class VAE:
def __init__(self, args):
self.num_epochs = args.epochs
self.cuda = args.cuda
self.verbose = args.verbose
self.batch_size = args.batch_size
self.batch_size_val = args.batch_size_val
self.learning_rate = args.learning_rate
self.decay_epoch = args.decay_epoch
self.lr_decay = args.lr_decay
self.w_cat = args.w_categ
self.w_gauss = args.w_gauss
self.w_rec = args.w_rec
self.rec_type = args.rec_type
self.gaussian_size = args.gaussian_size
self.input_size = args.input_size
self.output_size = args.output_size
self.network = VAENet(self.input_size, self.gaussian_size,
self.output_size)
self.losses = LossFunctions()
self.metrics = Metrics()
if self.cuda:
self.network = self.network.cuda()
# ===============================================================================
# This is where all the losses are computed; helper method for train_epoch.
def unlabeled_loss(self, chunk, out_net):
# obtain network variables
z, data_recon = out_net['gaussian'], out_net['x_rec']
mu, var = out_net['mean'], out_net['var']
# reconstruction loss
loss_rec = self.losses.reconstruction_loss(chunk, data_recon,
self.rec_type)
# gaussian loss
loss_gauss = self.losses.kl_divergence_v2(mu, var)
# total loss
loss_total = self.w_rec * loss_rec + self.w_gauss * loss_gauss
loss_dic = {
'total': loss_total,
'reconstruction': loss_rec,
'gaussian': loss_gauss,
}
return loss_dic
# ===============================================================================
# ===============================================================================
# Train the model for one epoch; helper method for train.
def train_epoch(self, optimizer, data_loader):
self.network.train()
total_loss = 0.
recon_loss = 0.
gauss_loss = 0.
accuracy = 0.
num_batches = 0.
for (path, chunk) in data_loader:
if self.cuda == 1:
path = path.cuda()
chunk = chunk.cuda()
optimizer.zero_grad()
# flatten data
path = path.view(path.size(0), -1)
chunk = chunk.view(chunk.size(0), -1)
# forward call
out_net = self.network(path)
unlab_loss_dic = self.unlabeled_loss(chunk, out_net)
total = unlab_loss_dic['total']
# accumulate values
total_loss += total.item()
recon_loss += unlab_loss_dic['reconstruction'].item()
gauss_loss += unlab_loss_dic['gaussian'].item()
# perform backpropagation
total.backward()
optimizer.step()
num_batches += 1.
# average per batch
total_loss /= num_batches
recon_loss /= num_batches
gauss_loss /= num_batches
return total_loss, recon_loss, gauss_loss
# ==============================================================================
# ==============================================================================
# Test the model. This method is essentially the same as train_epoch except that
# self.network.eval is called at the beginning of this method.
def test(self, data_loader):
self.network.eval()
total_loss = 0.
recon_loss = 0.
gauss_loss = 0.
accuracy = 0.
num_batches = 0.
with torch.no_grad():
for path, chunk in data_loader:
if self.cuda == 1:
path = path.cuda()
chunk = chunk.cuda()
# flatten data
path = path.view(path.size(0), -1)
chunk = chunk.view(chunk.size(0), -1)
# forward call
out_net = self.network(path)
unlab_loss_dic = self.unlabeled_loss(chunk, out_net)
# accumulate values
total_loss += unlab_loss_dic['total'].item()
recon_loss += unlab_loss_dic['reconstruction'].item()
gauss_loss += unlab_loss_dic['gaussian'].item()
num_batches += 1.
# average per batch
total_loss /= num_batches
recon_loss /= num_batches
gauss_loss /= num_batches
return total_loss, recon_loss, gauss_loss
# ==============================================================================
def train(self, train_loader, val_loader):
"""Train the model
Args:
train_loader: (DataLoader) corresponding loader containing the training data
val_loader: (DataLoader) corresponding loader containing the validation data
Returns:
output: (dict) contains the history of train/val loss
"""
optimizer = optim.Adam(self.network.parameters(),
lr=self.learning_rate)
train_total_losses = []
train_recon_losses = []
train_gauss_losses = []
valid_total_losses = []
valid_recon_losses = []
valid_gauss_losses = []
for epoch in range(1, self.num_epochs + 1):
train_total_loss, train_recon_loss, train_gauss_loss = self.train_epoch(
optimizer, train_loader)
valid_total_loss, valid_recon_loss, valid_gauss_loss = self.test(
val_loader)
print(
f"Epoch {epoch:5} / {self.num_epochs} | Total loss: {round(valid_total_loss, 2):7} | Recon loss: {round(valid_recon_loss, 2):7} | Gauss loss: {round(valid_gauss_loss, 2):7}"
)
train_total_losses.append(train_total_loss)
train_recon_losses.append(train_recon_loss)
train_gauss_losses.append(train_gauss_loss)
valid_total_losses.append(valid_total_loss)
valid_recon_losses.append(valid_recon_loss)
valid_gauss_losses.append(valid_gauss_loss)
return {
'train_total_losses': train_total_losses,
'train_recon_losses': train_recon_losses,
'train_gauss_losses': train_gauss_losses,
'valid_total_losses': valid_total_losses,
'valid_recon_losses': valid_recon_losses,
'valid_gauss_losses': valid_gauss_losses,
}
def latent_features(self, data_loader):
"""Obtain latent features learnt by the model
Args:
data_loader: (DataLoader) loader containing the data
return_labels: (boolean) whether to return true labels or not
Returns:
features: (array) array containing the features from the data
"""
self.network.eval()
N = len(data_loader.dataset)
features = np.zeros((N, self.gaussian_size))
start_ind = 0
with torch.no_grad():
for (data, ) in data_loader:
if self.cuda == 1:
data = data.cuda()
data = data.view(data.size(0), -1)
out = self.network(data)
latent_feat = out['mean']
end_ind = min(start_ind + data.size(0), N + 1)
features[start_ind:end_ind] = latent_feat.cpu().numpy()
start_ind += data.size(0)
return features
def reconstruct_data(self, data_loader, sample_size=-1):
self.network.eval()
# sample random data from loader
indices = np.random.randint(0,
len(data_loader.dataset),
size=sample_size)
test_random_loader = torch.utils.data.DataLoader(
data_loader.dataset,
batch_size=sample_size,
sampler=SubsetRandomSampler(indices))
# obtain values
it = iter(test_random_loader)
test_batch_data, _ = it.next()
original = test_batch_data.data.numpy()
if self.cuda:
test_batch_data = test_batch_data.cuda()
# obtain reconstructed data
out = self.network(test_batch_data)
reconstructed = out['x_rec']
return original, reconstructed.data.cpu().numpy()
def plot_latent_space(self, data_loader, save=False):
"""Plot the latent space learnt by the model
Args:
data: (array) corresponding array containing the data
labels: (array) corresponding array containing the labels
save: (bool) whether to save the latent space plot
Returns:
fig: (figure) plot of the latent space
"""
# obtain the latent features
features = self.latent_features(data_loader)
# plot only the first 2 dimensions
fig = plt.figure(figsize=(8, 6))
plt.scatter(features[:, 0],
features[:, 1],
c=labels,
marker='o',
edgecolor='none',
cmap=plt.cm.get_cmap('jet', 10),
s=10)
plt.colorbar()
if (save):
fig.savefig('latent_space.png')
return fig
def random_generation(self, num_chunks):
latent_vecs = torch.randn(num_chunks, 64).cuda()
chunks = self.network.generative.pxz(latent_vecs)
return chunks.cpu().detach().numpy()
class GMVAE:
def __init__(self, args):
self.num_epochs = args.epochs
self.cuda = args.cuda
self.verbose = args.verbose
self.batch_size = args.batch_size
self.batch_size_val = args.batch_size_val
self.learning_rate = args.learning_rate
self.decay_epoch = args.decay_epoch
self.lr_decay = args.lr_decay
self.w_cat = args.w_categ
self.w_gauss = args.w_gauss
self.w_rec = args.w_rec
self.rec_type = args.rec_type
self.num_classes = args.num_classes
self.gaussian_size = args.gaussian_size
self.input_size = args.input_size
# gumbel
self.init_temp = args.init_temp
self.decay_temp = args.decay_temp
self.hard_gumbel = args.hard_gumbel
self.min_temp = args.min_temp
self.decay_temp_rate = args.decay_temp_rate
self.gumbel_temp = self.init_temp
self.network = GMVAENet(self.input_size, self.gaussian_size,
self.num_classes)
self.losses = LossFunctions()
self.metrics = Metrics()
if self.cuda:
self.network = self.network.cuda()
def unlabeled_loss(self, data, out_net):
"""Method defining the loss functions derived from the variational lower bound
Args:
data: (array) corresponding array containing the input data
out_net: (dict) contains the graph operations or nodes of the network output
Returns:
loss_dic: (dict) contains the values of each loss function and predictions
"""
# obtain network variables
z, data_recon = out_net['gaussian'], out_net['x_rec']
logits, prob_cat = out_net['logits'], out_net['prob_cat']
y_mu, y_var = out_net['y_mean'], out_net['y_var']
mu, var = out_net['mean'], out_net['var']
# reconstruction loss
loss_rec = self.losses.reconstruction_loss(data, data_recon,
self.rec_type)
# gaussian loss
loss_gauss = self.losses.gaussian_loss(z, mu, var, y_mu, y_var)
# categorical loss
loss_cat = -self.losses.entropy(logits, prob_cat) - np.log(0.1)
# total loss
loss_total = self.w_rec * loss_rec + self.w_gauss * loss_gauss + self.w_cat * loss_cat
# obtain predictions
_, predicted_labels = torch.max(logits, dim=1)
loss_dic = {
'total': loss_total,
'predicted_labels': predicted_labels,
'reconstruction': loss_rec,
'gaussian': loss_gauss,
'categorical': loss_cat
}
return loss_dic
def train_epoch(self, optimizer, data_loader):
"""Train the model for one epoch
Args:
optimizer: (Optim) optimizer to use in backpropagation
data_loader: (DataLoader) corresponding loader containing the training data
Returns:
average of all loss values, accuracy, nmi
"""
self.network.train()
total_loss = 0.
recon_loss = 0.
cat_loss = 0.
gauss_loss = 0.
accuracy = 0.
nmi = 0.
num_batches = 0.
true_labels_list = []
predicted_labels_list = []
# iterate over the dataset
for (data, labels) in data_loader:
if self.cuda == 1:
data = data.cuda()
optimizer.zero_grad()
# flatten data
data = data.view(data.size(0), -1)
# forward call
out_net = self.network(data, self.gumbel_temp, self.hard_gumbel)
unlab_loss_dic = self.unlabeled_loss(data, out_net)
total = unlab_loss_dic['total']
# accumulate values
total_loss += total.item()
recon_loss += unlab_loss_dic['reconstruction'].item()
gauss_loss += unlab_loss_dic['gaussian'].item()
cat_loss += unlab_loss_dic['categorical'].item()
# perform backpropagation
total.backward()
optimizer.step()
# save predicted and true labels
predicted = unlab_loss_dic['predicted_labels']
true_labels_list.append(labels)
predicted_labels_list.append(predicted)
num_batches += 1.
# average per batch
total_loss /= num_batches
recon_loss /= num_batches
gauss_loss /= num_batches
cat_loss /= num_batches
# concat all true and predicted labels
true_labels = torch.cat(true_labels_list, dim=0).cpu().numpy()
predicted_labels = torch.cat(predicted_labels_list,
dim=0).cpu().numpy()
# compute metrics
accuracy = 100.0 * self.metrics.cluster_acc(predicted_labels,
true_labels)
nmi = 100.0 * self.metrics.nmi(predicted_labels, true_labels)
return total_loss, recon_loss, gauss_loss, cat_loss, accuracy, nmi
def test(self, data_loader, return_loss=False):
"""Test the model with new data
Args:
data_loader: (DataLoader) corresponding loader containing the test/validation data
return_loss: (boolean) whether to return the average loss values
Return:
accuracy and nmi for the given test data
"""
self.network.eval()
total_loss = 0.
recon_loss = 0.
cat_loss = 0.
gauss_loss = 0.
accuracy = 0.
nmi = 0.
num_batches = 0.
true_labels_list = []
predicted_labels_list = []
with torch.no_grad():
for data, labels in data_loader:
if self.cuda == 1:
data = data.cuda()
# flatten data
data = data.view(data.size(0), -1)
# forward call
out_net = self.network(data, self.gumbel_temp,
self.hard_gumbel)
unlab_loss_dic = self.unlabeled_loss(data, out_net)
# accumulate values
total_loss += unlab_loss_dic['total'].item()
recon_loss += unlab_loss_dic['reconstruction'].item()
gauss_loss += unlab_loss_dic['gaussian'].item()
cat_loss += unlab_loss_dic['categorical'].item()
# save predicted and true labels
predicted = unlab_loss_dic['predicted_labels']
true_labels_list.append(labels)
predicted_labels_list.append(predicted)
num_batches += 1.
# average per batch
if return_loss:
total_loss /= num_batches
recon_loss /= num_batches
gauss_loss /= num_batches
cat_loss /= num_batches
# concat all true and predicted labels
true_labels = torch.cat(true_labels_list, dim=0).cpu().numpy()
predicted_labels = torch.cat(predicted_labels_list,
dim=0).cpu().numpy()
# compute metrics
accuracy = 100.0 * self.metrics.cluster_acc(predicted_labels,
true_labels)
nmi = 100.0 * self.metrics.nmi(predicted_labels, true_labels)
if return_loss:
return total_loss, recon_loss, gauss_loss, cat_loss, accuracy, nmi
else:
return accuracy, nmi
def train(self, train_loader, val_loader):
"""Train the model
Args:
train_loader: (DataLoader) corresponding loader containing the training data
val_loader: (DataLoader) corresponding loader containing the validation data
Returns:
output: (dict) contains the history of train/val loss
"""
optimizer = optim.Adam(self.network.parameters(),
lr=self.learning_rate)
train_history_acc, val_history_acc = [], []
train_history_nmi, val_history_nmi = [], []
for epoch in range(1, self.num_epochs + 1):
train_loss, train_rec, train_gauss, train_cat, train_acc, train_nmi = self.train_epoch(
optimizer, train_loader)
val_loss, val_rec, val_gauss, val_cat, val_acc, val_nmi = self.test(
val_loader, True)
# if verbose then print specific information about training
if self.verbose == 1:
print("(Epoch %d / %d)" % (epoch, self.num_epochs))
print("Train - REC: %.5lf; Gauss: %.5lf; Cat: %.5lf;" % \
(train_rec, train_gauss, train_cat))
print("Valid - REC: %.5lf; Gauss: %.5lf; Cat: %.5lf;" % \
(val_rec, val_gauss, val_cat))
print("Accuracy=Train: %.5lf; Val: %.5lf NMI=Train: %.5lf; Val: %.5lf Total Loss=Train: %.5lf; Val: %.5lf" % \
(train_acc, val_acc, train_nmi, val_nmi, train_loss, val_loss))
else:
print('(Epoch %d / %d) Train_Loss: %.3lf; Val_Loss: %.3lf Train_ACC: %.3lf; Val_ACC: %.3lf Train_NMI: %.3lf; Val_NMI: %.3lf' % \
(epoch, self.num_epochs, train_loss, val_loss, train_acc, val_acc, train_nmi, val_nmi))
# decay gumbel temperature
if self.decay_temp == 1:
self.gumbel_temp = np.maximum(
self.init_temp * np.exp(-self.decay_temp_rate * epoch),
self.min_temp)
if self.verbose == 1:
print("Gumbel Temperature: %.3lf" % self.gumbel_temp)
train_history_acc.append(train_acc)
val_history_acc.append(val_acc)
train_history_nmi.append(train_nmi)
val_history_nmi.append(val_nmi)
return {
'train_history_nmi': train_history_nmi,
'val_history_nmi': val_history_nmi,
'train_history_acc': train_history_acc,
'val_history_acc': val_history_acc
}
def latent_features(self, data_loader, return_labels=False):
"""Obtain latent features learnt by the model
Args:
data_loader: (DataLoader) loader containing the data
return_labels: (boolean) whether to return true labels or not
Returns:
features: (array) array containing the features from the data
"""
self.network.eval()
N = len(data_loader.dataset)
features = np.zeros((N, self.gaussian_size))
if return_labels:
true_labels = np.zeros(N, dtype=np.int64)
start_ind = 0
with torch.no_grad():
for (data, labels) in data_loader:
if self.cuda == 1:
data = data.cuda()
# flatten data
data = data.view(data.size(0), -1)
out = self.network.inference(data, self.gumbel_temp,
self.hard_gumbel)
latent_feat = out['mean']
end_ind = min(start_ind + data.size(0), N + 1)
# return true labels
if return_labels:
true_labels[start_ind:end_ind] = labels.cpu().numpy()
features[start_ind:end_ind] = latent_feat.cpu().detach().numpy(
)
start_ind += data.size(0)
if return_labels:
return features, true_labels
return features
def reconstruct_data(self, data_loader, sample_size=-1):
"""Reconstruct Data
Args:
data_loader: (DataLoader) loader containing the data
sample_size: (int) size of random data to consider from data_loader
Returns:
reconstructed: (array) array containing the reconstructed data
"""
self.network.eval()
# sample random data from loader
indices = np.random.randint(0,
len(data_loader.dataset),
size=sample_size)
test_random_loader = torch.utils.data.DataLoader(
data_loader.dataset,
batch_size=sample_size,
sampler=SubsetRandomSampler(indices))
# obtain values
it = iter(test_random_loader)
test_batch_data, _ = it.next()
original = test_batch_data.data.numpy()
if self.cuda:
test_batch_data = test_batch_data.cuda()
# obtain reconstructed data
out = self.network(test_batch_data, self.gumbel_temp, self.hard_gumbel)
reconstructed = out['x_rec']
return original, reconstructed.data.cpu().numpy()
def plot_latent_space(self, data_loader, save=False):
"""Plot the latent space learnt by the model
Args:
data: (array) corresponding array containing the data
labels: (array) corresponding array containing the labels
save: (bool) whether to save the latent space plot
Returns:
fig: (figure) plot of the latent space
"""
# obtain the latent features
features = self.latent_features(data_loader)
# plot only the first 2 dimensions
fig = plt.figure(figsize=(8, 6))
plt.scatter(features[:, 0],
features[:, 1],
c=labels,
marker='o',
edgecolor='none',
cmap=plt.cm.get_cmap('jet', 10),
s=10)
plt.colorbar()
if (save):
fig.savefig('latent_space.png')
return fig
def random_generation(self, num_elements=1):
"""Random generation for each category
Args:
num_elements: (int) number of elements to generate
Returns:
generated data according to num_elements
"""
# categories for each element
arr = np.array([])
for i in range(self.num_classes):
arr = np.hstack([arr, np.ones(num_elements) * i])
indices = arr.astype(int).tolist()
categorical = F.one_hot(torch.tensor(indices),
self.num_classes).float()
if self.cuda:
categorical = categorical.cuda()
# infer the gaussian distribution according to the category
mean, var = self.network.generative.pzy(categorical)
# gaussian random sample by using the mean and variance
noise = torch.randn_like(var)
std = torch.sqrt(var)
gaussian = mean + noise * std
# generate new samples with the given gaussian
generated = self.network.generative.pxz(gaussian)
return generated.cpu().detach().numpy()
