from otdd.pytorch.datasets import load_torchvision_data
from otdd.pytorch.distance import DatasetDistance
# Load data
loaders_src = load_torchvision_data(data=data_src,
label=label_src,
valid_size=0,
resize=28,
maxsize=2000)[0]
loaders_tgt = load_torchvision_data(data=data_tgt,
label=label_tgt,
valid_size=0,
resize=28,
maxsize=2000)[0]
# Instantiate distance
dist = DatasetDistance(loaders_src['train'],
loaders_tgt['train'],
inner_ot_method='exact',
debiased_loss=True,
p=2,
entreg=1e-1,
device='cpu')
d = dist.distance(maxsamples=1000)
print(f'OTDD(MNIST,USPS)={d:8.2f}')
from otdd.pytorch.datasets import load_torchvision_data
from otdd.pytorch.distance import DatasetDistance
# Load data
loaders_src = load_torchvision_data('MNIST',
valid_size=0,
resize=28,
maxsize=2000)[0]
loaders_tgt = load_torchvision_data('USPS',
valid_size=0,
resize=28,
maxsize=2000)[0]
# Instantiate distance
dist = DatasetDistance(loaders_src['train'],
loaders_tgt['train'],
inner_ot_method='exact',
debiased_loss=True,
p=2,
entreg=1e-1,
device='cpu')
d = dist.distance(maxsamples=1000)
print(f'OTDD(MNIST,USPS)={d:8.2f}')
from otdd.pytorch.datasets import load_torchvision_data
from otdd.pytorch.distance import DatasetDistance
import torch
import numpy as np
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Load datasets
loaders_src = load_torchvision_data('MNIST',
valid_size=0,
resize=32,
to3channels=True,
maxsize=2000)[0]
loaders_tgt = load_torchvision_data('FashionMNIST',
valid_size=0,
resize=32,
to3channels=True,
maxsize=2000)[0]
#print(np.shape(loaders_src['train']))
# Instantiate distance
dist = DatasetDistance(loaders_src['train'],
loaders_tgt['train'],
inner_ot_method='exact',
debiased_loss=True,
p=2,
entreg=1e-1,
device=device)
import torch
from torchvision.models import resnet18
from otdd.pytorch.datasets import load_torchvision_data
from otdd.pytorch.distance import DatasetDistance, FeatureCost
# Load MNIST/CIFAR in 3channels (needed by torchvision models)
loaders_src = load_torchvision_data('CIFAR10', resize=28, maxsize=2000)[0]
loaders_tgt = load_torchvision_data('MNIST', resize=28, to3channels=True, maxsize=2000)[0]
# Embed using a pretrained (+frozen) resnet
embedder = resnet18(pretrained=True).eval()
embedder.fc = torch.nn.Identity()
for p in embedder.parameters():
p.requires_grad = False
# Here we use same embedder for both datasets
feature_cost = FeatureCost(src_embedding = embedder,
src_dim = (3,28,28),
tgt_embedding = embedder,
tgt_dim = (3,28,28),
p = 2,
device='cpu')
dist = DatasetDistance(loaders_src['train'], loaders_tgt['train'],
inner_ot_method = 'exact',
debiased_loss = True,
feature_cost = feature_cost,
sqrt_method = 'spectral',
sqrt_niters=10,
precision='single',
'between - distances between dataset \n inter - distances between classes')
args = parser.parse_args()
TO3CHANNELS = True
# Load datasets
DATASETS = ['MNIST', 'FashionMNIST', 'KMNIST', 'CIFAR10', 'SVHN']
# 'USPS', 'SVHN', 'EMNIST'
datasets = {}
n_datasets = len(DATASETS)
for ds_name in DATASETS:
datasets[ds_name] = load_torchvision_data(ds_name,
to3channels=TO3CHANNELS,
resize=34,
valid_size=0,
maxsize=5000)[0]['train']
if args.dist_type == 'between':
print('Calculating distance between datasets')
distances = np.zeros((n_datasets, n_datasets))
for i, set1 in enumerate(datasets):
for j, set2 in enumerate(datasets):
if i >= j:
continue
dist = DatasetDistance(datasets[set1],
datasets[set2],
inner_ot_method='exact',
debiased_loss=True,
p=2,