def __init__(self, args):
self.args = args
self.nClass = args.nClass
self.device = torch.device('cpu')
self.autoencoder = AutoencoderBN().to(self.device)
self.regressor = OrdinalRegressionModel(self.nClass).to(self.device)
self.classification = rainFallClassification().to(self.device)
def __init__(self, args,
trainRegressionDataLoader, trainRegressionClassificationLoader,
testDataLoader, trainRainFallLoader,
means, std):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.trainRegressionDataLoader = trainRegressionDataLoader
self.trainRegressionClassificationLoader = trainRegressionClassificationLoader
self.testDataLoader = testDataLoader
self.classificationLoader = trainRainFallLoader
self.run_datetime = datetime.datetime.now()
self.out_path = args.out
self.sigma = args.sigma
self.beta = args.beta
self.earlyStop = args.earlyStop
self.nClass = args.nClass
self.noiseMean = torch.zeros(args.batch_size, args.featureNums, 17, 17)
self.noiseStd = 1e-3
self.model = AutoencoderBN(self.noiseMean, self.noiseStd).to(self.device)
self.regressionModel = Regression(self.nClass).to(self.device)
self.classificationModel = regressionClassification(self.nClass).to(self.device)
self.rainFallClassifierModel = rainFallClassification().to(self.device)
self.meanStdNormalizer = MeanVarianceNormalizer(means, std).to(self.device)
self.meanvarLoss = MeanVarLoss(self.nClass).to(self.device)
self.normaliedLoss = NormalizerLoss(std).to(self.device)
self.focalLoss = FocalLoss(self.nClass, alpha=0.25, gamma=2).to(self.device)
self.rainFocalLoss = FocalLoss(2, alpha=0.25, gamma=2).to(self.device)
self.regressionOptim = torch.optim.Adam([
{'params': self.regressionModel.parameters(), 'lr': args.lr,
'weight_decay': args.weight_decay},
{'params': self.model.parameters(), 'lr': args.lr,
'weight_decay': args.weight_decay},
],
lr=args.lr * 10, weight_decay=args.weight_decay * 10)
self.classificationOptim = torch.optim.Adam(self.classificationModel.parameters(), lr=args.lr * 100)
self.rainFallOptim = torch.optim.Adam(self.rainFallClassifierModel.parameters(), lr=args.lr * 10)
# self.reconstructOptim = torch.optim.Adam(self.model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.regressionOptim, step_size=750 * 2)
self.criterion = nn.MSELoss()
self.classificationCriterion = nn.CrossEntropyLoss()
if not os.path.exists(self.out_path):
os.makedirs(self.out_path)
self.logger = Logger(self.out_path)
with open(os.path.join(self.out_path, "para.json"), "w") as f:
json.dump(args.__dict__, f)
self.epoch = 0
self.iteration = 0
self.classificationIteration = 0
self.rainfallclassificationIteration = 0
self.test_step = 0
self.max_epoch = args.epochs
self.val_interval = args.interval
self.res = 0
self.bestConstructLoss = 1e7
self.bestConstructEpoch = 0
self.best_error = 1e7;
self.best_res_epoch = 0