class MLP_Regression():
def __init__(self, label, parameters):
super().__init__()
self.writer = SummaryWriter(comment=f"_{label}_training")
self.label = label
self.lr = parameters['lr']
self.hidden_units = parameters['hidden_units']
self.mode = parameters['mode']
self.batch_size = parameters['batch_size']
self.num_batches = parameters['num_batches']
self.x_shape = parameters['x_shape']
self.y_shape = parameters['y_shape']
self.save_model_path = f'{parameters["save_dir"]}/{label}_model.pt'
self.best_loss = np.inf
self.init_net(parameters)
def init_net(self, parameters):
if not os.path.exists(parameters["save_dir"]):
os.makedirs(parameters["save_dir"])
model_params = {
'input_shape': self.x_shape,
'classes': self.y_shape,
'batch_size': self.batch_size,
'hidden_units': self.hidden_units,
'mode': self.mode
}
self.net = MLP(model_params).to(DEVICE)
self.optimiser = torch.optim.Adam(self.net.parameters(), lr=self.lr)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimiser,
step_size=5000,
gamma=0.5)
print("MLP Parameters: ")
print(
f'batch size: {self.batch_size}, input shape: {model_params["input_shape"]}, hidden units: {model_params["hidden_units"]}, output shape: {model_params["classes"]}, lr: {self.lr}'
)
def train_step(self, train_data):
self.net.train()
for _, (x, y) in enumerate(train_data):
x, y = x.to(DEVICE), y.to(DEVICE)
self.net.zero_grad()
self.loss_info = torch.nn.functional.mse_loss(self.net(x),
y,
reduction='sum')
self.loss_info.backward()
self.optimiser.step()
self.epoch_loss = self.loss_info.item()
def evaluate(self, x_test):
self.net.eval()
with torch.no_grad():
y_test = self.net(x_test.to(DEVICE)).detach().cpu().numpy()
return y_test
def log_progress(self, step):
write_loss(self.writer, self.loss_info, step)
def model(data, labels):
network = MLP(n_pixels, 10, args.units, args.nonlinear)
if cuda:
network.cuda()
optimizer = th.optim.Adam(network.parameters(), args.lr)
prediction_list = []
for i in range(args.T):
category = network(data)
prediction_list.append(category)
if i != args.T - 1:
loss = F.cross_entropy(category, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
return prediction_list
class Greedy_Bandit(Bandit):
def __init__(self, label, *args):
super().__init__(label, *args)
self.writer = SummaryWriter(comment=f"_{label}_training"),
def init_net(self, parameters):
model_params = {
'input_shape': self.x.shape[1] + 2,
'classes': 1 if len(self.y.shape) == 1 else self.y.shape[1],
'batch_size': self.batch_size,
'hidden_units': parameters['hidden_units'],
'mode': parameters['mode']
}
self.net = MLP(model_params).to(DEVICE)
self.optimiser = torch.optim.Adam(self.net.parameters(), lr=self.lr)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimiser,
step_size=5000,
gamma=0.5)
print(f'Bandit {self.label} Parameters: ')
print(
f'buffer_size: {self.buffer_size}, batch size: {self.batch_size}, number of samples: {self.n_samples}, epsilon: {self.epsilon}'
)
print("MLP Parameters: ")
print(
f'input shape: {model_params["input_shape"]}, hidden units: {model_params["hidden_units"]}, output shape: {model_params["classes"]}, lr: {self.lr}'
)
def loss_step(self, x, y, batch_id):
self.net.train()
self.net.zero_grad()
net_loss = torch.nn.functional.mse_loss(self.net(x).squeeze(),
y,
reduction='sum')
net_loss.backward()
self.optimiser.step()
return net_loss
def log_progress(self, step):
write_loss(self.writer[0], self.loss_info, step)
self.writer[0].add_scalar('logs/cumulative_regret',
self.cumulative_regrets[-1], step)
class MLP_Classification():
def __init__(self, label, parameters):
super().__init__()
self.writer = SummaryWriter(comment=f"_{label}_training")
self.label = label
self.lr = parameters['lr']
self.hidden_units = parameters['hidden_units']
self.mode = parameters['mode']
self.batch_size = parameters['batch_size']
self.num_batches = parameters['num_batches']
self.x_shape = parameters['x_shape']
self.classes = parameters['classes']
self.save_model_path = f'{parameters["save_dir"]}/{label}_model.pt'
self.best_acc = 0.
self.dropout = parameters['dropout']
self.init_net(parameters)
def init_net(self, parameters):
if not os.path.exists(parameters["save_dir"]):
os.makedirs(parameters["save_dir"])
model_params = {
'input_shape': self.x_shape,
'classes': self.classes,
'batch_size': self.batch_size,
'hidden_units': self.hidden_units,
'mode': self.mode,
'dropout': self.dropout,
}
if self.dropout:
self.net = MLP_Dropout(model_params).to(DEVICE)
print('MLP Dropout Parameters: ')
print(f'batch size: {self.batch_size}, input shape: {model_params["input_shape"]}, hidden units: {model_params["hidden_units"]}, output shape: {model_params["classes"]}, lr: {self.lr}')
else:
self.net = MLP(model_params).to(DEVICE)
print('MLP Parameters: ')
print(f'batch size: {self.batch_size}, input shape: {model_params["input_shape"]}, hidden units: {model_params["hidden_units"]}, output shape: {model_params["classes"]}, lr: {self.lr}')
self.optimiser = torch.optim.SGD(self.net.parameters(), lr=self.lr)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimiser, step_size=100, gamma=0.5)
def train_step(self, train_data):
self.net.train()
for _, (x, y) in enumerate(tqdm(train_data)):
x, y = x.to(DEVICE), y.to(DEVICE)
self.net.zero_grad()
self.loss_info = torch.nn.functional.cross_entropy(self.net(x), y, reduction='sum')
self.loss_info.backward()
self.optimiser.step()
def predict(self, X):
probs = torch.nn.Softmax(dim=1)(self.net(X))
preds = torch.argmax(probs, dim=1)
return preds, probs
def evaluate(self, test_loader):
self.net.eval()
print('Evaluating on validation data')
correct = 0
total = 0
with torch.no_grad():
for data in tqdm(test_loader):
X, y = data
X, y = X.to(DEVICE), y.to(DEVICE)
preds, _ = self.predict(X)
total += self.batch_size
correct += (preds == y).sum().item()
self.acc = correct / total
print(f'{self.label} validation accuracy: {self.acc}')
def log_progress(self, step):
write_loss(self.writer, self.loss_info, step)
write_acc(self.writer, self.acc, step)
def main(args):
torch.manual_seed(args.seed)
if not os.path.exists(args.res_dir):
os.mkdir(args.res_dir)
if not os.path.exists(
os.path.join(args.res_dir, args.type + str(args.noise))):
os.mkdir(os.path.join(args.res_dir, args.type + str(args.noise)))
if not os.path.exists(
os.path.join(args.res_dir, args.type + str(args.noise),
str(args.pace))):
os.mkdir(
os.path.join(args.res_dir, args.type + str(args.noise),
str(args.pace)))
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
res_dir = os.path.join(args.res_dir, args.type + str(args.noise),
str(args.pace))
data1 = dd.io.load(os.path.join(args.vec_dir, 'NYU_correlation_matrix.h5'))
data2 = dd.io.load(os.path.join(args.vec_dir, 'UM_correlation_matrix.h5'))
data3 = dd.io.load(os.path.join(args.vec_dir, 'USM_correlation_matrix.h5'))
data4 = dd.io.load(os.path.join(args.vec_dir,
'UCLA_correlation_matrix.h5'))
x1 = torch.from_numpy(data1['data']).float()
y1 = torch.from_numpy(data1['label']).long()
x2 = torch.from_numpy(data2['data']).float()
y2 = torch.from_numpy(data2['label']).long()
x3 = torch.from_numpy(data3['data']).float()
y3 = torch.from_numpy(data3['label']).long()
x4 = torch.from_numpy(data4['data']).float()
y4 = torch.from_numpy(data4['label']).long()
if args.overlap:
idNYU = dd.io.load('./idx/NYU_sub_overlap.h5')
idUM = dd.io.load('./idx/UM_sub_overlap.h5')
idUSM = dd.io.load('./idx/USM_sub_overlap.h5')
idUCLA = dd.io.load('./idx/UCLA_sub_overlap.h5')
else:
idNYU = dd.io.load('./idx/NYU_sub.h5')
idUM = dd.io.load('./idx/UM_sub.h5')
idUSM = dd.io.load('./idx/USM_sub.h5')
idUCLA = dd.io.load('./idx/UCLA_sub.h5')
if args.split == 0:
tr1 = idNYU['1'] + idNYU['2'] + idNYU['3'] + idNYU['4']
tr2 = idUM['1'] + idUM['2'] + idUM['3'] + idUM['4']
tr3 = idUSM['1'] + idUSM['2'] + idUSM['3'] + idUSM['4']
tr4 = idUCLA['1'] + idUCLA['2'] + idUCLA['3'] + idUCLA['4']
te1 = idNYU['0']
te2 = idUM['0']
te3 = idUSM['0']
te4 = idUCLA['0']
elif args.split == 1:
tr1 = idNYU['0'] + idNYU['2'] + idNYU['3'] + idNYU['4']
tr2 = idUM['0'] + idUM['2'] + idUM['3'] + idUM['4']
tr3 = idUSM['0'] + idUSM['2'] + idUSM['3'] + idUSM['4']
tr4 = idUCLA['0'] + idUCLA['2'] + idUCLA['3'] + idUCLA['4']
te1 = idNYU['1']
te2 = idUM['1']
te3 = idUSM['1']
te4 = idUCLA['1']
elif args.split == 2:
tr1 = idNYU['0'] + idNYU['1'] + idNYU['3'] + idNYU['4']
tr2 = idUM['0'] + idUM['1'] + idUM['3'] + idUM['4']
tr3 = idUSM['0'] + idUSM['1'] + idUSM['3'] + idUSM['4']
tr4 = idUCLA['0'] + idUCLA['1'] + idUCLA['3'] + idUCLA['4']
te1 = idNYU['2']
te2 = idUM['2']
te3 = idUSM['2']
te4 = idUCLA['2']
elif args.split == 3:
tr1 = idNYU['0'] + idNYU['1'] + idNYU['2'] + idNYU['4']
tr2 = idUM['0'] + idUM['1'] + idUM['2'] + idUM['4']
tr3 = idUSM['0'] + idUSM['1'] + idUSM['2'] + idUSM['4']
tr4 = idUCLA['0'] + idUCLA['1'] + idUCLA['2'] + idUCLA['4']
te1 = idNYU['3']
te2 = idUM['3']
te3 = idUSM['3']
te4 = idUCLA['3']
elif args.split == 4:
tr1 = idNYU['0'] + idNYU['1'] + idNYU['2'] + idNYU['3']
tr2 = idUM['0'] + idUM['1'] + idUM['2'] + idUM['3']
tr3 = idUSM['0'] + idUSM['1'] + idUSM['2'] + idUSM['3']
tr4 = idUCLA['0'] + idUCLA['1'] + idUCLA['2'] + idUCLA['3']
te1 = idNYU['4']
te2 = idUM['4']
te3 = idUSM['4']
te4 = idUCLA['4']
x1_train = x1[tr1]
y1_train = y1[tr1]
x2_train = x2[tr2]
y2_train = y2[tr2]
x3_train = x3[tr3]
y3_train = y3[tr3]
x4_train = x4[tr4]
y4_train = y4[tr4]
x1_test = x1[te1]
y1_test = y1[te1]
x2_test = x2[te2]
y2_test = y2[te2]
x3_test = x3[te3]
y3_test = y3[te3]
x4_test = x4[te4]
y4_test = y4[te4]
if args.sepnorm:
mean = x1_train.mean(0, keepdim=True)
dev = x1_train.std(0, keepdim=True)
x1_train = (x1_train - mean) / dev
x1_test = (x1_test - mean) / dev
mean = x2_train.mean(0, keepdim=True)
dev = x2_train.std(0, keepdim=True)
x2_train = (x2_train - mean) / dev
x2_test = (x2_test - mean) / dev
mean = x3_train.mean(0, keepdim=True)
dev = x3_train.std(0, keepdim=True)
x3_train = (x3_train - mean) / dev
x3_test = (x3_test - mean) / dev
mean = x4_train.mean(0, keepdim=True)
dev = x4_train.std(0, keepdim=True)
x4_train = (x4_train - mean) / dev
x4_test = (x4_test - mean) / dev
else:
mean = torch.cat((x1_train, x2_train, x3_train, x4_train),
0).mean(0, keepdim=True)
dev = torch.cat((x1_train, x2_train, x3_train, x4_train),
0).std(0, keepdim=True)
x1_train = (x1_train - mean) / dev
x1_test = (x1_test - mean) / dev
x2_train = (x2_train - mean) / dev
x2_test = (x2_test - mean) / dev
x3_train = (x3_train - mean) / dev
x3_test = (x3_test - mean) / dev
x4_train = (x4_train - mean) / dev
x4_test = (x4_test - mean) / dev
train1 = TensorDataset(x1_train, y1_train)
train_loader1 = DataLoader(train1,
batch_size=len(train1) // args.nsteps,
shuffle=True)
train2 = TensorDataset(x2_train, y2_train)
train_loader2 = DataLoader(train2,
batch_size=len(train2) // args.nsteps,
shuffle=True)
train3 = TensorDataset(x3_train, y3_train)
train_loader3 = DataLoader(train3,
batch_size=len(train3) // args.nsteps,
shuffle=True)
train4 = TensorDataset(x4_train, y4_train)
train_loader4 = DataLoader(train4,
batch_size=len(train4) // args.nsteps,
shuffle=True)
train_all = ConcatDataset([train1, train2, train3, train4])
train_loader = DataLoader(train_all, batch_size=500, shuffle=False)
test1 = TensorDataset(x1_test, y1_test)
test2 = TensorDataset(x2_test, y2_test)
test3 = TensorDataset(x3_test, y3_test)
test4 = TensorDataset(x4_test, y4_test)
test_loader1 = DataLoader(test1,
batch_size=args.test_batch_size1,
shuffle=False)
test_loader2 = DataLoader(test2,
batch_size=args.test_batch_size2,
shuffle=False)
test_loader3 = DataLoader(test3,
batch_size=args.test_batch_size3,
shuffle=False)
test_loader4 = DataLoader(test4,
batch_size=args.test_batch_size4,
shuffle=False)
tbs = [
args.test_batch_size1, args.test_batch_size2, args.test_batch_size3,
args.test_batch_size4
]
model1 = MLP(6105, args.dim, 2).to(device)
model2 = MLP(6105, args.dim, 2).to(device)
model3 = MLP(6105, args.dim, 2).to(device)
model4 = MLP(6105, args.dim, 2).to(device)
optimizer1 = optim.Adam(model1.parameters(),
lr=args.lr1,
weight_decay=5e-2)
optimizer2 = optim.Adam(model2.parameters(),
lr=args.lr2,
weight_decay=5e-2)
optimizer3 = optim.Adam(model3.parameters(),
lr=args.lr3,
weight_decay=5e-2)
optimizer4 = optim.Adam(model4.parameters(),
lr=args.lr4,
weight_decay=5e-2)
models = [model1, model2, model3, model4]
train_loaders = [
train_loader1, train_loader2, train_loader3, train_loader4
]
optimizers = [optimizer1, optimizer2, optimizer3, optimizer4]
model = MLP(6105, args.dim, 2).to(device)
print(model)
nnloss = nn.NLLLoss()
def train(epoch):
pace = args.pace
for i in range(4):
models[i].train()
if epoch <= 50 and epoch % 20 == 0:
for param_group1 in optimizers[i].param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
elif epoch > 50 and epoch % 20 == 0:
for param_group1 in optimizers[i].param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
#define weights
w = dict()
denominator = np.sum(np.array(tbs))
for i in range(4):
w[i] = 0.25 #tbs[i]/denominator
loss_all = dict()
num_data = dict()
for i in range(4):
loss_all[i] = 0
num_data[i] = 0
count = 0
for t in range(args.nsteps):
for i in range(4):
optimizers[i].zero_grad()
a, b = next(iter(train_loaders[i]))
num_data[i] += b.size(0)
a = a.to(device)
b = b.to(device)
output = models[i](a)
loss = nnloss(output, b)
loss.backward()
loss_all[i] += loss.item() * b.size(0)
optimizers[i].step()
count += 1
if count % pace == 0 or t == args.nsteps - 1:
with torch.no_grad():
for key in model.state_dict().keys():
if models[0].state_dict()[key].dtype == torch.int64:
model.state_dict()[key].data.copy_(
models[0].state_dict()[key])
else:
temp = torch.zeros_like(model.state_dict()[key])
# add noise
for s in range(4):
if args.type == 'G':
nn = tdist.Normal(
torch.tensor([0.0]),
args.noise *
torch.std(models[s].state_dict()
[key].detach().cpu()))
else:
nn = tdist.Laplace(
torch.tensor([0.0]),
args.noise *
torch.std(models[s].state_dict()
[key].detach().cpu()))
noise = nn.sample(models[s].state_dict()
[key].size()).squeeze()
noise = noise.to(device)
temp += w[s] * (models[s].state_dict()[key] +
noise)
# update global model
model.state_dict()[key].data.copy_(temp)
# updata local model
for s in range(4):
models[s].state_dict()[key].data.copy_(
model.state_dict()[key])
return loss_all[0] / num_data[0], loss_all[1] / num_data[1], \
loss_all[2] / num_data[2], loss_all[3] / num_data[3]
def test(federated_model, dataloader, train=False):
federated_model.eval()
test_loss = 0
correct = 0
outputs = []
preds = []
targets = []
for data, target in dataloader:
targets.append(target[0].detach().numpy())
data = data.to(device)
target = target.to(device)
output = federated_model(data)
outputs.append(output.detach().cpu().numpy())
test_loss += nnloss(output, target).item() * target.size(0)
pred = output.data.max(1)[1]
preds.append(pred.detach().cpu().numpy())
correct += pred.eq(target.view(-1)).sum().item()
test_loss /= len(dataloader.dataset)
correct /= len(dataloader.dataset)
if train:
print('Train set local: Average loss: {:.4f}, Average acc: {:.4f}'.
format(test_loss, correct))
else:
print('Test set local: Average loss: {:.4f}, Average acc: {:.4f}'.
format(test_loss, correct))
return test_loss, correct, targets, outputs, preds
best_acc = 0
best_epoch = 0
train_loss = dict()
for i in range(4):
train_loss[i] = list()
for epoch in range(args.epochs):
start_time = time.time()
print(f"Epoch Number {epoch + 1}")
l1, l2, l3, l4 = train(epoch)
print(
' L1 loss: {:.4f}, L2 loss: {:.4f}, L3 loss: {:.4f}, L4 loss: {:.4f}'
.format(l1, l2, l3, l4))
train_loss[0].append(l1)
train_loss[1].append(l2)
train_loss[2].append(l3)
train_loss[3].append(l4)
test(model, train_loader, train=True)
test(model, train_loader, train=True)
print('===NYU===')
_, acc1, targets1, outputs1, preds1 = test(model,
test_loader1,
train=False)
print('===UM===')
_, acc2, targets2, outputs2, preds2 = test(model,
test_loader2,
train=False)
print('===USM===')
_, acc3, targets3, outputs3, preds3 = test(model,
test_loader3,
train=False)
print('===UCLA===')
_, acc4, targets4, outputs4, preds4 = test(model,
test_loader4,
train=False)
if (acc1 + acc2 + acc3 + acc4) / 4 > best_acc:
best_acc = (acc1 + acc2 + acc3 + acc4) / 4
best_epoch = epoch
total_time = time.time() - start_time
print('Communication time over the network', round(total_time, 2),
's\n')
model_wts = copy.deepcopy(model.state_dict())
torch.save(model_wts, os.path.join(args.model_dir,
str(args.split) + '.pth'))
dd.io.save(os.path.join(res_dir, 'NYU_' + str(args.split) + '.h5'), {
'outputs': outputs1,
'preds': preds1,
'targets': targets1
})
dd.io.save(os.path.join(res_dir, 'UM_' + str(args.split) + '.h5'), {
'outputs': outputs2,
'preds': preds2,
'targets': targets2
})
dd.io.save(os.path.join(res_dir, 'USM_' + str(args.split) + '.h5'), {
'outputs': outputs3,
'preds': preds3,
'targets': targets3
})
dd.io.save(os.path.join(res_dir, 'UCLA_' + str(args.split) + '.h5'), {
'outputs': outputs4,
'preds': preds4,
'targets': targets4
})
dd.io.save(os.path.join(res_dir, 'train_loss.h5'), {'loss': train_loss})
print('Best Acc:', best_acc)
print('split:', args.split, ' noise:', args.noise, ' pace:', args.pace)
class VanillaAE(nn.Module):
def __init__(self, opt):
super(VanillaAE, self).__init__()
self.opt = opt
self.device = torch.device("cuda:0" if not opt.no_cuda else "cpu")
nc = int(opt.nc)
imageSize = int(opt.imageSize)
nz = int(opt.nz)
nblk = int(opt.nblk)
# generator
self.netG = MLP(input_dim=nc * imageSize * imageSize,
output_dim=nc * imageSize * imageSize,
dim=nz,
n_blk=nblk,
norm='none',
activ='relu').to(self.device)
weights_init(self.netG)
if opt.netG != '':
self.netG.load_state_dict(
torch.load(opt.netG, map_location=self.device))
print_and_write_log(opt.train_log_file, 'netG:')
print_and_write_log(opt.train_log_file, str(self.netG))
# losses
self.criterion = nn.MSELoss()
# define focal frequency loss
self.criterion_freq = FFL(loss_weight=opt.ffl_w,
alpha=opt.alpha,
patch_factor=opt.patch_factor,
ave_spectrum=opt.ave_spectrum,
log_matrix=opt.log_matrix,
batch_matrix=opt.batch_matrix).to(
self.device)
# misc
self.to(self.device)
# optimizer
self.optimizerG = optim.Adam(self.netG.parameters(),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
def forward(self):
pass
def gen_update(self, data, epoch, matrix=None):
self.netG.zero_grad()
real = data.to(self.device)
if matrix is not None:
matrix = matrix.to(self.device)
recon = self.netG(real)
# apply pixel-level loss
errG_pix = self.criterion(recon, real) * self.opt.mse_w
# apply focal frequency loss
if epoch >= self.opt.freq_start_epoch:
errG_freq = self.criterion_freq(recon, real, matrix)
else:
errG_freq = torch.tensor(0.0).to(self.device)
errG = errG_pix + errG_freq
errG.backward()
self.optimizerG.step()
return errG_pix, errG_freq
def sample(self, x):
x = x.to(self.device)
self.netG.eval()
with torch.no_grad():
recon = self.netG(x)
self.netG.train()
return recon
def save_checkpoints(self, ckpt_dir, epoch):
torch.save(self.netG.state_dict(),
'%s/netG_epoch_%03d.pth' % (ckpt_dir, epoch))
def main(args):
torch.manual_seed(args.seed)
if not os.path.exists(args.res_dir):
os.mkdir(args.res_dir)
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
data1 = dd.io.load(os.path.join(args.vec_dir, 'NYU_correlation_matrix.h5'))
data2 = dd.io.load(os.path.join(args.vec_dir, 'UM_correlation_matrix.h5'))
data3 = dd.io.load(os.path.join(args.vec_dir, 'USM_correlation_matrix.h5'))
data4 = dd.io.load(os.path.join(args.vec_dir,
'UCLA_correlation_matrix.h5'))
x1 = torch.from_numpy(data1['data']).float()
y1 = torch.from_numpy(data1['label']).long()
x2 = torch.from_numpy(data2['data']).float()
y2 = torch.from_numpy(data2['label']).long()
x3 = torch.from_numpy(data3['data']).float()
y3 = torch.from_numpy(data3['label']).long()
x4 = torch.from_numpy(data4['data']).float()
y4 = torch.from_numpy(data4['label']).long()
if args.overlap:
idNYU = dd.io.load('./idx/NYU_sub_overlap.h5')
idUM = dd.io.load('./idx/UM_sub_overlap.h5')
idUSM = dd.io.load('./idx/USM_sub_overlap.h5')
idUCLA = dd.io.load('./idx/UCLA_sub_overlap.h5')
else:
idNYU = dd.io.load('./idx/NYU_sub.h5')
idUM = dd.io.load('./idx/UM_sub.h5')
idUSM = dd.io.load('./idx/USM_sub.h5')
idUCLA = dd.io.load('./idx/UCLA_sub.h5')
if args.split == 0:
tr1 = idNYU['1'] + idNYU['2'] + idNYU['3'] + idNYU['4']
tr2 = idUM['1'] + idUM['2'] + idUM['3'] + idUM['4']
tr3 = idUSM['1'] + idUSM['2'] + idUSM['3'] + idUSM['4']
tr4 = idUCLA['1'] + idUCLA['2'] + idUCLA['3'] + idUCLA['4']
te1 = idNYU['0']
te2 = idUM['0']
te3 = idUSM['0']
te4 = idUCLA['0']
elif args.split == 1:
tr1 = idNYU['0'] + idNYU['2'] + idNYU['3'] + idNYU['4']
tr2 = idUM['0'] + idUM['2'] + idUM['3'] + idUM['4']
tr3 = idUSM['0'] + idUSM['2'] + idUSM['3'] + idUSM['4']
tr4 = idUCLA['0'] + idUCLA['2'] + idUCLA['3'] + idUCLA['4']
te1 = idNYU['1']
te2 = idUM['1']
te3 = idUSM['1']
te4 = idUCLA['1']
elif args.split == 2:
tr1 = idNYU['0'] + idNYU['1'] + idNYU['3'] + idNYU['4']
tr2 = idUM['0'] + idUM['1'] + idUM['3'] + idUM['4']
tr3 = idUSM['0'] + idUSM['1'] + idUSM['3'] + idUSM['4']
tr4 = idUCLA['0'] + idUCLA['1'] + idUCLA['3'] + idUCLA['4']
te1 = idNYU['2']
te2 = idUM['2']
te3 = idUSM['2']
te4 = idUCLA['2']
elif args.split == 3:
tr1 = idNYU['0'] + idNYU['1'] + idNYU['2'] + idNYU['4']
tr2 = idUM['0'] + idUM['1'] + idUM['2'] + idUM['4']
tr3 = idUSM['0'] + idUSM['1'] + idUSM['2'] + idUSM['4']
tr4 = idUCLA['0'] + idUCLA['1'] + idUCLA['2'] + idUCLA['4']
te1 = idNYU['3']
te2 = idUM['3']
te3 = idUSM['3']
te4 = idUCLA['3']
elif args.split == 4:
tr1 = idNYU['0'] + idNYU['1'] + idNYU['2'] + idNYU['3']
tr2 = idUM['0'] + idUM['1'] + idUM['2'] + idUM['3']
tr3 = idUSM['0'] + idUSM['1'] + idUSM['2'] + idUSM['3']
tr4 = idUCLA['0'] + idUCLA['1'] + idUCLA['2'] + idUCLA['3']
te1 = idNYU['4']
te2 = idUM['4']
te3 = idUSM['4']
te4 = idUCLA['4']
x1_train = x1[tr1]
y1_train = y1[tr1]
x2_train = x2[tr2]
y2_train = y2[tr2]
x3_train = x3[tr3]
y3_train = y3[tr3]
x4_train = x4[tr4]
y4_train = y4[tr4]
x1_test = x1[te1]
y1_test = y1[te1]
x2_test = x2[te2]
y2_test = y2[te2]
x3_test = x3[te3]
y3_test = y3[te3]
x4_test = x4[te4]
y4_test = y4[te4]
if args.sepnorm:
mean = x1_train.mean(0, keepdim=True)
dev = x1_train.std(0, keepdim=True)
x1_train = (x1_train - mean) / dev
x1_test = (x1_test - mean) / dev
mean = x2_train.mean(0, keepdim=True)
dev = x2_train.std(0, keepdim=True)
x2_train = (x2_train - mean) / dev
x2_test = (x2_test - mean) / dev
mean = x3_train.mean(0, keepdim=True)
dev = x3_train.std(0, keepdim=True)
x3_train = (x3_train - mean) / dev
x3_test = (x3_test - mean) / dev
mean = x4_train.mean(0, keepdim=True)
dev = x4_train.std(0, keepdim=True)
x4_train = (x4_train - mean) / dev
x4_test = (x4_test - mean) / dev
else:
mean = torch.cat((x1_train, x2_train, x3_train, x4_train),
0).mean(0, keepdim=True)
dev = torch.cat((x1_train, x2_train, x3_train, x4_train),
0).std(0, keepdim=True)
x1_train = (x1_train - mean) / dev
x1_test = (x1_test - mean) / dev
x2_train = (x2_train - mean) / dev
x2_test = (x2_test - mean) / dev
x3_train = (x3_train - mean) / dev
x3_test = (x3_test - mean) / dev
x4_train = (x4_train - mean) / dev
x4_test = (x4_test - mean) / dev
train = TensorDataset(
torch.cat((x1_train, x2_train, x3_train, x4_train), 0),
torch.cat((y1_train, y2_train, y3_train, y4_train), 0))
train_loader = DataLoader(train, batch_size=args.batch_size, shuffle=True)
test1 = TensorDataset(x1_test, y1_test)
test_loader1 = DataLoader(test1,
batch_size=args.test_batch_size1,
shuffle=False)
test2 = TensorDataset(x2_test, y2_test)
test_loader2 = DataLoader(test2,
batch_size=args.test_batch_size2,
shuffle=False)
test3 = TensorDataset(x3_test, y3_test)
test_loader3 = DataLoader(test3,
batch_size=args.test_batch_size3,
shuffle=False)
test4 = TensorDataset(x4_test, y4_test)
test_loader4 = DataLoader(test4,
batch_size=args.test_batch_size4,
shuffle=False)
model = MLP(6105, args.dim, 2).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=5e-2)
print(model)
nnloss = nn.NLLLoss()
def train(data_loader, epoch):
model.train()
if epoch <= 50 and epoch % 20 == 0:
for param_group1 in optimizer.param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
elif epoch > 50 and epoch % 20 == 0:
for param_group1 in optimizer.param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
loss_all1 = 0
for data, target in data_loader:
optimizer.zero_grad()
data = data.to(device)
target = target.to(device)
output1 = model(data)
loss1 = nnloss(output1, target)
loss1.backward()
loss_all1 += loss1.item() * target.size(0)
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
return loss_all1 / (len(data_loader.dataset))
def test(data_loader, train=False):
model.eval()
test_loss = 0
correct = 0
outputs = []
preds = []
targets = []
for data, target in data_loader:
data = data.to(device)
targets.append(target[0].detach().numpy())
target = target.to(device)
output = model(data)
outputs.append(output.detach().cpu().numpy())
test_loss += nnloss(output, target).item() * target.size(0)
pred = output.data.max(1)[1]
preds.append(pred.detach().cpu().numpy())
correct += pred.eq(target.view(-1)).sum().item()
test_loss /= len(data_loader.dataset)
correct /= len(data_loader.dataset)
if train:
print(
'Train set: Average loss: {:.4f}, Average acc: {:.4f}'.format(
test_loss, correct))
else:
print('Test set: Average loss: {:.4f}, Average acc: {:.4f}'.format(
test_loss, correct))
return test_loss, correct, targets, outputs, preds
best_acc = 0
best_epoch = 0
for epoch in range(args.epochs):
start_time = time.time()
print(f"Epoch Number {epoch + 1}")
l1 = train(train_loader, epoch)
test(train_loader, train=True)
print(' L1 loss: {:.4f}'.format(l1))
print('===NYU===')
_, acc1, targets1, outputs1, preds1 = test(test_loader1, train=False)
print('===UM===')
_, acc2, targets2, outputs2, preds2 = test(test_loader2, train=False)
print('===USM===')
_, acc3, targets3, outputs3, preds3 = test(test_loader3, train=False)
print('===UCLA===')
_, acc4, targets4, outputs4, preds4 = test(test_loader4, train=False)
if (acc1 + acc2 + acc3 + acc4) / 4 > best_acc:
best_acc = (acc1 + acc2 + acc3 + acc4) / 4
best_epoch = epoch
total_time = time.time() - start_time
print('Communication time over the network', round(total_time, 2),
's\n')
model_wts = copy.deepcopy(model.state_dict())
torch.save(model_wts, os.path.join(args.model_dir,
str(args.split) + '.pth'))
dd.io.save(os.path.join(args.res_dir, 'NYU_' + str(args.split) + '.h5'), {
'outputs': outputs1,
'preds': preds1,
'targets': targets1
})
dd.io.save(os.path.join(args.res_dir, 'UM_' + str(args.split) + '.h5'), {
'outputs': outputs2,
'preds': preds2,
'targets': targets2
})
dd.io.save(os.path.join(args.res_dir, 'USM_' + str(args.split) + '.h5'), {
'outputs': outputs3,
'preds': preds3,
'targets': targets3
})
dd.io.save(os.path.join(args.res_dir, 'UCLA_' + str(args.split) + '.h5'), {
'outputs': outputs4,
'preds': preds4,
'targets': targets4
})
print('Best Acc:', best_acc, 'Best Epoch:', best_epoch)
print('split:', args.split)
def main(args):
if not os.path.exists(args.res_dir):
os.mkdir(args.res_dir)
if not os.path.exists(os.path.join(args.res_dir, args.trainsite)):
os.mkdir(os.path.join(args.res_dir, args.trainsite))
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
torch.manual_seed(args.seed)
data1 = dd.io.load(os.path.join(args.vec_dir, 'NYU_correlation_matrix.h5'))
data2 = dd.io.load(os.path.join(args.vec_dir, 'UM_correlation_matrix.h5'))
data3 = dd.io.load(os.path.join(args.vec_dir, 'USM_correlation_matrix.h5'))
data4 = dd.io.load(os.path.join(args.vec_dir,
'UCLA_correlation_matrix.h5'))
x1 = torch.from_numpy(data1['data']).float()
y1 = torch.from_numpy(data1['label']).long()
x2 = torch.from_numpy(data2['data']).float()
y2 = torch.from_numpy(data2['label']).long()
x3 = torch.from_numpy(data3['data']).float()
y3 = torch.from_numpy(data3['label']).long()
x4 = torch.from_numpy(data4['data']).float()
y4 = torch.from_numpy(data4['label']).long()
if args.sepnorm:
mean = x1.mean(0, keepdim=True)
dev = x1.std(0, keepdim=True)
x1 = (x1 - mean) / dev
mean = x2.mean(0, keepdim=True)
dev = x2.std(0, keepdim=True)
x2 = (x2 - mean) / dev
mean = x3.mean(0, keepdim=True)
dev = x3.std(0, keepdim=True)
x3 = (x3 - mean) / dev
mean = x4.mean(0, keepdim=True)
dev = x4.std(0, keepdim=True)
x4 = (x4 - mean) / dev
else:
if args.trainsite == 'NYU':
mean = x1.mean(0, keepdim=True)
dev = x1.std(0, keepdim=True)
elif args.trainsite == 'UM':
mean = x2.mean(0, keepdim=True)
dev = x2.std(0, keepdim=True)
elif args.trainsite == 'USM':
mean = x3.mean(0, keepdim=True)
dev = x3.std(0, keepdim=True)
elif args.trainsite == 'UCLA':
mean = x4.mean(0, keepdim=True)
dev = x4.std(0, keepdim=True)
x1 = (x1 - mean) / dev
x2 = (x2 - mean) / dev
x3 = (x3 - mean) / dev
x4 = (x4 - mean) / dev
datas = [
TensorDataset(x1, y1),
TensorDataset(x2, y2),
TensorDataset(x3, y3),
TensorDataset(x4, y4)
]
if args.trainsite == 'NYU':
train_loader = DataLoader(datas[0],
batch_size=args.batch_size,
shuffle=True)
elif args.trainsite == 'UM':
train_loader = DataLoader(datas[1],
batch_size=args.batch_size,
shuffle=True)
elif args.trainsite == 'USM':
train_loader = DataLoader(datas[2],
batch_size=args.batch_size,
shuffle=True)
elif args.trainsite == 'UCLA':
train_loader = DataLoader(datas[3],
batch_size=args.batch_size,
shuffle=True)
test_loader1 = DataLoader(datas[0],
batch_size=args.test_batch_size1,
shuffle=False)
test_loader2 = DataLoader(datas[1],
batch_size=args.test_batch_size2,
shuffle=False)
test_loader3 = DataLoader(datas[2],
batch_size=args.test_batch_size3,
shuffle=False)
test_loader4 = DataLoader(datas[3],
batch_size=args.test_batch_size4,
shuffle=False)
model = MLP(6105, args.dim, 2).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=5e-2)
print(model)
nnloss = nn.NLLLoss()
def train(data_loader, epoch):
model.train()
if epoch <= 50 and epoch % 20 == 0:
for param_group1 in optimizer.param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
elif epoch > 50 and epoch % 20 == 0:
for param_group1 in optimizer.param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
loss_all1 = 0
for data, target in data_loader:
optimizer.zero_grad()
data = data.to(device)
target = target.to(device)
output1 = model(data)
loss1 = nnloss(output1, target)
loss1.backward()
loss_all1 += loss1.item() * target.size(0)
optimizer.step()
return loss_all1 / (len(data_loader.dataset)), model
def test(data_loader, train=False):
model.eval()
test_loss = 0
correct = 0
outputs = []
preds = []
targets = []
for data, target in data_loader:
data = data.to(device)
targets.append(target[0].detach().numpy())
target = target.to(device)
output = federated_model(data)
outputs.append(output.detach().cpu().numpy())
test_loss += nnloss(output, target).item() * target.size(0)
pred = output.data.max(1)[1]
preds.append(pred.detach().cpu().numpy())
correct += pred.eq(target.view(-1)).sum().item()
test_loss /= len(data_loader.dataset)
correct /= len(data_loader.dataset)
if train:
print(
'Train set: Average loss: {:.4f}, Average acc: {:.4f}'.format(
test_loss, correct))
else:
print('Test set: Average loss: {:.4f}, Average acc: {:.4f}'.format(
test_loss, correct))
return test_loss, correct, targets, outputs, preds
for epoch in range(args.epochs):
start_time = time.time()
print(f"Epoch Number {epoch + 1}")
l1, federated_model = train(train_loader, epoch)
print(' L1 loss: {:.4f}'.format(l1))
print('===NYU===')
_, acc1, targets1, outputs1, preds1 = test(test_loader1, train=False)
print('===UM===')
_, acc2, targets2, outputs2, preds2 = test(test_loader2, train=False)
print('===USM===')
_, acc3, targets3, outputs3, preds3 = test(test_loader3, train=False)
print('===UCLA===')
_, acc4, targets4, outputs4, preds4 = test(test_loader4, train=False)
total_time = time.time() - start_time
print('Communication time over the network', round(total_time, 2),
's\n')
model_wts = copy.deepcopy(model.state_dict())
torch.save(model_wts, os.path.join(args.model_dir,
args.trainsite + '.pth'))
dd.io.save(os.path.join(args.res_dir, args.trainsite, 'NYU.h5'), {
'outputs': outputs1,
'preds': preds1,
'targets': targets1
})
dd.io.save(os.path.join(args.res_dir, args.trainsite, 'UM.h5'), {
'outputs': outputs2,
'preds': preds2,
'targets': targets2
})
dd.io.save(os.path.join(args.res_dir, args.trainsite, 'USM.h5'), {
'outputs': outputs3,
'preds': preds3,
'targets': targets3
})
dd.io.save(os.path.join(args.res_dir, args.trainsite, 'UCLA.h5'), {
'outputs': outputs4,
'preds': preds4,
'targets': targets4
})
def file_classify_demo(fobjs: List[FileInfo]):
words = {}
for fobj in filter(lambda x: not x.istest, fobjs):
for kw, freq in zip(fobj.keywords, fobj.kwfreq):
if kw in words:
words[kw] += freq
else:
words[kw] = freq
# make keyword score vec: train and test
all_wordvec = []
all_wordvec_test = []
labels_train = []
labels_test = []
for fobj in fobjs:
fobj.set_wordvec(words)
if not fobj.kwfreq:
continue
if not fobj.istest:
all_wordvec.append(fobj.wordvec)
labels_train.append(fobj.label)
# curlabel = [0] * 5
# curlabel[fobj.label] = 1
# labels.append(curlabel)
else:
all_wordvec_test.append(fobj.wordvec)
labels_test.append(fobj.label)
# pca make fingerprints
inputdim = 20
outputdim = 7
pca = PCA(n_components=inputdim)
pca.fit(all_wordvec)
fprints = pca.transform(all_wordvec)
fprints_test = pca.transform(all_wordvec_test)
print('PCA ratio sum:', sum(pca.explained_variance_ratio_))
print()
x_train = torch.from_numpy(fprints).float()
x_test = torch.from_numpy(fprints_test).float()
y_train = torch.Tensor(labels_train).long() # float()
train_dataset = TensorDataset(x_train, y_train)
dloader = DataLoader(train_dataset, batch_size=6, shuffle=True)
model = MLP(inputdim, outputdim)
optimizer = optim.Adam(model.parameters(), lr=0.01)
lossfunc = nn.CrossEntropyLoss()
epoch = 300 + 1
for ecnt in range(epoch):
for i, data in enumerate(dloader):
optimizer.zero_grad()
inputs, labels = data
inputs = torch.autograd.Variable(inputs)
labels = torch.autograd.Variable(labels)
outputs = model(inputs)
loss = lossfunc(outputs, labels) # / outputs.size()[0]
# loss = torch.Tensor([0])
# for b in range(outputs.size()[0]):
# loss += sum(abs(outputs[b] - labels[b]))
# loss /= outputs.size()[0]
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
5) # clip param important
optimizer.step()
# if i % 1 == 0:
# print(i, ":", loss)
# print(outputs)
# print(labels)
if ecnt % 20 == 0:
print('Epoch:', ecnt)
model.eval()
y_train_step = model(x_train)
y_train_step_label = np.argmax(y_train_step.data, axis=1)
y_test_step = model(x_test)
y_test_step_label = np.argmax(y_test_step.data, axis=1)
tran_accu = len(
list(
filter(lambda x: x == 0, y_train_step_label -
np.array(labels_train)))) / len(labels_train)
test_accu = len(
list(
filter(lambda x: x == 0, y_test_step_label -
np.array(labels_test)))) / len(labels_test)
print('tran_accu', tran_accu)
print('test_accu', test_accu)
print()
model.train()
# save model
save_path = r'.\ai\classify-demo.pth'
torch.save(model.state_dict(), save_path)
# load model
new_model = MLP(inputdim, outputdim)
new_model.load_state_dict(torch.load(save_path))
y_train_look = new_model(x_train)
y_test = new_model(x_test)
print(y_train_look)
print(y_test)
print(np.argmax(y_test.data, axis=1))
print(labels_test)
print(len(labels_test))
print(len(labels_train))
def main(args):
torch.manual_seed(args.seed)
if not os.path.exists(args.res_dir):
os.mkdir(args.res_dir)
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
if not os.path.exists(os.path.join(args.model_dir, args.site)):
os.mkdir(os.path.join(args.model_dir, args.site))
save_model_dir = os.path.join(args.model_dir, args.site)
data = dd.io.load(
os.path.join(args.vec_dir, args.site + '_correlation_matrix.h5'))
x = torch.from_numpy(data['data']).float()
y = torch.from_numpy(data['label']).long()
if args.site == 'NYU':
rep = 145 #7
elif args.site == 'UM':
rep = 265 #9
elif args.site == 'USM':
rep = 205 #8
elif args.site == 'UCLA':
rep = 85 #7
split_dir = os.path.join(args.id_dir, args.site + '_sub_overlap.h5')
if not os.path.exists(split_dir): #save splitting
n = len(y) // rep
ll = list(range(n))
random.seed(args.seed)
random.shuffle(ll)
list1 = dict()
for i in range(5): # 5 splits
list1[i] = list()
if i != 4:
temp = ll[i * n // 5:(i + 1) * n // 5]
else:
temp = ll[4 * n // 5:]
for t in temp:
list1[i] += [t * rep + j for j in range(rep)]
dd.io.save(
split_dir, {
'0': list1[0],
'1': list1[1],
'2': list1[2],
'3': list1[3],
'4': list1[4]
})
print("data saved")
id = dd.io.load(split_dir)
if args.split == 0:
tr = id['1'] + id['2'] + id['3'] + id['4']
te = id['0']
elif args.split == 1:
tr = id['0'] + id['2'] + id['3'] + id['4']
te = id['1']
elif args.split == 2:
tr = id['0'] + id['1'] + id['3'] + id['4']
te = id['2']
elif args.split == 3:
tr = id['0'] + id['1'] + id['2'] + id['4']
te = id['3']
elif args.split == 4:
tr = id['0'] + id['1'] + id['2'] + id['3']
te = id['4']
x_train = x[tr]
y_train = y[tr]
x_test = x[te]
y_test = y[te]
mean = x_train.mean(0, keepdim=True)
dev = x_train.std(0, keepdim=True)
x_train = (x_train - mean) / dev
x_test = (x_test - mean) / dev
train = TensorDataset(x_train, y_train)
train_loader = DataLoader(train, batch_size=args.batch_size, shuffle=True)
test = TensorDataset(x_test, y_test)
test_loader = DataLoader(test, batch_size=rep, shuffle=False)
model = MLP(6105, args.dim, 2).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=5e-2)
print(model)
def train(data_loader, optimizer, epoch):
model.train()
if epoch <= 50 and epoch % 20 == 0:
for param_group1 in optimizer.param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
elif epoch > 50 and epoch % 20 == 0:
for param_group1 in optimizer.param_groups:
param_group1['lr'] = 0.5 * param_group1['lr']
loss_all = 0
for data, target in data_loader:
optimizer.zero_grad()
data = data.to(device)
target = target.to(device)
output = model(data)
loss = nnloss(output, target)
loss.backward()
loss_all += loss.item() * target.size(0)
optimizer.step()
return loss_all / (len(data_loader.dataset))
def test(data_loader, train):
model.eval()
test_loss = 0
correct = 0
outputs = []
preds = []
targets = []
for data, target in data_loader:
data = data.to(device)
targets.append(target[0].detach().numpy())
target = target.to(device)
output = model(data)
outputs.append(output.detach().cpu().numpy())
test_loss += nnloss(output, target).item() * target.size(0)
pred = output.data.max(1)[1]
preds.append(pred.detach().cpu().numpy())
correct += pred.eq(target.view(-1)).sum().item()
test_loss /= len(data_loader.dataset)
correct /= len(data_loader.dataset)
if train:
print(
'Train set: Average loss: {:.4f}, Average acc: {:.4f}'.format(
test_loss, correct))
else:
print('Test set: Average loss: {:.4f}, Average acc: {:.4f}'.format(
test_loss, correct))
return targets, outputs, preds
for epoch in range(args.epochs):
start_time = time.time()
print(f"Epoch Number {epoch + 1}")
l1 = train(train_loader, optimizer, epoch)
print(' L1 loss: {:.4f}'.format(l1))
test(train_loader, train=True)
targets, outputs, preds = test(test_loader, train=False)
total_time = time.time() - start_time
print('Communication time over the network', round(total_time, 2),
's\n')
model_wts = copy.deepcopy(model.state_dict())
torch.save(model_wts, os.path.join(save_model_dir,
str(args.split) + '.pth'))
dd.io.save(
os.path.join(args.res_dir, args.site + '_' + str(args.split) + '.h5'),
{
'outputs': outputs,
'preds': preds,
'targets': targets
})
print('site:', args.site, ' split:', args.split)