def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" % (model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10000, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("RNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset in ["mnist", "fmnist", "emnist"]:
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim, flatten=True)
elif model_name == "cnn" and args.dataset in ["mnist", "fmnist", "emnist"]:
if args.dataset in ["mnist", "fmnist"]:
logging.info("CNN + MNIST")
model = CNN_DropOut(True)
elif args.dataset == "emnist":
logging.info("CNN + MNIST")
model = CNN_DropOut(only_digits=47)
elif model_name == "cnn" and args.dataset in ["har", "har_subject"]:
logging.info("CNN + HAR")
model = HAR_CNN(data_size=(9, 128), n_classes=6)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "cnn" and args.dataset == "cifar10":
logging.info("CNN + CIFAR10")
model = CNNCifar()
elif model_name == "purchasemlp":
if args.dataset == "purchase100":
model = PurchaseMLP(input_dim=600, n_classes=100)
elif model_name == "texasmlp":
if args.dataset == "texas100":
model = TexasMLP(input_dim=6169, n_classes=100)
elif model_name == 'lr' and args.dataset == "adult":
model = LogisticRegression(105, 2, flatten=False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10000, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("RNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "vgg11":
model = VGG("VGG11")
elif model_name == "resnet20":
if args.dataset == "cifar10":
model = resnet20_cifar(num_classes=10)
elif args.dataset == "chmnist":
model = resnet20_cifar(num_classes=8)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10004, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("CNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
# TODO
elif model_name == 'mobilenet_v3':
'''model_mode \in {LARGE: 5.15M, SMALL: 2.94M}'''
model = MobileNetV3(model_mode='LARGE', num_classes=output_dim)
elif model_name == 'efficientnet':
# model = EfficientNet()
efficientnet_dict = {
# Coefficients: width,depth,res,dropout
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
}
# default is 'efficientnet-b0'
model = EfficientNet.from_name(model_name='efficientnet-b0',
num_classes=output_dim)
# model = EfficientNet.from_pretrained(model_name='efficientnet-b0')
else:
raise NotImplementedError
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10004, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("CNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
elif model_name == "resnet20":
model = resnet20(class_num=output_dim)
elif model_name == "resnet38":
try:
# logging.info('Test model!!!!!!!!!!!!!!!!!!!!!!!!!!')
model = resnet38(class_num=output_dim)
logging.info(str(model))
except Exception as error:
logging.info(str(error))
elif model_name == "resnet74":
model = resnet74(class_num=output_dim)
elif model_name == "resnet110":
model = resnet110(class_num=output_dim)
elif model_name == "resnet18_imagenet":
model = resnet18_imagenet(num_classes=output_dim)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
elif model_name == 'mlp_fedcom':
model = MLP_fedcom()
elif model_name == 'mobilenetv2':
model = MobileNetV2(num_classes=output_dim)
return model
def main():
# test the data loader
# Hyper Parameters
num_epochs = 100
batch_size = 10
learning_rate = 0.8
np.random.seed(0)
torch.manual_seed(10)
device = torch.device("cuda:0")
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(batch_size)
model = RNN_OriginalFedAvg().to(device)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# Training the Model
for epoch in range(num_epochs):
for i, (x, labels) in enumerate(train_data_global):
x = x.to(device)
labels = labels.to(device)
# Forward + Backward + Optimize
optimizer.zero_grad()
output = model(x)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
# if (i + 1) % 100 == 0:
# print('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f'
# % (epoch + 1, num_epochs, i + 1, len(train_data_global), loss.item()))
# Test the Model
# if epoch % 10 == 0:
correct = 0
total = 0
for x, labels in test_data_global:
x = x.to(device)
labels = labels.to(device)
outputs = model(x)
_, predicted = torch.max(outputs.data, -1)
total += labels.size(0)
correct += (predicted == labels).sum()
# 52% in the last round
print('Accuracy of the model: %d %%' % (100 * correct // total))
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim)
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10004, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("CNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
elif model_name == "mobilenet_v2":
model = models.mobilenet_v2()
# TODO
elif model_name == 'mobilenet_v3':
'''model_mode \in {LARGE: 5.15M, SMALL: 2.94M}'''
model = MobileNetV3(model_mode='LARGE')
elif model_name == 'efficientnet':
model = EfficientNet()
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" % (model_name, output_dim))
model = None
if model_name == "lr" and args.dataset == "mnist":
model = LogisticRegression(28 * 28, output_dim)
args.client_optimizer = "sgd"
elif model_name == "rnn" and args.dataset == "shakespeare":
model = RNN_OriginalFedAvg(28 * 28, output_dim)
args.client_optimizer = "sgd"
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" % (model_name, output_dim))
model = None
if model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10004, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("RNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
else:
raise Exception("No model named {} scripted!"%model_name)
return model
def create_model(args, model_name, output_dim):
logging.info("create_model. model_name = %s, output_dim = %s" %
(model_name, output_dim))
model = None
if model_name == "lr" and args.dataset in ["mnist", "fmnist", "emnist"]:
logging.info("LogisticRegression + MNIST")
model = LogisticRegression(28 * 28, output_dim, flatten=True)
elif model_name == "cnn" and args.dataset in ["mnist", "fmnist", "emnist"]:
if args.dataset in ["mnist", "fmnist"]:
logging.info("CNN + MNIST")
model = CNN_DropOut(True)
elif args.dataset == "emnist":
logging.info("CNN + MNIST")
model = CNN_DropOut(only_digits=47)
elif model_name == "cnn" and args.dataset == "har":
logging.info("CNN + HAR")
# model = init_specific_model("Cnn1", data_size=(9, 128), num_classes=6)
model = HAR_CNN(data_size=(9, 128), n_classes=6)
elif model_name == "cnn" and args.dataset == "femnist":
logging.info("CNN + FederatedEMNIST")
model = CNN_DropOut(False)
elif model_name == "purchasemlp":
if args.dataset == "purchase100":
model = PurchaseMLP(input_dim=600, n_classes=100)
elif model_name == "texasmlp":
if args.dataset == "texas100":
model = TexasMLP(input_dim=6169, n_classes=100)
elif model_name == 'lr' and args.dataset == "adult":
model = LogisticRegression(105, 2, flatten=False)
elif model_name == "resnet18_gn" and args.dataset == "fed_cifar100":
logging.info("ResNet18_GN + Federated_CIFAR100")
model = resnet18()
elif model_name == "rnn" and args.dataset == "shakespeare":
logging.info("RNN + shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "rnn" and args.dataset == "fed_shakespeare":
logging.info("RNN + fed_shakespeare")
model = RNN_OriginalFedAvg()
elif model_name == "lr" and args.dataset == "stackoverflow_lr":
logging.info("lr + stackoverflow_lr")
model = LogisticRegression(10004, output_dim)
elif model_name == "rnn" and args.dataset == "stackoverflow_nwp":
logging.info("CNN + stackoverflow_nwp")
model = RNN_StackOverFlow()
elif model_name == "resnet56":
model = resnet56(class_num=output_dim)
elif model_name == "vgg11":
model = VGG("VGG11")
elif model_name == "resnet20":
if args.dataset == "cifar10":
model = resnet20_cifar(num_classes=10)
elif args.dataset == "chmnist":
model = resnet20_cifar(num_classes=8)
elif model_name == "mobilenet":
model = mobilenet(class_num=output_dim)
elif model_name == 'mobilenet_v3':
'''model_mode \in {LARGE: 5.15M, SMALL: 2.94M}'''
model = MobileNetV3(model_mode='LARGE', num_classes=output_dim)
elif model_name == 'efficientnet':
# model = EfficientNet()
efficientnet_dict = {
# Coefficients: width,depth,res,dropout
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
}
# default is 'efficientnet-b0'
model = EfficientNet.from_name(model_name='efficientnet-b0',
num_classes=output_dim)
return model