def sr(x, opt):
sc = opt.scale
sum = ensemble(
x, opt.ensemble, {
'opt': opt,
'model': getModel(opt),
'padding': 2 if sc == 3 else 1,
'sc': sc
})
if opt.ensemble:
return sum / (opt.ensemble + 1)
else:
return sum
def sr(x, opt):
sc = opt.scale
if opt.mode == 'lite.old':
x = interpolate(x)
sum = ensemble(x, opt.ensemble, {
'opt': opt,
'model': opt.modelCached,
'padding': 2 if sc == 3 else 1,
'sc': sc
})
if opt.ensemble:
return sum / (opt.ensemble + 1)
else:
return sum
import numpy as np
from imageProcess import ensemble, genGetModel
from models import NetDN, SEDN
from config import config
model_dict = {
'15': './model/l15/model_new.pth',
'25': './model/l25/model_new.pth',
'50': './model/l50/model_new.pth',
'lite5': './model/dn_lite5/model_new.pth',
'lite10': './model/dn_lite10/model_new.pth',
'lite15': './model/dn_lite15/model_new.pth'
}
ramCoef = .9 / np.array([2700., 4106.9, 2400., 7405., 1253.4, 4304.2])
dn = lambda x, opt: ensemble(x, 0, {'opt': opt, 'model': getModel(opt)})
getModel = genGetModel(lambda opt, *args: NetDN()
if 'dn_lite' in opt.model else SEDN())
##################################
def getOpt(model):
def opt():
pass
if not model in model_dict:
return
opt.model = model_dict[model]
refFile = 0 #'test/1566005911.7879605_ci.png'
def context():
pass
opt = Option(
('test/{}.pth' if test else 'model/demoire/{}.pth').format(modelName))
opt.padding = 31
opt.ramCoef = 1 / 8000.
opt.align = 128
opt.modelCached = initModel(opt, weights=opt.model, f=lambda _: Net())
toTorch = lambda x: torch.from_numpy(np.array(x)).permute(2, 0, 1).to(
dtype=config.dtype(), device=config.device()) / 256
time = 0.0
for pic in os.listdir(inputFolder):
original = toTorch(readFile(context=context)(inputFolder + '/' + pic))
ref = toTorch(readFile(context=context)(refFile + '/' +
pic)) if refFile else original
start = perf_counter()
y = ensemble(opt)(original)
time += perf_counter() - start
print(pic, float(y.mean(dtype=torch.float)),
float((y - ref).abs().mean(dtype=torch.float)))
out = toOutput(8)(toFloat(y))
writeFile(
out,
'download/{}.{}.png'.format(splitext(split(pic)[1])[0],
modelName), context)
print(time)
