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