371 lines
12 KiB
Python
371 lines
12 KiB
Python
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,
|
|
do_checkpoint=True,
|
|
):
|
|
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
|
|
self.do_checkpoint = do_checkpoint
|
|
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):
|
|
if self.do_checkpoint:
|
|
return checkpoint(
|
|
self._forward, x
|
|
)
|
|
else:
|
|
return 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)])
|