def generate_unconditionally(cell_size=400, num_clusters=20, steps=800, random_state=700, \
state_dict_file='trained_models/unconditional_epoch_50.pt'):
model = LSTMRandWriter(cell_size, num_clusters)
# load trained model weights
model.load_state_dict(torch.load(state_dict_file)['model'])
np.random.seed(random_state)
zero_tensor = torch.zeros((1, 1, 3))
# initialize null hidden states and memory states
init_states = [torch.zeros((1, 1, cell_size))] * 4
if cuda:
model.cuda()
zero_tensor = zero_tensor.cuda()
init_states = [state.cuda() for state in init_states]
x = Variable(zero_tensor)
init_states = [
Variable(state, requires_grad=False) for state in init_states
]
h1_init, c1_init, h2_init, c2_init = init_states
prev = (h1_init, c1_init)
prev2 = (h2_init, c2_init)
record = [np.array([0, 0, 0])]
for i in range(steps):
end, weights, mu_1, mu_2, log_sigma_1, log_sigma_2, p, prev, prev2 = model(
x, prev, prev2)
# sample end stroke indicator
prob_end = end.data[0][0][0]
sample_end = np.random.binomial(1, prob_end.cpu())
sample_index = np.random.choice(range(20),
p=weights.data[0][0].cpu().numpy())
# sample new stroke point
mu = np.array([
mu_1.data[0][0][sample_index].item(),
mu_2.data[0][0][sample_index].item()
])
v1 = log_sigma_1.exp().data[0][0][sample_index].item()**2
v2 = log_sigma_2.exp().data[0][0][sample_index].item()**2
c = p.data[0][0][sample_index].item()*log_sigma_1.exp().data[0][0][sample_index].item()\
*log_sigma_2.exp().data[0][0][sample_index].item()
cov = np.array([[v1, c], [c, v2]])
sample_point = np.random.multivariate_normal(mu, cov)
out = np.insert(sample_point, 0, sample_end)
record.append(out)
x = torch.from_numpy(out).type(torch.FloatTensor)
if cuda:
x = x.cuda()
x = Variable(x, requires_grad=False)
x = x.view((1, 1, 3))
plot_stroke(np.array(record))
def generate_conditionally(text, cell_size=400, num_clusters=20, K=10, random_state=700, \
bias=1., bias2=1., state_dict_file='trained_models/conditional_epoch_60.pt'):
char_to_code = torch.load('char_to_code.pt')
np.random.seed(random_state)
text = text + ' '
model = LSTMSynthesis(len(text),
len(char_to_code) + 1, cell_size, num_clusters, K)
model.load_state_dict(torch.load(state_dict_file)['model'])
onehots = np.zeros((len(text), len(char_to_code) + 1))
for _ in range(len(text)):
try:
onehots[_][char_to_code[text[_]]] = 1
except:
onehots[_][-1] = 1
zero_tensor = torch.zeros((1, 1, 3))
h1_init, c1_init = torch.zeros((1, cell_size)), torch.zeros((1, cell_size))
h2_init, c2_init = torch.zeros((1, 1, cell_size)), torch.zeros(
(1, 1, cell_size))
kappa_old = torch.zeros(1, K)
onehots = torch.from_numpy(onehots).type(torch.FloatTensor)
text_len = torch.from_numpy(np.array([[len(text)]
])).type(torch.FloatTensor)
if cuda:
model.cuda()
zero_tensor = zero_tensor.cuda()
h1_init, c1_init = h1_init.cuda(), c1_init.cuda()
h2_init, c2_init = h2_init.cuda(), c2_init.cuda()
kappa_old = kappa_old.cuda()
onehots = onehots.cuda()
text_len = text_len.cuda()
x = Variable(zero_tensor)
h1_init, c1_init = Variable(h1_init), Variable(c1_init)
h2_init, c2_init = Variable(h2_init), Variable(c2_init)
prev = (h1_init, c1_init)
prev2 = (h2_init, c2_init)
kappa_old = Variable(kappa_old)
onehots = Variable(onehots, requires_grad=False)
w_old = onehots.narrow(0, 0, 1) # attention on the first input text char
text_len = Variable(text_len)
record = [np.zeros(3)]
phis = []
stop = False
count = 0
while not stop:
outputs = model(x, onehots, text_len, w_old, kappa_old, prev, prev2,
bias)
end, weights, mu_1, mu_2, log_sigma_1, log_sigma_2, rho, w_old, kappa_old, prev, prev2, old_phi = outputs
#bernoulli sample
prob_end = end.data[0][0][0]
sample_end = np.random.binomial(1, prob_end)
#mog sample
sample_index = np.random.choice(range(20),
p=weights.data[0][0].cpu().numpy())
mu = np.array(
[mu_1.data[0][0][sample_index], mu_2.data[0][0][sample_index]])
log_sigma_1 = log_sigma_1 - bias2
log_sigma_2 = log_sigma_2 - bias2
v1 = (log_sigma_1).exp().data[0][0][sample_index]**2
v2 = (log_sigma_2).exp().data[0][0][sample_index]**2
c = rho.data[0][0][sample_index]*log_sigma_1.exp().data[0][0][sample_index]\
*log_sigma_2.exp().data[0][0][sample_index]
cov = np.array([[v1, c], [c, v2]])
sample_point = np.random.multivariate_normal(mu, cov)
out = np.insert(sample_point, 0, sample_end)
record.append(out)
x = torch.from_numpy(out).type(torch.FloatTensor)
if cuda:
x = x.cuda()
x = Variable(x, requires_grad=False)
x = x.view(1, 1, 3)
# attention
old_phi = old_phi.squeeze(0)
phis.append(old_phi)
old_phi = old_phi.data.cpu().numpy()
# hack to prevent early exit (attention is unstable at the beginning)
if count >= 20 and np.max(old_phi) == old_phi[-1]:
stop = True
count += 1
phis = torch.stack(phis).data.cpu().numpy().T
plot_stroke(np.array(record))
attention_plot(phis)