def generate(self, z, t=1.):
A = z.new(z.size(0), 155, 155).bool() | 1
X = z.new(z.size(0), 155, self.n_features)
X = X * 0 + 1
c = self.z_to_c(z).view(z.shape[0], 155, -1)
X_dec = self.decoder(X, A, c)
X_hat = mask_parameters(self.logits(X_dec))
X_hat = torch.distributions.Categorical(logits=X_hat / t)
return X_hat
def generate(self, X, X_a, sample=True, t=1.):
q = self.features(X, X_a)
z = q.sample()
c_gamma, c_beta = self.z_to_c(z).chunk(2, -1)
X_hat = mask_parameters(self.logits(c_gamma))
X_hat = torch.distributions.Categorical(logits=X_hat / t)
return X_hat
def generate(self, X, X_a):
A = (~X_a.unsqueeze(-1)) & (X_a.unsqueeze(-2))
eye = torch.eye(A.shape[-1]).bool().to(self.device) & (~X_a.unsqueeze(-2))
A = A | eye
X = self.embedder(X) * X_a.unsqueeze(-1).float()
X = self.encoder(X, A)
X_hat = mask_parameters(self.logits(X))
X_hat = torch.distributions.Categorical(logits=X_hat)
return X_hat
def generate_z(self, X, X_a, z, t=1.):
A = (~X_a.unsqueeze(-1)) & (X_a.unsqueeze(-2))
eye = torch.eye(A.shape[-1]).bool().to(
self.device) & (~X_a.unsqueeze(-2))
A = A | eye
X = self.embedder(X)
X = X * X_a.unsqueeze(-1).float()
c = self.z_to_c(z).view(z.shape[0], 155, -1)
X_dec = self.decoder(X, A, c)
X_hat = mask_parameters(self.logits(X_dec))
X_hat = torch.distributions.Categorical(logits=X_hat / t)
return X_hat
def generate(self, z, t=1.):
"""
Given a sample from the latent distribution, reporojects it back to data space
z - the array of dx7 voices, torch.FloatTensor(batch_size, latent_dim)
t - the temperature of the output distribution. approaches determenistic as t->0 and approach uniforms as t->infty, requires t>0
"""
A = z.new(z.size(0), 155, 155).bool() | 1
X = z.new(z.size(0), 155, self.n_features)
X = X * 0 + 1
c = self.z_to_c(z).view(z.shape[0], 155, -1)
X_dec = self.decoder(X, A, c)
X_hat = mask_parameters(self.logits(X_dec))
X_hat = torch.distributions.Categorical(logits=X_hat / t)
return X_hat
# plt.imshow(logits.log_prob(batch))
# %%
from itertools import count
from neuralDX7.utils import dx7_bulk_pack, mask_parameters
import mido
iter_X = iter(loader)
for n in range(10):
X = next(iter_X)['x']
# syx = dx7_bulk_pack(X.numpy().tolist())
# mido.write_syx_file('/home/nintorac/.local/share/DigitalSuburban/Dexed/Cartridges/neuralDX7/OG.syx', [syx])
X_d = torch.distributions.Categorical(
logits=mask_parameters(torch.zeros(32, 155, 128)))
X_a = torch.rand_like(X.float()) < 0.3
X_a = torch.ones_like(X).bool()
X_a[:, :-10] = 0
X = X[[0] * 32]
X[~X_a] = X_d.sample()[~X_a]
max_to_sample = max((~X_a).sum(-1))
# for i in tqdm(range(max_to_sample)):
logits = model.generate(X, X_a)
samples = logits.sample()
