165 lines
5.7 KiB
Python
165 lines
5.7 KiB
Python
import torch
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import numpy as np
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from torch import nn
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from einops import rearrange
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from .kl import Encoder, Decoder
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class VQModel(nn.Module):
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def __init__(self,
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config,
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remap=None,
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sane_index_shape=False, # tell vector quantizer to return indices as bhw
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):
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super().__init__()
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self.embed_dim = config["embed_dim"]
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self.n_embed = config["n_embed"]
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self.encoder = Encoder(**config)
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self.decoder = Decoder(**config)
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self.quantize = VectorQuantizer(self.n_embed, self.embed_dim, beta=0.25,
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remap=remap,
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sane_index_shape=sane_index_shape)
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self.quant_conv = torch.nn.Conv2d(config["z_channels"], self.embed_dim, 1)
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self.post_quant_conv = torch.nn.Conv2d(self.embed_dim, config["z_channels"], 1)
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def encode(self, x):
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h = self.encoder(x)
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h = self.quant_conv(h)
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return h
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def decode(self, h, force_not_quantize=False):
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# also go through quantization layer
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if not force_not_quantize:
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quant, emb_loss, info = self.quantize(h)
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else:
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quant = h
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quant = self.post_quant_conv(quant)
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dec = self.decoder(quant)
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return dec
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def forward(self, input, return_pred_indices=False):
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quant, diff, (_,_,ind) = self.encode(input)
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dec = self.decode(quant)
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if return_pred_indices:
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return dec, diff, ind
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return dec, diff
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class VectorQuantizer(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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# 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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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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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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self.sane_index_shape = sane_index_shape
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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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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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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==False, "Only for interface compatible with Gumbel"
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assert return_logits==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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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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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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# 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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# preserve gradients
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z_q = z + (z_q - z).detach()
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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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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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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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return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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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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# get quantized latent vectors
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z_q = self.embedding(indices)
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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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return z_q
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