54 lines
2.4 KiB
Python
54 lines
2.4 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, Literal
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import torch.nn as nn
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from dgl import DGLGraph
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from dgl.nn.pytorch import AvgPooling, MaxPooling
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from torch import Tensor
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class GPooling(nn.Module):
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"""
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Graph max/average pooling on a given feature type.
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The average can be taken for any feature type, and equivariance will be maintained.
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The maximum can only be taken for invariant features (type 0).
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If you want max-pooling for type > 0 features, look into Vector Neurons.
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"""
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def __init__(self, feat_type: int = 0, pool: Literal['max', 'avg'] = 'max'):
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"""
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:param feat_type: Feature type to pool
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:param pool: Type of pooling: max or avg
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"""
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super().__init__()
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assert pool in ['max', 'avg'], f'Unknown pooling: {pool}'
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assert feat_type == 0 or pool == 'avg', 'Max pooling on type > 0 features will break equivariance'
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self.feat_type = feat_type
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self.pool = MaxPooling() if pool == 'max' else AvgPooling()
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def forward(self, features: Dict[str, Tensor], graph: DGLGraph, **kwargs) -> Tensor:
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pooled = self.pool(graph, features[str(self.feat_type)])
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return pooled.squeeze(dim=-1)
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