spike_ready=False,
batch_size=batch_size,
shuffle=shuffle)
test_loader, test_dataset = get_testLoader(data_dir,
spike_ready=False,
batch_size=batch_size,
shuffle=shuffle)
# Initialize models
model_B = VRAEC(num_class=num_class,
sequence_length=sequence_length_B,
number_of_features=number_of_features_B,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,
latent_length=latent_length,
batch_size=batch_size,
learning_rate=learning_rate,
n_epochs=n_epochs,
dropout_rate=dropout_rate,
cuda=cuda,
model_name=model_name_B,
header=header_B,
device=device)
model_B.to(device)
model_I = VRAEC(num_class=num_class,
sequence_length=sequence_length_I,
number_of_features=number_of_features_I,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,
latent_length=latent_length,
batch_size=batch_size,
test_loader, test_dataset = get_testLoader(data_dir, spike_ready=False, batch_size=batch_size, shuffle=shuffle)
# In[6]:
# Create model
model = VRAEC(num_class=num_class,
sequence_length=sequence_length,
number_of_features = number_of_features,
hidden_size = hidden_size,
hidden_layer_depth = hidden_layer_depth,
latent_length = latent_length,
batch_size = batch_size,
learning_rate = learning_rate,
n_epochs = n_epochs,
dropout_rate = dropout_rate,
cuda = cuda,
print_every=print_every,
clip=clip,
max_grad_norm=max_grad_norm,
dload = logDir,
model_name=model_name,
header=header,
device = device)
model.to(device)
# In[7]:
test_loader, test_dataset = get_testLoader(data_dir,
spike_ready=False,
batch_size=batch_size,
shuffle=shuffle)
# In[5]:
model_B_pretrained = VRAEC(num_class=num_class,
sequence_length=sequence_length_B,
number_of_features=number_of_features_B,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,
latent_length=latent_length,
batch_size=batch_size,
learning_rate=learning_rate,
n_epochs=n_epochs,
dropout_rate=dropout_rate,
cuda=cuda,
print_every=print_every,
clip=clip,
max_grad_norm=max_grad_norm,
dload=logDir,
model_name=model_name_B,
header=header_B,
device=device)
model_B_pretrained_dir = logDir + model_name_B + '.pt'
if device == torch.device('cpu'):
model_B_pretrained.load_state_dict(
torch.load(model_B_pretrained_dir, map_location=torch.device('cpu')))
else:
model_B_pretrained.load_state_dict(torch.load(model_B_pretrained_dir))
spike_ready=False,
batch_size=batch_size,
shuffle=shuffle)
test_loader, test_dataset = get_testLoader(data_dir,
spike_ready=False,
batch_size=batch_size,
shuffle=shuffle)
# Initialize models
model_B = VRAEC(num_class=num_class,
sequence_length=sequence_length_B,
number_of_features=number_of_features_B,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,
latent_length=latent_length,
batch_size=batch_size,
learning_rate=learning_rate,
n_epochs=n_epochs,
dropout_rate=dropout_rate,
cuda=cuda,
model_name=model_name_B,
header=header_B,
device=device)
model_B.to(device)
model_I = VRAEC(num_class=num_class,
sequence_length=sequence_length_I,
number_of_features=number_of_features_I,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,
latent_length=latent_length,
batch_size=batch_size,
if torch.cuda.is_available():
device = torch.device("cuda:{}".format(args.cuda))
else:
device = torch.device('cpu')
print("Loading data \n, load {} kfold number, put to device: {}".format(args.kfold, device))
train_loader, val_loader, train_dataset, val_dataset = get_trainValLoader(data_dir, k=kfold_number, spike_ready=False, batch_size=batch_size, shuffle=shuffle)
test_loader, test_dataset = get_testLoader(data_dir, spike_ready=False, batch_size=batch_size, shuffle=shuffle)
model_B_pretrained = VRAEC(num_class=num_class,
sequence_length=sequence_length_B,
number_of_features = number_of_features_B,
hidden_size = hidden_size,
hidden_layer_depth = hidden_layer_depth,
latent_length = latent_length,
batch_size = batch_size,
learning_rate = learning_rate,
n_epochs = n_epochs,
dropout_rate = dropout_rate,
cuda = cuda,
model_name=model_name_B,
header=header_B,
device = device)
model_B_pretrained_dir = logDir+model_name_B+'.pt'
if device == torch.device('cpu'):
model_B_pretrained.load_state_dict(torch.load(model_B_pretrained_dir, map_location=torch.device('cpu')))
else:
model_B_pretrained.load_state_dict(torch.load(model_B_pretrained_dir))
model_B_pretrained.to(device)
model_B_pretrained.eval()
test_loader, test_dataset = get_testLoader(data_dir,
spike_ready=False,
batch_size=batch_size,
shuffle=shuffle)
# In[6]:
model_B = VRAEC(num_class=num_class,
sequence_length=sequence_length_B,
number_of_features=number_of_features_B,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,
latent_length=latent_length,
batch_size=batch_size,
learning_rate=learning_rate,
n_epochs=n_epochs,
dropout_rate=dropout_rate,
cuda=cuda,
print_every=print_every,
clip=clip,
max_grad_norm=max_grad_norm,
dload=logDir,
model_name=model_name_B,
header=header_B,
device=device)
model_B.to(device)
model_I = VRAEC(num_class=num_class,
sequence_length=sequence_length_I,
number_of_features=number_of_features_I,
hidden_size=hidden_size,
hidden_layer_depth=hidden_layer_depth,