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test.py
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test.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
import warnings
warnings.filterwarnings("ignore",category=UserWarning)
def batched_predict(model, inp, coord, cell, bsize):
with torch.no_grad():
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,
verbose=False):
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).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.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
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()
#import cv2
#import numpy as np
#cv2.imwrite('./out/pred.png',cv2.cvtColor(pred.squeeze(0).permute(1,2,0).cpu().detach().numpy() * 255, cv2.COLOR_RGB2BGR))
#cv2.imwrite('./out/gt.png', cv2.cvtColor(batch['gt'].squeeze(0).permute(1,2,0).cpu().detach().numpy() * 255, cv2.COLOR_RGB2BGR))
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', default='./configs/test/test-div2k-12.yaml')
parser.add_argument('--model', default='./save/edsr_baseline-A-liif/epoch-last.pth')
parser.add_argument('--gpu', default='1')
args = parser.parse_args()
torch.backends.cudnn.enabled = False
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)
model_spec = torch.load(args.model)['model']
if model_spec['args']['imnet_spec']['name'] == 'mlp_pw' :
model_spec['args']['basis_spec']['args']['state']='test'
model_spec['args']['pdn_spec']['name'] = 'ExpansionNet'
model_spec['args']['basis_spec']['args']['path'] = args.model
model = models.make(model_spec, load_sd=True).cuda()
res = eval_psnr(loader, model,
data_norm=config.get('data_norm'),
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
verbose=True)
print('result: {:.4f}'.format(res))