def forward(self, input_sequences, reset):
if reset:
self.reset_hidden_states()
(batch_size, _) = input_sequences.size()
upper_tier_conditioning = None
for rnn in reversed(self.model.frame_level_rnns):
from_index = self.model.lookback - rnn.n_frame_samples
to_index = -rnn.n_frame_samples + 1
prev_samples = 2 * utils.linear_dequantize(
input_sequences[:, from_index : to_index],
self.model.q_levels
)
prev_samples = prev_samples.contiguous().view(
batch_size, -1, rnn.n_frame_samples
)
upper_tier_conditioning = self.run_rnn(
rnn, prev_samples, upper_tier_conditioning
)
bottom_frame_size = self.model.frame_level_rnns[0].frame_size
mlp_input_sequences = input_sequences \
[:, self.model.lookback - bottom_frame_size :]
return self.model.sample_level_mlp(
mlp_input_sequences, upper_tier_conditioning
)
def forward(self, input_sequences, reset):
if reset:
self.reset_hidden_states()
len_input_sequence = input_sequences.shape[1] - self.nb_classes
input_sequences, cond = utils.size_splits(
input_sequences, [len_input_sequence, self.nb_classes],
1) #cond is one hot vector
(batch_size, _) = input_sequences.size()
upper_tier_conditioning = None
for rnn in reversed(self.model.frame_level_rnns):
from_index = self.model.lookback - rnn.n_frame_samples
to_index = -rnn.n_frame_samples + 1
prev_samples = 2 * utils.linear_dequantize(
input_sequences[:, from_index:to_index], self.model.q_levels)
#Duplicate cond so that each sample of size (rnn.n_frame_samples) is concatenated with the one hot vector
number_of_repeat = int(prev_samples.shape[1] / rnn.n_frame_samples)
cond_multi = cond[:, None, :]
cond_multi = cond_multi.repeat(1, number_of_repeat, 1)
prev_samples = prev_samples.contiguous().view(
batch_size, -1, rnn.n_frame_samples)
cond_multi = cond_multi.float()
prev_samples = torch.cat([prev_samples, cond_multi], 2)
upper_tier_conditioning = self.run_rnn(rnn, prev_samples,
upper_tier_conditioning)
bottom_frame_size = self.model.frame_level_rnns[0].frame_size
mlp_input_sequences = input_sequences \
[:, self.model.lookback - bottom_frame_size :]
return self.model.sample_level_mlp(mlp_input_sequences,
upper_tier_conditioning)
def __call__(self, n_seqs, seq_len):
# generation doesn't work with CUDNN for some reason
#torch.backends.cudnn.enabled = False
self.reset_hidden_states()
bottom_frame_size = self.model.frame_level_rnns[0].n_frame_samples
sequences = torch.LongTensor(n_seqs, self.model.lookback + seq_len) \
.fill_(utils.q_zero(self.model.q_levels))
frame_level_outputs = [None for _ in self.model.frame_level_rnns]
print('Generating sample...')
for i in tqdm.tqdm(range(self.model.lookback, self.model.lookback + seq_len), mininterval=1, ascii=True):
for (tier_index, rnn) in \
reversed(list(enumerate(self.model.frame_level_rnns))):
if i % rnn.n_frame_samples != 0:
continue
prev_samples = torch.autograd.Variable(
2 * utils.linear_dequantize(
sequences[:, i - rnn.n_frame_samples : i],
self.model.q_levels
).unsqueeze(1),
volatile=True
)
if self.cuda:
prev_samples = prev_samples.cuda()
if tier_index == len(self.model.frame_level_rnns) - 1:
upper_tier_conditioning = None
else:
frame_index = (i // rnn.n_frame_samples) % \
self.model.frame_level_rnns[tier_index + 1].frame_size
upper_tier_conditioning = \
frame_level_outputs[tier_index + 1][:, frame_index, :] \
.unsqueeze(1)
frame_level_outputs[tier_index] = self.run_rnn(
rnn, prev_samples, upper_tier_conditioning
)
prev_samples = torch.autograd.Variable(
sequences[:, i - bottom_frame_size : i],
volatile=True
)
if self.cuda:
prev_samples = prev_samples.cuda()
upper_tier_conditioning = \
frame_level_outputs[0][:, i % bottom_frame_size, :] \
.unsqueeze(1)
sample_dist = self.model.sample_level_mlp(
prev_samples, upper_tier_conditioning
).squeeze(1).exp_().data
sequences[:, i] = sample_dist.multinomial(1).squeeze(1)
#torch.backends.cudnn.enabled = True
return sequences[:, self.model.lookback :]
def forward(self, input_sequences, reset, z=None):
if reset:
self.reset_hidden_states()
(batch_size, _) = input_sequences.size()
if z is None:
upper_tier_conditioning = None
else:
upper_tier_conditioning = None
# else:
# upper_tier_conditioning = self.model.class_embedding(z).view(batch_size, 1, -1) + self.model.class_bias
# for rnn in reversed(self.model.frame_level_rnns):
# # from_index = self.model.lookback - rnn.n_frame_samples
# from_index = 0
# to_index = -rnn.n_frame_samples + 1
# prev_samples = 2 * utils.linear_dequantize(
# input_sequences[:, from_index: to_index],
# self.model.q_levels
# )
# # if (len(prev_samples.flatten())) < batch_size * rnn.n_frame_samples:
# prev_samples = prev_samples.contiguous().view(
# batch_size, -1, rnn.frame_size
# )
# upper_tier_conditioning = self.run_rnn(
# rnn, prev_samples, upper_tier_conditioning, z
# )
rnn = self.model.frame_level_rnns[0]
prev_samples = 2 * utils.linear_dequantize(
input_sequences[:, :-rnn.frame_size + 1], self.model.q_levels)
prev_samples = prev_samples.contiguous().view(batch_size, -1,
rnn.frame_size)
upper_tier_conditioning = self.run_rnn(rnn, prev_samples,
upper_tier_conditioning, z)
# bottom_frame_size = self.model.frame_level_rnns[0].frame_size
# mlp_input_sequences = input_sequences[:, self.model.lookback - bottom_frame_size:]
# mlp_input_sequences = input_sequences[:, :]
# seq_len = mlp_input_sequences.size(-1) - bottom_frame_size
# mlp_input = torch.zeros(batch_size, seq_len, bottom_frame_size)
# for idx in range(seq_len):
# mlp_input[:, idx, :] = mlp_input_sequences[:, idx: idx + bottom_frame_size]
return self.model.sample_level_mlp(input_sequences,
upper_tier_conditioning)
def __call__(self,
n_seqs,
seq_len,
sampling_temperature=0.9,
initial_seq=None):
# generation doesn't work with CUDNN for some reason
torch.backends.cudnn.enabled = False
self.reset_hidden_states()
bottom_frame_size = self.model.frame_level_rnns[0].n_frame_samples
sequences = torch.LongTensor(n_seqs, self.model.lookback + seq_len) \
.fill_(utils.q_zero(self.model.q_levels))
if initial_seq is None:
initial_i = self.model.lookback
final_i = initial_i + seq_len
else: # CONDITIONAL
sequences[:, 0:np.shape(initial_seq)[1]] = initial_seq
initial_i = np.shape(initial_seq)[1] - self.model.lookback
# initial_i = np.shape(initial_seq)[1] + self.model.lookback
final_i = self.model.lookback + seq_len
frame_level_outputs = [None for _ in self.model.frame_level_rnns]
for i in range(initial_i, final_i):
for (tier_index, rnn) in \
reversed(list(enumerate(self.model.frame_level_rnns))):
if i % rnn.n_frame_samples != 0:
continue
prev_samples = torch.autograd.Variable(
2 * utils.linear_dequantize(
sequences[:, i - rnn.n_frame_samples:i],
self.model.q_levels).unsqueeze(1),
volatile=True)
# print("Tier {}: prev_samples from {} to {}, shape {}: {}".format(tier_index, i - rnn.n_frame_samples, i, np.shape(prev_samples), prev_samples))
if self.cuda:
prev_samples = prev_samples.cuda()
l = len(self.model.frame_level_rnns) - 1
if tier_index == l:
print("No upper tier conditioning")
upper_tier_conditioning = None
else:
frame_index = (i // rnn.n_frame_samples) % \
self.model.frame_level_rnns[tier_index + 1].frame_size
upper_tier_conditioning = \
frame_level_outputs[tier_index + 1][:, frame_index, :] \
.unsqueeze(1)
print("Frame index {}, upper_tier_conditioning shape {}".
format(frame_index,
np.shape(upper_tier_conditioning)))
frame_level_outputs[tier_index] = self.run_rnn(
rnn, prev_samples, upper_tier_conditioning)
print("Tier {} frame level outputs shape {}".format(
tier_index, np.shape(frame_level_outputs[tier_index])))
# print(sequences[:, i - bottom_frame_size : i])
prev_samples = torch.autograd.Variable(
sequences[:, i - bottom_frame_size:i], volatile=True)
# print("Tier {}: prev_samples from {} to {}, shape {}: {}".format(tier_index, i - bottom_frame_size, i, np.shape(prev_samples), prev_samples))
if self.cuda:
prev_samples = prev_samples.cuda()
upper_tier_conditioning = \
frame_level_outputs[0][:, i % bottom_frame_size, :] \
.unsqueeze(1)
sample_dist = self.model.sample_level_mlp(prev_samples,
upper_tier_conditioning)
sample_dist = sample_dist.div(sampling_temperature).squeeze(
1).exp_().data
print("Sample dist {}".format(np.shape(sample_dist)))
print("Before: {}".format(sequences[:, i]))
sequences[:, i] = sample_dist.multinomial(1).squeeze(1)
print("After {}".format(sequences[:, i]))
torch.backends.cudnn.enabled = True
return sequences[:, self.model.lookback:]
def __call__(self, n_seqs, seq_len):
torch.backends.cudnn.enabled = True
self.reset_hidden_states()
bottom_frame_size = self.model.frame_level_rnns[0].n_frame_samples
sequences = torch.LongTensor(n_seqs*self.nb_classes, self.model.lookback + seq_len) \
.fill_(utils.q_zero(self.model.q_levels))
frame_level_outputs = [None for _ in self.model.frame_level_rnns]
for i in range(self.model.lookback, self.model.lookback + seq_len):
for (tier_index, rnn) in \
reversed(list(enumerate(self.model.frame_level_rnns))):
if i % rnn.n_frame_samples != 0:
continue
prev_samples = torch.autograd.Variable(
2 * utils.linear_dequantize(
sequences[:, i - rnn.n_frame_samples:i],
self.model.q_levels).unsqueeze(1),
volatile=True)
if self.cond == False: #no conditiong,generate all classes
one_hot_tensor = torch.tensor([]).float()
for label in range(self.nb_classes):
for nb_sample in range(n_seqs):
one_hot_vec = utils.one_hot(
torch.tensor([label]),
self.nb_classes).float()
one_hot_tensor = torch.cat(
[one_hot_tensor, one_hot_vec])
else: #use custom one hot vector
one_hot_tensor = torch.tensor([]).float()
one_hot_vec = torch.tensor([self.cond]).float()
for label in range(self.nb_classes):
for nb_sample in range(n_seqs):
one_hot_tensor = torch.cat(
[one_hot_tensor, one_hot_vec])
one_hot_tensor = one_hot_tensor[:, None, :]
prev_samples = torch.cat([prev_samples, one_hot_tensor], 2)
if self.cuda:
prev_samples = prev_samples.cuda()
if tier_index == len(self.model.frame_level_rnns) - 1:
upper_tier_conditioning = None
else:
frame_index = (i // rnn.n_frame_samples) % \
self.model.frame_level_rnns[tier_index + 1].frame_size
upper_tier_conditioning = \
frame_level_outputs[tier_index + 1][:, frame_index, :] \
.unsqueeze(1)
frame_level_outputs[tier_index] = self.run_rnn(
rnn, prev_samples, upper_tier_conditioning)
prev_samples = torch.autograd.Variable(
sequences[:, i - bottom_frame_size:i], volatile=True)
if self.cuda:
prev_samples = prev_samples.cuda()
upper_tier_conditioning = \
frame_level_outputs[0][:, i % bottom_frame_size, :] \
.unsqueeze(1)
sample_dist = self.model.sample_level_mlp(
prev_samples, upper_tier_conditioning).squeeze(1).exp_().data
sequences[:, i] = sample_dist.multinomial(1).squeeze(1)
torch.backends.cudnn.enabled = True
return sequences[:, self.model.lookback:]
def __call__(self, n_seqs, seq_len, class_label=0, data_seed=None):
# generation doesn't work with CUDNN for some reason
torch.backends.cudnn.enabled = False
label_tensor = torch.LongTensor([class_label])
with torch.no_grad():
self.reset_hidden_states()
# bottom_frame_size = self.model.frame_level_rnns[0].n_frame_samples
bottom_frame_size = 16
sequences = torch.LongTensor(n_seqs, self.model.lookback + seq_len) \
.fill_(utils.q_zero(self.model.q_levels))
if data_seed is not None:
seeds = []
for _ in range(n_seqs):
seeds.append(
utils.linear_quantize(
torch.from_numpy(
data_seed.getClassSplit(
class_num=class_label,
seq_len=self.model.lookback)),
self.model.q_levels))
seed = torch.stack(seeds)
sequences[:, :self.model.lookback] = seed
frame_level_outputs = [None for _ in self.model.frame_level_rnns]
for i in range(self.model.lookback, self.model.lookback + seq_len):
for (tier_index, rnn) in \
reversed(list(enumerate(self.model.frame_level_rnns))):
# if i % rnn.n_frame_samples != 0:
# continue
prev_samples = sequences[:, i - 16:i]
prev_samples = torch.autograd.Variable(
2 * utils.linear_dequantize(
prev_samples, self.model.q_levels).unsqueeze(1))
if self.cuda:
prev_samples = prev_samples.cuda()
label_tensor = label_tensor.cuda()
if tier_index == len(self.model.frame_level_rnns) - 1:
upper_tier_conditioning = None
# if self.model.num_classes > 1:
# upper_tier_conditioning = self.model.class_embedding(label_tensor) + self.model.class_bias.cuda()
# if self.cuda:
# upper_tier_conditioning = upper_tier_conditioning.cuda()
else:
frame_index = (i // rnn.n_frame_samples) % \
self.model.frame_level_rnns[tier_index + 1].frame_size
upper_tier_conditioning = \
frame_level_outputs[tier_index + 1][:, frame_index, :] \
.unsqueeze(1)
if isinstance(class_label, int):
class_label = torch.Tensor([class_label])
frame_level_outputs[tier_index] = self.run_rnn(
rnn, prev_samples, upper_tier_conditioning,
class_label)
prev_samples = torch.autograd.Variable(
sequences[:, i - bottom_frame_size:i])
if self.cuda:
prev_samples = prev_samples.cuda()
frame_level_outputs[0] = frame_level_outputs[0].view(
n_seqs, bottom_frame_size, -1)
upper_tier_conditioning = \
frame_level_outputs[0][:, i % bottom_frame_size, :].unsqueeze(1)
sample_dist = self.model.sample_level_mlp(
prev_samples,
upper_tier_conditioning).squeeze(1).exp_().data
sequences[:, i] = sample_dist.multinomial(1).squeeze(1)
torch.backends.cudnn.enabled = True
return sequences[:, self.model.lookback:]
