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# MIT License
# Copyright (c) Microsoft
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Copyright (c) [2025] [Microsoft]
# Copyright (c) [2025] [Chongjie Ye]
# SPDX-License-Identifier: MIT
# This file has been modified by Chongjie Ye on 2025/04/10
# Original file was released under MIT, with the full license text # available at https://github.com/atong01/conditional-flow-matching/blob/1.0.7/LICENSE.
# This modified file is released under the same license.
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
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