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| 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() | |
| # Zero-out output layers: | |
| 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) | |
| # Sample from the posterior distribution | |
| 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 | |