def test_set_obj_other():
fed_client = FederatedClient()
dummy_data = torch.tensor(42)
dummy_data.id = 43
fed_client.set_obj(dummy_data)
assert len(fed_client._objects) == 1
assert fed_client._objects[dummy_data.id] == dummy_data
def test_set_obj_train_config():
fed_client = FederatedClient()
fed_client.optimizer = True
train_config = TrainConfig(id=100, model=None, loss_fn=None)
fed_client.set_obj(train_config)
assert fed_client.train_config.id == train_config.id
assert fed_client.optimizer is None
def test_evaluate(): # pragma: no cover
data, target = utils.iris_data_partial()
fed_client = FederatedClient()
dataset = sy.BaseDataset(data, target)
dataset_key = "iris"
fed_client.add_dataset(dataset, key=dataset_key)
def loss_fn(pred, target):
return torch.nn.functional.cross_entropy(input=pred, target=target)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = torch.nn.Linear(4, 3)
def forward(self, x):
x = torch.nn.functional.relu(self.fc1(x))
return x
model_untraced = Net()
with torch.no_grad():
model_untraced.fc1.weight.set_(
torch.tensor(
[
[0.0160, 1.3753, -0.1202, -0.9129],
[0.1539, 0.3092, 0.0749, 0.2142],
[0.0984, 0.6248, 0.0274, 0.1735],
]
)
)
model_untraced.fc1.bias.set_(torch.tensor([0.3477, 0.2970, -0.0799]))
model = torch.jit.trace(model_untraced, data)
model_id = 0
model_ow = ObjectWrapper(obj=model, id=model_id)
loss_id = 1
loss_ow = ObjectWrapper(obj=loss_fn, id=loss_id)
pred = model(data)
loss_before = loss_fn(target=target, pred=pred)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
# Create and send train config
train_config = sy.TrainConfig(
batch_size=8,
model=None,
loss_fn=None,
model_id=model_id,
loss_fn_id=loss_id,
optimizer_args=None,
epochs=1,
)
fed_client.set_obj(model_ow)
fed_client.set_obj(loss_ow)
fed_client.set_obj(train_config)
fed_client.optimizer = None
result = fed_client.evaluate(
dataset_key=dataset_key, return_histograms=True, nr_bins=3, return_loss=True
)
test_loss_before = result["loss"]
correct_before = result["nr_correct_predictions"]
len_dataset = result["nr_predictions"]
hist_pred_before = result["histogram_predictions"]
hist_target = result["histogram_target"]
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Evaluation result before training: {}".format(result))
assert len_dataset == 30
assert (hist_target == [10, 10, 10]).all()
train_config = sy.TrainConfig(
batch_size=8,
model=None,
loss_fn=None,
model_id=model_id,
loss_fn_id=loss_id,
optimizer="SGD",
optimizer_args={"lr": 0.01},
shuffle=True,
epochs=2,
)
fed_client.set_obj(train_config)
train_model(
fed_client, dataset_key, available_dataset_key=dataset_key, nr_rounds=50, device="cpu"
)
result = fed_client.evaluate(
dataset_key=dataset_key, return_histograms=True, nr_bins=3, return_loss=True
)
test_loss_after = result["loss"]
correct_after = result["nr_correct_predictions"]
len_dataset = result["nr_predictions"]
hist_pred_after = result["histogram_predictions"]
hist_target = result["histogram_target"]
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Evaluation result: {}".format(result))
assert len_dataset == 30
assert (hist_target == [10, 10, 10]).all()
assert correct_after > correct_before
assert torch.norm(torch.tensor(hist_target - hist_pred_after)) < torch.norm(
torch.tensor(hist_target - hist_pred_before)
)
def test_fit(fit_dataset_key, epochs, device):
if device == "cuda" and not torch.cuda.is_available():
return
data, target = utils.create_gaussian_mixture_toy_data(nr_samples=100)
fed_client = FederatedClient()
dataset = sy.BaseDataset(data, target)
dataset_key = "gaussian_mixture"
fed_client.add_dataset(dataset, key=dataset_key)
def loss_fn(pred, target):
return torch.nn.functional.cross_entropy(input=pred, target=target)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = torch.nn.Linear(2, 3)
self.fc2 = torch.nn.Linear(3, 2)
torch.nn.init.xavier_normal_(self.fc1.weight)
torch.nn.init.xavier_normal_(self.fc2.weight)
def forward(self, x):
x = torch.nn.functional.relu(self.fc1(x))
x = torch.nn.functional.relu(self.fc2(x))
return x
data_device = data.to(torch.device(device))
target_device = target.to(torch.device(device))
model_untraced = Net().to(torch.device(device))
model = torch.jit.trace(model_untraced, data_device)
model_id = 0
model_ow = ObjectWrapper(obj=model, id=model_id)
loss_id = 1
loss_ow = ObjectWrapper(obj=loss_fn, id=loss_id)
pred = model(data_device)
loss_before = loss_fn(target=target_device, pred=pred)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
# Create and send train config
train_config = sy.TrainConfig(
batch_size=8,
model=None,
loss_fn=None,
model_id=model_id,
loss_fn_id=loss_id,
optimizer_args={"lr": 0.05, "weight_decay": 0.01},
epochs=epochs,
)
fed_client.set_obj(model_ow)
fed_client.set_obj(loss_ow)
fed_client.set_obj(train_config)
fed_client.optimizer = None
train_model(
fed_client, fit_dataset_key, available_dataset_key=dataset_key, nr_rounds=3, device=device
)
if dataset_key == fit_dataset_key:
loss_after = evaluate_model(fed_client, model_id, loss_fn, data_device, target_device)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss after training: {}".format(loss_after))
if loss_after >= loss_before: # pragma: no cover
if PRINT_IN_UNITTESTS:
print("Loss not reduced, train more: {}".format(loss_after))
train_model(
fed_client, fit_dataset_key, available_dataset_key=dataset_key, nr_rounds=10
)
loss_after = evaluate_model(fed_client, model_id, loss_fn, data, target)
assert loss_after < loss_before