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jblitzar
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github-python-corpus

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def get_dist(self, params: torch.Tensor, input_params=None, t=None) -> DiscreteDistribution:
"""Note: input_params and t are only required by PredDistToDataDistFactory."""
pass
class BernoulliFactory(DiscreteDistributionFactory):
def get_dist(self, params, input_params=None, t=None):
return Bernoulli(logits=params.squeeze(-1))
class CategoricalFactory(DiscreteDistributionFactory):
def get_dist(self, params, input_params=None, t=None):
return Categorical(logits=params)
class DiscretizedBernoulliFactory(DiscreteDistributionFactory):
def get_dist(self, params, input_params=None, t=None):
return DiscretizedBernoulli(logits=params.squeeze(-1))
class DiscretizedCategoricalFactory(DiscreteDistributionFactory):
def get_dist(self, params, input_params=None, t=None):
return DiscretizedCategorical(logits=params)
class DiscretizedGMMFactory(DiscreteDistributionFactory):
def __init__(self, num_bins, clip=True, min_std_dev=1e-3, max_std_dev=10, min_prob=1e-5, log_dev=True):
self.num_bins = num_bins
self.clip = clip
self.min_std_dev = min_std_dev
self.max_std_dev = max_std_dev
self.min_prob = min_prob
self.log_dev = log_dev
def get_dist(self, params, input_params=None, t=None):
return DiscretizedGMM(
params,
num_bins=self.num_bins,
clip=self.clip,
min_std_dev=self.min_std_dev,
max_std_dev=self.max_std_dev,
min_prob=self.min_prob,
log_dev=self.log_dev,
)
class DiscretizedNormalFactory(DiscreteDistributionFactory):
def __init__(self, num_bins, clip=True, min_std_dev=1e-3, max_std_dev=10, min_prob=1e-5, log_dev=True):
self.num_bins = num_bins
self.clip = clip
self.min_std_dev = min_std_dev
self.max_std_dev = max_std_dev
self.min_prob = min_prob
self.log_dev = log_dev
def get_dist(self, params, input_params=None, t=None):
return DiscretizedNormal(
params,
num_bins=self.num_bins,
clip=self.clip,
min_std_dev=self.min_std_dev,
max_std_dev=self.max_std_dev,
min_prob=self.min_prob,
log_dev=self.log_dev,
)
def noise_pred_params_to_data_pred_params(noise_pred_params: torch.Tensor, input_mean: torch.Tensor, t: torch.Tensor, min_variance: float, min_t=1e-6):
"""Convert output parameters that predict the noise added to data, to parameters that predict the data."""
data_shape = list(noise_pred_params.shape)[:-1]
noise_pred_params = sandwich(noise_pred_params)
input_mean = input_mean.flatten(start_dim=1)
if torch.is_tensor(t):
t = t.flatten(start_dim=1)
else:
t = (input_mean * 0) + t
alpha_mask = (t < min_t).unsqueeze(-1)
posterior_var = torch.pow(min_variance, t.clamp(min=min_t))
gamma = 1 - posterior_var
A = (input_mean / gamma).unsqueeze(-1)
B = (posterior_var / gamma).sqrt().unsqueeze(-1)
data_pred_params = []
if noise_pred_params.size(-1) == 1:
noise_pred_mean = noise_pred_params
elif noise_pred_params.size(-1) == 2:
noise_pred_mean, noise_pred_log_dev = noise_pred_params.chunk(2, -1)
else:
assert noise_pred_params.size(-1) % 3 == 0
mix_wt_logits, noise_pred_mean, noise_pred_log_dev = noise_pred_params.chunk(3, -1)
data_pred_params.append(mix_wt_logits)
data_pred_mean = A - (B * noise_pred_mean)
data_pred_mean = torch.where(alpha_mask, 0 * data_pred_mean, data_pred_mean)
data_pred_params.append(data_pred_mean)
if noise_pred_params.size(-1) >= 2:
noise_pred_dev = safe_exp(noise_pred_log_dev)
data_pred_dev = B * noise_pred_dev
data_pred_dev = torch.where(alpha_mask, 1 + (0 * data_pred_dev), data_pred_dev)
data_pred_params.append(data_pred_dev)
data_pred_params = torch.cat(data_pred_params, -1)
data_pred_params = data_pred_params.reshape(data_shape + [-1])