Initial commit: RoseTTAFold-All-Atom configured for Wes with Harbor images and s3:// paths

rf2aa/SE3Transformer/se3_transformer/model/layers/pooling.py

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+# 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)