def check_dataset(dataset, dataroot, augment, download): if dataset == "cifar10": dataset = get_CIFAR10(augment, dataroot, download) if dataset == "svhn": dataset = get_SVHN(augment, dataroot, download) if dataset == "awa2": dataset = get_AwA2(augment, dataroot) return dataset
def check_dataset(dataset, dataroot, augment, download): if dataset == 'cifar10': cifar10 = get_CIFAR10(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = cifar10 if dataset == 'svhn': svhn = get_SVHN(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = svhn return input_size, num_classes, train_dataset, test_dataset
def check_dataset(dataset, dataroot, augment, download): if dataset == "cifar64": cifar64 = get_CIFAR64(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = cifar64 if dataset == "cifar10": cifar10 = get_CIFAR10(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = cifar10 if dataset == "svhn": svhn = get_SVHN(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = svhn return input_size, num_classes, train_dataset, test_dataset
def check_dataset(dataset, dataroot, augment, download): if dataset == "cifar10": cifar10 = get_CIFAR10(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = cifar10 if dataset == "svhn": svhn = get_SVHN(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = svhn if dataset == "mnist": mnist = get_MNIST(augment, dataroot, download) input_size, num_classes, train_dataset, test_dataset = mnist return input_size, num_classes, train_dataset, test_dataset
import ipdb device = torch.device("cuda") output_folder = 'glow/' model_name = 'glow_affine_coupling.pt' with open(output_folder + 'hparams.json') as json_file: hparams = json.load(json_file) hparams['dataroot'] = '../mutual-information' image_shape, num_classes, _, test_cifar = get_CIFAR10(hparams['augment'], hparams['dataroot'], hparams['download']) image_shape, num_classes, _, test_svhn = get_SVHN(hparams['augment'], hparams['dataroot'], hparams['download']) model = Glow(image_shape, hparams['hidden_channels'], hparams['K'], hparams['L'], hparams['actnorm_scale'], hparams['flow_permutation'], hparams['flow_coupling'], hparams['LU_decomposed'], num_classes, hparams['learn_top'], hparams['y_condition']) model.load_state_dict(torch.load(output_folder + model_name)) model.set_actnorm_init() model = model.to(device) model = model.eval()
from model import Glow from datasets import get_CIFAR10, get_SVHN device = torch.device("cuda") output_folder = 'pretrained/' model_name = 'glow_affine_coupling.pt' with open(output_folder + 'hparams.json') as json_file: hparams = json.load(json_file) print(hparams) image_shape, num_classes, train_cifar, test_cifar = get_CIFAR10( augment=False, dataroot=hparams['dataroot'], download=True) image_shape, num_classes_svhn, train_svhn, test_svhn = get_SVHN( augment=False, dataroot=hparams['dataroot'], download=True) # The data is in the range [-0.5, 0.5] train_dataloader_cifar = torch.utils.data.DataLoader(train_cifar, batch_size=32, num_workers=0, pin_memory=True) test_dataloader_cifar = torch.utils.data.DataLoader(test_cifar, batch_size=32, num_workers=0, pin_memory=True) # The data is in the range [-0.5, 0.5] train_dataloader_svhn = torch.utils.data.DataLoader(train_svhn, batch_size=32, num_workers=0,