| from typing import * |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from ...modules import sparse as sp |
| from .base import SparseTransformerBase |
|
|
|
|
| class SLatEncoder(SparseTransformerBase): |
| def __init__( |
| self, |
| resolution: int, |
| in_channels: int, |
| model_channels: int, |
| latent_channels: int, |
| num_blocks: int, |
| num_heads: Optional[int] = None, |
| num_head_channels: Optional[int] = 64, |
| mlp_ratio: float = 4, |
| attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", |
| window_size: int = 8, |
| pe_mode: Literal["ape", "rope"] = "ape", |
| use_fp16: bool = False, |
| use_checkpoint: bool = False, |
| qk_rms_norm: bool = False, |
| ): |
| super().__init__( |
| in_channels=in_channels, |
| model_channels=model_channels, |
| num_blocks=num_blocks, |
| num_heads=num_heads, |
| num_head_channels=num_head_channels, |
| mlp_ratio=mlp_ratio, |
| attn_mode=attn_mode, |
| window_size=window_size, |
| pe_mode=pe_mode, |
| use_fp16=use_fp16, |
| use_checkpoint=use_checkpoint, |
| qk_rms_norm=qk_rms_norm, |
| ) |
| self.resolution = resolution |
| self.out_layer = sp.SparseLinear(model_channels, 2 * latent_channels) |
|
|
| self.initialize_weights() |
| if use_fp16: |
| self.convert_to_fp16() |
|
|
| def initialize_weights(self) -> None: |
| super().initialize_weights() |
| |
| nn.init.constant_(self.out_layer.weight, 0) |
| nn.init.constant_(self.out_layer.bias, 0) |
|
|
| def forward(self, x: sp.SparseTensor, sample_posterior=True, return_raw=False): |
| h = super().forward(x) |
| h = h.type(x.dtype) |
| h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) |
| h = self.out_layer(h) |
| |
| |
| mean, logvar = h.feats.chunk(2, dim=-1) |
| if sample_posterior: |
| std = torch.exp(0.5 * logvar) |
| z = mean + std * torch.randn_like(std) |
| else: |
| z = mean |
| z = h.replace(z) |
| |
| if return_raw: |
| return z, mean, logvar |
| else: |
| return z |
|
|
