mirror of
https://github.com/aqlaboratory/openfold.git
synced 2026-06-04 12:44:26 +08:00
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import torch
|
|
import numpy as np
|
|
import unittest
|
|
from openfold.model.outer_product_mean import OuterProductMean
|
|
from openfold.utils.tensor_utils import tree_map
|
|
import tests.compare_utils as compare_utils
|
|
from tests.config import consts
|
|
|
|
if compare_utils.alphafold_is_installed():
|
|
alphafold = compare_utils.import_alphafold()
|
|
import jax
|
|
import haiku as hk
|
|
|
|
|
|
class TestOuterProductMean(unittest.TestCase):
|
|
def test_shape(self):
|
|
c = 31
|
|
|
|
opm = OuterProductMean(consts.c_m, consts.c_z, c)
|
|
|
|
m = torch.rand(
|
|
(consts.batch_size, consts.n_seq, consts.n_res, consts.c_m)
|
|
)
|
|
mask = torch.randint(
|
|
0, 2, size=(consts.batch_size, consts.n_seq, consts.n_res)
|
|
)
|
|
m = opm(m, mask=mask, chunk_size=None)
|
|
|
|
self.assertTrue(
|
|
m.shape ==
|
|
(consts.batch_size, consts.n_res, consts.n_res, consts.c_z)
|
|
)
|
|
|
|
@compare_utils.skip_unless_alphafold_installed()
|
|
def test_opm_compare(self):
|
|
def run_opm(msa_act, msa_mask):
|
|
config = compare_utils.get_alphafold_config()
|
|
c_evo = config.model.embeddings_and_evoformer.evoformer
|
|
opm = alphafold.model.modules.OuterProductMean(
|
|
c_evo.outer_product_mean,
|
|
config.model.global_config,
|
|
consts.c_z,
|
|
)
|
|
act = opm(act=msa_act, mask=msa_mask)
|
|
return act
|
|
|
|
f = hk.transform(run_opm)
|
|
|
|
n_res = consts.n_res
|
|
n_seq = consts.n_seq
|
|
c_m = consts.c_m
|
|
|
|
msa_act = np.random.rand(n_seq, n_res, c_m).astype(np.float32) * 100
|
|
msa_mask = np.random.randint(low=0, high=2, size=(n_seq, n_res)).astype(
|
|
np.float32
|
|
)
|
|
|
|
# Fetch pretrained parameters (but only from one block)]
|
|
params = compare_utils.fetch_alphafold_module_weights(
|
|
"alphafold/alphafold_iteration/evoformer/"
|
|
+ "evoformer_iteration/outer_product_mean"
|
|
)
|
|
params = tree_map(lambda n: n[0], params, jax.Array)
|
|
|
|
out_gt = f.apply(params, None, msa_act, msa_mask).block_until_ready()
|
|
out_gt = torch.as_tensor(np.array(out_gt))
|
|
|
|
model = compare_utils.get_global_pretrained_openfold()
|
|
out_repro = (
|
|
model.evoformer.blocks[0]
|
|
.outer_product_mean(
|
|
torch.as_tensor(msa_act).cuda(),
|
|
chunk_size=4,
|
|
mask=torch.as_tensor(msa_mask).cuda(),
|
|
)
|
|
.cpu()
|
|
)
|
|
|
|
# Even when correct, OPM has large, precision-related errors. It gets
|
|
# a special pass from consts.eps.
|
|
compare_utils.assert_max_abs_diff_small(out_gt, out_repro, 5e-4)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|