Spaces:
Runtime error
Runtime error
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
# | |
# 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. | |
# | |
# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES | |
# SPDX-License-Identifier: MIT | |
from typing import Dict, Literal | |
import torch.nn as nn | |
from dgl import DGLGraph | |
from dgl.nn.pytorch import AvgPooling, MaxPooling | |
from torch import Tensor | |
class GPooling(nn.Module): | |
""" | |
Graph max/average pooling on a given feature type. | |
The average can be taken for any feature type, and equivariance will be maintained. | |
The maximum can only be taken for invariant features (type 0). | |
If you want max-pooling for type > 0 features, look into Vector Neurons. | |
""" | |
def __init__(self, feat_type: int = 0, pool: Literal['max', 'avg'] = 'max'): | |
""" | |
:param feat_type: Feature type to pool | |
:param pool: Type of pooling: max or avg | |
""" | |
super().__init__() | |
assert pool in ['max', 'avg'], f'Unknown pooling: {pool}' | |
assert feat_type == 0 or pool == 'avg', 'Max pooling on type > 0 features will break equivariance' | |
self.feat_type = feat_type | |
self.pool = MaxPooling() if pool == 'max' else AvgPooling() | |
def forward(self, features: Dict[str, Tensor], graph: DGLGraph, **kwargs) -> Tensor: | |
pooled = self.pool(graph, features[str(self.feat_type)]) | |
return pooled.squeeze(dim=-1) | |