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Zero
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"""Vector quantizer.
Copyright (2024) Bytedance Ltd. and/or its affiliates
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Reference:
https://github.com/CompVis/taming-transformers/blob/master/taming/modules/vqvae/quantize.py
https://github.com/google-research/magvit/blob/main/videogvt/models/vqvae.py
"""
from typing import Mapping, Text, Tuple
import torch
from einops import rearrange
from torch.cuda.amp import autocast
class VectorQuantizer(torch.nn.Module):
def __init__(self,
codebook_size: int = 1024,
token_size: int = 256,
commitment_cost: float = 0.25,
use_l2_norm: bool = False,
):
super().__init__()
self.commitment_cost = commitment_cost
self.embedding = torch.nn.Embedding(codebook_size, token_size)
self.embedding.weight.data.uniform_(-1.0 / codebook_size, 1.0 / codebook_size)
self.use_l2_norm = use_l2_norm
# Ensure quantization is performed using f32
@autocast(enabled=False)
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Mapping[Text, torch.Tensor]]:
z = z.float()
z = rearrange(z, 'b c h w -> b h w c').contiguous()
z_flattened = rearrange(z, 'b h w c -> (b h w) c')
if self.use_l2_norm:
z_flattened = torch.nn.functional.normalize(z_flattened, dim=-1)
embedding = torch.nn.functional.normalize(self.embedding.weight, 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, embedding.T)
min_encoding_indices = torch.argmin(d, dim=1) # num_ele
z_quantized = self.get_codebook_entry(min_encoding_indices).view(z.shape)
if self.use_l2_norm:
z_quantized = torch.nn.functional.normalize(z_quantized, dim=-1)
z = torch.nn.functional.normalize(z, dim=-1)
# compute loss for embedding
commitment_loss = self.commitment_cost * torch.mean((z_quantized.detach() - z) **2)
codebook_loss = torch.mean((z_quantized - z.detach()) **2)
loss = commitment_loss + codebook_loss
# preserve gradients
z_quantized = z + (z_quantized - z).detach()
# reshape back to match original input shape
z_quantized = rearrange(z_quantized, 'b h w c -> b c h w').contiguous()
result_dict = dict(
quantizer_loss=loss,
commitment_loss=commitment_loss,
codebook_loss=codebook_loss,
min_encoding_indices=min_encoding_indices.view(z_quantized.shape[0], z_quantized.shape[2], z_quantized.shape[3])
)
return z_quantized, result_dict
def get_codebook_entry(self, indices):
if len(indices.shape) == 1:
z_quantized = self.embedding(indices)
elif len(indices.shape) == 2:
z_quantized = torch.einsum('bd,dn->bn', indices, self.embedding.weight)
else:
raise NotImplementedError
return z_quantized |