def test_federated_dataset_search(workers):
bob = workers["bob"]
alice = workers["alice"]
grid = sy.PrivateGridNetwork(*[bob, alice])
train_bob = th.Tensor(th.zeros(1000, 100)).tag("data").send(bob)
target_bob = th.Tensor(th.zeros(1000, 100)).tag("target").send(bob)
train_alice = th.Tensor(th.zeros(1000, 100)).tag("data").send(alice)
target_alice = th.Tensor(th.zeros(1000, 100)).tag("target").send(alice)
data = grid.search("data")
target = grid.search("target")
datasets = [
BaseDataset(data["bob"][0], target["bob"][0]),
BaseDataset(data["alice"][0], target["alice"][0]),
]
fed_dataset = sy.FederatedDataset(datasets)
train_loader = sy.FederatedDataLoader(fed_dataset,
batch_size=4,
shuffle=False,
drop_last=False)
counter = 0
for batch_idx, (data, target) in enumerate(train_loader):
counter += 1
assert counter == len(train_loader), f"{counter} == {len(fed_dataset)}"
def test_federated_dataloader_shuffle(workers):
bob = workers["bob"]
alice = workers["alice"]
datasets = [
fl.BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
fl.BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5,
6])).send(alice),
]
fed_dataset = sy.FederatedDataset(datasets)
fdataloader = sy.FederatedDataLoader(fed_dataset,
batch_size=2,
shuffle=True)
for epoch in range(3):
counter = 0
for batch_idx, (data, target) in enumerate(fdataloader):
if counter < 1: # one batch for bob, two batches for alice (batch_size == 2)
assert (
data.location.id == "bob"
), f"id should be bob, counter = {counter}, epoch = {epoch}"
else:
assert (
data.location.id == "alice"
), f"id should be alice, counter = {counter}, epoch = {epoch}"
counter += 1
assert counter == len(fdataloader), f"{counter} == {len(fdataloader)}"
num_iterators = 2
fdataloader = sy.FederatedDataLoader(fed_dataset,
batch_size=2,
num_iterators=num_iterators,
shuffle=True)
assert (fdataloader.num_iterators == num_iterators -
1), f"{fdataloader.num_iterators} == {num_iterators - 1}"
def test_federated_dataloader(workers):
bob = workers["bob"]
alice = workers["alice"]
datasets = [
fl.BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
fl.BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5,
6])).send(alice),
]
fed_dataset = sy.FederatedDataset(datasets)
fdataloader = sy.FederatedDataLoader(fed_dataset, batch_size=2)
counter = 0
for batch_idx, (data, target) in enumerate(fdataloader):
counter += 1
assert counter == len(fdataloader), f"{counter} == {len(fdataloader)}"
fdataloader = sy.FederatedDataLoader(fed_dataset,
batch_size=2,
drop_last=True)
counter = 0
for batch_idx, (data, target) in enumerate(fdataloader):
counter += 1
assert counter == len(fdataloader), f"{counter} == {len(fdataloader)}"
def construct_FL_loader(data_pointer, **kwargs):
""" Cast paired data & labels into configured tensor dataloaders
Args:
dataset (list(sy.BaseDataset)): A tuple of X features & y labels
kwargs: Additional parameters to configure PyTorch's Dataloader
Returns:
Configured dataloader (th.utils.data.DataLoader)
"""
federated_dataset = sy.FederatedDataset(data_pointer)
# print(federated_dataset)
federated_data_loader = sy.FederatedDataLoader(
federated_dataset,
batch_size=(
model_hyperparams['batch_size']
if model_hyperparams['batch_size']
else len(federated_dataset)
),
shuffle=True,
iter_per_worker=True, # for subsequent parallelization
**kwargs
)
return federated_data_loader
def test_federated_dataset(workers):
bob = workers["bob"]
alice = workers["alice"]
grid = sy.VirtualGrid(*[bob, alice])
train_bob = th.Tensor(th.zeros(1000, 100)).tag("data").send(bob)
target_bob = th.Tensor(th.zeros(1000, 100)).tag("target").send(bob)
train_alice = th.Tensor(th.zeros(1000, 100)).tag("data").send(alice)
target_alice = th.Tensor(th.zeros(1000, 100)).tag("target").send(alice)
data, _ = grid.search("data")
target, _ = grid.search("target")
dataset = sy.FederatedDataset(data, target)
train_loader = sy.FederatedDataLoader(dataset,
batch_size=4,
shuffle=False,
drop_last=False)
epochs = 2
for epoch in range(1, epochs + 1):
for batch_idx, (data, target) in enumerate(train_loader):
pass
def dataset_federate_noniid(dataset, workers, Ratio=[1, 1, 1], net='NOT CNN'):
"""
Add a method to easily transform a torch.Dataset or a sy.BaseDataset
into a sy.FederatedDataset. The dataset given is split in len(workers)
part and sent to each workers
"""
logger.info(
f"Scanning and sending data to {', '.join([w.id for w in workers])}..."
)
datasets = []
N = 0
dataset_list = list(dataset)
for n in range(0, len(workers)):
ratio = Ratio[n] / sum(Ratio) #计算比例
num = round(ratio * len(dataset)) #根据比例计算要抽取的数据的长度
Subset = dataset_list[N:N + num] #抽取数据
N = N + num
data = []
targets = []
for d, t in Subset:
data.append(d)
targets.append(t)
data = torch.cat(data)
if net == 'CNN':
data = torch.unsqueeze(data, 1)
targets = torch.tensor(targets)
worker = workers[n]
logger.debug("Sending data to worker %s", worker.id)
data = data.send(worker)
targets = targets.send(worker)
datasets.append(sy.BaseDataset(data, targets)) # .send(worker)
logger.debug("Done!")
return sy.FederatedDataset(datasets)
def _distribute_among_workers(dataset, workers):
datasets = []
for i, data in dataset.items():
x_train, y_train = _data_target_split(data)
data = x_train.send(workers[i])
targets = y_train.send(workers[i])
datasets.append(sy.BaseDataset(data, targets))
return sy.FederatedDataset(datasets)
def get_federated_dataset(data, users, context_size, hook):
users_data = []
workers = []
for user in users:
user_worker = sy.VirtualWorker(hook, id = user)
cur_data = data[data.user == user]
X, Y = extend_data(cur_data.X, cur_data.Y, context_size)
X = th.tensor(X)
Y = th.tensor(Y)
users_data.append(sy.BaseDataset(X, Y).send(user_worker))
workers.append(user_worker)
return sy.FederatedDataset(users_data), workers
def test_illegal_get(workers):
"""test getting error message when calling .get() on a dataset that's a part of fedratedDataset object"""
bob = workers["bob"]
alice = workers["alice"]
alice_base_dataset = BaseDataset(th.tensor([3, 4, 5, 6]),
th.tensor([3, 4, 5, 6]))
datasets = [
BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
alice_base_dataset.send(alice),
]
fed_dataset = sy.FederatedDataset(datasets)
with pytest.raises(ValueError):
fed_dataset["alice"].get()
def test_federated_dataloader_one_worker(workers):
bob = workers["bob"]
datasets = [
fl.BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5,
6])).send(bob)
]
fed_dataset = sy.FederatedDataset(datasets)
num_iterators = len(datasets)
fdataloader = sy.FederatedDataLoader(fed_dataset,
batch_size=2,
shuffle=True)
assert fdataloader.num_iterators == 1, f"{fdataloader.num_iterators} == {1}"
def test_get_dataset(workers):
bob = workers["bob"]
alice = workers["alice"]
alice_base_dataset = BaseDataset(th.tensor([3, 4, 5, 6]),
th.tensor([3, 4, 5, 6]))
datasets = [
BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
alice_base_dataset.send(alice),
]
fed_dataset = sy.FederatedDataset(datasets)
dataset = fed_dataset.get_dataset("alice")
assert len(fed_dataset) == 2
assert len(dataset) == 4
def test_extract_batches_per_worker(workers):
bob = workers["bob"]
alice = workers["alice"]
datasets = [
fl.BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
fl.BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5, 6])).send(alice),
]
fed_dataset = sy.FederatedDataset(datasets)
fdataloader = sy.FederatedDataLoader(fed_dataset, batch_size=2, shuffle=True)
batches = utils.extract_batches_per_worker(fdataloader)
assert len(batches.keys()) == len(
datasets
), "each worker should appear as key in the batches dictionary"
def collect_datasets(self, grid):
loaders = []
tags = ['train', 'valid', 'test']
for tag in tags:
found_X = grid.search("#X", f"#{tag}")
found_y = grid.search("#Y", f"#{tag}")
datasets = []
for worker in found_X.keys():
datasets.append(
sy.BaseDataset(found_X[worker][0], found_y[worker][0]))
dataset = sy.FederatedDataset(datasets)
loaders.append(
sy.FederatedDataLoader(
dataset, batch_size=self.model_config.batch_size))
return loaders
def _distribute_among_workers(samplers, dataset, workers):
datasets = []
# Each worker have it's own sampler; len(samplers)== len(workers)
for idx, sampler in enumerate(samplers):
loader = DataLoader(dataset,
batch_size=len(sampler),
shuffle=False,
sampler=sampler)
# Loader always contains only one batch (because batch_size=len(sampler))
for batch in loader:
data = batch[0].send(workers[idx].id)
targets = batch[1].send(workers[idx].id)
datasets.append(sy.BaseDataset(data, targets))
return sy.FederatedDataset(datasets)
def test_federated_dataloader_num_iterators(workers):
bob = workers["bob"]
alice = workers["alice"]
james = workers["james"]
datasets = [
fl.BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
fl.BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5,
6])).send(alice),
fl.BaseDataset(th.tensor([7, 8, 9, 10]), th.tensor([7, 8, 9,
10])).send(james),
]
fed_dataset = sy.FederatedDataset(datasets)
num_iterators = len(datasets)
fdataloader = sy.FederatedDataLoader(fed_dataset,
batch_size=2,
num_iterators=num_iterators,
shuffle=True)
assert (fdataloader.num_iterators == num_iterators -
1), f"{fdataloader.num_iterators} == {num_iterators - 1}"
counter = 0
for batch_idx, batches in enumerate(fdataloader):
assert (len(batches.keys()) == num_iterators -
1), f"len(batches.keys()) == {num_iterators} - 1"
if batch_idx < 1:
data_bob, target_bob = batches[bob]
assert data_bob.location.id == "bob", "id should be bob, batch_idx = {0}".format(
batch_idx)
else: # bob is replaced by james
data_james, target_james = batches[james]
assert data_james.location.id == "james", "id should be james, batch_idx = {0}".format(
batch_idx)
if batch_idx < 2:
data_alice, target_alice = batches[alice]
assert data_alice.location.id == "alice", "id should be alice, batch_idx = {0}".format(
batch_idx)
counter += 1
epochs = num_iterators - 1
assert counter * (num_iterators - 1) == epochs * len(
fdataloader), " == epochs * len(fdataloader)"
def test_federated_dataset(workers):
bob = workers["bob"]
alice = workers["alice"]
alice_base_dataset = BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5, 6]))
datasets = [
BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
alice_base_dataset.send(alice),
]
fed_dataset = sy.FederatedDataset(datasets)
assert fed_dataset.workers == ["bob", "alice"]
assert len(fed_dataset) == 6
fed_dataset["alice"].get()
assert (fed_dataset["alice"].data == alice_base_dataset.data).all()
assert fed_dataset["alice"][2] == (5, 5)
assert len(fed_dataset["alice"]) == 4
assert len(fed_dataset) == 6
assert isinstance(fed_dataset.__str__(), str)
def test_federated_dataloader_iter_per_worker(workers):
bob = workers["bob"]
alice = workers["alice"]
james = workers["james"]
datasets = [
fl.BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),
fl.BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5,
6])).send(alice),
fl.BaseDataset(th.tensor([7, 8, 9, 10]), th.tensor([7, 8, 9,
10])).send(james),
]
fed_dataset = sy.FederatedDataset(datasets)
fdataloader = sy.FederatedDataLoader(fed_dataset,
batch_size=2,
iter_per_worker=True,
shuffle=True)
nr_workers = len(datasets)
assert (fdataloader.num_iterators == nr_workers
), "num_iterators should be equal to number or workers"
for batch_idx, batches in enumerate(fdataloader):
assert len(
batches.keys()) == nr_workers, "return a batch for each worker"
train_inputs = th.tensor(X_train,dtype=th.float) train_labels = th.tensor(y_train) test_inputs = th.tensor(X_test,dtype=th.float) test_labels = th.tensor(y_test) # Send the training and test data to the gatways in equal proportion. # since there are two gateways we are splitting into two : train_idx = int(len(train_labels)/2) test_idx = int(len(test_labels)/2) gateway1_train_dataset = sy.BaseDataset(train_inputs[:train_idx], train_labels[:train_idx]).send(gateway1) gateway2_train_dataset = sy.BaseDataset(train_inputs[train_idx:], train_labels[train_idx:]).send(gateway2) gateway1_test_dataset = sy.BaseDataset(test_inputs[:test_idx], test_labels[:test_idx]).send(gateway1) gateway2_test_dataset = sy.BaseDataset(test_inputs[test_idx:], test_labels[test_idx:]).send(gateway2) # Create federated datasets, an extension of Pytorch TensorDataset class federated_train_dataset = sy.FederatedDataset([gateway1_train_dataset, gateway2_train_dataset]) federated_test_dataset = sy.FederatedDataset([gateway1_test_dataset, gateway2_test_dataset]) # Create federated dataloaders, an extension of Pytorch DataLoader class federated_train_loader = sy.FederatedDataLoader(federated_train_dataset, shuffle=True, batch_size=BATCH_SIZE) federated_test_loader = sy.FederatedDataLoader(federated_test_dataset, shuffle=False, batch_size=BATCH_SIZE) len(federated_train_loader) """**Initialising Neural Network**""" print(n_class) import torch.nn as nn nn.Dropout(0.5) class Net(nn.Module):
return train_dataloaders, val_dataloaders
# In[13]:
# %%time
precision_fractional = 3
private_ds, central_val_dl = get_private_data_loaders(
workers=workers,
# crypto_provider=crypto_provider,
# precision_fractional=precision_fractional,
)
print(len(private_ds), len(central_val_dl))
federated_train_ds = sy.FederatedDataset(private_ds)
federated_train_dl = sy.FederatedDataLoader(federated_train_ds,
shuffle=False,
batch_size=args.batch_size)
# In[ ]:
# w = workers[1]
# len(list(w._objects.keys()))
# x = w._objects[list(w._objects.keys())[0]]
# print(x)
# len(w.search(['#wesad', '#chest_device', '#target']))
# In[3]:
get_ipython().run_line_magic('autoreload', '')
def main():
args = define_and_get_arguments()
hook = sy.TorchHook(torch)
if args.use_virtual:
alice = VirtualWorker(id="alice", hook=hook, verbose=args.verbose)
bob = VirtualWorker(id="bob", hook=hook, verbose=args.verbose)
else:
kwargs_websocket = {"host": "localhost", "hook": hook, "verbose": args.verbose}
alice = WebsocketClientWorker(id="alice", port=8777, **kwargs_websocket)
bob = WebsocketClientWorker(id="bob", port=8778, **kwargs_websocket)
workers = [alice, bob]
use_cuda = args.cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {}
if DATATHON:
bob_data, bob_target = preprocessed_data("EICU_DB", True, 0)
alice_data, alice_target = preprocessed_data("MIMIC_DB", True, 1)
alice_train_dataset = sy.BaseDataset(alice_data, alice_target).send(alice)
bob_train_dataset = sy.BaseDataset(bob_data, bob_target).send(bob)
federated_train_dataset = sy.FederatedDataset([alice_train_dataset, bob_train_dataset])
federated_train_loader = sy.FederatedDataLoader(federated_train_dataset, shuffle = True, batch_size=args.batch_size, iter_per_worker=True, **kwargs,)
test_loader_mimic = get_dataloader(is_train=False, batch_size=args.batch_size, is_mimic = 1 )
test_loader_eicu = get_dataloader(is_train=False, batch_size=args.batch_size, is_mimic = 0 )
else:
federated_train_loader = sy.FederatedDataLoader(
datasets.MNIST(
"../data",
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
).federate(tuple(workers)),
batch_size=args.batch_size,
shuffle=True,
iter_per_worker=True,
**kwargs,
)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"../data",
train=False,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs,
)
model = Net().to(device)
for epoch in range(1, args.epochs + 1):
logger.info("Starting epoch %s/%s", epoch, args.epochs)
model = train(model, device, federated_train_loader, args.lr, args.federate_after_n_batches)
test(model, device, test_loader_mimic, 1)
test(model, device, test_loader_eicu, 0)
if args.save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
res=emotion_analysis(custom[0])
res=res+".mp3"
x= img1
x = x.reshape([48, 48]);
plt.gray()
plt.imshow(x)
plt.show()
ipd.Audio(filename='/content/gdrive/My Drive/Songs/{}'.format(res),autoplay=True)
"""## Federated Learning"""
!pip install syft
import syft as sy # <-- NEW: import the Pysyft library
hook = sy.TorchHook(torch) # <-- NEW: hook PyTorch ie add extra functionalities to support Federated Learning
bob = sy.VirtualWorker(hook, id="bob") # <-- NEW: define remote worker bob
alice = sy.VirtualWorker(hook, id="alice") # <-- NEW: and alice
size = len(train_loader)
bobs_data = sy.BaseDataset(torch.tensor(x_train[:size//2]).type(torch.LongTensor), torch.tensor(y_train[:size//2]).type(torch.LongTensor)).send(bob)
alices_data = sy.BaseDataset(torch.tensor(x_train[size//2+1:]).type(torch.LongTensor), torch.tensor(y_train[size//2+1:]).type(torch.LongTensor)).send(alice)
federated_train_dataset = sy.FederatedDataset([bobs_data, alices_data])
federated_train_loader = sy.FederatedDataLoader(federated_train_dataset, batch_size=batch_size)
test_dataset = torch.utils.data.TensorDataset(torch.tensor(x_test).type(torch.LongTensor), torch.tensor(y_test).type(torch.LongTensor))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)
def run_train_test(workers, ports, ids, X_train, X_test, y_train, y_test,
encoder):
# Hook PyTorch ie add extra functionalities to support Federated Learning
hook = sy.TorchHook(torch)
# Sets the seed for generating random numbers.
torch.manual_seed(1)
# Select CPU computation, in case you want GPU use "cuda" instead
device = torch.device("cpu")
# Data will be distributed among these VirtualWorkers.
# Remote training of the model will happen here.
kwargs_websocket_bob = {"host": workers[1], "hook": hook}
#gatway2 = sy.VirtualWorker(hook, id="gatway2")
gatway2 = WebsocketClientWorker(id=ids[1],
port=ports[1],
**kwargs_websocket_bob)
kwargs_websocket_alice = {"host": workers[0], "hook": hook}
#gatway1 = sy.VirtualWorker(hook, id="gatway1")
gatway1 = WebsocketClientWorker(id=ids[0],
port=ports[0],
**kwargs_websocket_alice)
#kwargs_websocket_bob2 = {"host": workers[2], "hook": hook}
#gatway2 = sy.VirtualWorker(hook, id="gatway2")
#gatway3 = WebsocketClientWorker(id=ids[2], port=ports[2], **kwargs_websocket_bob2)
#kwargs_websocket_alice2 = {"host": workers[3], "hook": hook}
#gatway1 = sy.VirtualWorker(hook, id="gatway1")
#gatway4= WebsocketClientWorker(id=ids[3], port=ports[3], **kwargs_websocket_alice2)
# Number of times we want to iterate over whole training data
#gatway3=sy.VirtualWorker(hook, id="gatway3")
#gatway4=sy.VirtualWorker(hook, id="gatway4")
BATCH_SIZE = 1000
EPOCHS = 5
LOG_INTERVAL = 5
lr = 0.01
n_feature = X_train.shape[1]
n_class = np.unique(y_train).shape[0]
print("Number of training features : ", n_feature)
print("Number of training classes : ", n_class)
# Create pytorch tensor from X_train,y_train,X_test,y_test
train_inputs = torch.tensor(X_train,
dtype=torch.float).tag("#iot", "#network",
"#data", "#train")
train_labels = torch.tensor(y_train).tag("#iot", "#network", "#target",
"#train")
test_inputs = torch.tensor(X_test,
dtype=torch.float).tag("#iot", "#network",
"#data", "#test")
test_labels = torch.tensor(y_test).tag("#iot", "#network", "#target",
"#test")
# Send the training and test data to the gatways in equal proportion.
train_idx = int(len(train_labels) / 2)
test_idx = int(len(test_labels) / 2)
gatway1_train_dataset = sy.BaseDataset(
train_inputs[:train_idx], train_labels[:train_idx]).send(gatway1)
gatway2_train_dataset = sy.BaseDataset(
train_inputs[train_idx:2 * train_idx],
train_labels[train_idx:2 * train_idx]).send(gatway2)
#gatway3_train_dataset = sy.BaseDataset(train_inputs[2*train_idx:3*train_idx], train_labels[2*train_idx:3*train_idx]).send(gatway3)
#gatway4_train_dataset = sy.BaseDataset(train_inputs[3*train_idx:], train_labels[3*train_idx:]).send(gatway4)
gatway1_test_dataset = sy.BaseDataset(test_inputs[:test_idx],
test_labels[:test_idx]).send(gatway1)
gatway2_test_dataset = sy.BaseDataset(test_inputs[test_idx:2 * test_idx],
test_labels[test_idx:2 *
test_idx]).send(gatway2)
#gatway3_test_dataset = sy.BaseDataset(test_inputs[2*test_idx:2*test_idx], test_labels[2*test_idx:3*test_idx]).send(gatway3)
#gatway4_test_dataset = sy.BaseDataset(test_inputs[3*test_idx:], test_labels[3*test_idx:]).send(gatway3)
# Create federated datasets, an extension of Pytorch TensorDataset class
federated_train_dataset = sy.FederatedDataset([
gatway1_train_dataset, gatway2_train_dataset
]) #,gatway3_train_dataset,gatway4_train_dataset])
federated_test_dataset = sy.FederatedDataset(
[gatway1_test_dataset,
gatway2_test_dataset]) #,gatway3_test_dataset,gatway4_test_dataset])
# Create federated dataloaders, an extension of Pytorch DataLoader class
federated_train_loader = sy.FederatedDataLoader(federated_train_dataset,
shuffle=True,
batch_size=BATCH_SIZE)
federated_test_loader = sy.FederatedDataLoader(federated_test_dataset,
shuffle=False,
batch_size=BATCH_SIZE)
# Initialize the model
model = Net(n_feature, n_class)
#Initialize the SGD optimizer
optimizer = optim.SGD(model.parameters(), lr=lr)
for epoch in range(1, EPOCHS + 1):
# Train on the training data in a federated way
start1 = datetime.datetime.now()
model = train(model, device, federated_train_loader, optimizer, epoch,
BATCH_SIZE, LOG_INTERVAL)
end1 = datetime.datetime.now()
print("Time Taken to train epoch %d is ", end1 - start1)
if epoch == 1:
total = end1 - start1
else:
total += end1 - start1
# Check the test accuracy on unseen test data in a federated way
start2 = datetime.datetime.now()
test(model, device, federated_test_loader)
end2 = datetime.datetime.now()
print("Time Taken to test epoch %d is ", end2 - start2)
print("Total training time = ", total)
# Save the model
torch.save(model.state_dict(), "binaize-threat-model_10.pt")
# Reload the model in a new model object
model_new = Net(n_feature, n_class)
model_new.load_state_dict(torch.load("binaize-threat-model_10.pt"))
model_new.eval()
process = os.popen(
"sudo scp -i /root/.ssh/id_rsa -o stricthostkeychecking=no binaize-threat-model_fully_trained.pt root@%s:/home/ayush/ADS/predict_workers"
% (workers[0]))
output = process.read()
process = os.popen(
"sudo scp -i /root/.ssh/id_rsa -o stricthostkeychecking=no binaize-threat-model_fully_trained.pt root@%s:/home/ayush/ADS/predict_workers"
% (workers[1]))
output = process.read()
# Take the 122th record from the test data
idx = 122
data = test_inputs[idx]
pred = model_new(data)
pred_label = int(pred.argmax().data.cpu().numpy())
pred_threat = encoder.inverse_transform([pred_label])[0]
print("Predicted threat type : ", pred_threat)
actual_label = int(test_labels[idx].data.cpu().numpy())
actual_threat = encoder.inverse_transform([actual_label])[0]
print("Actual threat type : ", actual_threat)
# Take the 159th record from the test data
idx = 159
data = test_inputs[idx]
pred = model_new(data)
pred_label = int(pred.argmax().data.cpu().numpy())
pred_threat = encoder.inverse_transform([pred_label])[0]
print("Predicted threat type : ", pred_threat)
actual_label = int(test_labels[idx].data.cpu().numpy())
actual_threat = encoder.inverse_transform([actual_label])[0]
print("Actual threat type : ", actual_threat)
"""We load a model and an optimizer"""
n_features = boston_data['alice'][0].shape[1]
n_targets = 1
model = th.nn.Linear(n_features, n_targets).to(device)
"""Here we cast the data fetched in a `FederatedDataset`. See the workers which hold part of the data."""
print("A total of {} workers".format(len(boston_data.keys())))
# Cast the result in BaseDatasets
datasets = []
for worker in boston_data.keys():
dataset = sy.BaseDataset(boston_data[worker][0], boston_target[worker][0])
datasets.append(dataset)
# Build the FederatedDataset object
dataset = sy.FederatedDataset(datasets)
print(dataset.workers)
optimizers = {}
for worker in dataset.workers:
optimizers[worker] = th.optim.Adam(params=model.parameters(), lr=1e-2)
# optimizers[worker] = th.optim.SGD(model.parameters(), lr=0.05)
"""We put it in a `FederatedDataLoader` and specify options"""
train_loader = sy.FederatedDataLoader(dataset,
batch_size=32,
shuffle=False,
drop_last=False)
"""And finally we iterate over epochs. You can see how similar this is compared to pure and local PyTorch training!"""
epochs = 5
from collections import defaultdict
