107 lines
3.7 KiB
Python
107 lines
3.7 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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import torch
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from se3_transformer.model import SE3Transformer
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from se3_transformer.model.fiber import Fiber
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if __package__ is None or __package__ == '':
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from utils import get_random_graph, assign_relative_pos, get_max_diff, rot
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else:
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from .utils import get_random_graph, assign_relative_pos, get_max_diff, rot
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# Tolerances for equivariance error abs( f(x) @ R - f(x @ R) )
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TOL = 1e-3
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CHANNELS, NODES = 32, 512
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def _get_outputs(model, R):
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feats0 = torch.randn(NODES, CHANNELS, 1)
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feats1 = torch.randn(NODES, CHANNELS, 3)
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coords = torch.randn(NODES, 3)
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graph = get_random_graph(NODES)
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if torch.cuda.is_available():
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feats0 = feats0.cuda()
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feats1 = feats1.cuda()
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R = R.cuda()
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coords = coords.cuda()
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graph = graph.to('cuda')
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model.cuda()
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graph1 = assign_relative_pos(graph, coords)
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out1 = model(graph1, {'0': feats0, '1': feats1}, {})
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graph2 = assign_relative_pos(graph, coords @ R)
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out2 = model(graph2, {'0': feats0, '1': feats1 @ R}, {})
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return out1, out2
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def _get_model(**kwargs):
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return SE3Transformer(
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num_layers=4,
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fiber_in=Fiber.create(2, CHANNELS),
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fiber_hidden=Fiber.create(3, CHANNELS),
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fiber_out=Fiber.create(2, CHANNELS),
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fiber_edge=Fiber({}),
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num_heads=8,
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channels_div=2,
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**kwargs
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)
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def test_equivariance():
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model = _get_model()
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R = rot(*torch.rand(3))
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if torch.cuda.is_available():
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R = R.cuda()
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out1, out2 = _get_outputs(model, R)
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assert torch.allclose(out2['0'], out1['0'], atol=TOL), \
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f'type-0 features should be invariant {get_max_diff(out1["0"], out2["0"])}'
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assert torch.allclose(out2['1'], (out1['1'] @ R), atol=TOL), \
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f'type-1 features should be equivariant {get_max_diff(out1["1"] @ R, out2["1"])}'
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def test_equivariance_pooled():
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model = _get_model(pooling='avg', return_type=1)
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R = rot(*torch.rand(3))
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if torch.cuda.is_available():
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R = R.cuda()
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out1, out2 = _get_outputs(model, R)
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assert torch.allclose(out2, (out1 @ R), atol=TOL), \
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f'type-1 features should be equivariant {get_max_diff(out1 @ R, out2)}'
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def test_invariance_pooled():
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model = _get_model(pooling='avg', return_type=0)
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R = rot(*torch.rand(3))
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if torch.cuda.is_available():
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R = R.cuda()
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out1, out2 = _get_outputs(model, R)
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assert torch.allclose(out2, out1, atol=TOL), \
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f'type-0 features should be invariant {get_max_diff(out1, out2)}'
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