mirror of
https://github.com/aqlaboratory/openfold.git
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111 lines
2.8 KiB
Python
111 lines
2.8 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import numpy as np
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import unittest
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from openfold.model.embedders import (
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InputEmbedder,
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RecyclingEmbedder,
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TemplateAngleEmbedder,
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TemplatePairEmbedder,
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)
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class TestInputEmbedder(unittest.TestCase):
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def test_shape(self):
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tf_dim = 2
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msa_dim = 3
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c_z = 5
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c_m = 7
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relpos_k = 11
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b = 13
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n_res = 17
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n_clust = 19
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tf = torch.rand((b, n_res, tf_dim))
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ri = torch.rand((b, n_res))
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msa = torch.rand((b, n_clust, n_res, msa_dim))
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ie = InputEmbedder(tf_dim, msa_dim, c_z, c_m, relpos_k)
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msa_emb, pair_emb = ie(tf, ri, msa)
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self.assertTrue(msa_emb.shape == (b, n_clust, n_res, c_m))
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self.assertTrue(pair_emb.shape == (b, n_res, n_res, c_z))
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class TestRecyclingEmbedder(unittest.TestCase):
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def test_shape(self):
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batch_size = 2
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n = 3
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c_z = 5
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c_m = 7
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min_bin = 0
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max_bin = 10
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no_bins = 9
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re = RecyclingEmbedder(c_m, c_z, min_bin, max_bin, no_bins)
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m_1 = torch.rand((batch_size, n, c_m))
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z = torch.rand((batch_size, n, n, c_z))
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x = torch.rand((batch_size, n, 3))
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m_1, z = re(m_1, z, x)
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self.assertTrue(z.shape == (batch_size, n, n, c_z))
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self.assertTrue(m_1.shape == (batch_size, n, c_m))
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class TestTemplateAngleEmbedder(unittest.TestCase):
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def test_shape(self):
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template_angle_dim = 51
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c_m = 256
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batch_size = 4
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n_templ = 4
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n_res = 256
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tae = TemplateAngleEmbedder(
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template_angle_dim,
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c_m,
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)
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x = torch.rand((batch_size, n_templ, n_res, template_angle_dim))
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x = tae(x)
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self.assertTrue(x.shape == (batch_size, n_templ, n_res, c_m))
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class TestTemplatePairEmbedder(unittest.TestCase):
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def test_shape(self):
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batch_size = 2
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n_templ = 3
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n_res = 5
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template_pair_dim = 7
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c_t = 11
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tpe = TemplatePairEmbedder(
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template_pair_dim,
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c_t,
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)
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x = torch.rand((batch_size, n_templ, n_res, n_res, template_pair_dim))
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x = tpe(x)
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self.assertTrue(x.shape == (batch_size, n_templ, n_res, n_res, c_t))
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if __name__ == "__main__":
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unittest.main()
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