forked from mrq/DL-Art-School
282 lines
9.7 KiB
Python
282 lines
9.7 KiB
Python
import functools
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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 vector_quantize_pytorch import VectorQuantize
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from models.gpt_voice.dvae_arch_playground.discretization_loss import DiscretizationLoss
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from models.vqvae.vqvae import Quantize
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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, activation):
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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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activation(),
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conv(chan, chan, 3, padding = 1),
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activation(),
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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 UpsampledConv(nn.Module):
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def __init__(self, conv, *args, **kwargs):
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super().__init__()
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assert 'stride' in kwargs.keys()
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self.stride = kwargs['stride']
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del kwargs['stride']
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self.conv = conv(*args, **kwargs)
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def forward(self, x):
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up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest')
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return self.conv(up)
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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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stride = 2,
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kernel_size = 4,
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use_transposed_convs = True,
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encoder_norm = False,
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activation = 'relu',
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smooth_l1_loss = False,
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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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discretization_loss_averaging_steps = 100,
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use_lr_quantizer = False,
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lr_quantizer_args = {},
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):
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super().__init__()
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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.straight_through = straight_through
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self.positional_dims = positional_dims
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self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps)
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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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if not use_transposed_convs:
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conv_transpose = functools.partial(UpsampledConv, conv)
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if activation == 'relu':
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act = nn.ReLU
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elif activation == 'silu':
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act = nn.SiLU
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else:
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assert NotImplementedError()
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enc_layers = []
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dec_layers = []
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if num_layers > 0:
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enc_chans = [hidden_dim * 2 ** i for i in range(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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pad = (kernel_size - 1) // 2
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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, kernel_size, stride = stride, padding = pad), act()))
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if encoder_norm:
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enc_layers.append(nn.GroupNorm(8, enc_out))
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dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act()))
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dec_out_chans = dec_chans[-1]
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innermost_dim = dec_chans[0]
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else:
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enc_layers.append(nn.Sequential(conv(channels, hidden_dim, 1), act()))
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dec_out_chans = hidden_dim
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innermost_dim = hidden_dim
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for _ in range(num_resnet_blocks):
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dec_layers.insert(0, ResBlock(innermost_dim, conv, act))
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enc_layers.append(ResBlock(innermost_dim, conv, act))
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if num_resnet_blocks > 0:
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dec_layers.insert(0, conv(codebook_dim, innermost_dim, 1))
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enc_layers.append(conv(innermost_dim, codebook_dim, 1))
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dec_layers.append(conv(dec_out_chans, 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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if use_lr_quantizer:
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self.codebook = VectorQuantize(dim=codebook_dim, n_embed=num_tokens, **lr_quantizer_args)
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else:
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self.codebook = Quantize(codebook_dim, num_tokens, new_return_order=True)
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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((1228800,), dtype=torch.long)
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self.code_ind = 0
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self.total_codes = 0
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self.internal_step = 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 get_debug_values(self, step, __):
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if self.record_codes:
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# Report annealing schedule
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return {'histogram_codes': self.codes[:self.total_codes]}
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else:
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return {}
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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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img = self.norm(images)
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logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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sampled, codes, _ = self.codebook(logits)
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return codes
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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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self.log_codes(img_seq)
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if hasattr(self.codebook, 'embed_code'):
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image_embeds = self.codebook.embed_code(img_seq)
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else:
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image_embeds = F.embedding(img_seq, self.codebook.embed.transpose(1,0))
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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 = [image_embeds]
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for layer in self.decoder:
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images.append(layer(images[-1]))
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return images[-1], images[-2]
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def infer(self, img):
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img = self.norm(img)
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logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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sampled, codes, commitment_loss = self.codebook(logits)
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return self.decode(codes)
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# Note: This module is not meant to be run in forward() except while training. It has special logic which performs
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# evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially
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# more lossy (but useful for determining network performance).
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def forward(
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self,
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img
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):
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img = self.norm(img)
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logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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sampled, codes, commitment_loss = self.codebook(logits)
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sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1))
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if self.training:
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out = sampled
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for d in self.decoder:
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out = d(out)
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else:
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# This is non-differentiable, but gives a better idea of how the network is actually performing.
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out, _ = self.decode(codes)
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# reconstruction loss
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recon_loss = self.loss_fn(img, out, reduction='none')
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# This is so we can debug the distribution of codes being learned.
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self.log_codes(codes)
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return recon_loss, commitment_loss, out
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def log_codes(self, codes):
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# This is so we can debug the distribution of codes being learned.
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if self.record_codes and self.internal_step % 50 == 0:
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codes = codes.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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self.total_codes += 1
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self.internal_step += 1
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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=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048,
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hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False,
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use_lr_quantizer=True)
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#v.load_state_dict(torch.load('../experiments/clips_dvae_8192_rev2.pth'))
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#v.eval()
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r,l,o=v(torch.randn(1,80,256))
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v.decode(torch.randint(0,8192,(1,256)))
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print(o.shape, l.shape)
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