Spaces:
Configuration error
Configuration error
| import torch | |
| import numpy as np | |
| from torch import nn | |
| from einops import rearrange | |
| from .kl import Encoder, Decoder | |
| class VQModel(nn.Module): | |
| def __init__(self, | |
| config, | |
| remap=None, | |
| sane_index_shape=False, # tell vector quantizer to return indices as bhw | |
| ): | |
| super().__init__() | |
| self.embed_dim = config["embed_dim"] | |
| self.n_embed = config["n_embed"] | |
| self.encoder = Encoder(**config) | |
| self.decoder = Decoder(**config) | |
| self.quantize = VectorQuantizer(self.n_embed, self.embed_dim, beta=0.25, | |
| remap=remap, | |
| sane_index_shape=sane_index_shape) | |
| self.quant_conv = torch.nn.Conv2d(config["z_channels"], self.embed_dim, 1) | |
| self.post_quant_conv = torch.nn.Conv2d(self.embed_dim, config["z_channels"], 1) | |
| def encode(self, x): | |
| h = self.encoder(x) | |
| h = self.quant_conv(h) | |
| return h | |
| def decode(self, h, force_not_quantize=False): | |
| # also go through quantization layer | |
| if not force_not_quantize: | |
| quant, emb_loss, info = self.quantize(h) | |
| else: | |
| quant = h | |
| quant = self.post_quant_conv(quant) | |
| dec = self.decoder(quant) | |
| return dec | |
| def forward(self, input, return_pred_indices=False): | |
| quant, diff, (_,_,ind) = self.encode(input) | |
| dec = self.decode(quant) | |
| if return_pred_indices: | |
| return dec, diff, ind | |
| return dec, diff | |
| class VectorQuantizer(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 | |