def test_adjoint_autograd():
"""Compare ODE Adjoint vs Autograd gradients, s := [0, 1], adaptive-step"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='moons', noise=.4)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = nn.Sequential(nn.Linear(2, 64), nn.Tanh(), nn.Linear(64, 2))
model = NeuralODE(f,
solver='dopri5',
sensitivity='adjoint',
atol=1e-5,
rtol=1e-8).to(device)
x, y = next(iter(trainloader))
# adjoint gradients
y_hat = model(x)
loss = nn.CrossEntropyLoss()(y_hat, y)
loss.backward()
adj_grad = torch.cat([p.grad.flatten() for p in model.parameters()])
# autograd gradients
model.zero_grad()
model.sensitivity = 'autograd'
y_hat = model(x)
loss = nn.CrossEntropyLoss()(y_hat, y)
loss.backward()
bp_grad = torch.cat([p.grad.flatten() for p in model.parameters()])
assert (torch.abs(bp_grad - adj_grad) <= 1e-3
).all(), f'Gradient error: {torch.abs(bp_grad - adj_grad).sum()}'
def moons_dataloader():
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='moons', noise=.4)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
return X_train, data.DataLoader(train, batch_size=len(X), shuffle=False)
def moons_trainloader():
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='moons', noise=.4)
X_train = torch.Tensor(X)
y_train = torch.LongTensor(yn.long())
train = TensorDataset(X_train, y_train)
trainloader = DataLoader(train, batch_size=len(X), shuffle=False)
return trainloader
def test_stable_neural_de(testlearner):
"""Stable: basic functionality"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='moons', noise=.4)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = Stable(nn.Sequential(nn.Linear(2, 64), nn.Tanh(), nn.Linear(64, 1)))
model = NeuralODE(f).to(device)
learn = testlearner(model, trainloader=trainloader)
trainer = pl.Trainer(min_epochs=10, max_epochs=30)
trainer.fit(learn)
def test_stable_neural_de(testlearner):
"""Stable: basic functionality"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='moons', noise=.4)
X_train = torch.Tensor(X)
y_train = torch.LongTensor(yn.long())
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = GNF(nn.Sequential(nn.Linear(2, 64), nn.Tanh(), nn.Linear(64, 1)))
model = NeuralODE(f)
t_span = torch.linspace(0, 1, 30)
learn = testlearner(t_span, model, trainloader=trainloader)
trainer = pl.Trainer(min_epochs=5, max_epochs=5)
trainer.fit(learn)
def test_2nd_order():
"""2nd order (MLP) Galerkin Neural ODE"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='spirals', noise=.4)
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = nn.Sequential(DepthCat(1), nn.Linear(5, 64), nn.Tanh(), DepthCat(1),
nn.Linear(65, 2))
model = nn.Sequential(
Augmenter(augment_idx=1, augment_func=nn.Linear(2, 2)),
NeuralDE(f, solver='dopri5', order=2)).to(device)
learn = TestLearner(model, trainloader=trainloader)
trainer = pl.Trainer(min_epochs=1, max_epochs=1)
trainer.fit(learn)
def test_vanilla_galerkin():
"""Vanilla Galerkin (MLP) Neural ODE"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='spirals', noise=.4)
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = nn.Sequential(DepthCat(1), GalLinear(6, 64, basisfunc=Fourier(5)),
nn.Tanh(), DepthCat(1),
GalLinear(64, 6, basisfunc=Polynomial(2)))
model = nn.Sequential(
Augmenter(augment_idx=1, augment_func=nn.Linear(2, 4)),
NeuralDE(f, solver='dopri5')).to(device)
learn = TestLearner(model, trainloader=trainloader)
trainer = pl.Trainer(min_epochs=1, max_epochs=1)
trainer.fit(learn)
def test_augmented_data_control():
"""Data-controlled NeuralDE with IL-Augmentation"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='spirals', noise=.4)
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = nn.Sequential(DataControl(), nn.Linear(12, 64), nn.Tanh(),
nn.Linear(64, 6))
model = nn.Sequential(
Augmenter(augment_idx=1, augment_func=nn.Linear(2, 4)),
NeuralDE(f, solver='dopri5')).to(device)
learn = TestLearner(model, trainloader=trainloader)
trainer = pl.Trainer(min_epochs=1, max_epochs=1)
trainer.fit(learn)
def test_augmenter_func_is_trained():
"""Test if augment function is trained without explicit definition"""
d = ToyDataset()
X, yn = d.generate(n_samples=512, dataset_type='spirals', noise=.4)
X_train = torch.Tensor(X).to(device)
y_train = torch.LongTensor(yn.long()).to(device)
train = data.TensorDataset(X_train, y_train)
trainloader = data.DataLoader(train, batch_size=len(X), shuffle=False)
f = nn.Sequential(DataControl(), nn.Linear(12, 64), nn.Tanh(),
nn.Linear(64, 6))
model = nn.Sequential(
Augmenter(augment_idx=1, augment_func=nn.Linear(2, 4)),
NeuralDE(f, solver='dopri5')).to(device)
learn = TestLearner(model, trainloader=trainloader)
trainer = pl.Trainer(min_epochs=1, max_epochs=1)
p = torch.cat([p.flatten() for p in model[0].parameters()])
trainer.fit(learn)
p_after = torch.cat([p.flatten() for p in model[0].parameters()])
assert (p != p_after).any()
def test_adjoint_autograd():
"""Test generation of (vanilla) version of all static_datasets"""
d = ToyDataset()
for dataset_type in ['moons', 'spirals', 'spheres', 'gaussians', 'gaussians_spiral', 'diffeqml']:
X, yn = d.generate(n_samples=512, noise=0.2, dataset_type=dataset_type)
