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Get rid of discretization loss

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This commit is contained in:
James Betker 2022-03-03 13:36:25 -07:00
parent beb7c8a39d
commit 619da9ea28
1 changed files with 0 additions and 42 deletions
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codes/models/gpt_voice/lucidrains_dvae.py
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@ -28,46 +28,6 @@ def eval_decorator(fn):
return inner
# Fits a soft-discretized input to a normal-PDF across the specified dimension.
# In other words, attempts to force the discretization function to have a mean equal utilization across all discrete
# values with the specified expected variance.
class DiscretizationLoss(nn.Module):
def __init__(self, discrete_bins, dim, expected_variance, store_past=0):
super().__init__()
self.discrete_bins = discrete_bins
self.dim = dim
self.dist = torch.distributions.Normal(0, scale=expected_variance)
if store_past > 0:
self.record_past = True
self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
self.register_buffer("accumulator", torch.zeros(store_past, discrete_bins))
else:
self.record_past = False
def forward(self, x):
other_dims = set(range(len(x.shape)))-set([self.dim])
averaged = x.sum(dim=tuple(other_dims)) / x.sum()
averaged = averaged - averaged.mean()
if self.record_past:
acc_count = self.accumulator.shape[0]
avg = averaged.detach().clone()
if self.accumulator_filled > 0:
averaged = torch.mean(self.accumulator, dim=0) * (acc_count-1) / acc_count + \
averaged / acc_count
# Also push averaged into the accumulator.
self.accumulator[self.accumulator_index] = avg
self.accumulator_index += 1
if self.accumulator_index >= acc_count:
self.accumulator_index *= 0
if self.accumulator_filled <= 0:
self.accumulator_filled += 1
return torch.sum(-self.dist.log_prob(averaged))
class ResBlock(nn.Module):
def __init__(self, chan, conv, activation):
super().__init__()
@ -115,7 +75,6 @@ class DiscreteVAE(nn.Module):
straight_through = False,
normalization = None, # ((0.5,) * 3, (0.5,) * 3),
record_codes = False,
discretization_loss_averaging_steps = 100,
use_lr_quantizer = False,
lr_quantizer_args = {},
):
@ -126,7 +85,6 @@ class DiscreteVAE(nn.Module):
self.num_layers = num_layers
self.straight_through = straight_through
self.positional_dims = positional_dims
self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps)
assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now.
if positional_dims == 2: