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+# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
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+# where the license is as follows:
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+#
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+# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
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+#
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+# Permission is hereby granted, free of charge, to any person obtaining a copy
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+# of this software and associated documentation files (the "Software"), to deal
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+# in the Software without restriction, including without limitation the rights
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+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+# copies of the Software, and to permit persons to whom the Software is
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+# furnished to do so, subject to the following conditions:
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+#
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+# The above copyright notice and this permission notice shall be included in all
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+# copies or substantial portions of the Software.
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+#
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+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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+# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
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+# OR OTHER DEALINGS IN THE SOFTWARE./
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+
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+import torch
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+import torch.nn as nn
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+import numpy as np
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+from einops import rearrange
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+
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+
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+class VectorQuantizer2(nn.Module):
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+ """
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+ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
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+ avoids costly matrix multiplications and allows for post-hoc remapping of indices.
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+ """
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+
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+ # NOTE: due to a bug the beta term was applied to the wrong term. for
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+ # backwards compatibility we use the buggy version by default, but you can
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+ # specify legacy=False to fix it.
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+ def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
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+ sane_index_shape=False, legacy=True):
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+ super().__init__()
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+ self.n_e = n_e
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+ self.e_dim = e_dim
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+ self.beta = beta
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+ self.legacy = legacy
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+
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+ self.embedding = nn.Embedding(self.n_e, self.e_dim)
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+ self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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+
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+ self.remap = remap
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+ if self.remap is not None:
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+ self.register_buffer("used", torch.tensor(np.load(self.remap)))
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+ self.re_embed = self.used.shape[0]
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+ self.unknown_index = unknown_index # "random" or "extra" or integer
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+ if self.unknown_index == "extra":
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+ self.unknown_index = self.re_embed
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+ self.re_embed = self.re_embed + 1
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+ print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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+ f"Using {self.unknown_index} for unknown indices.")
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+ else:
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+ self.re_embed = n_e
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+
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+ self.sane_index_shape = sane_index_shape
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+
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+ def remap_to_used(self, inds):
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+ ishape = inds.shape
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+ assert len(ishape) > 1
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+ inds = inds.reshape(ishape[0], -1)
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+ used = self.used.to(inds)
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+ match = (inds[:, :, None] == used[None, None, ...]).long()
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+ new = match.argmax(-1)
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+ unknown = match.sum(2) < 1
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+ if self.unknown_index == "random":
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+ new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
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+ else:
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+ new[unknown] = self.unknown_index
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+ return new.reshape(ishape)
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+
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+ def unmap_to_all(self, inds):
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+ ishape = inds.shape
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+ assert len(ishape) > 1
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+ inds = inds.reshape(ishape[0], -1)
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+ used = self.used.to(inds)
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+ if self.re_embed > self.used.shape[0]: # extra token
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+ inds[inds >= self.used.shape[0]] = 0 # simply set to zero
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+ back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
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+ return back.reshape(ishape)
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+
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+ def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
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+ assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
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+ assert rescale_logits is False, "Only for interface compatible with Gumbel"
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+ assert return_logits is False, "Only for interface compatible with Gumbel"
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+ # reshape z -> (batch, height, width, channel) and flatten
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+ z = rearrange(z, 'b c h w -> b h w c').contiguous()
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+ z_flattened = z.view(-1, self.e_dim)
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+ # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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+
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+ d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
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+ torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
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+ torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
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+
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+ min_encoding_indices = torch.argmin(d, dim=1)
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+ z_q = self.embedding(min_encoding_indices).view(z.shape)
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+ perplexity = None
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+ min_encodings = None
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+
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+ # compute loss for embedding
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+ if not self.legacy:
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+ loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
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+ torch.mean((z_q - z.detach()) ** 2)
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+ else:
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+ loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
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+ torch.mean((z_q - z.detach()) ** 2)
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+
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+ # preserve gradients
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+ z_q = z + (z_q - z).detach()
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+
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+ # reshape back to match original input shape
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+ z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
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+
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+ if self.remap is not None:
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+ min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
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+ min_encoding_indices = self.remap_to_used(min_encoding_indices)
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+ min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
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+
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+ if self.sane_index_shape:
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+ min_encoding_indices = min_encoding_indices.reshape(
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+ z_q.shape[0], z_q.shape[2], z_q.shape[3])
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+
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+ return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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+
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+ def get_codebook_entry(self, indices, shape):
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+ # shape specifying (batch, height, width, channel)
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+ if self.remap is not None:
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+ indices = indices.reshape(shape[0], -1) # add batch axis
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+ indices = self.unmap_to_all(indices)
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+ indices = indices.reshape(-1) # flatten again
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+
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+ # get quantized latent vectors
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+ z_q = self.embedding(indices)
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+
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+ if shape is not None:
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+ z_q = z_q.view(shape)
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+ # reshape back to match original input shape
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+ z_q = z_q.permute(0, 3, 1, 2).contiguous()
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+
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+ return z_q
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Aarni Koskela