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 models.diffusion.nn import conv_nd, normalization, zero_module
from models.diffusion.unet_diffusion import Upsample, Downsample, AttentionBlock
from models.vqvae.vqvae import Quantize
from trainer.networks import register_model
from utils.util import opt_get, checkpoint
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,
channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
up=False,
down=False,
kernel_size=3,
):
super().__init__()
self.channels = channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_scale_shift_norm = use_scale_shift_norm
padding = 1 if kernel_size == 3 else 2
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, kernel_size, padding=padding
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x):
return checkpoint(
self._forward, x
)
def _forward(self, x):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
h = self.out_layers(h)
return self.skip_connection(x) + h
class DisjointUnet(nn.Module):
def __init__(
self,
attention_resolutions,
channel_mult_down,
channel_mult_up,
in_channels = 3,
model_channels = 64,
out_channels = 3,
dims=2,
num_res_blocks = 2,
stride = 2,
dropout=0,
num_heads=4,
):
super().__init__()
self.enc_input_blocks = nn.ModuleList(
[
conv_nd(dims, in_channels, model_channels, 3, padding=1)
]
)
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult_down):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
dropout,
out_channels=mult * model_channels,
dims=dims,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=-1,
)
)
self.enc_input_blocks.append(nn.Sequential(*layers))
input_block_chans.append(ch)
if level != len(channel_mult_down) - 1:
out_ch = ch
self.enc_input_blocks.append(
Downsample(
ch, True, dims=dims, out_channels=out_ch, factor=stride
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self.enc_middle_block = nn.Sequential(
ResBlock(
ch,
dropout,
dims=dims,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=-1,
),
ResBlock(
ch,
dropout,
dims=dims,
),
)
self.enc_output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult_up)):
for i in range(num_res_blocks + 1):
if len(input_block_chans) > 0:
ich = input_block_chans.pop()
else:
ich = 0
layers = [
ResBlock(
ch + ich,
dropout,
out_channels=model_channels * mult,
dims=dims,
)
]
ch = model_channels * mult
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=-1,
)
)
if level != len(channel_mult_up)-1 and i == num_res_blocks:
out_ch = ch
layers.append(
Upsample(ch, True, dims=dims, out_channels=out_ch, factor=stride)
)
ds //= 2
self.enc_output_blocks.append(nn.Sequential(*layers))
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
conv_nd(dims, ch, out_channels, 3, padding=1),
)
def forward(self, x):
hs = []
h = x
for module in self.enc_input_blocks:
h = module(h)
hs.append(h)
h = self.enc_middle_block(h)
for module in self.enc_output_blocks:
if len(hs) > 0:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h)
h = h.type(x.dtype)
return self.out(h)
class DiscreteVAE(nn.Module):
def __init__(
self,
attention_resolutions,
in_channels = 3,
model_channels = 64,
out_channels = 3,
channel_mult=(1, 2, 4, 8),
dims=2,
num_tokens = 512,
codebook_dim = 512,
convergence_layer=2,
num_res_blocks = 0,
stride = 2,
straight_through = False,
dropout=0,
num_heads=4,
record_codes=True,
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.num_tokens = num_tokens
self.num_layers = len(channel_mult)
self.straight_through = straight_through
self.codebook = Quantize(codebook_dim, num_tokens)
self.positional_dims = dims
self.dropout = dropout
self.num_heads = num_heads
self.record_codes = record_codes
if record_codes:
self.codes = torch.zeros((32768,), dtype=torch.long)
self.code_ind = 0
self.internal_step = 0
enc_down = channel_mult
enc_up = list(reversed(channel_mult[convergence_layer:]))
self.encoder = DisjointUnet(attention_resolutions, enc_down, enc_up, in_channels=in_channels, model_channels=model_channels,
out_channels=codebook_dim, dims=dims, num_res_blocks=num_res_blocks, num_heads=num_heads, dropout=dropout,
stride=stride)
dec_down = list(reversed(enc_up))
dec_up = list(reversed(enc_down))
self.decoder = DisjointUnet(attention_resolutions, dec_down, dec_up, in_channels=codebook_dim, model_channels=model_channels,
out_channels=out_channels, dims=dims, num_res_blocks=num_res_blocks, num_heads=num_heads, dropout=dropout,
stride=stride)
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 = 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
def infer(self, 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)
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
):
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 = sampled
out = self.decoder(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 = F.mse_loss(img, out, reduction='none')
# 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_my_dvae(opt_net, opt):
return DiscreteVAE(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
net = DiscreteVAE((8, 16), channel_mult=(1,2,4,8,8), in_channels=80, model_channels=128, out_channels=80, dims=1, num_res_blocks=2)
inp = torch.randn((2,80,512))
print([j.shape for j in net(inp)])