145 lines
5.8 KiB
Python
145 lines
5.8 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 collections import namedtuple
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from itertools import product
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from typing import Dict
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import torch
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from torch import Tensor
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from se3_transformer.runtime.utils import degree_to_dim
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FiberEl = namedtuple('FiberEl', ['degree', 'channels'])
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class Fiber(dict):
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"""
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Describes the structure of some set of features.
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Features are split into types (0, 1, 2, 3, ...). A feature of type k has a dimension of 2k+1.
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Type-0 features: invariant scalars
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Type-1 features: equivariant 3D vectors
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Type-2 features: equivariant symmetric traceless matrices
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...
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As inputs to a SE3 layer, there can be many features of the same types, and many features of different types.
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The 'multiplicity' or 'number of channels' is the number of features of a given type.
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This class puts together all the degrees and their multiplicities in order to describe
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the inputs, outputs or hidden features of SE3 layers.
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"""
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def __init__(self, structure):
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if isinstance(structure, dict):
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structure = [FiberEl(int(d), int(m)) for d, m in sorted(structure.items(), key=lambda x: x[1])]
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elif not isinstance(structure[0], FiberEl):
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structure = list(map(lambda t: FiberEl(*t), sorted(structure, key=lambda x: x[1])))
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self.structure = structure
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super().__init__({d: m for d, m in self.structure})
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@property
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def degrees(self):
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return sorted([t.degree for t in self.structure])
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@property
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def channels(self):
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return [self[d] for d in self.degrees]
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@property
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def num_features(self):
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""" Size of the resulting tensor if all features were concatenated together """
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return sum(t.channels * degree_to_dim(t.degree) for t in self.structure)
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@staticmethod
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def create(num_degrees: int, num_channels: int):
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""" Create a Fiber with degrees 0..num_degrees-1, all with the same multiplicity """
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return Fiber([(degree, num_channels) for degree in range(num_degrees)])
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@staticmethod
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def from_features(feats: Dict[str, Tensor]):
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""" Infer the Fiber structure from a feature dict """
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structure = {}
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for k, v in feats.items():
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degree = int(k)
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assert len(v.shape) == 3, 'Feature shape should be (N, C, 2D+1)'
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assert v.shape[-1] == degree_to_dim(degree)
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structure[degree] = v.shape[-2]
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return Fiber(structure)
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def __getitem__(self, degree: int):
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""" fiber[degree] returns the multiplicity for this degree """
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return dict(self.structure).get(degree, 0)
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def __iter__(self):
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""" Iterate over namedtuples (degree, channels) """
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return iter(self.structure)
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def __mul__(self, other):
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"""
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If other in an int, multiplies all the multiplicities by other.
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If other is a fiber, returns the cartesian product.
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"""
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if isinstance(other, Fiber):
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return product(self.structure, other.structure)
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elif isinstance(other, int):
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return Fiber({t.degree: t.channels * other for t in self.structure})
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def __add__(self, other):
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"""
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If other in an int, add other to all the multiplicities.
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If other is a fiber, add the multiplicities of the fibers together.
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"""
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if isinstance(other, Fiber):
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return Fiber({t.degree: t.channels + other[t.degree] for t in self.structure})
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elif isinstance(other, int):
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return Fiber({t.degree: t.channels + other for t in self.structure})
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def __repr__(self):
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return str(self.structure)
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@staticmethod
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def combine_max(f1, f2):
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""" Combine two fiber by taking the maximum multiplicity for each degree in both fibers """
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new_dict = dict(f1.structure)
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for k, m in f2.structure:
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new_dict[k] = max(new_dict.get(k, 0), m)
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return Fiber(list(new_dict.items()))
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@staticmethod
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def combine_selectively(f1, f2):
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""" Combine two fiber by taking the sum of multiplicities for each degree in the first fiber """
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# only use orders which occur in fiber f1
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new_dict = dict(f1.structure)
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for k in f1.degrees:
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if k in f2.degrees:
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new_dict[k] += f2[k]
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return Fiber(list(new_dict.items()))
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def to_attention_heads(self, tensors: Dict[str, Tensor], num_heads: int):
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# dict(N, num_channels, 2d+1) -> (N, num_heads, -1)
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fibers = [tensors[str(degree)].reshape(*tensors[str(degree)].shape[:-2], num_heads, -1) for degree in
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self.degrees]
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fibers = torch.cat(fibers, -1)
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return fibers
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