#training_set, test_set = data.generate_clean_data(data_dir="../data/REDD/", appliance='Dishwasher', window_size=params.DISHWASHER_WINDOW_SIZE, proportion=[0, 0], threshold=10, test=True, test_on='All', stride=1) #stride=1 #training_set = dataset.REDDDataset(data=training_set) #test_set = dataset.REDDDataset(data=test_set) #mean, std = training_set.get_mean_and_std() mean = 395.890660893 std = 615.928606564 # mean = 200 # std = 450 #training_set.init_transformation(torchvision.transforms.Compose([Normalize(mean=mean, sd=std)])) #del training_set, test_set training_set, test_set = data.generate_clean_data( data_dir="../data/REDD/", appliance='Dishwasher', window_size=params.DISHWASHER_WINDOW_SIZE, proportion=[3, 0], threshold=10, test=True, test_on='h1', stride=100) #stride=1 mean = 395.890660893 std = 615.928606564 # test_set = data.read_from_home('../data/REDD/', 'h2', appliance='Dishwasher', window_size=params.DISHWASHER_WINDOW_SIZE) test_set = dataset.REDDDataset(data=test_set) test_set.init_transformation( torchvision.transforms.Compose([Normalize(mean=mean, sd=std)])) print('Size of test set: ', len(test_set)) # del training_set net = ConvDishNILM() try: net = torch.load('models/dishwasher_lucky_shot_house1.pt')
import redd_parameters as params import redd_data as data import dataset from matplotlib import pyplot as plt from transformations import Normalize import torch.nn as nn import torch.optim as optim from models import * from torch.autograd import Variable import torchvision import scores #Creating custom dataset train_set, test_set = data.generate_clean_data( data_dir="../data/REDD/", appliance='Refrigerator', window_size=params.REFRIGERATOR_WINDOW_SIZE, proportion=[0, 0], threshold=80, test=True, test_on='All') #stride=5 # initialization of custom pytorch datasets refrigerator_train_set = dataset.REDDDataset(data=train_set) refrigerator_test_set = dataset.REDDDataset(data=test_set) #Getting mean and standard deviation so Normalization can be performed mean, std = refrigerator_train_set.get_mean_and_std() refrigerator_train_set.init_transformation( torchvision.transforms.Compose([Normalize(mean=mean, sd=std)])) refrigerator_test_set.init_transformation( torchvision.transforms.Compose([Normalize(mean=mean, sd=std)])) print('Training set size: ', len(refrigerator_train_set)) print('Test set size: ', len(refrigerator_test_set))
import redd_parameters as params import redd_data as data import dataset from matplotlib import pyplot as plt from transformations import Normalize import torch.nn as nn import torch.optim as optim from models import * from torch.autograd import Variable import torchvision import scores #Initialization of dataset train_set, test_set = data.generate_clean_data( data_dir="../data/REDD/", appliance='Microwave', window_size=params.MICROWAVE_WINDOW_SIZE, proportion=[15, 0], threshold=200, test=True, test_on='All') #stride=1 # initialization of custom pytorch datasets microwave_train_set = dataset.REDDDataset(data=train_set) microwave_test_set = dataset.REDDDataset(data=test_set) #Getting mean and standard deviation so Normalization can be performed #mean, std = microwave_train_set.get_mean_and_std() mean = 444.516250434 std = 828.08954202 microwave_train_set.init_transformation( torchvision.transforms.Compose([Normalize(mean=mean, sd=std)])) microwave_test_set.init_transformation( torchvision.transforms.Compose([Normalize(mean=mean, sd=std)]))