Hugging Face's logo

Spaces:
MLSB
/
DockFormerPP
Running

App Files Community
DockFormerPP
File size: 6,722 Bytes 
0fdcb79
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
 
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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 sys

from env_consts import TEST_INPUT_DIR, TEST_OUTPUT_DIR, CKPT_PATH
import json
import logging
import numpy as np
import os
import pickle

from dockformerpp.data.data_modules import OpenFoldSingleDataset

logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(level=logging.INFO)

import torch
torch_versions = torch.__version__.split(".")
torch_major_version = int(torch_versions[0])
torch_minor_version = int(torch_versions[1])
if (
    torch_major_version > 1 or
    (torch_major_version == 1 and torch_minor_version >= 12)
):
    # Gives a large speedup on Ampere-class GPUs
    torch.set_float32_matmul_precision("high")

torch.set_grad_enabled(False)

from dockformerpp.config import model_config
from dockformerpp.utils.script_utils import (load_models_from_command_line, run_model, save_output_structure,
                                             get_latest_checkpoint)
from dockformerpp.utils.tensor_utils import tensor_tree_map


def list_files_with_extensions(dir, extensions):
    return [f for f in os.listdir(dir) if f.endswith(extensions)]


def override_config(base_config, overriding_config):
    for k, v in overriding_config.items():
        if isinstance(v, dict):
            base_config[k] = override_config(base_config[k], v)
        else:
            base_config[k] = v
    return base_config


def run_on_folder(input_dir: str, output_dir: str, run_config_path: str, skip_relaxation=True,
                  long_sequence_inference=False, skip_exists=False):
    config_preset = "initial_training"
    save_outputs = False
    device_name = "cuda" if torch.cuda.is_available() else "cpu"

    run_config = json.load(open(run_config_path))

    ckpt_path = CKPT_PATH
    if ckpt_path is None:
        ckpt_path = get_latest_checkpoint(os.path.join(run_config["train_output_dir"], "checkpoint"))
    print("Using checkpoint: ", ckpt_path)

    config = model_config(config_preset, long_sequence_inference=long_sequence_inference)
    config = override_config(config, run_config.get("override_conf", {}))

    model_generator = load_models_from_command_line(
        config,
        model_device=device_name,
        model_checkpoint_path=ckpt_path,
        output_dir=output_dir)
    print("Model loaded")
    model, output_directory = next(model_generator)

    dataset = OpenFoldSingleDataset(data_dir=input_dir, config=config.data, mode="predict")
    for i, processed_feature_dict in enumerate(dataset):
        tag = dataset.get_metadata_for_idx(i)["input_name"]
        print("Processing", tag)
        output_name = f"{tag}_predicted"
        output_path = os.path.join(output_directory, f'{output_name}_joined.pdb')
        if os.path.exists(output_path) and skip_exists:
            print("skipping exists", output_name)
            continue

        # turn into a batch of size 1
        processed_feature_dict = {key: value.unsqueeze(0).to(device_name)
                                  for key, value in processed_feature_dict.items()}

        out = run_model(model, processed_feature_dict, tag, output_dir)

        # Toss out the recycling dimensions --- we don't need them anymore
        processed_feature_dict = tensor_tree_map(
            lambda x: np.array(x[..., -1].cpu()),
            processed_feature_dict
        )
        out = tensor_tree_map(lambda x: np.array(x.cpu()), out)

        protein_mask = processed_feature_dict["structural_mask"][0].astype(bool)

        in_chain_residue_index = np.concatenate([processed_feature_dict["in_chain_residue_index_r"][0],
                                                 processed_feature_dict["in_chain_residue_index_l"][0]])
        chain_index = [0] * len(processed_feature_dict["in_chain_residue_index_r"][0])
        chain_index += [1] * len(processed_feature_dict["in_chain_residue_index_l"][0])
        chain_index = np.array(chain_index)

        save_output_structure(
            aatype=processed_feature_dict["aatype"][0][protein_mask],
            residue_index=in_chain_residue_index,
            chain_index=chain_index,
            plddt=out["plddt"][0][protein_mask],
            final_atom_protein_positions=out["final_atom_positions"][0][protein_mask],
            final_atom_mask=out["final_atom_mask"][0][protein_mask],
            output_path=output_path,
        )

        logger.info(f"Output written to {output_path}...")

        # TODO: fix relaxation
        # if not skip_relaxation:
        #     # Relax the prediction.
        #     logger.info(f"Running relaxation on {output_path}...")
        #     from dockformerpp.utils.relax import relax_complex
        #     try:
        #         relax_complex(output_path,
        #                       ligand_output_path,
        #                       os.path.join(output_directory, f'{output_name}_protein_relaxed.pdb'),
        #                       os.path.join(output_directory, f'{output_name}_ligand_relaxed.sdf'))
        #     except Exception as e:
        #         logger.error(f"Failed to relax {protein_output_path} due to {e}...")

        if save_outputs:
            output_dict_path = os.path.join(
                output_directory, f'{output_name}_output_dict.pkl'
            )
            with open(output_dict_path, "wb") as fp:
                pickle.dump(out, fp, protocol=pickle.HIGHEST_PROTOCOL)

            logger.info(f"Model output written to {output_dict_path}...")


if __name__ == "__main__":
    config_path = sys.argv[1] if len(sys.argv) > 1 else os.path.join(os.path.dirname(__file__), "run_config.json")
    input_dir, output_dir = TEST_INPUT_DIR, TEST_OUTPUT_DIR
    options = {"skip_relaxation": True, "long_sequence_inference": False}
    if len(sys.argv) > 3:
        input_dir = sys.argv[2]
        output_dir = sys.argv[3]
        if "--relax" in sys.argv:
            options["skip_relaxation"] = False
        if "--long" in sys.argv:
            options["long_sequence_inference"] = True
        if "--allow-skip" in sys.argv:
            options["skip_exists"] = True

    run_on_folder(input_dir, output_dir, config_path, **options)