mirror of
https://github.com/aqlaboratory/openfold.git
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93 lines
3.1 KiB
Python
93 lines
3.1 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.pair_transition import PairTransition
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from openfold.utils.tensor_utils import tree_map
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import tests.compare_utils as compare_utils
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from tests.config import consts
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if compare_utils.alphafold_is_installed():
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alphafold = compare_utils.import_alphafold()
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import jax
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import haiku as hk
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class TestPairTransition(unittest.TestCase):
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def test_shape(self):
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c_z = consts.c_z
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n = 4
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pt = PairTransition(c_z, n)
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batch_size = consts.batch_size
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n_res = consts.n_res
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z = torch.rand((batch_size, n_res, n_res, c_z))
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mask = torch.randint(0, 2, size=(batch_size, n_res, n_res))
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shape_before = z.shape
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z = pt(z, mask=mask, chunk_size=None)
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shape_after = z.shape
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self.assertTrue(shape_before == shape_after)
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@compare_utils.skip_unless_alphafold_installed()
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def test_compare(self):
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def run_pair_transition(pair_act, pair_mask):
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config = compare_utils.get_alphafold_config()
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c_e = config.model.embeddings_and_evoformer.evoformer
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pt = alphafold.model.modules.Transition(
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c_e.pair_transition,
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config.model.global_config,
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name="pair_transition",
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)
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act = pt(act=pair_act, mask=pair_mask)
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return act
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f = hk.transform(run_pair_transition)
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n_res = consts.n_res
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pair_act = np.random.rand(n_res, n_res, consts.c_z).astype(np.float32)
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pair_mask = np.ones((n_res, n_res)).astype(np.float32) # no mask
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# Fetch pretrained parameters (but only from one block)]
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params = compare_utils.fetch_alphafold_module_weights(
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"alphafold/alphafold_iteration/evoformer/evoformer_iteration/"
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+ "pair_transition"
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)
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params = tree_map(lambda n: n[0], params, jax.Array)
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out_gt = f.apply(params, None, pair_act, pair_mask).block_until_ready()
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out_gt = torch.as_tensor(np.array(out_gt.block_until_ready()))
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model = compare_utils.get_global_pretrained_openfold()
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out_repro = (
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model.evoformer.blocks[0].pair_stack
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.pair_transition(
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torch.as_tensor(pair_act, dtype=torch.float32).cuda(),
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chunk_size=4,
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mask=torch.as_tensor(pair_mask, dtype=torch.float32).cuda(),
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)
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.cpu()
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)
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self.assertTrue(torch.max(torch.abs(out_gt - out_repro) < consts.eps))
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if __name__ == "__main__":
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unittest.main()
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