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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from torch import einsum | |
| from einops import rearrange | |
| 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 == False, "Only for interface compatible with Gumbel" | |
| assert return_logits == 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 | |
| class GumbelQuantize(nn.Module): | |
| """ | |
| credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) | |
| Gumbel Softmax trick quantizer | |
| Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 | |
| https://arxiv.org/abs/1611.01144 | |
| """ | |
| def __init__( | |
| self, | |
| num_hiddens, | |
| embedding_dim, | |
| n_embed, | |
| straight_through=True, | |
| kl_weight=5e-4, | |
| temp_init=1.0, | |
| use_vqinterface=True, | |
| remap=None, | |
| unknown_index="random", | |
| ): | |
| super().__init__() | |
| self.embedding_dim = embedding_dim | |
| self.n_embed = n_embed | |
| self.straight_through = straight_through | |
| self.temperature = temp_init | |
| self.kl_weight = kl_weight | |
| self.proj = nn.Conv2d(num_hiddens, n_embed, 1) | |
| self.embed = nn.Embedding(n_embed, embedding_dim) | |
| self.use_vqinterface = use_vqinterface | |
| 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_embed} indices to {self.re_embed} indices. " | |
| f"Using {self.unknown_index} for unknown indices." | |
| ) | |
| else: | |
| self.re_embed = n_embed | |
| 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, return_logits=False): | |
| # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work | |
| hard = self.straight_through if self.training else True | |
| temp = self.temperature if temp is None else temp | |
| logits = self.proj(z) | |
| if self.remap is not None: | |
| # continue only with used logits | |
| full_zeros = torch.zeros_like(logits) | |
| logits = logits[:, self.used, ...] | |
| soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) | |
| if self.remap is not None: | |
| # go back to all entries but unused set to zero | |
| full_zeros[:, self.used, ...] = soft_one_hot | |
| soft_one_hot = full_zeros | |
| z_q = einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) | |
| # + kl divergence to the prior loss | |
| qy = F.softmax(logits, dim=1) | |
| diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() | |
| ind = soft_one_hot.argmax(dim=1) | |
| if self.remap is not None: | |
| ind = self.remap_to_used(ind) | |
| if self.use_vqinterface: | |
| if return_logits: | |
| return z_q, diff, (None, None, ind), logits | |
| return z_q, diff, (None, None, ind) | |
| return z_q, diff, ind | |
| def get_codebook_entry(self, indices, shape): | |
| b, h, w, c = shape | |
| assert b * h * w == indices.shape[0] | |
| indices = rearrange(indices, "(b h w) -> b h w", b=b, h=h, w=w) | |
| if self.remap is not None: | |
| indices = self.unmap_to_all(indices) | |
| one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() | |
| z_q = einsum("b n h w, n d -> b d h w", one_hot, self.embed.weight) | |
| return z_q | |