(self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
batch_resize_range=None,
scheduler_config=None,
lr_g_factor=1.0,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
use_ema=False
)
| 134 | |
| 135 | class VQModel(pl.LightningModule): |
| 136 | def __init__(self, |
| 137 | ddconfig, |
| 138 | lossconfig, |
| 139 | n_embed, |
| 140 | embed_dim, |
| 141 | ckpt_path=None, |
| 142 | ignore_keys=[], |
| 143 | image_key="image", |
| 144 | colorize_nlabels=None, |
| 145 | monitor=None, |
| 146 | batch_resize_range=None, |
| 147 | scheduler_config=None, |
| 148 | lr_g_factor=1.0, |
| 149 | remap=None, |
| 150 | sane_index_shape=False, # tell vector quantizer to return indices as bhw |
| 151 | use_ema=False |
| 152 | ): |
| 153 | super().__init__() |
| 154 | self.embed_dim = embed_dim |
| 155 | self.n_embed = n_embed |
| 156 | self.image_key = image_key |
| 157 | self.encoder = Encoder(**ddconfig) |
| 158 | self.decoder = Decoder(**ddconfig) |
| 159 | self.loss = instantiate_from_config(lossconfig) |
| 160 | self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, |
| 161 | remap=remap, |
| 162 | sane_index_shape=sane_index_shape) |
| 163 | self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) |
| 164 | self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) |
| 165 | if colorize_nlabels is not None: |
| 166 | assert type(colorize_nlabels)==int |
| 167 | self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) |
| 168 | if monitor is not None: |
| 169 | self.monitor = monitor |
| 170 | self.batch_resize_range = batch_resize_range |
| 171 | if self.batch_resize_range is not None: |
| 172 | print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") |
| 173 | |
| 174 | self.use_ema = use_ema |
| 175 | if self.use_ema: |
| 176 | self.model_ema = LitEma(self) |
| 177 | print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") |
| 178 | |
| 179 | if ckpt_path is not None: |
| 180 | self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) |
| 181 | self.scheduler_config = scheduler_config |
| 182 | self.lr_g_factor = lr_g_factor |
| 183 | |
| 184 | @contextmanager |
| 185 | def ema_scope(self, context=None): |
no test coverage detected