forked from lucidrains/DALLE2-pytorch
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vqgan_vae.py
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vqgan_vae.py
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import copy
import math
from math import sqrt
from functools import partial, wraps
from vector_quantize_pytorch import VectorQuantize as VQ
import torch
from torch import nn, einsum
import torch.nn.functional as F
from torch.autograd import grad as torch_grad
import torchvision
from einops import rearrange, reduce, repeat
from einops_exts import rearrange_many
from einops.layers.torch import Rearrange
# constants
MList = nn.ModuleList
# helper functions
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
# decorators
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
def remove_vgg(fn):
@wraps(fn)
def inner(self, *args, **kwargs):
has_vgg = hasattr(self, 'vgg')
if has_vgg:
vgg = self.vgg
delattr(self, 'vgg')
out = fn(self, *args, **kwargs)
if has_vgg:
self.vgg = vgg
return out
return inner
# keyword argument helpers
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
def group_dict_by_key(cond, d):
return_val = [dict(),dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def group_by_key_prefix(prefix, d):
return group_dict_by_key(partial(string_begins_with, prefix), d)
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs
# tensor helper functions
def log(t, eps = 1e-10):
return torch.log(t + eps)
def gradient_penalty(images, output, weight = 10):
batch_size = images.shape[0]
gradients = torch_grad(outputs = output, inputs = images,
grad_outputs = torch.ones(output.size(), device = images.device),
create_graph = True, retain_graph = True, only_inputs = True)[0]
gradients = rearrange(gradients, 'b ... -> b (...)')
return weight * ((gradients.norm(2, dim = 1) - 1) ** 2).mean()
def l2norm(t):
return F.normalize(t, dim = -1)
def leaky_relu(p = 0.1):
return nn.LeakyReLU(0.1)
def stable_softmax(t, dim = -1, alpha = 32 ** 2):
t = t / alpha
t = t - torch.amax(t, dim = dim, keepdim = True).detach()
return (t * alpha).softmax(dim = dim)
def safe_div(numer, denom, eps = 1e-8):
return numer / (denom + eps)
# gan losses
def hinge_discr_loss(fake, real):
return (F.relu(1 + fake) + F.relu(1 - real)).mean()
def hinge_gen_loss(fake):
return -fake.mean()
def bce_discr_loss(fake, real):
return (-log(1 - torch.sigmoid(fake)) - log(torch.sigmoid(real))).mean()
def bce_gen_loss(fake):
return -log(torch.sigmoid(fake)).mean()
def grad_layer_wrt_loss(loss, layer):
return torch_grad(
outputs = loss,
inputs = layer,
grad_outputs = torch.ones_like(loss),
retain_graph = True
)[0].detach()
# vqgan vae
class LayerNormChan(nn.Module):
def __init__(
self,
dim,
eps = 1e-5
):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
def forward(self, x):
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
mean = torch.mean(x, dim = 1, keepdim = True)
return (x - mean) / (var + self.eps).sqrt() * self.gamma
# discriminator
class Discriminator(nn.Module):
def __init__(
self,
dims,
channels = 3,
groups = 16,
init_kernel_size = 5
):
super().__init__()
dim_pairs = zip(dims[:-1], dims[1:])
self.layers = MList([nn.Sequential(nn.Conv2d(channels, dims[0], init_kernel_size, padding = init_kernel_size // 2), leaky_relu())])
for dim_in, dim_out in dim_pairs:
self.layers.append(nn.Sequential(
nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1),
nn.GroupNorm(groups, dim_out),
leaky_relu()
))
dim = dims[-1]
self.to_logits = nn.Sequential( # return 5 x 5, for PatchGAN-esque training
nn.Conv2d(dim, dim, 1),
leaky_relu(),
nn.Conv2d(dim, 1, 4)
)
def forward(self, x):
for net in self.layers:
x = net(x)
return self.to_logits(x)
# positional encoding
class ContinuousPositionBias(nn.Module):
""" from https://arxiv.org/abs/2111.09883 """
def __init__(self, *, dim, heads, layers = 2):
super().__init__()
self.net = MList([])
self.net.append(nn.Sequential(nn.Linear(2, dim), leaky_relu()))
for _ in range(layers - 1):
self.net.append(nn.Sequential(nn.Linear(dim, dim), leaky_relu()))
self.net.append(nn.Linear(dim, heads))
self.register_buffer('rel_pos', None, persistent = False)
def forward(self, x):
n, device = x.shape[-1], x.device
fmap_size = int(sqrt(n))
if not exists(self.rel_pos):
pos = torch.arange(fmap_size, device = device)
grid = torch.stack(torch.meshgrid(pos, pos, indexing = 'ij'))
grid = rearrange(grid, 'c i j -> (i j) c')
rel_pos = rearrange(grid, 'i c -> i 1 c') - rearrange(grid, 'j c -> 1 j c')
rel_pos = torch.sign(rel_pos) * torch.log(rel_pos.abs() + 1)
self.register_buffer('rel_pos', rel_pos, persistent = False)
rel_pos = self.rel_pos.float()
for layer in self.net:
rel_pos = layer(rel_pos)
bias = rearrange(rel_pos, 'i j h -> h i j')
return x + bias
# resnet encoder / decoder
class ResnetEncDec(nn.Module):
def __init__(
self,
dim,
*,
channels = 3,
layers = 4,
layer_mults = None,
num_resnet_blocks = 1,
resnet_groups = 16,
first_conv_kernel_size = 5,
use_attn = True,
attn_dim_head = 64,
attn_heads = 8,
attn_dropout = 0.,
):
super().__init__()
assert dim % resnet_groups == 0, f'dimension {dim} must be divisible by {resnet_groups} (groups for the groupnorm)'
self.layers = layers
self.encoders = MList([])
self.decoders = MList([])
layer_mults = default(layer_mults, list(map(lambda t: 2 ** t, range(layers))))
assert len(layer_mults) == layers, 'layer multipliers must be equal to designated number of layers'
layer_dims = [dim * mult for mult in layer_mults]
dims = (dim, *layer_dims)
self.encoded_dim = dims[-1]
dim_pairs = zip(dims[:-1], dims[1:])
append = lambda arr, t: arr.append(t)
prepend = lambda arr, t: arr.insert(0, t)
if not isinstance(num_resnet_blocks, tuple):
num_resnet_blocks = (*((0,) * (layers - 1)), num_resnet_blocks)
if not isinstance(use_attn, tuple):
use_attn = (*((False,) * (layers - 1)), use_attn)
assert len(num_resnet_blocks) == layers, 'number of resnet blocks config must be equal to number of layers'
assert len(use_attn) == layers
for layer_index, (dim_in, dim_out), layer_num_resnet_blocks, layer_use_attn in zip(range(layers), dim_pairs, num_resnet_blocks, use_attn):
append(self.encoders, nn.Sequential(nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1), leaky_relu()))
prepend(self.decoders, nn.Sequential(nn.ConvTranspose2d(dim_out, dim_in, 4, 2, 1), leaky_relu()))
if layer_use_attn:
prepend(self.decoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
for _ in range(layer_num_resnet_blocks):
append(self.encoders, ResBlock(dim_out, groups = resnet_groups))
prepend(self.decoders, GLUResBlock(dim_out, groups = resnet_groups))
if layer_use_attn:
append(self.encoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
prepend(self.encoders, nn.Conv2d(channels, dim, first_conv_kernel_size, padding = first_conv_kernel_size // 2))
append(self.decoders, nn.Conv2d(dim, channels, 1))
def get_encoded_fmap_size(self, image_size):
return image_size // (2 ** self.layers)
def encode(self, x):
for enc in self.encoders:
x = enc(x)
return x
def decode(self, x):
for dec in self.decoders:
x = dec(x)
return x
class GLUResBlock(nn.Module):
def __init__(self, chan, groups = 16):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(chan, chan * 2, 3, padding = 1),
nn.GLU(dim = 1),
nn.GroupNorm(groups, chan),
nn.Conv2d(chan, chan * 2, 3, padding = 1),
nn.GLU(dim = 1),
nn.GroupNorm(groups, chan),
nn.Conv2d(chan, chan, 1)
)
def forward(self, x):
return self.net(x) + x
class ResBlock(nn.Module):
def __init__(self, chan, groups = 16):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(chan, chan, 3, padding = 1),
nn.GroupNorm(groups, chan),
leaky_relu(),
nn.Conv2d(chan, chan, 3, padding = 1),
nn.GroupNorm(groups, chan),
leaky_relu(),
nn.Conv2d(chan, chan, 1)
)
def forward(self, x):
return self.net(x) + x
# vqgan attention layer
class VQGanAttention(nn.Module):
def __init__(
self,
*,
dim,
dim_head = 64,
heads = 8,
dropout = 0.
):
super().__init__()
self.heads = heads
self.scale = dim_head ** -0.5
inner_dim = heads * dim_head
self.dropout = nn.Dropout(dropout)
self.pre_norm = LayerNormChan(dim)
self.cpb = ContinuousPositionBias(dim = dim // 4, heads = heads)
self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
def forward(self, x):
h = self.heads
height, width, residual = *x.shape[-2:], x.clone()
x = self.pre_norm(x)
q, k, v = self.to_qkv(x).chunk(3, dim = 1)
q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = h), (q, k, v))
sim = einsum('b h c i, b h c j -> b h i j', q, k) * self.scale
sim = self.cpb(sim)
attn = stable_softmax(sim, dim = -1)
attn = self.dropout(attn)
out = einsum('b h i j, b h c j -> b h c i', attn, v)
out = rearrange(out, 'b h c (x y) -> b (h c) x y', x = height, y = width)
out = self.to_out(out)
return out + residual
# ViT encoder / decoder
class RearrangeImage(nn.Module):
def forward(self, x):
n = x.shape[1]
w = h = int(sqrt(n))
return rearrange(x, 'b (h w) ... -> b h w ...', h = h, w = w)
class Attention(nn.Module):
def __init__(
self,
dim,
*,
heads = 8,
dim_head = 32
):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.heads = heads
self.scale = dim_head ** -0.5
inner_dim = dim_head * heads
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Linear(inner_dim, dim)
def forward(self, x):
h = self.heads
x = self.norm(x)
q, k, v = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = rearrange_many((q, k, v), 'b n (h d) -> b h n d', h = h)
q = q * self.scale
sim = einsum('b h i d, b h j d -> b h i j', q, k)
sim = sim - sim.amax(dim = -1, keepdim = True).detach()
attn = sim.softmax(dim = -1)
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
def FeedForward(dim, mult = 4):
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, dim * mult, bias = False),
nn.GELU(),
nn.Linear(dim * mult, dim, bias = False)
)
class Transformer(nn.Module):
def __init__(
self,
dim,
*,
layers,
dim_head = 32,
heads = 8,
ff_mult = 4
):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(layers):
self.layers.append(nn.ModuleList([
Attention(dim = dim, dim_head = dim_head, heads = heads),
FeedForward(dim = dim, mult = ff_mult)
]))
self.norm = nn.LayerNorm(dim)
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return self.norm(x)
class ViTEncDec(nn.Module):
def __init__(
self,
dim,
channels = 3,
layers = 4,
patch_size = 8,
dim_head = 32,
heads = 8,
ff_mult = 4
):
super().__init__()
self.encoded_dim = dim
self.patch_size = patch_size
input_dim = channels * (patch_size ** 2)
self.encoder = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
nn.Linear(input_dim, dim),
Transformer(
dim = dim,
dim_head = dim_head,
heads = heads,
ff_mult = ff_mult,
layers = layers
),
RearrangeImage(),
Rearrange('b h w c -> b c h w')
)
self.decoder = nn.Sequential(
Rearrange('b c h w -> b (h w) c'),
Transformer(
dim = dim,
dim_head = dim_head,
heads = heads,
ff_mult = ff_mult,
layers = layers
),
nn.Sequential(
nn.Linear(dim, dim * 4, bias = False),
nn.Tanh(),
nn.Linear(dim * 4, input_dim, bias = False),
),
RearrangeImage(),
Rearrange('b h w (p1 p2 c) -> b c (h p1) (w p2)', p1 = patch_size, p2 = patch_size)
)
def get_encoded_fmap_size(self, image_size):
return image_size // self.patch_size
def encode(self, x):
return self.encoder(x)
def decode(self, x):
return self.decoder(x)
# main vqgan-vae classes
class NullVQGanVAE(nn.Module):
def __init__(
self,
*,
channels
):
super().__init__()
self.encoded_dim = channels
self.layers = 0
def get_encoded_fmap_size(self, size):
return size
def copy_for_eval(self):
return self
def encode(self, x):
return x
def decode(self, x):
return x
class VQGanVAE(nn.Module):
def __init__(
self,
*,
dim,
image_size,
channels = 3,
layers = 4,
l2_recon_loss = False,
use_hinge_loss = True,
vgg = None,
vq_codebook_dim = 256,
vq_codebook_size = 512,
vq_decay = 0.8,
vq_commitment_weight = 1.,
vq_kmeans_init = True,
vq_use_cosine_sim = True,
use_vgg_and_gan = True,
vae_type = 'resnet',
discr_layers = 4,
**kwargs
):
super().__init__()
vq_kwargs, kwargs = groupby_prefix_and_trim('vq_', kwargs)
encdec_kwargs, kwargs = groupby_prefix_and_trim('encdec_', kwargs)
self.image_size = image_size
self.channels = channels
self.codebook_size = vq_codebook_size
if vae_type == 'resnet':
enc_dec_klass = ResnetEncDec
elif vae_type == 'vit':
enc_dec_klass = ViTEncDec
else:
raise ValueError(f'{vae_type} not valid')
self.enc_dec = enc_dec_klass(
dim = dim,
channels = channels,
layers = layers,
**encdec_kwargs
)
self.vq = VQ(
dim = self.enc_dec.encoded_dim,
codebook_dim = vq_codebook_dim,
codebook_size = vq_codebook_size,
decay = vq_decay,
commitment_weight = vq_commitment_weight,
accept_image_fmap = True,
kmeans_init = vq_kmeans_init,
use_cosine_sim = vq_use_cosine_sim,
**vq_kwargs
)
# reconstruction loss
self.recon_loss_fn = F.mse_loss if l2_recon_loss else F.l1_loss
# turn off GAN and perceptual loss if grayscale
self.vgg = None
self.discr = None
self.use_vgg_and_gan = use_vgg_and_gan
if not use_vgg_and_gan:
return
# preceptual loss
if exists(vgg):
self.vgg = vgg
else:
self.vgg = torchvision.models.vgg16(pretrained = True)
self.vgg.classifier = nn.Sequential(*self.vgg.classifier[:-2])
# gan related losses
layer_mults = list(map(lambda t: 2 ** t, range(discr_layers)))
layer_dims = [dim * mult for mult in layer_mults]
dims = (dim, *layer_dims)
self.discr = Discriminator(dims = dims, channels = channels)
self.discr_loss = hinge_discr_loss if use_hinge_loss else bce_discr_loss
self.gen_loss = hinge_gen_loss if use_hinge_loss else bce_gen_loss
@property
def encoded_dim(self):
return self.enc_dec.encoded_dim
def get_encoded_fmap_size(self, image_size):
return self.enc_dec.get_encoded_fmap_size(image_size)
def copy_for_eval(self):
device = next(self.parameters()).device
vae_copy = copy.deepcopy(self.cpu())
if vae_copy.use_vgg_and_gan:
del vae_copy.discr
del vae_copy.vgg
vae_copy.eval()
return vae_copy.to(device)
@remove_vgg
def state_dict(self, *args, **kwargs):
return super().state_dict(*args, **kwargs)
@remove_vgg
def load_state_dict(self, *args, **kwargs):
return super().load_state_dict(*args, **kwargs)
@property
def codebook(self):
return self.vq.codebook
def encode(self, fmap):
fmap = self.enc_dec.encode(fmap)
return fmap
def decode(self, fmap, return_indices_and_loss = False):
fmap, indices, commit_loss = self.vq(fmap)
fmap = self.enc_dec.decode(fmap)
if not return_indices_and_loss:
return fmap
return fmap, indices, commit_loss
def forward(
self,
img,
return_loss = False,
return_discr_loss = False,
return_recons = False,
add_gradient_penalty = True
):
batch, channels, height, width, device = *img.shape, img.device
assert height == self.image_size and width == self.image_size, 'height and width of input image must be equal to {self.image_size}'
assert channels == self.channels, 'number of channels on image or sketch is not equal to the channels set on this VQGanVAE'
fmap = self.encode(img)
fmap, indices, commit_loss = self.decode(fmap, return_indices_and_loss = True)
if not return_loss and not return_discr_loss:
return fmap
assert return_loss ^ return_discr_loss, 'you should either return autoencoder loss or discriminator loss, but not both'
# whether to return discriminator loss
if return_discr_loss:
assert exists(self.discr), 'discriminator must exist to train it'
fmap.detach_()
img.requires_grad_()
fmap_discr_logits, img_discr_logits = map(self.discr, (fmap, img))
discr_loss = self.discr_loss(fmap_discr_logits, img_discr_logits)
if add_gradient_penalty:
gp = gradient_penalty(img, img_discr_logits)
loss = discr_loss + gp
if return_recons:
return loss, fmap
return loss
# reconstruction loss
recon_loss = self.recon_loss_fn(fmap, img)
# early return if training on grayscale
if not self.use_vgg_and_gan:
if return_recons:
return recon_loss, fmap
return recon_loss
# perceptual loss
img_vgg_input = img
fmap_vgg_input = fmap
if img.shape[1] == 1:
# handle grayscale for vgg
img_vgg_input, fmap_vgg_input = map(lambda t: repeat(t, 'b 1 ... -> b c ...', c = 3), (img_vgg_input, fmap_vgg_input))
img_vgg_feats = self.vgg(img_vgg_input)
recon_vgg_feats = self.vgg(fmap_vgg_input)
perceptual_loss = F.mse_loss(img_vgg_feats, recon_vgg_feats)
# generator loss
gen_loss = self.gen_loss(self.discr(fmap))
# calculate adaptive weight
last_dec_layer = self.decoders[-1].weight
norm_grad_wrt_gen_loss = grad_layer_wrt_loss(gen_loss, last_dec_layer).norm(p = 2)
norm_grad_wrt_perceptual_loss = grad_layer_wrt_loss(perceptual_loss, last_dec_layer).norm(p = 2)
adaptive_weight = safe_div(norm_grad_wrt_perceptual_loss, norm_grad_wrt_gen_loss)
adaptive_weight.clamp_(max = 1e4)
# combine losses
loss = recon_loss + perceptual_loss + commit_loss + adaptive_weight * gen_loss
if return_recons:
return loss, fmap
return loss