def run(count, memory_size, iteration, device='cuda'):
traintransform = transforms.Compose(
[transforms.RandomRotation(20),
transforms.ToTensor()])
trainset = torchvision.datasets.MNIST(root='./data/mnist',
train=True,
download=True,
transform=traintransform)
trainloader = torch.utils.data.DataLoader(trainset,
pin_memory=True,
batch_size=128,
shuffle=True,
num_workers=10)
testtransform = transforms.Compose([transforms.ToTensor()])
testset = torchvision.datasets.MNIST(root='./data/mnist',
train=False,
download=True,
transform=testtransform)
testloader = torch.utils.data.DataLoader(testset,
pin_memory=True,
batch_size=128,
shuffle=True,
num_workers=10)
base_dir = os.path.join('mnist_' + str(memory_size), "4")
model = MnistDraw(count, memory_size)
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=1e-3)
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
trial = Trial(
model,
optimizer,
nn.MSELoss(reduction='sum'), ['loss'],
pass_state=True,
callbacks=[
tm.kl_divergence(MU, LOGVAR),
callbacks.MostRecent(
os.path.join(base_dir,
'iter_' + str(iteration) + '.{epoch:02d}.pt')),
callbacks.GradientClipping(5),
callbacks.ExponentialLR(0.99),
callbacks.TensorBoardImages(comment=current_time,
name='Prediction',
write_each_epoch=True,
key=torchbearer.Y_PRED),
callbacks.TensorBoardImages(comment=current_time + '_mnist',
name='Target',
write_each_epoch=True,
key=torchbearer.Y_TRUE)
]).with_generators(train_generator=trainloader,
val_generator=testloader).to(device)
trial.run(100)
def run(count, memory_size, device='cuda'):
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=10)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=10)
base_dir = os.path.join('cifar_' + str(memory_size), str(count))
model = nn.DataParallel(CifarClassifier(count, memory_size))
optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.001, momentum=0.9, weight_decay=5e-4)
trial = Trial(model, optimizer, nn.NLLLoss(), [torchbearer.metrics.CategoricalAccuracy(), 'loss'], callbacks=[
callbacks.MostRecent(os.path.join(base_dir, '{epoch:02d}.pt')),
callbacks.GradientClipping(5),
callbacks.MultiStepLR(milestones=[150, 250]),
callbacks.TensorBoard(write_graph=False, comment=base_dir)
]).with_train_generator(trainloader).to(device)
trial.run(350)
trial.with_test_generator(testloader).evaluate(data_key=torchbearer.TEST_DATA)
def run(count, memory_size, file, device='cuda'):
transform_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(0.25, 0.25, 0.25, 0.25),
transforms.ToTensor()
])
transform_test = transforms.Compose([transforms.ToTensor()])
trainset = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=128,
shuffle=True,
num_workers=10)
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=100,
shuffle=False,
num_workers=10)
base_dir = os.path.join('cifarss_' + str(memory_size), "16")
model = CifarDraw(count, memory_size)
model.load_state_dict(torch.load(file)[torchbearer.MODEL])
model = SelfTaught(count, 512, memory_size, model.memory)
for param in model.memory.parameters():
param.requires_grad = False
model.memory.decay.requires_grad = True
model.memory.learn.requires_grad = True
model.memory.learn2.requires_grad = True
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=1e-3)
trial = Trial(model,
optimizer,
nn.NLLLoss(), ['acc', 'loss'],
pass_state=True,
callbacks=[
callbacks.MultiStepLR([25, 40, 45]),
callbacks.MostRecent(
os.path.join(base_dir, '{epoch:02d}.pt')),
callbacks.GradientClipping(5)
]).with_generators(
train_generator=trainloader,
val_generator=testloader).for_val_steps(5).to(device)
trial.run(50)
def run(count, memory_size, device='cuda'):
traintransform = transforms.Compose([
transforms.RandomRotation(20),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
trainset = torchvision.datasets.MNIST(root='./data/mnist',
train=True,
download=True,
transform=traintransform)
trainloader = torch.utils.data.DataLoader(trainset,
pin_memory=True,
batch_size=128,
shuffle=True,
num_workers=10)
testtransform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
testset = torchvision.datasets.MNIST(root='./data/mnist',
train=False,
download=True,
transform=testtransform)
testloader = torch.utils.data.DataLoader(testset,
pin_memory=True,
batch_size=128,
shuffle=False,
num_workers=10)
base_dir = os.path.join('mnist_' + str(memory_size), str(count))
model = MnistClassifier(count, memory_size)
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=0.001)
trial = Trial(
model,
optimizer,
nn.NLLLoss(), ['acc', 'loss'],
callbacks=[
callbacks.MostRecent(os.path.join(base_dir, '{epoch:02d}.pt')),
callbacks.GradientClipping(5),
callbacks.MultiStepLR(milestones=[50, 100, 150, 190, 195]),
callbacks.ExponentialLR(0.99),
callbacks.TensorBoard(write_graph=False, comment=base_dir)
]).with_train_generator(trainloader).to(device)
trial.run(200)
trial.with_test_generator(testloader).evaluate(
data_key=torchbearer.TEST_DATA)
def run(count, glimpse_size, memory_size, iteration, device='cuda'):
base_dir = os.path.join('celeba_' + str(memory_size), str(glimpse_size))
if not os.path.exists(base_dir):
os.makedirs(base_dir)
transform_train = transforms.Compose([
transforms.ToTensor()
])
dataset = torchvision.datasets.ImageFolder(root='./cropped_celeba/', transform=transform_train)
splitter = DatasetValidationSplitter(len(dataset), 0.05)
trainset = splitter.get_train_dataset(dataset)
# Save the ids
torch.save((splitter.train_ids, splitter.valid_ids), os.path.join(base_dir, 'split.dat'))
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=10)
model = CelebDraw(count, glimpse_size, memory_size)
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
call_a = callbacks.TensorBoardImages(comment=current_time, name='Prediction', write_each_epoch=True, key=torchbearer.Y_PRED)
call_a.on_step_training = call_a.on_step_validation # Hack to make this log training samples
call_b = callbacks.TensorBoardImages(comment=current_time + '_celeba', name='Target', write_each_epoch=True,
key=torchbearer.Y_TRUE)
call_b.on_step_training = call_b.on_step_validation # Hack to make this log training samples
trial = Trial(model, optimizer, nn.MSELoss(reduction='sum'), ['acc', 'loss'], pass_state=True, callbacks=[
joint_kl_divergence(MU, LOGVAR),
callbacks.MostRecent(os.path.join(base_dir, 'iter_' + str(iteration) + '.{epoch:02d}.pt')),
callbacks.GradientClipping(5),
call_a,
call_b
]).with_generators(train_generator=trainloader).to(device)
trial.run(100)
def test_callbacks(self):
from torch.utils.data import TensorDataset
traingen = TensorDataset(torch.rand(10, 1, 3), torch.rand(10, 1))
valgen = TensorDataset(torch.rand(10, 1, 3), torch.rand(10, 1))
testgen = TensorDataset(torch.rand(10, 1, 3), torch.rand(10, 1))
model = torch.nn.Linear(3, 1)
optim = torch.optim.SGD(model.parameters(), lr=0.01)
cbs = []
cbs.extend([
c.EarlyStopping(),
c.GradientClipping(10, model.parameters()),
c.Best('test.pt'),
c.MostRecent('test.pt'),
c.ReduceLROnPlateau(),
c.CosineAnnealingLR(0.1, 0.01),
c.ExponentialLR(1),
c.Interval('test.pt'),
c.CSVLogger('test_csv.pt'),
c.L1WeightDecay(),
c.L2WeightDecay(),
c.TerminateOnNaN(monitor='fail_metric')
])
trial = torchbearer.Trial(model,
optim,
torch.nn.MSELoss(),
metrics=['loss'],
callbacks=cbs)
trial = trial.with_generators(traingen, valgen, testgen)
trial.run(2)
trial.predict()
trial.evaluate(data_key=torchbearer.TEST_DATA)
trial.evaluate()
import os
os.remove('test.pt')
os.remove('test_csv.pt')
def run(iteration, device='cuda:1'):
transform_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(0.25, 0.25, 0.25, 0.25),
transforms.ToTensor()
])
transform_test = transforms.Compose([transforms.ToTensor()])
trainset = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=128,
shuffle=True,
num_workers=10)
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=100,
shuffle=False,
num_workers=10)
base_dir = 'cifar_vae'
model = CifarVAE()
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=5e-4)
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
trial = Trial(
model,
optimizer,
nn.MSELoss(reduction='sum'), ['acc', 'loss'],
pass_state=True,
callbacks=[
tm.kl_divergence(MU, LOGVAR, beta=2),
callbacks.MultiStepLR([50, 90]),
callbacks.MostRecent(
os.path.join(base_dir,
'iter_' + str(iteration) + '.{epoch:02d}.pt')),
callbacks.GradientClipping(5),
callbacks.TensorBoardImages(comment=current_time,
name='Prediction',
write_each_epoch=True,
key=torchbearer.Y_PRED),
callbacks.TensorBoardImages(comment=current_time + '_cifar_vae',
name='Target',
write_each_epoch=False,
key=torchbearer.Y_TRUE),
]).with_generators(
train_generator=trainloader,
val_generator=testloader).for_val_steps(5).to(device)
trial.run(100)
