File size: 7,802 Bytes
1d409a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 |
import torch
# import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from typing import Any, Dict, List, Optional, Tuple, Union
from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm_patched.ldm.util import instantiate_from_config
from ldm_patched.ldm.modules.ema import LitEma
import ldm_patched.modules.ops
class DiagonalGaussianRegularizer(torch.nn.Module):
def __init__(self, sample: bool = True):
super().__init__()
self.sample = sample
def get_trainable_parameters(self) -> Any:
yield from ()
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
log = dict()
posterior = DiagonalGaussianDistribution(z)
if self.sample:
z = posterior.sample()
else:
z = posterior.mode()
kl_loss = posterior.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
log["kl_loss"] = kl_loss
return z, log
class AbstractAutoencoder(torch.nn.Module):
"""
This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
unCLIP models, etc. Hence, it is fairly general, and specific features
(e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
"""
def __init__(
self,
ema_decay: Union[None, float] = None,
monitor: Union[None, str] = None,
input_key: str = "jpg",
**kwargs,
):
super().__init__()
self.input_key = input_key
self.use_ema = ema_decay is not None
if monitor is not None:
self.monitor = monitor
if self.use_ema:
self.model_ema = LitEma(self, decay=ema_decay)
logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
def get_input(self, batch) -> Any:
raise NotImplementedError()
def on_train_batch_end(self, *args, **kwargs):
# for EMA computation
if self.use_ema:
self.model_ema(self)
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.parameters())
self.model_ema.copy_to(self)
if context is not None:
logpy.info(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.parameters())
if context is not None:
logpy.info(f"{context}: Restored training weights")
def encode(self, *args, **kwargs) -> torch.Tensor:
raise NotImplementedError("encode()-method of abstract base class called")
def decode(self, *args, **kwargs) -> torch.Tensor:
raise NotImplementedError("decode()-method of abstract base class called")
def instantiate_optimizer_from_config(self, params, lr, cfg):
logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
return get_obj_from_str(cfg["target"])(
params, lr=lr, **cfg.get("params", dict())
)
def configure_optimizers(self) -> Any:
raise NotImplementedError()
class AutoencodingEngine(AbstractAutoencoder):
"""
Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
(we also restore them explicitly as special cases for legacy reasons).
Regularizations such as KL or VQ are moved to the regularizer class.
"""
def __init__(
self,
*args,
encoder_config: Dict,
decoder_config: Dict,
regularizer_config: Dict,
**kwargs,
):
super().__init__(*args, **kwargs)
self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
self.regularization: AbstractRegularizer = instantiate_from_config(
regularizer_config
)
def get_last_layer(self):
return self.decoder.get_last_layer()
def encode(
self,
x: torch.Tensor,
return_reg_log: bool = False,
unregularized: bool = False,
) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
z = self.encoder(x)
if unregularized:
return z, dict()
z, reg_log = self.regularization(z)
if return_reg_log:
return z, reg_log
return z
def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
x = self.decoder(z, **kwargs)
return x
def forward(
self, x: torch.Tensor, **additional_decode_kwargs
) -> Tuple[torch.Tensor, torch.Tensor, dict]:
z, reg_log = self.encode(x, return_reg_log=True)
dec = self.decode(z, **additional_decode_kwargs)
return z, dec, reg_log
class AutoencodingEngineLegacy(AutoencodingEngine):
def __init__(self, embed_dim: int, **kwargs):
self.max_batch_size = kwargs.pop("max_batch_size", None)
ddconfig = kwargs.pop("ddconfig")
super().__init__(
encoder_config={
"target": "ldm_patched.ldm.modules.diffusionmodules.model.Encoder",
"params": ddconfig,
},
decoder_config={
"target": "ldm_patched.ldm.modules.diffusionmodules.model.Decoder",
"params": ddconfig,
},
**kwargs,
)
self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(
(1 + ddconfig["double_z"]) * ddconfig["z_channels"],
(1 + ddconfig["double_z"]) * embed_dim,
1,
)
self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
self.embed_dim = embed_dim
def get_autoencoder_params(self) -> list:
params = super().get_autoencoder_params()
return params
def encode(
self, x: torch.Tensor, return_reg_log: bool = False
) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
if self.max_batch_size is None:
z = self.encoder(x)
z = self.quant_conv(z)
else:
N = x.shape[0]
bs = self.max_batch_size
n_batches = int(math.ceil(N / bs))
z = list()
for i_batch in range(n_batches):
z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
z_batch = self.quant_conv(z_batch)
z.append(z_batch)
z = torch.cat(z, 0)
z, reg_log = self.regularization(z)
if return_reg_log:
return z, reg_log
return z
def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
if self.max_batch_size is None:
dec = self.post_quant_conv(z)
dec = self.decoder(dec, **decoder_kwargs)
else:
N = z.shape[0]
bs = self.max_batch_size
n_batches = int(math.ceil(N / bs))
dec = list()
for i_batch in range(n_batches):
dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
dec_batch = self.decoder(dec_batch, **decoder_kwargs)
dec.append(dec_batch)
dec = torch.cat(dec, 0)
return dec
class AutoencoderKL(AutoencodingEngineLegacy):
def __init__(self, **kwargs):
if "lossconfig" in kwargs:
kwargs["loss_config"] = kwargs.pop("lossconfig")
super().__init__(
regularizer_config={
"target": (
"ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"
)
},
**kwargs,
)
|