repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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DDOD | DDOD-main/mmdet/datasets/crowdhuman.py | import itertools
import logging
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from mmdet.core import eval_recalls
from .api_wrappers import COCO, COCOeval
from .builder imp... | 22,425 | 40.07326 | 124 | py |
DDOD | DDOD-main/mmdet/datasets/cityscapes.py | # Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
import glob
import os
import os.path as osp
import tempfile
fr... | 14,288 | 41.653731 | 135 | py |
DDOD | DDOD-main/mmdet/datasets/utils.py | import copy
import warnings
from mmcv.cnn import VGG
from mmcv.runner.hooks import HOOKS, Hook
from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import LoadAnnotations, LoadImageFromFile
from mmdet.models.dense_heads import GARPNHead, RPNHead
from mmdet.models.roi_heads.mask_heads import Fuse... | 6,486 | 38.554878 | 78 | py |
DDOD | DDOD-main/mmdet/datasets/dataset_wrappers.py | import bisect
import math
from collections import defaultdict
import numpy as np
from mmcv.utils import print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A... | 11,072 | 38.127208 | 167 | py |
DDOD | DDOD-main/mmdet/datasets/xml_style.py | import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import numpy as np
from PIL import Image
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class XMLDataset(CustomDataset):
"""XML dataset for detection.
Args:
min_size (int | float, optio... | 5,886 | 33.426901 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/__init__.py | from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
RepeatDataset)
from .deepfashion import D... | 1,219 | 45.923077 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/lvis.py | import itertools
import logging
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class LVISV05Dat... | 46,136 | 61.51626 | 157 | py |
DDOD | DDOD-main/mmdet/datasets/builder.py | import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from .samplers import DistributedGroupSampler, DistributedSampler, ... | 5,284 | 35.701389 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/coco.py | import itertools
import logging
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from mmdet.core import eval_recalls
from .api_wrappers import COCO, COCOeval
from .builder imp... | 23,463 | 40.974955 | 124 | py |
DDOD | DDOD-main/mmdet/datasets/wider_face.py | import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
from .builder import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class WIDERFaceDataset(XMLDataset):
"""Reader for the WIDER Face dataset in PASCAL VOC format.
Conversion scripts can be found in
https://gith... | 1,501 | 27.884615 | 68 | py |
DDOD | DDOD-main/mmdet/datasets/api_wrappers/coco_api.py | # This file add snake case alias for coco api
import warnings
import pycocotools
from pycocotools.coco import COCO as _COCO
from pycocotools.cocoeval import COCOeval as _COCOeval
class COCO(_COCO):
"""This class is almost the same as official pycocotools package.
It implements some snake case function alia... | 1,458 | 30.042553 | 126 | py |
DDOD | DDOD-main/mmdet/datasets/api_wrappers/__init__.py | from .coco_api import COCO, COCOeval
__all__ = ['COCO', 'COCOeval']
| 69 | 16.5 | 36 | py |
DDOD | DDOD-main/mmdet/datasets/samplers/group_sampler.py | from __future__ import division
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):
assert hasattr(dataset, 'flag')
self.dataset = dataset
self.... | 5,368 | 35.033557 | 78 | py |
DDOD | DDOD-main/mmdet/datasets/samplers/distributed_sampler.py | import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas=None,
rank=None,
shuffle=True,
seed=0):
... | 1,310 | 31.775 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/samplers/__init__.py | from .distributed_sampler import DistributedSampler
from .group_sampler import DistributedGroupSampler, GroupSampler
__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler']
| 194 | 38 | 75 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/loading.py | import os.path as osp
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
from mmdet.core import BitmapMasks, PolygonMasks
from ..builder import PIPELINES
@PIPELINES.register_module()
class LoadImageFromFile:
"""Load an image from file.
Required keys are "img_prefix" and "img_info" (a dict ... | 15,860 | 33.555556 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/instaboost.py | import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class InstaBoost:
r"""Data augmentation method in `InstaBoost: Boosting Instance
Segmentation Via Probability Map Guided Copy-Pasting
<https://arxiv.org/abs/1908.07801>`_.
Refer to https://github.com/GothicAi/Instaboost ... | 3,486 | 34.222222 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/compose.py | import collections
from mmcv.utils import build_from_cfg
from ..builder import PIPELINES
@PIPELINES.register_module()
class Compose:
"""Compose multiple transforms sequentially.
Args:
transforms (Sequence[dict | callable]): Sequence of transform object or
config dict to be composed.
... | 1,456 | 27.019231 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/auto_augment.py | import copy
import cv2
import mmcv
import numpy as np
from ..builder import PIPELINES
from .compose import Compose
_MAX_LEVEL = 10
def level_to_value(level, max_value):
"""Map from level to values based on max_value."""
return (level / _MAX_LEVEL) * max_value
def enhance_level_to_value(level, a=1.8, b=0.... | 36,327 | 39.772166 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/formating.py | from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.T... | 11,981 | 31.827397 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/__init__.py | from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formating import (Collect, DefaultFormatBundle, ImageToTensor,
ToD... | 1,482 | 53.925926 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/transforms.py | import copy
import inspect
import mmcv
import numpy as np
from numpy import random
from mmdet.core import PolygonMasks
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from ..builder import PIPELINES
try:
from imagecorruptions import corrupt
except ImportError:
corrupt = None
try:
import al... | 75,130 | 38.418153 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/pipelines/test_time_aug.py | import warnings
import mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-block::
img_scale=[(1333, 400), (1333, 8... | 4,421 | 35.545455 | 78 | py |
DDOD | DDOD-main/mmdet/utils/contextmanagers.py | import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
name='',
... | 4,077 | 32.42623 | 79 | py |
DDOD | DDOD-main/mmdet/utils/util_mixins.py | """This module defines the :class:`NiceRepr` mixin class, which defines a
``__repr__`` and ``__str__`` method that only depend on a custom ``__nice__``
method, which you must define. This means you only have to overload one
function instead of two. Furthermore, if the object defines a ``__len__``
method, then the ``__... | 3,664 | 33.904762 | 78 | py |
DDOD | DDOD-main/mmdet/utils/profiling.py | import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
@contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end_stream=None):
"""Print time spent by CP... | 1,288 | 31.225 | 73 | py |
DDOD | DDOD-main/mmdet/utils/util_random.py | """Helpers for random number generators."""
import numpy as np
def ensure_rng(rng=None):
"""Coerces input into a random number generator.
If the input is None, then a global random state is returned.
If the input is a numeric value, then that is used as a seed to construct a
random state. Otherwise ... | 977 | 27.764706 | 119 | py |
DDOD | DDOD-main/mmdet/utils/logger.py | import logging
from mmcv.utils import get_logger
def get_root_logger(log_file=None, log_level=logging.INFO):
"""Get root logger.
Args:
log_file (str, optional): File path of log. Defaults to None.
log_level (int, optional): The level of logger.
Defaults to logging.INFO.
Retu... | 481 | 23.1 | 77 | py |
DDOD | DDOD-main/mmdet/utils/collect_env.py | from mmcv.utils import collect_env as collect_base_env
from mmcv.utils import get_git_hash
import mmdet
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7]
return env_info
... | 423 | 23.941176 | 74 | py |
DDOD | DDOD-main/mmdet/utils/__init__.py | from .collect_env import collect_env
from .logger import get_root_logger
__all__ = ['get_root_logger', 'collect_env']
| 119 | 23 | 44 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/utils.py | import os
def mkpath(*paths):
"""Make path."""
path = os.path.join(*[str(path) for path in paths])
path = os.path.realpath(path)
return path
| 151 | 15.888889 | 53 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/dataset.py | from __future__ import annotations
import torch
import torch.nn.functional as F
from glob import glob
from typing import Literal, List
from pedalboard.io import ReadableAudioFile
from torch.utils.data import Dataset
from tfcrnn.utils import mkpath
from tfcrnn.config import Config
CLASSES = ['backward', 'bed', 'bird'... | 3,763 | 30.630252 | 120 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/config.py | import os
import argparse
import wandb
from dataclasses import dataclass, asdict
from typing import Literal
from tfcrnn.utils import mkpath
@dataclass
class Config:
# Path configurations.
dataset_dir: str = mkpath(os.path.dirname(__file__), '../dataset')
# Data configurations.
input_seconds: float = 1.0
... | 2,099 | 24.925926 | 68 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/__init__.py | 0 | 0 | 0 | py | |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/train.py | import wandb
from tfcrnn.runners import SpeechCommandsRunner
from tfcrnn.config import Config
def main():
config = Config()
config.init_wandb()
config.parse_cli()
config.print()
runner = SpeechCommandsRunner(config)
print(runner.model)
print(f'\n=> Num params: {sum([p.numel() for p in runner.model... | 1,916 | 27.191176 | 84 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/__init__.py | from .blocks import *
from .skeletons import *
| 47 | 15 | 24 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/blocks/tf_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from .plain_blocks import BasicBlock
class TFBasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, hidden_size, amp_rate):
super().__init__()
self.base_block = BasicBlock(in_channels, out_ch... | 3,213 | 36.372093 | 87 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/blocks/plain_blocks.py | import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class BasicBlock(nn.Sequential):
def __init__(self, in_channels, out_channels):
super().__init__()
self.add_module('conv', nn.Conv1d(in_channels, out_channels, 3, 1, 1))
self.add_module('norm', nn.BatchNorm1d(out_c... | 2,546 | 35.385714 | 85 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/blocks/__init__.py | from .plain_blocks import *
from .tf_blocks import *
| 53 | 17 | 27 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/skeletons/crnn.py | from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from tfcrnn.config import Config
from ..blocks import BasicBlock, SEBlock, ResSEBlock
class CRNN(nn.Module):
def __init__(self, config: Config):
super(CRNN, sel... | 3,438 | 35.2 | 106 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/skeletons/cnn.py | from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from tfcrnn.config import Config
from ..blocks import BasicBlock, SEBlock, ResSEBlock
class CNN(nn.Module):
def __init__(self, config: Config):
super(CNN, self)... | 3,175 | 36.364706 | 115 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/skeletons/tf_crnn.py | from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from tfcrnn.config import Config
from ..blocks import TFBasicBlock, TFSEBlock, TFResSEBlock
class TFCRNN(nn.Module):
def __init__(self, config: Config):
super(T... | 3,636 | 35.009901 | 119 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/models/skeletons/__init__.py | from .cnn import CNN
from .crnn import CRNN
from .tf_crnn import TFCRNN
| 72 | 17.25 | 27 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/runners/speech_commands_runner.py | from __future__ import annotations
import os
import wandb
import torch
import torch.nn.functional as F
from collections import OrderedDict
from tqdm import tqdm
from torch.utils.data import DataLoader
from tfcrnn.config import Config
from tfcrnn.dataset import SpeechCommandsDataset
from .base_runner import BaseRunner... | 3,649 | 28.918033 | 72 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/runners/__init__.py | from .base_runner import BaseRunner
from .speech_commands_runner import SpeechCommandsRunner
| 93 | 30.333333 | 56 | py |
temporal-feedback-crnn | temporal-feedback-crnn-main/tfcrnn/runners/base_runner.py | from __future__ import annotations
import numpy as np
import os
import abc
import wandb
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.optim.lr_scheduler import ReduceLROnPlateau
from tfcrnn.models import CNN, CRNN, TFCRNN
from tfcrnn.config import Config
from tfcrnn.utils import ... | 5,233 | 32.33758 | 118 | py |
switchprompt | switchprompt-main/databuilding_script.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 4,322 | 41.80198 | 108 | py |
switchprompt | switchprompt-main/arguments.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 8,062 | 33.021097 | 119 | py |
switchprompt | switchprompt-main/app.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 986 | 36.961538 | 72 | py |
switchprompt | switchprompt-main/run.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 5,263 | 34.809524 | 140 | py |
switchprompt | switchprompt-main/training/trainer_base.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 3,691 | 41.436782 | 128 | py |
switchprompt | switchprompt-main/training/trainer_exp.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 25,340 | 48.015474 | 130 | py |
switchprompt | switchprompt-main/model/sequence_classification.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 15,176 | 38.626632 | 140 | py |
switchprompt | switchprompt-main/model/utils.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 6,699 | 35.612022 | 131 | py |
switchprompt | switchprompt-main/model/keyword_extractor.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 10,273 | 40.261044 | 135 | py |
switchprompt | switchprompt-main/model/prefix_encoder.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 5,075 | 51.329897 | 132 | py |
switchprompt | switchprompt-main/tasks/utils.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 1,285 | 30.365854 | 72 | py |
switchprompt | switchprompt-main/tasks/clinic/dataset.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 5,412 | 42.304 | 139 | py |
switchprompt | switchprompt-main/tasks/clinic/get_trainer.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 2,474 | 33.375 | 86 | py |
switchprompt | switchprompt-main/tasks/clinic/datasets/clinic.py | """ Utility classes and functions related to SwitchPrompt (EACL 2023).
Copyright (c) 2022 Robert Bosch GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
... | 4,266 | 34.558333 | 232 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/draw_crossover.py | from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem import rdMolHash
# smiles0 = "C1CC2=C3C(=CC=C2)C(=CN3C1)[C@H]4[C@@H](C(=O)NC4=O)C5=CNC6=CC=CC=C65"
smiles0 = "C1CC2=C3C(=CC=C2)C(=CN3C1)[C]4[C](C(=O)NC4=O)C5=CNC6=CC=CC=C65"
smiles1 = "C1CC2=C3C(=CC=C2)N(CN3C1)C4C(Cl)(C(=O)NC4=O)"
smiles2 = "C4(S)C(C(=... | 1,033 | 27.722222 | 111 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/evaluate_baseline.py | # from tdc import utils
# names = utils.retrieve_benchmark_names('Docking_Group')
# print(names)
# pyscreener_path = '/project/molecular_data/graphnn/pyscreener/'
# from tdc.benchmark_group import docking_group
# group = docking_group(path = 'data/',
# file_format='1iep_docking',
# pys... | 2,944 | 31.722222 | 118 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/RGA.py | '''
- import and config
- policy network
- for i in 1,...,generation
- crossover
- RGA use policy network to select ligand ***
- crossover
- docking
- RGA update policy network ***
- mutation
- RGA use policy network to select ligand ***
- mutation
- docking
- RGA update policy network ***
-... | 53,684 | 42.329298 | 204 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/RunAutogrow.py | # !/usr/bin/env python
"""This is the executable file for Autogrow 4.0.3. This script should come
first. It should obtain and verify all the parameters work. This than should
pass these parameters variables to the main execution function titled
AutogrowMainExecute.py found in MainFunctions
If you use AutoGrow 4.0.3 i... | 25,053 | 33.942817 | 107 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/demo_docking.py | import argparse
PARSER = argparse.ArgumentParser()
# Allows the run commands to be submitted via a .json file.
PARSER.add_argument(
"--json",
"-j",
metavar="param.json",
help="Name of a json file containing all parameters. \
Overrides other arguments.",
)
# Allows the run in debug mode. Doesn't d... | 37,208 | 35.055233 | 131 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/model.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors
def smiles2fp(smiles_string):
mol = Chem.MolFromSmiles(smiles_string)
Chem.SanitizeMol(mol)
fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2,... | 12,570 | 33.535714 | 133 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/run.py | import argparse
PARSER = argparse.ArgumentParser()
# Allows the run commands to be submitted via a .json file.
PARSER.add_argument(
"--json",
"-j",
metavar="param.json",
help="Name of a json file containing all parameters. \
Overrides other arguments.",
)
# Allows the run in debug mode. Doesn't d... | 32,721 | 33.553326 | 131 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/smiles2dockscore.py | import argparse
PARSER = argparse.ArgumentParser()
# Allows the run commands to be submitted via a .json file.
PARSER.add_argument(
"--json",
"-j",
metavar="param.json",
help="Name of a json file containing all parameters. \
Overrides other arguments.",
)
# Allows the run in debug mode. Doesn't d... | 31,771 | 36.511216 | 137 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/demo_GAoperation.py | import argparse
PARSER = argparse.ArgumentParser()
# Allows the run commands to be submitted via a .json file.
PARSER.add_argument(
"--json",
"-j",
metavar="param.json",
help="Name of a json file containing all parameters. \
Overrides other arguments.",
)
# Allows the run in debug mode. Doesn't d... | 26,805 | 33.994778 | 137 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/draw_mutation.py | from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
with open('mutation_example.txt', 'r') as fin:
lines = fin.readlines()
idx = 0
for line in lines[idx:idx+1]:
input_smiles, smart, output_smiles = line.split()[:3]
mol = Chem.MolFromSmiles(input_smiles, sanitize=False)
Draw.MolToF... | 2,257 | 30.802817 | 286 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/file_concatenate_and_compression.py | """
This script is used to decompress or recompress AutoGrow data.
If you use the reduce_files_sizes option AutoGrow will convert concatenate and compress
all files in the PDBs directory of each generation. This is useful when doing larger runs as
data transfer is faster and data storage is reduced when files are merg... | 10,196 | 32.432787 | 108 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/test_complementary_mol_library.py | """
This script will test a complementary molecule library to ensure all compounds
react in all reactions they may be used in.
Example submit:
python autogrow4/accessory_scripts/test_complementary_mol_library.py \
--rxn_library_file \
autogrow4/autogrow/operators/mutation/smiles_click_chem/reaction_libraries/click_ch... | 37,953 | 38.617954 | 125 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/make_lineage_figures.py | """
This script creates figures for all ligands which parented a given ligand.
All compounds for the entire AutoGrow run will be compiled into a dictionary \
which is used to search when tracing lineages. We pickle these dictionaries so \
that if this script is run multiple times these dictionaries do not need to be \... | 46,445 | 40.843243 | 100 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/convert_vina_docked_pdbqt_to_pdbs.py | """
This script will convert a docked .pdbqt.vina file into separate .pdb file.
This is done by splitting up a single .pdbqt.vina into separate .pdbqt
files for each docked pose.
Then it removes a column of the .pdbqt and saves as a .pdb file.
If variable --max_num_of_poses is not set it will convert all poses.
I... | 13,901 | 39.530612 | 98 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/convert_single_ligand_pdbqt_to_pdb.py | """
This script will convert a pdbqt file into a .pdb file.
This is done by removing a column of the PDB file.
# Run example:
#
# output example:
# python convert_ligands_pdb_to_smi.py \
# -source_folder $PATH/OF/PDBS/ \
# -output_folder $PATH/TO/OUTPUT/ \
# -number_of_processors -1
"""
import __future__
imp... | 4,099 | 31.539683 | 91 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/fragmenter_of_smi_mol.py | """
This script will fragment a .smi
Example Run:
python fragmenter_of_smi_mol.py \
--smi_file autogrow4/source_compounds/PARPi.smi
"""
import itertools
import copy
import random
import os
import argparse
import rdkit
import rdkit.Chem as Chem
from rdkit.Chem.BRICS import BRICSDecompose
from rdkit import RDLogger
... | 21,810 | 30.114123 | 97 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/plot_autogrow_run.py | """
Plots a line plot of the average score for each generation of AutoGrow run.
Example submit:
python autogrow4/accessory_scripts/plot_autogrow_run.py\
-i $PATH/Run_1/Run_0/ \
--plot_reference_lines [['Olaparib Score',-12.8,'y'],\
['Niraparib',-10.7,'k'],['NAD/NADH',-10.3,'purple'],\
... | 22,547 | 35.192616 | 91 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/convert_directory_ligands_pdb_to_smi.py | """
convert pdbs into smiles
This script will take a folder and convert all pdb files into a single texted file.
The text file will contain smiles strings of the respective pdb and the name of the file.
Run example:
output example:
python convert_ligands_pdb_to_smi.py \
--source_folder $PATH/OF/PDBS/ \
--output_... | 5,274 | 31.361963 | 89 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/support_scripts/mol_object_handling.py | # Copyright 2018 Jacob D. Durrant
#
# 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 writ... | 10,623 | 34.531773 | 130 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/support_scripts/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/accessory_scripts/support_scripts/Multiprocess.py | """
Run commands on multiple processors in python.
Adapted from examples on https://docs.python.org/2/library/multiprocessing.html
"""
# These functions are also borrow from the Gypsum-DL script Parallelizer.py
# These functions were renamed to be pep8 compliant
# ie )
# def multi_threading became def multi_threading... | 3,732 | 24.744828 | 89 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/user_vars.py | """user_vars
This should contain the functions for defining input variables.
Both the default variables and the user input variables.
This should also validate them.
"""
import __future__
import os
import copy
import datetime
import json
import sys
import platform
from shutil import copyfile
def program_info():
... | 87,255 | 38.697907 | 112 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/model.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors
def smiles2fp(smiles_string):
mol = Chem.MolFromSmiles(smiles_string)
Chem.SanitizeMol(mol)
fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nB... | 2,365 | 24.170213 | 72 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/autogrow_main_execute.py | """
Top level for running AutoGrow.
Runs all population generation (operations) and docking.
Runs plotting at end.
"""
import __future__
import os
import glob
import sys
import shutil
import autogrow.docking.execute_docking as DockingClass
import autogrow.operators.operations as operations
import autogrow.docking.con... | 15,174 | 40.236413 | 250 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/plotting/generate_line_plot.py | """ Plots AutoGrow Run"""
import __future__
import os
import glob
import matplotlib
import matplotlib.pyplot as plt
def get_usable_format(infile):
"""
This code takes a string for an file which is formatted as an .smi file. It
opens the file and reads in the components into a usable list.
The .smi ... | 15,937 | 34.896396 | 89 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/plotting/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/operations1.py | """
Populates an AutoGrow generation via mutation, crossover, and elitism.
Also filters and converts SMILES to 3d SDFS.
"""
import __future__
import os
import random
import copy
import sys
import rdkit
import rdkit.Chem as Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import aut... | 45,015 | 37.974892 | 144 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/operations.py | """
Populates an AutoGrow generation via mutation, crossover, and elitism.
Also filters and converts SMILES to 3d SDFS.
"""
import __future__
import os
import random
import copy
import sys
import rdkit
import rdkit.Chem as Chem
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
import aut... | 42,982 | 36.970848 | 114 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/execute_filters.py | """
Top level for running filters.
"""
import __future__
import copy
import rdkit
from rdkit import Chem
from rdkit.Chem.MolStandardize import rdMolStandardize
# Disable the unnecessary RDKit warnings
rdkit.RDLogger.DisableLog("rdApp.*")
from autogrow.operators.filter.filter_classes.parent_filter_class import Paren... | 6,794 | 30.169725 | 94 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/__init__.py | 1 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/get_child_filter_class.py | """
An object for auto-detecting and creating jobs with the proper templates.
"""
# You'll need to import the base class first
def get_all_subclasses(base_class):
"""
Method for getting all child classes from a parent object. Taken from:
http://stackoverflow.com/questions/3862310/how-can-i-find-all-subcla... | 775 | 26.714286 | 102 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/parent_filter_class.py | """
This script holds the parent class for filtering.
This is used as the basis for all filter classes.
"""
import __future__
class ParentFilter(object):
"""
This is a script containing all of the filters for drug likeliness
Filters for orally bio-available drugs:
1) Lipinski
Filters for for l... | 1,124 | 24 | 70 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/__init__.py | 0 | 0 | 0 | py | |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/lipinski_lenient_filter.py | """Lipinski Lenient
This runs a Lenient Lipinski filter. Lipinski filter refines for orally
available drugs. It filters molecules by Molecular weight (MW), the number of
hydrogen donors, the number hydrogen acceptors, and the logP value.
To pass the Lipinski filter a molecule must be:
MW: Max 500 dalton
Number... | 3,537 | 34.029703 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/mozziconacci_filter.py | """Mozziconacci Filter
This runs a Mozziconacci filter. Mozziconacci filter is a filter for
Drug-likeliness which filters molecules by the number of: rotatable bonds,
rings, oxygens, and halogens.
To pass the filter a molecule must be:
# of Rotatable bonds: Max 15
# of Rings: Max 6
# of Oxygens: Min 1
... | 3,265 | 31.66 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/lipinski_strict_filter.py | """Lipinski Strict
This runs a Strict Lipinski filter. Lipinski filter refines for orally
available drugs. It filters molecules by Molecular weight (MW), the number of
hydrogen donors, the number hydrogen acceptors, and the logP value.
To pass the Lipinski filter a molecule must be:
MW: Max 500 dalton
Number o... | 3,329 | 33.6875 | 85 | py |
reinforced-genetic-algorithm | reinforced-genetic-algorithm-main/autogrow/operators/filter/filter_classes/filter_children_classes/brenk_filter.py | """#BRENK filter
This will filter a ligand using the BRENK filter for lead-likeliness, by
matching common false positive molecules to the current mol..
This script relies on the RDKit predefined FilterCatalog. FilterCatalog is
maintained by RDKit.
If using the BRENK filter please cite: Brenk R et al. Lessons Learnt f... | 2,846 | 32.104651 | 86 | py |
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