60 lines
2.6 KiB
Python
60 lines
2.6 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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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
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# THE 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
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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# DEALINGS IN THE SOFTWARE.
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#
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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
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# SPDX-License-Identifier: MIT
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from typing import Dict
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import numpy as np
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import torch
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import torch.nn as nn
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from torch import Tensor
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from se3_transformer.model.fiber import Fiber
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class LinearSE3(nn.Module):
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"""
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Graph Linear SE(3)-equivariant layer, equivalent to a 1x1 convolution.
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Maps a fiber to a fiber with the same degrees (channels may be different).
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No interaction between degrees, but interaction between channels.
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type-0 features (C_0 channels) ────> Linear(bias=False) ────> type-0 features (C'_0 channels)
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type-1 features (C_1 channels) ────> Linear(bias=False) ────> type-1 features (C'_1 channels)
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:
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type-k features (C_k channels) ────> Linear(bias=False) ────> type-k features (C'_k channels)
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"""
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def __init__(self, fiber_in: Fiber, fiber_out: Fiber):
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super().__init__()
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self.weights = nn.ParameterDict({
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str(degree_out): nn.Parameter(
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torch.randn(channels_out, fiber_in[degree_out]) / np.sqrt(fiber_in[degree_out]))
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for degree_out, channels_out in fiber_out
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})
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def forward(self, features: Dict[str, Tensor], *args, **kwargs) -> Dict[str, Tensor]:
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return {
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degree: self.weights[degree] @ features[degree]
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for degree, weight in self.weights.items()
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}
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