset.set_test_scale(eval_scale)
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).cuda()
inp_div = torch.FloatTensor(t['div']).view(1, -1, 1, 1).cuda()
t = data_norm['gt']
gt_sub = torch.FloatTensor(t['sub']).view(1, 1, -1).cuda()
gt_div = torch.FloatTensor(t['div']).view(1, 1, -1).cuda()
if eval_type is None:
metric_fn = utils.calc_psnr
elif eval_type=='realArb':
metric_fn = partial(utils.calc_psnr, dataset='realArb', scale=eval_scale)
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 = utils.Averager()
pbar = tqdm(loader, leave=False, desc='val')
for batch in pbar:
for k, v in batch.items():
batch[k] = v.cuda()
inp = (batch['inp'] - inp_sub) / inp_div
if eval_bsize is None:
with torch.no_grad():
pred = model(inp, batch['coord'], batch['cell'])
else:
pred = batched_predict(model, inp,
batch['coord'], batch['cell'], eval_bsize)
pred = pred * gt_div + gt_sub
pred.clamp_(0, 1)
if eval_type is not None: # reshape for shaving-eval
if eval_type =='realArb':
ih = batch['h']
iw = batch['w']
shape = [batch['gt'].shape[0], ih, iw, 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
batch['gt'] = batch['gt'].view(*shape) \
.permute(0, 3, 1, 2).contiguous()
else:
ih, iw = batch['inp'].shape[-2:]
s = math.sqrt(batch['coord'].shape[1] / (ih * iw))
shape = [batch['inp'].shape[0], round(ih * s), round(iw * s), 3]
pred = pred.view(*shape) \
.permute(0, 3, 1, 2).contiguous()
batch['gt'] = batch['gt'].view(*shape) \
.permute(0, 3, 1, 2).contiguous()
res = metric_fn(pred, batch['gt'])
val_res.add(res.item(), inp.shape[0])
if verbose:
pbar.set_description('val {:.4f}'.format(val_res.item()))
return val_res.item()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config')
parser.add_argument('--model', default='none')
parser.add_argument('--model_path', default='none')
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
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=8, pin_memory=True)
if args.model_path == 'none':
model_spec = torch.load(args.model)['model']
model = models.make(model_spec, load_sd=True).cuda()
eval_scale_list = config.get('eval_scale')
for eval_scale in eval_scale_list:
res = eval_psnr(loader, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
eval_scale=eval_scale,
verbose=True)
print('result: {:.4f}'.format(res))
else:
ckpt_list = [ckpt_name for ckpt_name in os.listdir(args.model_path) if ckpt_name.endswith('.pth')]
for ckpt in ckpt_list:
print(f"----- ckpt:{ckpt}")
model_spec = torch.load(os.path.join(args.model_path, ckpt))['model']
model = models.make(model_spec, load_sd=True).cuda()
eval_scale_list = config.get('eval_scale')
for eval_scale in eval_scale_list:
res = eval_psnr(loader, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
eval_scale=eval_scale,
verbose=True)
print('result: {:.4f}'.format(res))
print("----------")