import functools 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 vector_quantize_pytorch import VectorQuantize from models.gpt_voice.dvae_arch_playground.discretization_loss import DiscretizationLoss 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, activation): super().__init__() self.net = nn.Sequential( conv(chan, chan, 3, padding = 1), activation(), conv(chan, chan, 3, padding = 1), activation(), conv(chan, chan, 1) ) def forward(self, x): return self.net(x) + x class UpsampledConv(nn.Module): def __init__(self, conv, *args, **kwargs): super().__init__() assert 'stride' in kwargs.keys() self.stride = kwargs['stride'] del kwargs['stride'] self.conv = conv(*args, **kwargs) def forward(self, x): up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest') return self.conv(up) 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, stride = 2, kernel_size = 4, use_transposed_convs = True, encoder_norm = False, activation = 'relu', smooth_l1_loss = False, straight_through = False, normalization = None, # ((0.5,) * 3, (0.5,) * 3), record_codes = False, discretization_loss_averaging_steps = 100, use_lr_quantizer = False, lr_quantizer_args = {}, ): super().__init__() has_resblocks = num_resnet_blocks > 0 self.num_tokens = num_tokens self.num_layers = num_layers self.straight_through = straight_through self.positional_dims = positional_dims self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps) 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 if not use_transposed_convs: conv_transpose = functools.partial(UpsampledConv, conv) if activation == 'relu': act = nn.ReLU elif activation == 'silu': act = nn.SiLU else: assert NotImplementedError() enc_layers = [] dec_layers = [] if num_layers > 0: enc_chans = [hidden_dim * 2 ** i for i in range(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)) pad = (kernel_size - 1) // 2 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, kernel_size, stride = stride, padding = pad), act())) if encoder_norm: enc_layers.append(nn.GroupNorm(8, enc_out)) dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act())) dec_out_chans = dec_chans[-1] innermost_dim = dec_chans[0] else: enc_layers.append(nn.Sequential(conv(channels, hidden_dim, 1), act())) dec_out_chans = hidden_dim innermost_dim = hidden_dim for _ in range(num_resnet_blocks): dec_layers.insert(0, ResBlock(innermost_dim, conv, act)) enc_layers.append(ResBlock(innermost_dim, conv, act)) if num_resnet_blocks > 0: dec_layers.insert(0, conv(codebook_dim, innermost_dim, 1)) enc_layers.append(conv(innermost_dim, codebook_dim, 1)) dec_layers.append(conv(dec_out_chans, 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 if use_lr_quantizer: self.codebook = VectorQuantize(dim=codebook_dim, codebook_size=num_tokens, **lr_quantizer_args) else: self.codebook = Quantize(codebook_dim, num_tokens, new_return_order=True) # take care of normalization within class self.normalization = normalization self.record_codes = record_codes if record_codes: self.codes = torch.zeros((1228800,), dtype=torch.long) self.code_ind = 0 self.total_codes = 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 and self.total_codes > 0: # Report annealing schedule return {'histogram_codes': self.codes[:self.total_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, codes, _ = self.codebook(logits) return codes def decode( self, img_seq ): self.log_codes(img_seq) if hasattr(self.codebook, 'embed_code'): image_embeds = self.codebook.embed_code(img_seq) else: image_embeds = F.embedding(img_seq, self.codebook.codebook) 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 = [image_embeds] for layer in self.decoder: images.append(layer(images[-1])) return images[-1], images[-2] def infer(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, codes, commitment_loss = self.codebook(logits) return self.decode(codes) # 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, codes, commitment_loss = self.codebook(logits) sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1)) if self.training: out = sampled for d in self.decoder: out = d(out) 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, reduction='none') # This is so we can debug the distribution of codes being learned. self.log_codes(codes) return recon_loss, commitment_loss, out def log_codes(self, codes): # 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.total_codes += 1 self.internal_step += 1 @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=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048, hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False, use_lr_quantizer=True) #v.load_state_dict(torch.load('../experiments/clips_dvae_8192_rev2.pth')) #v.eval() r,l,o=v(torch.randn(1,80,256)) v.decode(torch.randint(0,8192,(1,256))) print(o.shape, l.shape)