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
def getOpt(optDN):
model = optDN['model']
if not model in mode_switch:
return
opt = Option(mode_switch[model][0])
opt.modelDef = mode_switch[model][1]
opt.ramCoef = mode_switch[model][2][config.getRunType()]
opt.cropsize = config.getConfig()[1 if model[:4] == 'lite' else 2]
opt.modelCached = initModel(opt, opt.model, 'DN' + model)
opt.squeeze = lambda x: x.squeeze(1)
opt.unsqueeze = lambda x: x.unsqueeze(1)
opt.padding = 5
return opt
def getOpt(optSR):
opt = Option()
opt.mode = optSR['model']
opt.scale = optSR['scale']
nmode = opt.mode + str(opt.scale)
if not nmode in mode_switch:
return
if opt.mode[:3] != 'gan':
opt.squeeze = lambda x: x.squeeze(1)
opt.unsqueeze = lambda x: x.unsqueeze(1)
opt.padding = 9 if opt.scale == 3 else 5
opt.model = mode_switch[nmode][0]
opt.modelDef = mode_switch[nmode][1]
opt.ensemble = optSR['ensemble'] if 'ensemble' in optSR and (
0 <= optSR['ensemble'] <= 7) else config.ensembleSR
opt.ramCoef = mode_switch[nmode][2][config.getRunType()]
opt.cropsize = config.getConfig()[0]
opt.modelCached = initModel(opt, opt.model, 'SR' + nmode)
return opt
from time import perf_counter
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)))