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# Copyright (c) 2025 FoundationVision
# SPDX-License-Identifier: MIT
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class DiagonalGaussianDistribution(object):
def __init__(self, parameters, deterministic=False):
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
def sample(self):
# x = self.mean + self.std * torch.randn(self.mean.shape).to(device)
x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device)
return x
def kl(self, other=None, reduction="sum"):
if reduction == "sum":
reduction_op = torch.sum
elif reduction == "mean":
reduction_op = torch.mean
if self.mean.ndim == 4:
dims = [1,2,3]
else:
dims = [1,2,3,4]
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * reduction_op(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=dims)
else:
return 0.5 * reduction_op(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=dims)
def nll(self, sample, dims=[1,2,3]):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
logvar1, logvar2 = [
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ torch.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
)
class VectorQuantizer(nn.Module):
def __init__(self, n_e, e_dim, beta, entropy_loss_ratio, l2_norm, show_usage):
super().__init__()
self.n_e = n_e
self.e_dim = e_dim
self.beta = beta
self.entropy_loss_ratio = entropy_loss_ratio
self.l2_norm = l2_norm
self.show_usage = show_usage
self.embedding = nn.Embedding(self.n_e, self.e_dim)
self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
if self.l2_norm:
self.embedding.weight.data = F.normalize(self.embedding.weight.data, p=2, dim=-1)
if self.show_usage:
self.register_buffer("codebook_used", nn.Parameter(torch.zeros(65536)))
def forward(self, z):
# reshape z -> (batch, height, width, channel) and flatten
z = torch.einsum('b c h w -> b h w c', z).contiguous()
z_flattened = z.view(-1, self.e_dim)
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
if self.l2_norm:
z = F.normalize(z, p=2, dim=-1)
z_flattened = F.normalize(z_flattened, p=2, dim=-1)
embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
else:
embedding = self.embedding.weight
d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
torch.sum(embedding**2, dim=1) - 2 * \
torch.einsum('bd,dn->bn', z_flattened, torch.einsum('n d -> d n', embedding))
min_encoding_indices = torch.argmin(d, dim=1)
z_q = embedding[min_encoding_indices].view(z.shape)
perplexity = None
min_encodings = None
vq_loss = None
commit_loss = None
entropy_loss = None
codebook_usage = 0
if self.show_usage and self.training:
cur_len = min_encoding_indices.shape[0]
self.codebook_used[:-cur_len] = self.codebook_used[cur_len:].clone()
self.codebook_used[-cur_len:] = min_encoding_indices
codebook_usage = len(torch.unique(self.codebook_used)) / self.n_e
# compute loss for embedding
if self.training:
vq_loss = torch.mean((z_q - z.detach()) ** 2)
commit_loss = self.beta * torch.mean((z_q.detach() - z) ** 2)
entropy_loss = self.entropy_loss_ratio * compute_entropy_loss(-d)
# preserve gradients
z_q = z + (z_q - z).detach()
# reshape back to match original input shape
z_q = torch.einsum('b h w c -> b c h w', z_q)
return z_q, (vq_loss, commit_loss, entropy_loss, codebook_usage), (perplexity, min_encodings, min_encoding_indices)
def get_codebook_entry(self, indices, shape=None, channel_first=True):
# shape = (batch, channel, height, width) if channel_first else (batch, height, width, channel)
if self.l2_norm:
embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
else:
embedding = self.embedding.weight
z_q = embedding[indices] # (b*h*w, c)
if shape is not None:
if channel_first:
z_q = z_q.reshape(shape[0], shape[2], shape[3], shape[1])
# reshape back to match original input shape
z_q = z_q.permute(0, 3, 1, 2).contiguous()
else:
z_q = z_q.view(shape)
return z_q
def compute_entropy_loss(affinity, loss_type="softmax", temperature=0.01):
flat_affinity = affinity.reshape(-1, affinity.shape[-1])
flat_affinity /= temperature
probs = F.softmax(flat_affinity, dim=-1)
log_probs = F.log_softmax(flat_affinity + 1e-5, dim=-1)
if loss_type == "softmax":
target_probs = probs
else:
raise ValueError("Entropy loss {} not supported".format(loss_type))
avg_probs = torch.mean(target_probs, dim=0)
avg_entropy = - torch.sum(avg_probs * torch.log(avg_probs + 1e-5))
sample_entropy = - torch.mean(torch.sum(target_probs * log_probs, dim=-1))
loss = sample_entropy - avg_entropy
return loss |