def __init__(self,
learning_rate=0.05,
cls_num=2,
domain_num=2,
input_size=768,
hidden_layer_size=25,
lambda_adapt=1.,
maxiter=5000,
verbose=False,
batch_size=64,
use_cuda=True,
name=None,
cached=False,
cpt_path=''):
"""
Domain Adversarial Neural Network for classification
option "learning_rate" is the learning rate of the neural network.
option "hidden_layer_size" is the hidden layer size.
option "lambda_adapt" weights the domain adaptation regularization term.
if 0 or None or False, then no domain adaptation regularization is performed
option "maxiter" number of training iterations.
option "epsilon_init" is a term used for initialization.
if None the weight matrices are weighted by 6/(sqrt(r+c))
(where r and c are the dimensions of the weight matrix)
option "adversarial_representation": if False, the adversarial classifier is trained
but has no impact on the hidden layer representation. The label predictor is
then the same as a standard neural-network one (see experiments_moon.py figures).
option "seed" is the seed of the random number generator.
"""
super(DANN, self).__init__()
self.hidden_layer_size = hidden_layer_size
self.maxiter = maxiter
self.lambda_adapt = lambda_adapt if lambda_adapt not in (None,
False) else 0.
self.learning_rate = learning_rate
self.verbose = verbose
self.input_size = input_size
self.feature_extractor = nn.Sequential(
Linear(self.input_size, self.hidden_layer_size), nn.Sigmoid())
self.classifier = nn.Linear(self.hidden_layer_size, cls_num)
self.domain_classifier = nn.Sequential(
nn.Linear(self.hidden_layer_size, 20), nn.Sigmoid(),
nn.Linear(20, domain_num))
self.batch_size = batch_size
self.rev_grad = RevGrad()
self.use_cuda = use_cuda
self.criterion = nn.CrossEntropyLoss(reduction='mean')
# self.d_optimizer = optim.SGD([{"params": self.classifier.parameters(), 'lr': 1e-3}])
# self.optimizer = optim.SGD(self.parameters(), lr = 0.01, momentum = 0.9)
self.optimizer = optim.Adam(self.parameters(), lr=0.001)
self.print_freq = 100
self.name = name
self.cached = cached
self.checkpoint_path = cpt_path
def __init__(
self,
input_size,
output_size,
num_hidden_layers,
hidden_dim,
flip_gradient=False,
batchnorm=False,
drop_prob=0.0,
activation=torch.nn.ReLU,
):
super(DNN, self).__init__()
layers = [
torch.nn.Linear(input_size, hidden_dim),
torch.nn.Dropout(drop_prob),
activation(),
]
if batchnorm:
raise NotImplementedError
for i in range(num_hidden_layers):
layers.append(torch.nn.Linear(hidden_dim, hidden_dim))
layers.append(torch.nn.Dropout(drop_prob))
layers.append(activation())
layers.append(torch.nn.Linear(hidden_dim, output_size))
if flip_gradient:
layers.append(RevGrad())
self._network = torch.nn.Sequential(*layers)
def __init__(self, config):
super(BLSTM, self).__init__()
self.config = config
self.vocab_size = get_vocab_size()
self.num_mels = config.data.num_mels
self.hidden_size = config.model.hidden_size
self.num_layers = config.model.num_layers
self.batch_first = config.model.batch_first
self.dropout = config.model.dropout
self.bidirectional = config.model.bidirectional
self.lstm = nn.LSTM(input_size=self.num_mels,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=self.batch_first,
dropout=self.dropout,
bidirectional=self.bidirectional)
self.full1 = nn.Linear(
in_features=self.hidden_size
if not self.bidirectional else self.hidden_size * 2,
out_features=500)
self.adv_layer = nn.Sequential(
RevGrad(),
nn.Linear(in_features=self.hidden_size
if not self.bidirectional else self.hidden_size * 2,
out_features=2), #21
nn.Softmax(dim=-1))
self.full2 = nn.Linear(in_features=500, out_features=self.vocab_size)
def __init__(self, embedding_size, hidden_size, cls_num = 12, device = torch.device('cuda:1')):
super(DANNClassifier, self).__init__()
self.encoderA = Linear(embedding_size, hidden_size)
self.encoderB = Linear(embedding_size, hidden_size)
self.classifier = Linear(hidden_size, cls_num)
self.device = device
self.rev_grad = RevGrad()
self.criterion = nn.CrossEntropyLoss()
def __init__(self,
dim=512,
input_dim=pose_dim,
num_classes=226,
num_signers=50):
super().__init__(sign_loss=args.sign_loss,
signer_loss=args.signer_loss,
signer_loss_patience=args.signer_loss_patience)
self.batch_norm = torch.nn.BatchNorm1d(num_features=input_dim)
self.dropout = torch.nn.Dropout(p=0.2)
self.proj = torch.nn.Linear(in_features=input_dim, out_features=dim)
heads = args.encoder_heads
depth = args.encoder_depth
# self.transformer = Linformer(
# dim=dim,
# seq_len=seq_len + 1, # + 1 cls token
# depth=depth,
# heads=heads,
# k=64,
# dropout=0.4
# )
if args.encoder == "lstm":
self.encoder = torch.nn.LSTM(input_size=dim,
hidden_size=dim // 2,
num_layers=depth,
batch_first=True,
dropout=0.1,
bidirectional=True)
else:
self.encoder = Transformer(dim=dim,
depth=depth,
heads=heads,
dim_head=dim // heads,
mlp_dim=dim,
dropout=0.4)
self.cls_token = torch.nn.Parameter(torch.randn(1, 1, dim))
self.pos_embedding = torch.nn.Parameter(
torch.randn(1, args.max_seq_size + 1, dim))
self.head_norm = torch.nn.LayerNorm(dim)
self.mlp_head = torch.nn.Linear(dim, num_classes)
self.mlp_signer = torch.nn.Sequential(
RevGrad(), torch.nn.Linear(dim, num_signers))
def test_gradients_inverted():
network = torch.nn.Sequential(torch.nn.Linear(5, 3), torch.nn.Linear(3, 1))
revnetwork = torch.nn.Sequential(copy.deepcopy(network), RevGrad())
inp = torch.randn(8, 5)
outp = torch.randn(8)
criterion = torch.nn.MSELoss()
criterion(network(inp), outp).backward()
criterion(revnetwork(inp), outp).backward()
assert all(
(p1.grad == -p2.grad).all()
for p1, p2 in zip(network.parameters(), revnetwork.parameters()))
def test_gradients_inverted_alpha(alpha_parameter):
network = torch.nn.Sequential(torch.nn.Linear(5, 3), torch.nn.Linear(3, 1))
revnetwork = torch.nn.Sequential(
copy.deepcopy(network), RevGrad(alpha=alpha_parameter)
)
inp = torch.randn(8, 5)
outp = torch.randn(8, 1)
criterion = torch.nn.MSELoss()
criterion(network(inp), outp).backward()
criterion(revnetwork(inp), outp).backward()
for p1, p2 in zip(network.parameters(), revnetwork.parameters()):
assert torch.isclose(p1.grad, -p2.grad/alpha_parameter).all()
def __init__(self, model_params):
super(Discriminator, self).__init__()
"""
self.emb_dim = 256
self.dis_hid_dim = 200
self.dis_layers = 1
self.dis_input_dropout = 0.2
self.dis_dropout = 0.2
layers = []#[RevGrad()]
for i in range(self.dis_layers + 1):
input_dim = self.emb_dim if i == 0 else self.dis_hid_dim
output_dim = 2 if i == self.dis_layers else self.dis_hid_dim
layers.append(nn.Linear(input_dim, output_dim))
if i < self.dis_layers:
layers.append(nn.LeakyReLU(0.2))
layers.append(nn.Dropout(self.dis_dropout))
#layers.append(nn.Sigmoid())
self.layers = nn.Sequential(*layers)
"""
self.Classifier = nn.Sequential(RevGrad(), nn.Linear(256, 2))
def __init__(self, device, input_size=768, lstm_hidden_size=500, num_layers=1, bidirectional=False, hidden_dimensions=[500],
cell_type='GRU', causal_layer=None, causal_hidden_dimensions=[30, 20], att_dim=30, dropout1=0.2,
dropout2=0.2, activation='ReLU', adversarial_out=None, task='classification'):
super(LSTMAttentionClassifier, self).__init__()
self.task = task
self.dropout1 = dropout1
self.dropout2 = dropout2
self.adversarial_out = adversarial_out
self.device = device
self.cell_type = cell_type
if cell_type == 'GRU':
self.rnn = nn.GRU(input_size=input_size, hidden_size=lstm_hidden_size, num_layers=num_layers, dropout=0.5,
batch_first=True, bidirectional=bidirectional)
self.hx = None
elif cell_type == 'LSTM':
self.rnn = nn.LSTM(input_size=input_size, hidden_size=lstm_hidden_size, num_layers=num_layers, dropout=0.5,
batch_first=True, bidirectional=bidirectional)
self.hx = None
self.cx = None
else:
raise Exception('Invalid RNN type')
self.bidirectional = bidirectional
self.lstm_hidden_size = lstm_hidden_size
self.num_layers = num_layers
self.hidden_dimensions = hidden_dimensions
if self.bidirectional:
self.directions = 2
else:
self.directions = 1
if causal_layer and causal_layer == 'residual':
layer_input = lstm_hidden_size + 1
# layer_input = lstm_hidden_size + 768 # + causal_hidden_dimensions[-1]
else:
layer_input = lstm_hidden_size * self.directions
self.fc_layers = nn.ModuleList([])
for layer_out in hidden_dimensions:
self.fc_layers.append(nn.Linear(layer_input, layer_out))
layer_input = layer_out
self.last_fc = nn.Linear(layer_input, 1)
self.sigmoid = nn.Sigmoid()
self.drop = nn.Dropout(self.dropout1)
if activation == 'ReLU':
self.activation = nn.ReLU()
else:
self.activation = nn.Tanh()
self.att1 = nn.Linear(self.directions * lstm_hidden_size, att_dim, bias=False)
self.att2 = nn.Linear(att_dim, 1, bias=False)
self.causal_layer = causal_layer
if causal_layer == 'adversarial':
self.rev = RevGrad()
self.drop2 = nn.Dropout(self.dropout2)
layer_input = lstm_hidden_size * self.directions
self.causal_layers = nn.ModuleList([])
for layer_out in causal_hidden_dimensions:
self.causal_layers.append(nn.Linear(layer_input, layer_out))
layer_input = layer_out
if not adversarial_out:
self.causal_last_fc = nn.Linear(layer_input, 10) # regression as multiclass classification
self.classes = torch.arange(1, 11).view(-1, 1).to(self.device, dtype=torch.float) # classes has shape 10, 1
self.softmax = nn.Softmax()
else:
# adversarial out is a tuple of (number_of_confounders, ids of confounders with sigmoid)
self.causal_last_fc = nn.Linear(layer_input, adversarial_out[0])
elif causal_layer == 'residual':
self.drop2 = nn.Dropout(self.dropout2)
if not adversarial_out:
layer_input = input_size
else:
layer_input = adversarial_out[0]
self.causal_layers = nn.ModuleList([])
for layer_out in causal_hidden_dimensions:
self.causal_layers.append(nn.Linear(layer_input, layer_out))
layer_input = layer_out
self.causal_last_fc = nn.Linear(layer_input, 1)
def get_classifier():
return nn.Sequential(RevGrad(), nn.Linear(model_dim, args.hidden),
nn.ReLU(), nn.Dropout(0.1),
nn.Linear(args.hidden, len(languages))).to(device)
class DANN(nn.Module):
def __init__(self,
learning_rate=0.05,
cls_num=2,
domain_num=2,
input_size=768,
hidden_layer_size=25,
lambda_adapt=1.,
maxiter=5000,
verbose=False,
batch_size=64,
use_cuda=True,
name=None,
cached=False,
cpt_path=''):
"""
Domain Adversarial Neural Network for classification
option "learning_rate" is the learning rate of the neural network.
option "hidden_layer_size" is the hidden layer size.
option "lambda_adapt" weights the domain adaptation regularization term.
if 0 or None or False, then no domain adaptation regularization is performed
option "maxiter" number of training iterations.
option "epsilon_init" is a term used for initialization.
if None the weight matrices are weighted by 6/(sqrt(r+c))
(where r and c are the dimensions of the weight matrix)
option "adversarial_representation": if False, the adversarial classifier is trained
but has no impact on the hidden layer representation. The label predictor is
then the same as a standard neural-network one (see experiments_moon.py figures).
option "seed" is the seed of the random number generator.
"""
super(DANN, self).__init__()
self.hidden_layer_size = hidden_layer_size
self.maxiter = maxiter
self.lambda_adapt = lambda_adapt if lambda_adapt not in (None,
False) else 0.
self.learning_rate = learning_rate
self.verbose = verbose
self.input_size = input_size
self.feature_extractor = nn.Sequential(
Linear(self.input_size, self.hidden_layer_size), nn.Sigmoid())
self.classifier = nn.Linear(self.hidden_layer_size, cls_num)
self.domain_classifier = nn.Sequential(
nn.Linear(self.hidden_layer_size, 20), nn.Sigmoid(),
nn.Linear(20, domain_num))
self.batch_size = batch_size
self.rev_grad = RevGrad()
self.use_cuda = use_cuda
self.criterion = nn.CrossEntropyLoss(reduction='mean')
# self.d_optimizer = optim.SGD([{"params": self.classifier.parameters(), 'lr': 1e-3}])
# self.optimizer = optim.SGD(self.parameters(), lr = 0.01, momentum = 0.9)
self.optimizer = optim.Adam(self.parameters(), lr=0.001)
self.print_freq = 100
self.name = name
self.cached = cached
self.checkpoint_path = cpt_path
def forward(self, x):
x = self.feature_extractor(x)
x = self.classifier(x)
return x
def _hidden_representation(self, x):
x = (self.feature_extractor(x))
return x
def predict_(self, x):
# outputs = self(torch.FloatTensor(x))
x = torch.FloatTensor(x)
outputs = self(x)
_, predicted = torch.max(outputs.data, 1)
return predicted.cpu().numpy()
def _predict(self, x):
outputs = self(x.cuda())
_, predicted = torch.max(outputs.data, 1)
return predicted.cpu().numpy()
def predict(self, x):
x = torch.FloatTensor(x)
outputs = self(x.cuda())
_, predicted = torch.max(outputs.data, 1)
return predicted.cpu().numpy()
def _predict_domain(self, x):
outputs = self._hidden_representation(x)
_, predicted = torch.max(self.domain_classifier(outputs), 1)
return predicted.cpu().numpy()
def L_y(self, x, y):
x = self.feature_extractor(x)
x = self.classifier(x)
return self.criterion(x, y)
def L_d(self, x, domain_y):
x = self.rev_grad(self.feature_extractor(x))
x = self.domain_classifier(x)
return self.criterion(x, domain_y)
def validate(self, x, y):
with torch.no_grad():
preds = self._predict(x)
acc = np.mean(preds == y)
return acc
def validate_domain(self, X, X_adapt):
with torch.no_grad():
domain_labels = np.array([0] * X_adapt.size(0) + [1] * X.size(0))
domain_ds = data_utils.TensorDataset(
torch.cat([X_adapt, X], dim=0), )
loader = data_utils.DataLoader(domain_ds,
batch_size=1024,
shuffle=True,
pin_memory=True,
num_workers=4,
drop_last=False)
preds = []
for x, in loader:
if (self.use_cuda):
x = x.cuda()
preds.extend(self._predict_domain(x))
acc = np.mean(preds == domain_labels)
return acc
def fit(self,
X,
Y,
X_adapt,
X_valid=None,
Y_valid=None,
do_random_init=True):
"""
Trains the domain adversarial neural network until it reaches a total number of
iterations of "self.maxiter" since it was initialize.
inputs:
X : Source data matrix
Y : Source labels
X_adapt : Target data matrix
(X_valid, Y_valid) : validation set used for early stopping.
do_random_init : A boolean indicating whether to use random initialization or not.
"""
if (self.cached and self.verbose):
print("Attempt to Load Model from {} ...".format(
self.checkpoint_path))
if (self.cached and os.path.exists(self.checkpoint_path)):
self.load_state_dict(torch.load(self.checkpoint_path))
preds = self.predict_(X)
correct = np.sum(preds == Y)
correct = correct / len(Y)
# print("Source Domain batch Acc.: {:.4f}".format(correct))
if (self.use_cuda):
self.cuda()
return correct
# X = X - np.mean(X, axis = 0)
# X_adapt = X_adapt - np.mean(X_adapt, axis = 0)
# print(X)
# print(X_adapt)
X, X_adapt = torch.FloatTensor(X), torch.FloatTensor(X_adapt)
if (self.verbose):
print("Adaptation size: {}".format(len(X_adapt)))
X_valid = torch.FloatTensor(X_valid)
Y_cpu = Y.copy()
Y = torch.LongTensor(Y)
# domain_labels = torch.LongTensor([1]*X_adapt.size(0) + [1]*X.size(0))
domain_ds = data_utils.TensorDataset(X_adapt, )
clf_ds = data_utils.TensorDataset(X, Y)
domain_loader = data_utils.DataLoader(domain_ds,
batch_size=self.batch_size,
shuffle=True,
pin_memory=True,
num_workers=4,
drop_last=True)
clf_loader = data_utils.DataLoader(clf_ds,
batch_size=self.batch_size,
shuffle=True,
pin_memory=True,
num_workers=4,
drop_last=True)
domain_loader = list(domain_loader)
clf_loader = list(clf_loader)
best_acc = 0.0
avg_acc = []
print_count = 0
if (self.use_cuda):
self.cuda()
running_loss = 0.0
running_ld = 0.0
running_ly = 0.0
batch_counter = 0
num_steps = (X.size(0) // self.batch_size) * self.maxiter
for i in tqdm(range(self.maxiter)):
for x, y in clf_loader:
p = float(batch_counter) / num_steps
l = 2. / (1. + np.exp(-10. * p)) - 1
self.rev_grad.set_scale(l)
# Adaptation param and learning rate schedule as described in the paper
self.optimizer.zero_grad()
# self.d_optimizer.zero_grad()
# remove the random choicing of the batch data
domain_x, = domain_loader[batch_counter % len(domain_loader)]
domain_x = torch.cat([domain_x, x], dim=0)
domain_y = torch.LongTensor([0] * self.batch_size +
[1] * self.batch_size)
if (self.use_cuda):
x, y = x.cuda(), y.cuda()
domain_x, domain_y = domain_x.cuda(), domain_y.cuda()
l_y = self.L_y(x, y)
l_d = self.L_d(domain_x, domain_y)
loss = l_y + self.lambda_adapt * l_d
loss.backward()
# self.d_optimizer.step()
self.optimizer.step()
lr = 0.01 / (1. + 10 * p)**0.75
# for g in self.optimizer.param_groups:
# g['lr'] = lr
batch_counter += 1
# update scale
#
running_loss += loss.item()
running_ld += l_d.item()
running_ly += l_y.item()
if ((i + 1) % self.print_freq == 0):
if self.verbose:
print(
'Iter {}/{} loss: {:.5f} Ly: {:.5f} Ld: {:5f}'.format(
i + 1, self.maxiter,
running_loss / self.print_freq,
running_ly / self.print_freq,
running_ld / self.print_freq))
print("p: {:.4f} l: {:.4f} lr: {:.4f}".format(p, l, lr))
running_loss = 0.0
running_ld = 0.0
running_ly = 0.0
target_acc = self.validate(X_valid, Y_valid)
avg_acc.append(target_acc)
if self.verbose:
print("Source Domain Acc.: {:.4f}".format(
self.validate(X, Y_cpu)))
print("Target Domain Acc.: {:.4f}".format(target_acc))
print("Domain Clf Acc.: {:.4f}".format(
self.validate_domain(
X,
X_adapt,
)))
if (target_acc >= best_acc):
best_acc = target_acc
print_count += 1
torch.save(self.state_dict(), self.checkpoint_path)
print(
"INFER {} Best ACC in Valid Dataset. {:.4f} Average ACC {}".format(
self.name, best_acc, avg_acc))
return best_acc
def __init__(self, model_params):
super(Discriminator_Matcher, self).__init__()
self.Classifier = nn.Sequential(RevGrad(),
nn.Linear(20, 20),
nn.LeakyReLU(0.1),
nn.Linear(20, 2))
def __init__(self, model_params):
super(Discriminator_Compressor, self).__init__()
self.Classifier = nn.Sequential(RevGrad(),
nn.Linear(256, 128),
nn.LeakyReLU(0.1),
nn.Linear(128, 2))
