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Kyle Taylor
46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
# Copyright 2021 DeepMind Technologies Limited
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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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"""Test enformer model by applying random sequence as input.
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Test:
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$ python enformer_test.py
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"""
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import random
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import unittest
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import enformer
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import numpy as np
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class TestEnformer(unittest.TestCase):
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def test_enformer(self):
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model = enformer.Enformer(channels=1536, num_transformer_layers=11)
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inputs = _get_random_input()
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outputs = model(inputs, is_training=True)
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self.assertEqual(outputs['human'].shape, (1, enformer.TARGET_LENGTH, 5313))
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self.assertEqual(outputs['mouse'].shape, (1, enformer.TARGET_LENGTH, 1643))
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def _get_random_input():
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seq = ''.join(
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[random.choice('ACGT') for _ in range(enformer.SEQUENCE_LENGTH)])
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return np.expand_dims(enformer.one_hot_encode(seq), 0).astype(np.float32)
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if __name__ == '__main__':
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unittest.main()
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