def evaluate_dataset(net, path):
testset = ImageFolder(root=path,
transform=Compose([
Resize((32, 32)),
ToTensor(),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset), OUT_FEATURES)
def evaluate_svhn(net):
testset = SVHN(root='data',
split="test",
download=True,
transform=Compose([
ToTensor(),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset), OUT_FEATURES)
def evaluate_cifar10(net):
testset = CIFAR10(root='data',
train=False,
download=True,
transform=Compose([
ToTensor(),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset))
def evaluate_mnist(net):
testset = MNIST(root='data',
train=False,
download=True,
transform=Compose([
Resize((32, 32)),
Grayscale(num_output_channels=3),
ToTensor(),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset), OUT_FEATURES)
def evaluate_svhn(net):
testset = SVHN(root='data',
split="train",
download=True,
transform=Compose([
ToTensor(),
Normalize((0.43767047, 0.44375867, 0.47279018),
(0.19798356, 0.20096427, 0.19697163)),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset), OUT_FEATURES)
def evaluate_cifar10(net):
testset = CIFAR10(root='data',
train=True,
download=True,
transform=Compose([
ToTensor(),
Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset), OUT_FEATURES)
def evaluate_dataset(net, path):
testset = ImageFolder(
root=path,
transform=Compose([
Resize((32, 32)),
ToTensor(),
Normalize((0.1307, 0.1307, 0.1307), (0.3081, 0.3081, 0.3081)),
# Normalize((0.43767047, 0.44375867, 0.47279018), (0.19798356, 0.20096427, 0.19697163)),
# Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]))
testloader = DataLoader(testset,
batch_size=64,
shuffle=False,
num_workers=2)
evaluate_network_with_classes(net, testloader, len(testset), OUT_FEATURES)