mirror of
https://github.com/aqlaboratory/openfold.git
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96 lines
3.0 KiB
Python
96 lines
3.0 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 os
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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 pathlib import Path
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from tests.config import consts
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from openfold.config import model_config
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from openfold.model.model import AlphaFold
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from openfold.utils.import_weights import import_jax_weights_, import_openfold_weights_
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class TestImportWeights(unittest.TestCase):
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def test_import_jax_weights_(self):
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npz_path = Path(__file__).parent.resolve() / f"../openfold/resources/params/params_{consts.model}.npz"
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c = model_config(consts.model)
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c.globals.blocks_per_ckpt = None
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model = AlphaFold(c)
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model.eval()
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import_jax_weights_(
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model,
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npz_path,
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version=consts.model
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)
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data = np.load(npz_path)
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prefix = "alphafold/alphafold_iteration/"
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test_pairs = [
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# Normal linear weight
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(
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torch.as_tensor(
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data[
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prefix + "structure_module/initial_projection//weights"
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]
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).transpose(-1, -2),
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model.structure_module.linear_in.weight,
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),
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# Normal layer norm param
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(
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torch.as_tensor(
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data[prefix + "evoformer/prev_pair_norm//offset"],
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),
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model.recycling_embedder.layer_norm_z.bias,
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),
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# From a stack
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(
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torch.as_tensor(
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data[
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prefix
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+ (
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"evoformer/evoformer_iteration/outer_product_mean/"
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"left_projection//weights"
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)
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][1].transpose(-1, -2)
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),
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model.evoformer.blocks[1].outer_product_mean.linear_1.weight,
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),
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]
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for w_alpha, w_repro in test_pairs:
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self.assertTrue(torch.all(w_alpha == w_repro))
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def test_import_openfold_weights_(self):
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model_name = 'initial_training'
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pt_path = Path(__file__).parent.resolve() / f"../openfold/resources/openfold_params/{model_name}.pt"
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if os.path.exists(pt_path):
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c = model_config(model_name)
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c.globals.blocks_per_ckpt = None
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model = AlphaFold(c)
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model.eval()
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d = torch.load(pt_path)
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import_openfold_weights_(
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model=model,
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state_dict=d,
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)
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