def FinalTest():
dataset = 'cus_mnist'
# dataroot = './data/cus_mnist'
opt = Options().parse(dataset)
opt.isTrain = False
opt.load_weights = True
##
# LOAD DATA
dataloader = load_data(opt)
print(opt)
##
# LOAD MODEL
model = Ganomaly(opt, dataloader)
##
# TRAIN MODEL
# model.train()
minVal = None
maxVal = None
threshold = None
with open(opt.dataroot + '/performance.txt', 'r+', encoding='utf-8') as f:
res = f.readline()
res = res.split('&')
res = [float(i) for i in res]
minVal = res[0]
maxVal = res[1]
threshold = res[2]
model.FinalTest(minVal, maxVal, threshold)
def run(self):
# self.option.isize = 128
self.dataloader = load_data(self.option)
model = Ganomaly(self.option, self.dataloader)
minVal = self.modelData[0]
maxVal = self.modelData[1]
threshold = self.modelData[2]
resNor, resAbn = model.FinalTest(minVal, maxVal, threshold)
opt = {}
opt['path'] = self.option.dataroot
opt['modelName'] = self.option.dataset
self.option.signal.emit(copy.deepcopy(resNor), copy.deepcopy(resAbn), copy.deepcopy(opt))
def run(self):
opt = self.opt
# model.testOne()
model = Ganomaly(self.opt, self.dataloader)
##
# TRAIN MODEL
# model.train()
minVal = None
maxVal = None
threshold = None
with open(opt.dataroot + '/performance.txt', 'r+', encoding='utf-8') as f:
res = f.readline()
res = res.split('&')
res = [float(i) for i in res]
minVal = res[0]
maxVal = res[1]
threshold = res[2]
model.FinalTest(minVal, maxVal, threshold)