mirror of
https://github.com/baker-laboratory/RoseTTAFold-All-Atom.git
synced 2024-11-24 22:37:20 +00:00
Rohith Krishna
GitHub
commit
d96e013a54
17 changed files with 426 additions and 172 deletions
2
examples/protein/3fap_A.fasta
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examples/protein/3fap_A.fasta
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>3FAP_1|Chain A|FK506-BINDING PROTEIN|Homo sapiens (9606)
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GVQVETISPGDGRTFPKRGQTCVVHYTGMLEDGKKFDSSRDRNKPFKFMLGKQEVIRGWEEGVAQMSVGQRAKLTISPDYAYGATGHPGIIPPHATLVFDVELLKLE
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examples/protein/3fap_B.fasta
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examples/protein/3fap_B.fasta
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>3FAP_2|Chain B|FKBP12-RAPAMYCIN ASSOCIATED PROTEIN|Homo sapiens (9606)
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VAILWHEMWHEGLEEASRLYFGERNVKGMFEVLEPLHAMMERGPQTLKETSFNQAYGRDLMEAQEWCRKYMKSGNVKDLTQAWDLYYHVFRRIS
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322
examples/small_molecule/ARD_ideal.sdf
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examples/small_molecule/ARD_ideal.sdf
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ARD
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-OEChem-02232415173D
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150154 0 1 0 0 0 0 0999 V2000
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||||||
|
39 42 1 0 0 0 0
|
||||||
|
39110 1 0 0 0 0
|
||||||
|
40 41 1 0 0 0 0
|
||||||
|
41111 1 0 0 0 0
|
||||||
|
41112 1 0 0 0 0
|
||||||
|
41113 1 0 0 0 0
|
||||||
|
42 43 1 0 0 0 0
|
||||||
|
42 44 1 0 0 0 0
|
||||||
|
42114 1 0 0 0 0
|
||||||
|
43115 1 0 0 0 0
|
||||||
|
44 45 1 0 0 0 0
|
||||||
|
44 46 2 0 0 0 0
|
||||||
|
45116 1 0 0 0 0
|
||||||
|
45117 1 0 0 0 0
|
||||||
|
45118 1 0 0 0 0
|
||||||
|
46 47 1 0 0 0 0
|
||||||
|
46119 1 0 0 0 0
|
||||||
|
47 48 1 0 0 0 0
|
||||||
|
47 49 1 0 0 0 0
|
||||||
|
47120 1 0 0 0 0
|
||||||
|
48121 1 0 0 0 0
|
||||||
|
48122 1 0 0 0 0
|
||||||
|
48123 1 0 0 0 0
|
||||||
|
49 50 2 0 0 0 0
|
||||||
|
49 51 1 0 0 0 0
|
||||||
|
51 52 1 0 0 0 0
|
||||||
|
51124 1 0 0 0 0
|
||||||
|
51125 1 0 0 0 0
|
||||||
|
52 53 1 0 0 0 0
|
||||||
|
52126 1 0 0 0 0
|
||||||
|
53 54 1 0 0 0 0
|
||||||
|
53 55 1 0 0 0 0
|
||||||
|
53127 1 0 0 0 0
|
||||||
|
54128 1 0 0 0 0
|
||||||
|
54129 1 0 0 0 0
|
||||||
|
54130 1 0 0 0 0
|
||||||
|
55 56 1 0 0 0 0
|
||||||
|
55131 1 0 0 0 0
|
||||||
|
55132 1 0 0 0 0
|
||||||
|
56 57 1 0 0 0 0
|
||||||
|
56 64 1 0 0 0 0
|
||||||
|
56133 1 0 0 0 0
|
||||||
|
57 58 1 0 0 0 0
|
||||||
|
57134 1 0 0 0 0
|
||||||
|
57135 1 0 0 0 0
|
||||||
|
58 59 1 0 0 0 0
|
||||||
|
58 61 1 0 0 0 0
|
||||||
|
58136 1 0 0 0 0
|
||||||
|
59 60 1 0 0 0 0
|
||||||
|
60137 1 0 0 0 0
|
||||||
|
60138 1 0 0 0 0
|
||||||
|
60139 1 0 0 0 0
|
||||||
|
61 62 1 0 0 0 0
|
||||||
|
61 63 1 0 0 0 0
|
||||||
|
61140 1 0 0 0 0
|
||||||
|
62141 1 0 0 0 0
|
||||||
|
63 64 1 0 0 0 0
|
||||||
|
63142 1 0 0 0 0
|
||||||
|
63143 1 0 0 0 0
|
||||||
|
64144 1 0 0 0 0
|
||||||
|
64145 1 0 0 0 0
|
||||||
|
65 66 2 0 0 0 0
|
||||||
|
65 68 1 0 0 0 0
|
||||||
|
65 69 1 0 0 0 0
|
||||||
|
66 67 1 0 0 0 0
|
||||||
|
66146 1 0 0 0 0
|
||||||
|
67147 1 0 0 0 0
|
||||||
|
68148 1 0 0 0 0
|
||||||
|
68149 1 0 0 0 0
|
||||||
|
68150 1 0 0 0 0
|
||||||
|
M END
|
||||||
|
> <OPENEYE_ISO_SMILES>
|
||||||
|
Cc1ccc(s1)[C@@H]\2C[C@@H]3CC[C@H]([C@@](O3)(C(=O)C(=O)N4CCCC[C@H]4C(=O)O[C@@H](CC(=O)[C@@H](/C=C(/[C@H]([C@H](C(=O)[C@@H](C[C@@H](/C=C/C=C/C=C2\C)C)C)OC)O)\C)C)[C@H](C)C[C@@H]5CC[C@H]([C@@H](C5)OC)O)O)C
|
||||||
|
|
||||||
|
> <OPENEYE_INCHI>
|
||||||
|
InChI=1S/C55H81NO12S/c1-32-16-12-11-13-17-33(2)42(48-24-20-39(8)69-48)30-41-22-19-38(7)55(64,68-41)52(61)53(62)56-25-15-14-18-43(56)54(63)67-46(35(4)28-40-21-23-44(57)47(29-40)65-9)31-45(58)34(3)27-37(6)50(60)51(66-10)49(59)36(5)26-32/h11-13,16-17,20,24,27,32,34-36,38,40-44,46-47,50-51,57,60,64H,14-15,18-19,21-23,25-26,28-31H2,1-10H3/b13-11+,16-12+,33-17+,37-27+/t32-,34-,35-,36-,38-,40+,41+,42-,43+,44-,46+,47-,50-,51+,55-/m1/s1
|
||||||
|
|
||||||
|
> <OPENEYE_INCHIKEY>
|
||||||
|
SDSGJAIFUCCAOV-MSLSVLDMSA-N
|
||||||
|
|
||||||
|
> <FORMULA>
|
||||||
|
C55H81NO12S
|
||||||
|
|
||||||
|
$$$$
|
||||||
14
rf2aa/config/inference/protein_complex_sm.yaml
Normal file
14
rf2aa/config/inference/protein_complex_sm.yaml
Normal file
|
|
@ -0,0 +1,14 @@
|
||||||
|
defaults:
|
||||||
|
- base
|
||||||
|
job_name: "3fap"
|
||||||
|
|
||||||
|
protein_inputs:
|
||||||
|
A:
|
||||||
|
fasta_file: examples/protein/3fap_A.fasta
|
||||||
|
B:
|
||||||
|
fasta_file: examples/protein/3fap_B.fasta
|
||||||
|
|
||||||
|
sm_inputs:
|
||||||
|
C:
|
||||||
|
input: examples/small_molecule/ARD_ideal.sdf
|
||||||
|
input_type: "sdf"
|
||||||
|
|
@ -28,6 +28,10 @@ class RawInputData:
|
||||||
def query_sequence(self):
|
def query_sequence(self):
|
||||||
return self.msa[0]
|
return self.msa[0]
|
||||||
|
|
||||||
|
def sequence_string(self):
|
||||||
|
three_letter_sequence = [ChemData().num2aa[num] for num in self.query_sequence()]
|
||||||
|
return "".join([ChemData().aa_321[three] for three in three_letter_sequence])
|
||||||
|
|
||||||
def is_atom(self):
|
def is_atom(self):
|
||||||
return is_atom(self.query_sequence())
|
return is_atom(self.query_sequence())
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -744,7 +744,7 @@ def load_minimal_multi_msa(hash_list, taxid_list, Ls, params):
|
||||||
return a3m_out, hashes_out, Ls_out
|
return a3m_out, hashes_out, Ls_out
|
||||||
|
|
||||||
|
|
||||||
def expand_multi_msa(a3m, hashes_in, hashes_out, Ls_in, Ls_out, params):
|
def expand_multi_msa(a3m, hashes_in, hashes_out, Ls_in, Ls_out):
|
||||||
"""Expands a multi-MSA of unique chains into an MSA of a
|
"""Expands a multi-MSA of unique chains into an MSA of a
|
||||||
hetero-homo-oligomer in which some chains appear more than once. The query
|
hetero-homo-oligomer in which some chains appear more than once. The query
|
||||||
sequences (1st sequence of MSA) are concatenated directly along the
|
sequences (1st sequence of MSA) are concatenated directly along the
|
||||||
|
|
|
||||||
|
|
@ -1,6 +1,7 @@
|
||||||
import torch
|
import torch
|
||||||
|
from hashlib import md5
|
||||||
|
|
||||||
from rf2aa.data.data_loader_utils import merge_a3m_hetero, merge_a3m_homo, merge_hetero_templates, get_term_feats
|
from rf2aa.data.data_loader_utils import merge_a3m_hetero, merge_a3m_homo, merge_hetero_templates, get_term_feats, join_msas_by_taxid, expand_multi_msa
|
||||||
from rf2aa.data.data_loader import RawInputData
|
from rf2aa.data.data_loader import RawInputData
|
||||||
from rf2aa.util import center_and_realign_missing, same_chain_from_bond_feats, random_rot_trans, idx_from_Ls
|
from rf2aa.util import center_and_realign_missing, same_chain_from_bond_feats, random_rot_trans, idx_from_Ls
|
||||||
|
|
||||||
|
|
@ -18,7 +19,71 @@ def merge_protein_inputs(protein_inputs, deterministic: bool = False):
|
||||||
# handle merging MSAs and such
|
# handle merging MSAs and such
|
||||||
# first determine which sequence are identical, then which one have mergeable MSAs
|
# first determine which sequence are identical, then which one have mergeable MSAs
|
||||||
# then cat the templates, other feats
|
# then cat the templates, other feats
|
||||||
pass
|
else:
|
||||||
|
a3m_list = [
|
||||||
|
{"msa": input.msa,
|
||||||
|
"ins": input.ins,
|
||||||
|
"taxid": input.taxids
|
||||||
|
}
|
||||||
|
for input in protein_inputs.values()
|
||||||
|
]
|
||||||
|
hash_list = [md5(input.sequence_string().encode()).hexdigest() for input in protein_inputs.values()]
|
||||||
|
lengths_list = [input.length() for input in protein_inputs.values()]
|
||||||
|
|
||||||
|
seen = set()
|
||||||
|
unique_indices = []
|
||||||
|
for idx, hash in enumerate(hash_list):
|
||||||
|
if hash not in seen:
|
||||||
|
unique_indices.append(idx)
|
||||||
|
seen.add(hash)
|
||||||
|
|
||||||
|
unique_a3m = [a3m for i, a3m in enumerate(a3m_list) if i in unique_indices ]
|
||||||
|
unique_hashes = [value for index, value in enumerate(hash_list) if index in unique_indices]
|
||||||
|
unique_lengths_list = [value for index, value in enumerate(lengths_list) if index in unique_indices]
|
||||||
|
|
||||||
|
if len(unique_a3m) >1:
|
||||||
|
a3m_out = unique_a3m[0]
|
||||||
|
for i in range(1, len(unique_a3m)):
|
||||||
|
a3m_out = join_msas_by_taxid(a3m_out, a3m_list[i])
|
||||||
|
a3m_out = expand_multi_msa(a3m_out, unique_hashes, hash_list, unique_lengths_list, lengths_list)
|
||||||
|
else:
|
||||||
|
a3m = unique_a3m[0]
|
||||||
|
msa, ins = a3m["msa"], a3m["ins"]
|
||||||
|
a3m_out = merge_a3m_homo(msa, ins, len(hash_list))
|
||||||
|
|
||||||
|
# merge templates
|
||||||
|
max_template_dim = max([input.xyz_t.shape[0] for input in protein_inputs.values()])
|
||||||
|
xyz_t_list = [input.xyz_t for input in protein_inputs.values()]
|
||||||
|
mask_t_list = [input.mask_t for input in protein_inputs.values()]
|
||||||
|
t1d_list = [input.t1d for input in protein_inputs.values()]
|
||||||
|
ids = ["inference"] * len(t1d_list)
|
||||||
|
xyz_t, t1d, mask_t, _ = merge_hetero_templates(xyz_t_list, t1d_list, mask_t_list, ids, lengths_list, deterministic=deterministic)
|
||||||
|
|
||||||
|
atom_frames = torch.zeros(0,3,2)
|
||||||
|
chirals = torch.zeros(0,5)
|
||||||
|
|
||||||
|
|
||||||
|
L_total = sum(lengths_list)
|
||||||
|
bond_feats = torch.zeros((L_total, L_total)).long()
|
||||||
|
offset = 0
|
||||||
|
for bf in [input.bond_feats for input in protein_inputs.values()]:
|
||||||
|
L = bf.shape[0]
|
||||||
|
bond_feats[offset:offset+L, offset:offset+L] = bf
|
||||||
|
offset += L
|
||||||
|
chain_lengths = list(zip(protein_inputs.keys(), lengths_list))
|
||||||
|
|
||||||
|
merged_input = RawInputData(
|
||||||
|
a3m_out["msa"],
|
||||||
|
a3m_out["ins"],
|
||||||
|
bond_feats,
|
||||||
|
xyz_t[:max_template_dim],
|
||||||
|
mask_t[:max_template_dim],
|
||||||
|
t1d[:max_template_dim],
|
||||||
|
chirals,
|
||||||
|
atom_frames,
|
||||||
|
taxids=None
|
||||||
|
)
|
||||||
|
return merged_input, chain_lengths
|
||||||
|
|
||||||
def merge_na_inputs(na_inputs):
|
def merge_na_inputs(na_inputs):
|
||||||
# should just be trivially catting features
|
# should just be trivially catting features
|
||||||
|
|
@ -101,14 +166,6 @@ def merge_all(
|
||||||
deterministic: bool = False,
|
deterministic: bool = False,
|
||||||
):
|
):
|
||||||
|
|
||||||
#protein_lengths = [protein_input.length() for protein_input in protein_inputs.values()]
|
|
||||||
#na_lengths = [na_input.length() for na_input in na_inputs.values()]
|
|
||||||
#sm_lengths = [sm_input.length() for sm_input in sm_inputs.values()]
|
|
||||||
#all_lengths = protein_lengths + na_lengths + sm_lengths
|
|
||||||
|
|
||||||
#term_info = get_term_feats(all_lengths)
|
|
||||||
#term_info[sum(protein_lengths):, :] = 0
|
|
||||||
|
|
||||||
protein_inputs, protein_chain_lengths = merge_protein_inputs(protein_inputs, deterministic=deterministic)
|
protein_inputs, protein_chain_lengths = merge_protein_inputs(protein_inputs, deterministic=deterministic)
|
||||||
|
|
||||||
na_inputs, na_chain_lengths = merge_na_inputs(na_inputs)
|
na_inputs, na_chain_lengths = merge_na_inputs(na_inputs)
|
||||||
|
|
|
||||||
|
|
@ -414,18 +414,21 @@ def parse_a3m(filename, maxseq=8000, paired=False):
|
||||||
else:
|
else:
|
||||||
fstream = open(filename, 'r')
|
fstream = open(filename, 'r')
|
||||||
|
|
||||||
for line in fstream:
|
for i, line in enumerate(fstream):
|
||||||
|
|
||||||
# skip labels
|
# skip labels
|
||||||
if line[0] == '>':
|
if line[0] == '>':
|
||||||
if paired: # paired MSAs only have a TAXID in the fasta header
|
if paired: # paired MSAs only have a TAXID in the fasta header
|
||||||
taxIDs.append(line[1:].strip())
|
taxIDs.append(line[1:].strip())
|
||||||
else: # unpaired MSAs have all the metadata so use regex to pull out TAXID
|
else: # unpaired MSAs have all the metadata so use regex to pull out TAXID
|
||||||
|
if i == 0:
|
||||||
|
taxIDs.append("query")
|
||||||
|
else:
|
||||||
match = re.search( r'TaxID=(\d+)', line)
|
match = re.search( r'TaxID=(\d+)', line)
|
||||||
if match:
|
if match:
|
||||||
taxIDs.append(match.group(1))
|
taxIDs.append(match.group(1))
|
||||||
else:
|
else:
|
||||||
taxIDs.append("query") # query sequence
|
taxIDs.append("") # query sequence
|
||||||
continue
|
continue
|
||||||
|
|
||||||
# remove right whitespaces
|
# remove right whitespaces
|
||||||
|
|
|
||||||
|
|
@ -8,11 +8,12 @@ import subprocess
|
||||||
|
|
||||||
def make_msa(
|
def make_msa(
|
||||||
fasta_file,
|
fasta_file,
|
||||||
|
chain,
|
||||||
model_runner
|
model_runner
|
||||||
):
|
):
|
||||||
out_dir_base = Path(model_runner.config.output_path)
|
out_dir_base = Path(model_runner.config.output_path)
|
||||||
hash = model_runner.config.job_name
|
hash = model_runner.config.job_name
|
||||||
out_dir = out_dir_base / hash
|
out_dir = out_dir_base / hash / chain
|
||||||
out_dir.mkdir(parents=True, exist_ok=True)
|
out_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
command = model_runner.config.database_params.command
|
command = model_runner.config.database_params.command
|
||||||
|
|
|
||||||
|
|
@ -88,6 +88,6 @@ def load_protein(msa_file, hhr_fn, atab_fn, model_runner):
|
||||||
taxids=taxIDs,
|
taxids=taxIDs,
|
||||||
)
|
)
|
||||||
|
|
||||||
def generate_msa_and_load_protein(fasta_file, model_runner):
|
def generate_msa_and_load_protein(fasta_file, chain, model_runner):
|
||||||
msa_file, hhr_file, atab_file = make_msa(fasta_file, model_runner)
|
msa_file, hhr_file, atab_file = make_msa(fasta_file, chain, model_runner)
|
||||||
return load_protein(str(msa_file), str(hhr_file), str(atab_file), model_runner)
|
return load_protein(str(msa_file), str(hhr_file), str(atab_file), model_runner)
|
||||||
|
|
|
||||||
|
|
@ -45,6 +45,7 @@ class ModelRunner:
|
||||||
chains.append(chain)
|
chains.append(chain)
|
||||||
protein_input = generate_msa_and_load_protein(
|
protein_input = generate_msa_and_load_protein(
|
||||||
self.config.protein_inputs[chain]["fasta_file"],
|
self.config.protein_inputs[chain]["fasta_file"],
|
||||||
|
chain,
|
||||||
self
|
self
|
||||||
)
|
)
|
||||||
protein_inputs[chain] = protein_input
|
protein_inputs[chain] = protein_input
|
||||||
|
|
|
||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -1,79 +0,0 @@
|
||||||
import torch
|
|
||||||
import pandas as pd
|
|
||||||
import numpy as np
|
|
||||||
import itertools
|
|
||||||
from collections import OrderedDict
|
|
||||||
from hydra import initialize, compose
|
|
||||||
|
|
||||||
from rf2aa.setup_model import trainer_factory, seed_all
|
|
||||||
from rf2aa.chemical import ChemicalData as ChemData
|
|
||||||
|
|
||||||
# configurations to test
|
|
||||||
configs = ["legacy_train"]
|
|
||||||
datasets = ["compl", "na_compl", "rna", "sm_compl", "sm_compl_covale", "sm_compl_asmb"]
|
|
||||||
|
|
||||||
cfg_overrides = [
|
|
||||||
"loader_params.p_msa_mask=0.0",
|
|
||||||
"loader_params.crop=100000",
|
|
||||||
"loader_params.mintplt=0",
|
|
||||||
"loader_params.maxtplt=2"
|
|
||||||
]
|
|
||||||
|
|
||||||
def make_deterministic(seed=0):
|
|
||||||
seed_all(seed)
|
|
||||||
if torch.cuda.is_available():
|
|
||||||
torch.backends.cudnn.deterministic = True
|
|
||||||
torch.backends.cudnn.benchmark = False
|
|
||||||
|
|
||||||
def setup_dataset_names():
|
|
||||||
data = {}
|
|
||||||
for name in datasets:
|
|
||||||
data[name] = [name]
|
|
||||||
return data
|
|
||||||
|
|
||||||
# set up models for regression tests
|
|
||||||
def setup_models(device="cpu"):
|
|
||||||
models, chem_cfgs = [], []
|
|
||||||
for config in configs:
|
|
||||||
with initialize(version_base=None, config_path="../config/train"):
|
|
||||||
cfg = compose(config_name=config, overrides=cfg_overrides)
|
|
||||||
|
|
||||||
# initializing the model needs the chemical DB initialized. Force a reload
|
|
||||||
ChemData.reset()
|
|
||||||
ChemData(cfg.chem_params)
|
|
||||||
|
|
||||||
trainer = trainer_factory[cfg.experiment.trainer](cfg)
|
|
||||||
seed_all()
|
|
||||||
trainer.construct_model(device=device)
|
|
||||||
models.append(trainer.model)
|
|
||||||
chem_cfgs.append(cfg.chem_params)
|
|
||||||
trainer = None
|
|
||||||
|
|
||||||
return dict(zip(configs, (zip(configs, models, chem_cfgs))))
|
|
||||||
|
|
||||||
# set up job array for regression
|
|
||||||
def setup_array(datasets, models, device="cpu"):
|
|
||||||
test_data = setup_dataset_names()
|
|
||||||
test_models = setup_models(device=device)
|
|
||||||
test_data = [test_data[dataset] for dataset in datasets]
|
|
||||||
test_models = [test_models[model] for model in models]
|
|
||||||
return (list(itertools.product(test_data, test_models)))
|
|
||||||
|
|
||||||
def random_param_init(model):
|
|
||||||
seed_all()
|
|
||||||
with torch.no_grad():
|
|
||||||
fake_state_dict = OrderedDict()
|
|
||||||
for name, param in model.model.named_parameters():
|
|
||||||
fake_state_dict[name] = torch.randn_like(param)
|
|
||||||
model.model.load_state_dict(fake_state_dict)
|
|
||||||
model.shadow.load_state_dict(fake_state_dict)
|
|
||||||
return model
|
|
||||||
|
|
||||||
def dataset_pickle_path(dataset_name):
|
|
||||||
return f"test_pickles/data/{dataset_name}_regression.pt"
|
|
||||||
|
|
||||||
def model_pickle_path(dataset_name, model_name):
|
|
||||||
return f"test_pickles/model/{model_name}_{dataset_name}_regression.pt"
|
|
||||||
|
|
||||||
def loss_pickle_path(dataset_name, model_name, loss_name):
|
|
||||||
return f"test_pickles/loss/{loss_name}_{model_name}_{dataset_name}_regression.pt"
|
|
||||||
|
|
@ -1,73 +0,0 @@
|
||||||
import os
|
|
||||||
import torch
|
|
||||||
import pytest
|
|
||||||
import warnings
|
|
||||||
warnings.filterwarnings("ignore")
|
|
||||||
|
|
||||||
from rf2aa.data.dataloader_adaptor import prepare_input
|
|
||||||
from rf2aa.training.recycling import run_model_forward_legacy
|
|
||||||
from rf2aa.tensor_util import assert_equal
|
|
||||||
from rf2aa.tests.test_conditions import setup_array,\
|
|
||||||
make_deterministic, dataset_pickle_path, model_pickle_path
|
|
||||||
from rf2aa.util_module import XYZConverter
|
|
||||||
from rf2aa.chemical import ChemicalData as ChemData
|
|
||||||
|
|
||||||
|
|
||||||
# goal is to test all the configs on a broad set of datasets
|
|
||||||
|
|
||||||
gpu = "cuda:0" if torch.cuda.is_available() else "cpu"
|
|
||||||
|
|
||||||
legacy_test_conditions = setup_array(["na_compl", "rna", "sm_compl", "sm_compl_covale"], ["legacy_train"], device=gpu)
|
|
||||||
|
|
||||||
@pytest.mark.parametrize("example,model", legacy_test_conditions)
|
|
||||||
def test_regression_legacy(example, model):
|
|
||||||
dataset_name, dataset_inputs, model_name, model = setup_test(example, model)
|
|
||||||
make_deterministic()
|
|
||||||
output_i = run_model_forward_legacy(model, dataset_inputs, gpu)
|
|
||||||
model_pickle = model_pickle_path(dataset_name, model_name)
|
|
||||||
output_names = ("logits_c6d", "logits_aa", "logits_pae", \
|
|
||||||
"logits_pde", "p_bind", "xyz", "alpha", "xyz_allatom", \
|
|
||||||
"lddt", "seq", "pair", "state")
|
|
||||||
|
|
||||||
if not os.path.exists(model_pickle):
|
|
||||||
torch.save(output_i, model_pickle)
|
|
||||||
else:
|
|
||||||
output_regression = torch.load(model_pickle, map_location=gpu)
|
|
||||||
for idx, output in enumerate(output_i):
|
|
||||||
got = output
|
|
||||||
want = output_regression[idx]
|
|
||||||
if output_names[idx] == "logits_c6d":
|
|
||||||
for i in range(len(want)):
|
|
||||||
|
|
||||||
got_i = got[i]
|
|
||||||
want_i = want[i]
|
|
||||||
try:
|
|
||||||
assert_equal(got_i, want_i)
|
|
||||||
except Exception as e:
|
|
||||||
raise ValueError(f"{output_names[idx]} not same for model: {model_name} on dataset: {dataset_name}") from e
|
|
||||||
elif output_names[idx] in ["alpha", "xyz_allatom", "seq", "pair", "state"]:
|
|
||||||
try:
|
|
||||||
assert torch.allclose(got, want, atol=1e-4)
|
|
||||||
except Exception as e:
|
|
||||||
raise ValueError(f"{output_names[idx]} not same for model: {model_name} on dataset: {dataset_name}") from e
|
|
||||||
else:
|
|
||||||
try:
|
|
||||||
assert_equal(got, want)
|
|
||||||
except Exception as e:
|
|
||||||
raise ValueError(f"{output_names[idx]} not same for model: {model_name} on dataset: {dataset_name}") from e
|
|
||||||
|
|
||||||
def setup_test(example, model):
|
|
||||||
model_name, model, config = model
|
|
||||||
|
|
||||||
# initialize chemical database
|
|
||||||
ChemData.reset() # force reload chemical data
|
|
||||||
ChemData(config)
|
|
||||||
|
|
||||||
model = model.to(gpu)
|
|
||||||
dataset_name = example[0]
|
|
||||||
dataloader_inputs = torch.load(dataset_pickle_path(dataset_name), map_location=gpu)
|
|
||||||
xyz_converter = XYZConverter().to(gpu)
|
|
||||||
task, item, network_input, true_crds, mask_crds, msa, mask_msa, unclamp, \
|
|
||||||
negative, symmRs, Lasu, ch_label = prepare_input(dataloader_inputs,xyz_converter, gpu)
|
|
||||||
return dataset_name, network_input, model_name, model
|
|
||||||
|
|
||||||
Loading…
Reference in a new issue