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