import math from math import sqrt import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from torch import einsum from models.vqvae.vqvae import Quantize from trainer.networks import register_model from utils.util import opt_get def default(val, d): return val if val is not None else d def eval_decorator(fn): def inner(model, *args, **kwargs): was_training = model.training model.eval() out = fn(model, *args, **kwargs) model.train(was_training) return out return inner class ResBlock(nn.Module): def __init__(self, chan, conv): super().__init__() self.net = nn.Sequential( conv(chan, chan, 3, padding = 1), nn.ReLU(), conv(chan, chan, 3, padding = 1), nn.ReLU(), conv(chan, chan, 1) ) def forward(self, x): return self.net(x) + x class DiscreteVAE(nn.Module): def __init__( self, positional_dims=2, num_tokens = 512, codebook_dim = 512, num_layers = 3, num_resnet_blocks = 0, hidden_dim = 64, channels = 3, smooth_l1_loss = False, straight_through = False, normalization = None, # ((0.5,) * 3, (0.5,) * 3), record_codes = False, ): super().__init__() assert num_layers >= 1, 'number of layers must be greater than or equal to 1' has_resblocks = num_resnet_blocks > 0 self.num_tokens = num_tokens self.num_layers = num_layers self.straight_through = straight_through self.codebook = Quantize(codebook_dim, num_tokens) self.positional_dims = positional_dims assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now. if positional_dims == 2: conv = nn.Conv2d conv_transpose = nn.ConvTranspose2d else: conv = nn.Conv1d conv_transpose = nn.ConvTranspose1d enc_chans = [hidden_dim] * num_layers dec_chans = list(reversed(enc_chans)) enc_chans = [channels, *enc_chans] dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0] dec_chans = [dec_init_chan, *dec_chans] enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans)) enc_layers = [] dec_layers = [] for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io): enc_layers.append(nn.Sequential(conv(enc_in, enc_out, 4, stride = 2, padding = 1), nn.ReLU())) dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, 4, stride = 2, padding = 1), nn.ReLU())) for _ in range(num_resnet_blocks): dec_layers.insert(0, ResBlock(dec_chans[1], conv)) enc_layers.append(ResBlock(enc_chans[-1], conv)) if num_resnet_blocks > 0: dec_layers.insert(0, conv(codebook_dim, dec_chans[1], 1)) enc_layers.append(conv(enc_chans[-1], codebook_dim, 1)) dec_layers.append(conv(dec_chans[-1], channels, 1)) self.encoder = nn.Sequential(*enc_layers) self.decoder = nn.Sequential(*dec_layers) self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss # take care of normalization within class self.normalization = normalization self.record_codes = record_codes if record_codes: self.codes = torch.zeros((32768,), dtype=torch.long) self.code_ind = 0 self.internal_step = 0 def norm(self, images): if not self.normalization is not None: return images means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization) arrange = 'c -> () c () ()' if self.positional_dims == 2 else 'c -> () c ()' means, stds = map(lambda t: rearrange(t, arrange), (means, stds)) images = images.clone() images.sub_(means).div_(stds) return images def get_debug_values(self, step, __): if self.record_codes: # Report annealing schedule return {'histogram_codes': self.codes} else: return {} @torch.no_grad() @eval_decorator def get_codebook_indices(self, images): img = self.norm(images) logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) sampled, commitment_loss, codes = self.codebook(logits) return codes def decode( self, img_seq ): image_embeds = self.codebook.embed_code(img_seq) b, n, d = image_embeds.shape kwargs = {} if self.positional_dims == 1: arrange = 'b n d -> b d n' else: h = w = int(sqrt(n)) arrange = 'b (h w) d -> b d h w' kwargs = {'h': h, 'w': w} image_embeds = rearrange(image_embeds, arrange, **kwargs) images = self.decoder(image_embeds) return images # Note: This module is not meant to be run in forward() except while training. It has special logic which performs # evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially # more lossy (but useful for determining network performance). def forward( self, img ): img = self.norm(img) logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) sampled, commitment_loss, codes = self.codebook(logits) sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1)) if self.training: out = self.decoder(sampled) else: # This is non-differentiable, but gives a better idea of how the network is actually performing. out = self.decode(codes) # reconstruction loss recon_loss = self.loss_fn(img, out) # This is so we can debug the distribution of codes being learned. if self.record_codes and self.internal_step % 50 == 0: codes = codes.flatten() l = codes.shape[0] i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l self.codes[i:i+l] = codes.cpu() self.code_ind = self.code_ind + l if self.code_ind >= self.codes.shape[0]: self.code_ind = 0 self.internal_step += 1 return recon_loss, commitment_loss, out @register_model def register_lucidrains_dvae(opt_net, opt): return DiscreteVAE(**opt_get(opt_net, ['kwargs'], {})) if __name__ == '__main__': #v = DiscreteVAE() #o=v(torch.randn(1,3,256,256)) #print(o.shape) v = DiscreteVAE(channels=1, normalization=None, positional_dims=1, num_tokens=4096, codebook_dim=2048, hidden_dim=256) v.eval() o=v(torch.randn(1,1,256)) print(o[-1].shape)