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from enum import Enum
import torch
from torch import Tensor
from torch.nn.functional import silu
from .DiffAE_model_latentnet import *
from .DiffAE_model_unet import *
from .DiffAE_support_choices import *
@dataclass
class BeatGANsAutoencConfig(BeatGANsUNetConfig):
# number of style channels
enc_out_channels: int = 512
enc_attn_resolutions: Tuple[int] = None
enc_pool: str = 'depthconv'
enc_num_res_block: int = 2
enc_channel_mult: Tuple[int] = None
enc_grad_checkpoint: bool = False
latent_net_conf: MLPSkipNetConfig = None
def make_model(self):
return BeatGANsAutoencModel(self)
class BeatGANsAutoencModel(BeatGANsUNetModel):
def __init__(self, conf: BeatGANsAutoencConfig):
super().__init__(conf)
self.conf = conf
# having only time, cond
self.time_embed = TimeStyleSeperateEmbed(
time_channels=conf.model_channels,
time_out_channels=conf.embed_channels,
)
self.encoder = BeatGANsEncoderConfig(
image_size=conf.image_size,
in_channels=conf.in_channels,
model_channels=conf.model_channels,
out_hid_channels=conf.enc_out_channels,
out_channels=conf.enc_out_channels,
num_res_blocks=conf.enc_num_res_block,
attention_resolutions=(conf.enc_attn_resolutions
or conf.attention_resolutions),
dropout=conf.dropout,
channel_mult=conf.enc_channel_mult or conf.channel_mult,
use_time_condition=False,
conv_resample=conf.conv_resample,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint or conf.enc_grad_checkpoint,
num_heads=conf.num_heads,
num_head_channels=conf.num_head_channels,
resblock_updown=conf.resblock_updown,
use_new_attention_order=conf.use_new_attention_order,
pool=conf.enc_pool,
).make_model()
if conf.latent_net_conf is not None:
self.latent_net = conf.latent_net_conf.make_model()
def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
"""
Reparameterization trick to sample from N(mu, var) from
N(0,1).
:param mu: (Tensor) Mean of the latent Gaussian [B x D]
:param logvar: (Tensor) Standard deviation of the latent Gaussian [B x D]
:return: (Tensor) [B x D]
"""
assert self.conf.is_stochastic
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps * std + mu
def sample_z(self, n: int, device):
assert self.conf.is_stochastic
return torch.randn(n, self.conf.enc_out_channels, device=device)
def noise_to_cond(self, noise: Tensor):
raise NotImplementedError()
assert self.conf.noise_net_conf is not None
return self.noise_net.forward(noise)
def encode(self, x):
cond = self.encoder.forward(x)
return {'cond': cond}
@property
def stylespace_sizes(self):
modules = list(self.input_blocks.modules()) + list(
self.middle_block.modules()) + list(self.output_blocks.modules())
sizes = []
for module in modules:
if isinstance(module, ResBlock):
linear = module.cond_emb_layers[-1]
sizes.append(linear.weight.shape[0])
return sizes
def encode_stylespace(self, x, return_vector: bool = True):
"""
encode to style space
"""
modules = list(self.input_blocks.modules()) + list(
self.middle_block.modules()) + list(self.output_blocks.modules())
# (n, c)
cond = self.encoder.forward(x)
S = []
for module in modules:
if isinstance(module, ResBlock):
# (n, c')
s = module.cond_emb_layers.forward(cond)
S.append(s)
if return_vector:
# (n, sum_c)
return torch.cat(S, dim=1)
else:
return S
def forward(self,
x,
t,
y=None,
x_start=None,
cond=None,
style=None,
noise=None,
t_cond=None,
**kwargs):
"""
Apply the model to an input batch.
Args:
x_start: the original image to encode
cond: output of the encoder
noise: random noise (to predict the cond)
"""
if t_cond is None:
t_cond = t
if noise is not None:
# if the noise is given, we predict the cond from noise
cond = self.noise_to_cond(noise)
if cond is None:
if x is not None:
assert len(x) == len(x_start), f'{len(x)} != {len(x_start)}'
tmp = self.encode(x_start)
cond = tmp['cond']
if t is not None:
_t_emb = timestep_embedding(t, self.conf.model_channels)
_t_cond_emb = timestep_embedding(t_cond, self.conf.model_channels)
else:
# this happens when training only autoenc
_t_emb = None
_t_cond_emb = None
if self.conf.resnet_two_cond:
res = self.time_embed.forward(
time_emb=_t_emb,
cond=cond,
time_cond_emb=_t_cond_emb,
)
else:
raise NotImplementedError()
if self.conf.resnet_two_cond:
# two cond: first = time emb, second = cond_emb
emb = res.time_emb
cond_emb = res.emb
else:
# one cond = combined of both time and cond
emb = res.emb
cond_emb = None
# override the style if given
style = style or res.style
assert (y is not None) == (
self.conf.num_classes is not None
), "must specify y if and only if the model is class-conditional"
if self.conf.num_classes is not None:
raise NotImplementedError()
# assert y.shape == (x.shape[0], )
# emb = emb + self.label_emb(y)
# where in the model to supply time conditions
enc_time_emb = emb
mid_time_emb = emb
dec_time_emb = emb
# where in the model to supply style conditions
enc_cond_emb = cond_emb
mid_cond_emb = cond_emb
dec_cond_emb = cond_emb
# hs = []
hs = [[] for _ in range(len(self.conf.channel_mult))]
if x is not None:
h = x.type(self.dtype)
# input blocks
k = 0
for i in range(len(self.input_num_blocks)):
for j in range(self.input_num_blocks[i]):
h = self.input_blocks[k](h,
emb=enc_time_emb,
cond=enc_cond_emb)
# print(i, j, h.shape)
hs[i].append(h)
k += 1
assert k == len(self.input_blocks)
# middle blocks
h = self.middle_block(h, emb=mid_time_emb, cond=mid_cond_emb)
else:
# no lateral connections
# happens when training only the autonecoder
h = None
hs = [[] for _ in range(len(self.conf.channel_mult))]
# output blocks
k = 0
for i in range(len(self.output_num_blocks)):
for j in range(self.output_num_blocks[i]):
# take the lateral connection from the same layer (in reserve)
# until there is no more, use None
try:
lateral = hs[-i - 1].pop()
# print(i, j, lateral.shape)
except IndexError:
lateral = None
# print(i, j, lateral)
h = self.output_blocks[k](h,
emb=dec_time_emb,
cond=dec_cond_emb,
lateral=lateral)
k += 1
pred = self.out(h)
return AutoencReturn(pred=pred, cond=cond)
class AutoencReturn(NamedTuple):
pred: Tensor
cond: Tensor = None
class EmbedReturn(NamedTuple):
# style and time
emb: Tensor = None
# time only
time_emb: Tensor = None
# style only (but could depend on time)
style: Tensor = None
class TimeStyleSeperateEmbed(nn.Module):
# embed only style
def __init__(self, time_channels, time_out_channels):
super().__init__()
self.time_embed = nn.Sequential(
linear(time_channels, time_out_channels),
nn.SiLU(),
linear(time_out_channels, time_out_channels),
)
self.style = nn.Identity()
def forward(self, time_emb=None, cond=None, **kwargs):
if time_emb is None:
# happens with autoenc training mode
time_emb = None
else:
time_emb = self.time_embed(time_emb)
style = self.style(cond)
return EmbedReturn(emb=style, time_emb=time_emb, style=style)
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