def newOpt(f, ramType): opt = Option() opt.modelCached = lambda x: (f(x),) opt.ramCoef = ramCoef[config.getRunType() * 2 + ramType] opt.align = 32 opt.padding = 45 opt.squeeze = identity opt.unsqueeze = identity return opt
def newOpt(func, ramCoef, align=32, padding=45, scale=1, **_): opt = Option() opt.modelCached = func opt.ramCoef = ramCoef opt.align = align opt.padding = padding opt.scale = scale opt.squeeze = identity opt.unsqueeze = identity return opt
from moire_obj import Net
modelName = 'moire_obj'
test = False
inputFolder = '../test-pics'
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(