Laurent Erignoux
144fb65246
git-subtree-dir: Stable3DGen git-subtree-mainline:846d6cfad8git-subtree-split:c29f668ece
83 lines
3.2 KiB
Python
Executable File
83 lines
3.2 KiB
Python
Executable File
# MIT License
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# Copyright (c) Microsoft
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# Copyright (c) [2025] [Microsoft]
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# SPDX-License-Identifier: MIT
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import torch
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import torch.nn as nn
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from . import SparseTensor
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from . import DEBUG
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__all__ = [
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'SparseGroupNorm',
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'SparseLayerNorm',
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'SparseGroupNorm32',
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'SparseLayerNorm32',
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]
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class SparseGroupNorm(nn.GroupNorm):
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def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
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super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine)
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def forward(self, input: SparseTensor) -> SparseTensor:
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nfeats = torch.zeros_like(input.feats)
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for k in range(input.shape[0]):
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if DEBUG:
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assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch"
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bfeats = input.feats[input.layout[k]]
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bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
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bfeats = super().forward(bfeats)
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bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
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nfeats[input.layout[k]] = bfeats
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return input.replace(nfeats)
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class SparseLayerNorm(nn.LayerNorm):
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def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
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super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine)
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def forward(self, input: SparseTensor) -> SparseTensor:
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nfeats = torch.zeros_like(input.feats)
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for k in range(input.shape[0]):
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bfeats = input.feats[input.layout[k]]
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bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
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bfeats = super().forward(bfeats)
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bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
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nfeats[input.layout[k]] = bfeats
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return input.replace(nfeats)
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class SparseGroupNorm32(SparseGroupNorm):
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"""
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A GroupNorm layer that converts to float32 before the forward pass.
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"""
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def forward(self, x: SparseTensor) -> SparseTensor:
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return super().forward(x.float()).type(x.dtype)
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class SparseLayerNorm32(SparseLayerNorm):
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"""
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A LayerNorm layer that converts to float32 before the forward pass.
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"""
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def forward(self, x: SparseTensor) -> SparseTensor:
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return super().forward(x.float()).type(x.dtype)
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