218 lines
7.2 KiB
Python
218 lines
7.2 KiB
Python
import math
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from math import sqrt
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from torch import einsum
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from trainer.networks import register_model
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from utils.util import opt_get
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def default(val, d):
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return val if val is not None else d
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def eval_decorator(fn):
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def inner(model, *args, **kwargs):
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was_training = model.training
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model.eval()
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out = fn(model, *args, **kwargs)
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model.train(was_training)
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return out
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return inner
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class ResBlock(nn.Module):
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def __init__(self, chan, conv):
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super().__init__()
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self.net = nn.Sequential(
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conv(chan, chan, 3, padding = 1),
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nn.ReLU(),
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conv(chan, chan, 3, padding = 1),
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nn.ReLU(),
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conv(chan, chan, 1)
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)
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def forward(self, x):
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return self.net(x) + x
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class DiscreteVAE(nn.Module):
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def __init__(
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self,
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positional_dims=2,
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num_tokens = 512,
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codebook_dim = 512,
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num_layers = 3,
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num_resnet_blocks = 0,
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hidden_dim = 64,
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channels = 3,
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smooth_l1_loss = False,
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starting_temperature = 0.5,
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temperature_annealing_rate = 0,
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min_temperature = .5,
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straight_through = False,
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normalization = None, # ((0.5,) * 3, (0.5,) * 3),
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record_codes = False,
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):
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super().__init__()
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assert num_layers >= 1, 'number of layers must be greater than or equal to 1'
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has_resblocks = num_resnet_blocks > 0
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self.num_tokens = num_tokens
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self.num_layers = num_layers
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self.starting_temperature = starting_temperature
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self.current_temperature = starting_temperature
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self.straight_through = straight_through
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self.codebook = nn.Embedding(num_tokens, codebook_dim)
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self.positional_dims = positional_dims
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self.temperature_annealing_rate = temperature_annealing_rate
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self.min_temperature = min_temperature
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assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now.
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if positional_dims == 2:
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conv = nn.Conv2d
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conv_transpose = nn.ConvTranspose2d
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else:
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conv = nn.Conv1d
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conv_transpose = nn.ConvTranspose1d
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enc_chans = [hidden_dim] * num_layers
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dec_chans = list(reversed(enc_chans))
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enc_chans = [channels, *enc_chans]
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dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0]
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dec_chans = [dec_init_chan, *dec_chans]
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enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans))
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enc_layers = []
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dec_layers = []
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for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
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enc_layers.append(nn.Sequential(conv(enc_in, enc_out, 4, stride = 2, padding = 1), nn.ReLU()))
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dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, 4, stride = 2, padding = 1), nn.ReLU()))
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for _ in range(num_resnet_blocks):
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dec_layers.insert(0, ResBlock(dec_chans[1], conv))
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enc_layers.append(ResBlock(enc_chans[-1], conv))
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if num_resnet_blocks > 0:
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dec_layers.insert(0, conv(codebook_dim, dec_chans[1], 1))
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enc_layers.append(conv(enc_chans[-1], num_tokens, 1))
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dec_layers.append(conv(dec_chans[-1], channels, 1))
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self.encoder = nn.Sequential(*enc_layers)
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self.decoder = nn.Sequential(*dec_layers)
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self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss
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# take care of normalization within class
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self.normalization = normalization
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self.record_codes = record_codes
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if record_codes:
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self.codes = torch.zeros((32768,), dtype=torch.long)
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self.code_ind = 0
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def norm(self, images):
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if not self.normalization is not None:
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return images
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means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization)
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arrange = 'c -> () c () ()' if self.positional_dims == 2 else 'c -> () c ()'
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means, stds = map(lambda t: rearrange(t, arrange), (means, stds))
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images = images.clone()
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images.sub_(means).div_(stds)
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return images
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def update_for_step(self, step, __):
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# Run the annealing schedule
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if self.temperature_annealing_rate != 0:
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self.current_temperature = max(self.starting_temperature * math.exp(-self.temperature_annealing_rate * step), self.min_temperature)
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def get_debug_values(self, step, __):
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# Report annealing schedule
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return {'current_annealing_temperature': self.current_temperature, 'histogram_codes': self.codes}
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@torch.no_grad()
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@eval_decorator
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def get_codebook_indices(self, images):
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logits = self(images, return_logits = True)
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codebook_indices = logits.argmax(dim = 1).flatten(1)
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return codebook_indices
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def decode(
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self,
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img_seq
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):
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image_embeds = self.codebook(img_seq)
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b, n, d = image_embeds.shape
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kwargs = {}
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if self.positional_dims == 1:
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arrange = 'b n d -> b d n'
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else:
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h = w = int(sqrt(n))
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arrange = 'b (h w) d -> b d h w'
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kwargs = {'h': h, 'w': w}
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image_embeds = rearrange(image_embeds, arrange, **kwargs)
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images = self.decoder(image_embeds)
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return images
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def forward(
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self,
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img
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):
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device, num_tokens = img.device, self.num_tokens
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img = self.norm(img)
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logits = self.encoder(img)
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soft_one_hot = F.gumbel_softmax(logits, tau = self.current_temperature, dim = 1, hard = self.straight_through)
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if self.positional_dims == 1:
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arrange = 'b n s, n d -> b d s'
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else:
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arrange = 'b n h w, n d -> b d h w'
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sampled = einsum(arrange, soft_one_hot, self.codebook.weight)
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out = self.decoder(sampled)
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# reconstruction loss
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recon_loss = self.loss_fn(img, out)
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# kl divergence
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arrange = 'b n h w -> b (h w) n' if self.positional_dims == 2 else 'b n s -> b s n'
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logits = rearrange(logits, arrange)
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log_qy = F.log_softmax(logits, dim = -1)
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log_uniform = torch.log(torch.tensor([1. / num_tokens], device = device))
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kl_div = F.kl_div(log_uniform, log_qy, None, None, 'batchmean', log_target = True)
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# This is so we can debug the distribution of codes being learned.
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if self.record_codes:
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codes = logits.argmax(dim = 2).flatten()
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l = codes.shape[0]
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i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
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self.codes[i:i+l] = codes.cpu()
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self.code_ind = self.code_ind + l
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if self.code_ind >= self.codes.shape[0]:
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self.code_ind = 0
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return recon_loss, kl_div, out
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@register_model
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def register_lucidrains_dvae(opt_net, opt):
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return DiscreteVAE(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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#v = DiscreteVAE()
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#o=v(torch.randn(1,3,256,256))
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#print(o.shape)
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v = DiscreteVAE(channels=1, normalization=None, positional_dims=1)
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o=v(torch.randn(1,1,256))
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print(o.shape)
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