def sample_single_window(zs, labels, sampling_kwargs, level, prior, start,
hps):
n_samples = hps.n_samples
n_ctx = prior.n_ctx
end = start + n_ctx
# get z already sampled at current level
z = zs[level][:, start:end].to(prior.device)
if 'sample_tokens' in sampling_kwargs:
# Support sampling a window shorter than n_ctx
sample_tokens = sampling_kwargs['sample_tokens']
else:
sample_tokens = (end - start)
conditioning_tokens, new_tokens = z.shape[1], sample_tokens - z.shape[1]
print_once(
f"Sampling {sample_tokens} tokens for [{start},{start+sample_tokens}]. Conditioning on {conditioning_tokens} tokens"
)
if new_tokens <= 0:
# Nothing new to sample
return zs
# get z_conds from level above
z_conds = prior.get_z_conds(zs, start, end)
if z_conds != None:
for k in range(len(z_conds)):
z_conds[k] = z_conds[k].to(prior.device)
# set y offset, sample_length and lyrics tokens
y = prior.get_y(labels, start)
empty_cache()
max_batch_size = sampling_kwargs['max_batch_size']
del sampling_kwargs['max_batch_size']
z_list = split_batch(z, n_samples, max_batch_size)
z_conds_list = split_batch(z_conds, n_samples, max_batch_size)
y_list = split_batch(y, n_samples, max_batch_size)
z_samples = []
for z_i, z_conds_i, y_i in zip(z_list, z_conds_list, y_list):
z_samples_i = prior.sample(n_samples=z_i.shape[0],
z=z_i,
z_conds=z_conds_i,
y=y_i,
**sampling_kwargs)
z_samples.append(z_samples_i)
z = t.cat(z_samples, dim=0)
sampling_kwargs['max_batch_size'] = max_batch_size
# Update z with new sample
z_new = z[:, -new_tokens:].cpu()
del z
del y
zs[level] = t.cat([zs[level], z_new], dim=1)
return zs
def sample_single_window(zs, labels, sampling_kwargs, level, prior, start, hps):
n_samples = hps.n_samples
n_ctx = prior.n_ctx
end = start + n_ctx
# get z already sampled at current level
z = zs[level][:,start:end]
if 'sample_tokens' in sampling_kwargs:
# Support sampling a window shorter than n_ctx
sample_tokens = sampling_kwargs['sample_tokens']
else:
sample_tokens = (end - start)
conditioning_tokens, new_tokens = z.shape[1], sample_tokens - z.shape[1]
print_once(f"Sampling {sample_tokens} tokens for [{start},{start+sample_tokens}]. Conditioning on {conditioning_tokens} tokens")
if new_tokens <= 0:
# Nothing new to sample
return zs
# get z_conds from level above
z_conds = prior.get_z_conds(zs, start, end)
# set y offset, sample_length and lyrics tokens
y = prior.get_y(labels, start)
empty_cache()
max_batch_size = sampling_kwargs['max_batch_size']
del sampling_kwargs['max_batch_size']
z_list = split_batch(z, n_samples, max_batch_size)
z_conds_list = split_batch(z_conds, n_samples, max_batch_size)
y_list = split_batch(y, n_samples, max_batch_size)
z_samples = []
for z_i, z_conds_i, y_i in zip(z_list, z_conds_list, y_list):
midi_path = r"C:\Users\Yousef\Desktop\UNiz\MidiDataset\Cleaned\acdc\Big Balls.mid"
midi = load_sample_midi(midi_path)
z_samples_i = prior.sample(n_samples=z_i.shape[0], z=z_i, z_conds=z_conds_i, y=y_i, **sampling_kwargs, midi=midi)
z_samples.append(z_samples_i)
z = t.cat(z_samples, dim=0)
sampling_kwargs['max_batch_size'] = max_batch_size
# Update z with new sample
z_new = z[:,-new_tokens:]
zs[level] = t.cat([zs[level], z_new], dim=1)
return zs
def sample_single_window(zs,
labels_1,
labels_2,
sampling_kwargs,
level,
prior,
start,
hps,
total_length=1):
n_samples = hps.n_samples
n_ctx = prior.n_ctx
end = start + n_ctx
# get z already sampled at current level
z = zs[level][:, start:end]
if 'sample_tokens' in sampling_kwargs:
# Support sampling a window shorter than n_ctx
sample_tokens = sampling_kwargs['sample_tokens']
else:
sample_tokens = (end - start)
conditioning_tokens, new_tokens = z.shape[1], sample_tokens - z.shape[1]
print_once(
f"Sampling {sample_tokens} tokens for [{start},{start+sample_tokens}]. Conditioning on {conditioning_tokens} tokens"
)
print_once(
f"{round( (start+sample_tokens)/total_length*100.0 )}%-ish, level {level}"
)
if new_tokens <= 0:
# Nothing new to sample
return zs
# get z_conds from level above
z_conds = prior.get_z_conds(zs, start, end)
# set y offset, sample_length and lyrics tokens
y1 = prior.get_y(labels_1, start)
y2 = prior.get_y(labels_2, start)
empty_cache()
max_batch_size = sampling_kwargs['max_batch_size']
del sampling_kwargs['max_batch_size']
z_list = split_batch(z, n_samples, max_batch_size)
z_conds_list = split_batch(z_conds, n_samples, max_batch_size)
y1_list = split_batch(y1, n_samples, max_batch_size)
y2_list = split_batch(y2, n_samples, max_batch_size)
z_samples = []
for z_i, z_conds_i, y1_i, y2_i in zip(z_list, z_conds_list, y1_list,
y2_list):
z_samples_i = prior.sample(n_samples=z_i.shape[0],
z=z_i,
z_conds=z_conds_i,
y1=y1_i,
y2=y2_i,
**sampling_kwargs)
z_samples.append(z_samples_i)
z = t.cat(z_samples, dim=0)
sampling_kwargs['max_batch_size'] = max_batch_size
# Update z with new sample
z_new = z[:, -new_tokens:]
zs[level] = t.cat([zs[level], z_new], dim=1)
return zs