sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2edf0ea2961af77f9107ce5d6281fdc56521f2aaa9394fb9c64850f4565d48be | Python | 3,679 | 112 |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
FI = {
"ADNI": "./metadata/data_processed/merged_dataframe_cox_noqc_pruned_final.csv",
"NACC": "./metadata/data_processed/merged_dataframe_cox_test_pruned_final.csv"
}
COLS = {
"ADNI": {
'RID': str,
... |
7fe3b14683fdbad70066de8f4804229aac067999f67e82b293e2676d21119b55 | Python | 3,683 | 96 | """
subsample slices and shift the short axis and long axis slices
"""
import cv2
import numpy as np
import random
from scipy import ndimage
from scipy.ndimage.interpolation import shift
class ExpRandomShiftSxlx:
"""
Subsample the 3D numpy [h, w, d] by slice max number.
"""
def __init__(self, random_... |
ab26ca88c4a6add9dbac98caf6552bd299eaf852d0053dad25da40f692c5a273 | Python | 3,684 | 104 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PAT... |
f07cc11b73b57f6961924d6d65d3fd45dad29809cb4fdf6bf0c1f8035a07c0f5 | Python | 3,684 | 80 | # ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved *
# * *
# * Redistribution and use in source and binary forms, with o... |
45f17c23e1aaa77ae75a1a6b39ceb16ddd30e93c1417ef3a04a04d5080cb3cda | Python | 3,687 | 121 | """This module contains simple helper functions """
from __future__ import print_function
import os
from random import randrange
import numpy as np
import torch
from PIL import Image
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input... |
bad36c5a422ed5c93d4d2a35e6a7661c32598d10e07b0d04c2454a312b3415fe | Python | 3,688 | 113 | # ------------------------------------------------------------------------------
# Adapted from https://github.com/HoBeom/OneEuroFilter-Numpy
# Original licence: Copyright (c) HoBeom Jeon, under the MIT License.
# ------------------------------------------------------------------------------
import warnings
from time ... |
d5bcf9efe7e15f8958256c5431c3524e942ae8f307751c0b4ca48b532c60fecd | Python | 3,689 | 114 | """
Created on Mon Jan 17 10:09:58 2022
@author: daniel
"""
import shelve
import os
from tempotron.main import Tempotron
import numpy as np
import matplotlib.pyplot as plt
import sqlite3
import sys
import multiprocessing
import argparse
import uuid
"""Parse Command Line inputs"""
pr = argparse.ArgumentParser(descrip... |
e3bb27cc0fcc7cffa2e70ad6ba42671bdc18ec1345d4b8571a4fef253f36e2e9 | Python | 3,690 | 139 | import numpy as np
import pandas as pd
import datetime
import time
from model import Net1
import torch.optim as optim
from scipy import io
import argparse
import os # nn.BatchNorm2d(2,affine=False),
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import h5py
impor... |
091fd0f03c6d2eda1bc25584aa601da2cbab08875b32df3554e579ce680df677 | Python | 3,691 | 105 | # import psi4
# import sys
from rdkit import Chem
# from rdkit.Chem import AllChem
import numpy as np
# import pandas as pd
import os
import time
from datetime import datetime
def get_mbis_charge(mol):
try:
mbis_charges = [float(x) for x in mol.GetProp("MBIS_CHARGES").split("|")]
if len(mbis_cha... |
f6563d79f4566dae30c91a9c2d96666ed9b10a9bff06594689cf824e9593c4d3 | Python | 3,692 | 127 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
7673d5585d1cbdfb28ed5011acd200f4b3611047d0fe965131941203aba3a5bf | Python | 3,693 | 110 | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except Imp... |
1489fcf9d412e5d58f12fc4f044ea451dfc10de993308d5e6eb73aeb6c2ae6be | Python | 3,694 | 104 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PAT... |
a41ad3d9d68811e1451d2b2b24d5995fd4a190f715d122fd97eebdf623eab081 | Python | 3,703 | 99 | from typing import List, Union, Tuple
from rdkit import Chem
import torch
import torch.nn as nn
from .mpn import MPN
from .args import ModelArgs
from .features import BatchMolGraph
from .nn_utils import get_activation_function, initialize_weights
class MoleculeModel(nn.Module):
"""A :class:`MoleculeModel` is a ... |
bfe420ac9a42572f130c4ae91d020fd8e090edb99936dab5815270c6cb1fa95a | Python | 3,703 | 104 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PAT... |
654d24df9e874a47e61910cd7a20f42925ed11906a9e70cb71236c51f8b79fff | Python | 3,705 | 118 | from collections import defaultdict, namedtuple
Hsp = namedtuple(
"Hsp",
["qname", "tname", "pid", "len", "qcoords", "tcoords", "evalue", "qlen", "tlen"],
)
def parse_blast(handle):
for line in handle:
line = line.split()
yield Hsp(
qname=line[0],
tname=line[1],
... |
f5b73d070172eb820e771f55f83c7b2c6f00ce7045f1433bd6b3e5adbd84934d | Python | 3,705 | 104 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PAT... |
0938eb0f273008b87cb0d247f37a48af5e9f5ab2de35e55e4ea6662d94589c84 | Python | 3,708 | 89 | # Copyright (c) 2020-present Joindbre.com
# Authors: Patricia Maraver.
#
# This program is free software: you can redistribute it and/or modify
# it under the GNU Affero General Public License version 3
# as published by the Free Software Foundation.
# You should have received a copy of the Server Side Public Lic... |
242c3b2e235ce488006f46bb434109f65d0e9811d8663de9a019ee7eea680933 | Python | 3,714 | 111 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
import contextlib
import importlib
import inspect
import pkgutil
import random
import sys
from types import ModuleType
def import_first_function(module_... |
7c28d919f9218f2077ac7fcfbf639085e21dd710fc26c2e0f1018d33b4fc6b3d | Python | 3,714 | 104 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PAT... |
b0aac6bdbeb2f2681a497b01bd0616f5486bb477013033c7b9bdc6839d5be0be | Python | 3,714 | 111 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from __future__ import annotations
from dataclasses import asdict
import pytest
import torch
from torch import Tensor, eye, ones, zeros
from torch.distr... |
1e6680c975ca7305805ccaa15162c704fa7c18f31be3d5645c23411f4637f76f | Python | 3,715 | 128 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aflw.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
d087786e675811e7907e469df5528c3a31862f6af2dbe2619db9f04756befceb | Python | 3,715 | 128 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/wflw.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
c45c1a035621429d1b586cd76845e1fbdee039de776511932a40561fee45aa99 | Python | 3,717 | 128 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/300w.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
0bf8d2a28b8cae976461af060751aedd1b654a137654ace623fdd87d30df2e9c | Python | 3,724 | 131 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
15421423f0d68853db2dc70d16beb250f593a9baca5203f1eced4ce3eb4eca1b | Python | 3,724 | 131 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
a4890169314f35bfa36c6241e98bc53e06d49cf3e208beb3836304e88d20d7a1 | Python | 3,724 | 126 | from typing import Union, Optional, Any
import numpy as np
from scipy.ndimage.filters import gaussian_filter
import eagerpy as ep
from ..devutils import atleast_kd
from ..models import Model
from ..criteria import Criterion
from ..distances import Distance
from .base import FlexibleDistanceMinimizationAttack
from ... |
4c0c322f801d41bde42819ebfcc732df0a9349a204d661c804f63a2b157c759f | Python | 3,726 | 79 | import torch as th
import numpy as np
import scanpy as sc
import scipy as scp
from sklearn.model_selection import train_test_split
from latentvelo.utils import normalize, sparse_mx_to_torch_sparse_tensor
class ANVIDataset(th.utils.data.Dataset):
def __init__(self, adata, batch_size, shuffle=True, test=0.1, rando... |
0b29fda772d816bda9d870c1d15bb21f3ec3fa2e8e0a8fc7418667cab6bbef84 | Python | 3,727 | 115 | """Command to run model inference on videos."""
import logging
from pathlib import Path
_logger = logging.getLogger('DAART.CLI.PREDICT')
def register_parser(subparsers):
"""Register the predict command parser."""
parser = subparsers.add_parser(
'predict',
description='Run inference using a ... |
ba5a4bbad1bca647100c96afde289dbf25c7cf2a0762d09b4339fd8579a37d89 | Python | 3,728 | 99 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmcv.runner import DistEvalHook as _DistEvalHook
from mmcv.runner import EvalHook as _EvalHook
MMPOSE_GREATER_KEYS = [
'acc', 'ap', 'ar', 'pck', 'auc', '3dpck', 'p-3dpck', '3dauc', 'p-3dauc',
'pcp'
]
MMPOSE_LESS_KEYS = ['loss', 'epe', 'nme',... |
341a63cfbcf1c8f0e4c1c32fd2ba2a9afae111fa27716305d9eabb243ad49a9b | Python | 3,730 | 123 | #!/usr/bin/python3
import json
import os
import subprocess
import multiprocessing
import sys
# Parse json
with open("/batchx/input/input.json", "r") as inputFile:
inputJson = inputFile.read()
parsedJson = json.loads(inputJson)
# BX_MEMORY & BX_VCPUS
bxMemory = os.environ['BX_MEMORY']
bxVcpus = os.environ['BX_VCPU... |
f11877683798a0ca458fd3c5910d7100261850974670a6281a172cea44181aa3 | Python | 3,733 | 125 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/wflw.py'
]
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup=... |
3676e6ef1737f73e6ae389037fcc732e6f2cdf0c400297d06c114d45d837012c | Python | 3,738 | 108 | # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org
# Distributed under the MIT License.
# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
from pytest import mark, raises
import torch
from torch.autograd import grad
from torchmdnet.models.model import create... |
88c0edb84922800589978e4a033fcb5627838b9adaf33e335448abe208be93c2 | Python | 3,739 | 103 | import json
import csv
import numpy as np
from typing_extensions import Literal
def read_json(config_file: str) -> dict:
"""
Reads json file given file name
Args:
config_file (str): file name
Returns:
dict: dictionary corresponding to json file
"""
with open(config_file, "r"... |
b2e4b38833d1a9b6cbf608ab0180de3f084e84754178b305325930684f2559d2 | Python | 3,739 | 117 | # Copyright (c) OpenMMLab. All rights reserved.
from collections import defaultdict
from contextlib import contextmanager
from functools import partial
import numpy as np
from mmcv import Timer
class RunningAverage():
r"""A helper class to calculate running average in a sliding window.
Args:
window ... |
046251ff4ec295b554b92310d39fd591025c597fec2c37ca700ef4c14c4a00ce | Python | 3,742 | 78 | '''
Script for writing out predictions for splitN
Train on the 5-fold, then combine all training data to predict to test set
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
'''
import numpy as np
import pandas as pd
import csv
import scipy
import pickle
from sklearn.ensem... |
2ad031a425f6f02b88db1918333a30937e41b18f435df90e6bcbb6470427221b | Python | 3,743 | 134 | from typing import List
import numpy as np
import pandas as pd
from pandas.tseries import offsets
from pandas.tseries.frequencies import to_offset
class TimeFeature:
def __init__(self):
pass
def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
pass
def __repr__(self):
retu... |
71be584d3438b4c83406677a6841f508dde7335f5202a7d57e0f4fca90f9ed0f | Python | 3,744 | 100 | """CPU tests of allocation safety required before speculative CUDA replay."""
from types import SimpleNamespace
import pytest
import torch
from aimnet.calculators.cuda_graph.cache import CUDAGraphCache, _Entry
def test_detached_keepalive_does_not_pin_resized_allocation():
value = torch.nn.Parameter(torch.arang... |
44765442438ce254f9a486c043cb7022e730c98028d982046cb601e0ecca12e6 | Python | 3,748 | 105 | """
Unittesting for tempotron
"""
import unittest
import numpy as np
from tempotron.main import Tempotron
class TestTempotron(unittest.TestCase):
"""
Test the functionality of the Tempotron class.
"""
def setUp(self):
# Change synaptic efficacies later!!
self.tempotron = Tempotron(0,... |
d50ad532f95c5fd97d21971eb7c64df25bbdaae56847157a2a4ee7574c087ddd | Python | 3,748 | 83 | import sys
sys.path.append('..')
import os
import argparse
import torch
from torch import nn
import numpy as np
from torch.optim import Adam
import utils
import metrics
import config_mixtures as cfg
import utils_mixture as mix_utils
from main_bayesian import train_model as train_bayesian, validate_model as validate_b... |
07cd0af77de99c6e07dd93f404be1e6abb78b6e203359dfe60bba1c8a0b0d5bf | Python | 3,752 | 127 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/zebra.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
4a87f5411d66b19e6cbba230e4a35e6586635688a4cc56a58dd8c5a9886e3153 | Python | 3,752 | 108 | from logging import getLogger
import numpy as np
import torch
from torch import nn
from base.config import Config
from base.evaluation.base_evaluator import BaseEvaluator
from base.results.base_result_tracker import BaseResultTracker
logger = getLogger(__name__)
# Value that marks artifacts in labels (excluded from... |
6f562663276d6a98c976a4093d0bc073b61f8fe2a14be0261332c533da1ddea3 | Python | 3,754 | 127 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/zebra.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
dabe9b02e5512517807fe40a7c1baa7951f0d44cb83079de05d0dd38a2a30148 | Python | 3,754 | 127 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/zebra.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
5cf132b66709776f4f7aa4c373317d73e922028fa8a51872d779aec286d864c8 | Python | 3,763 | 83 | import numpy as np
from importlib import reload
import sys
sys.path.insert(2,'support')
import functions
reload(functions)
import functions as f
import parameters
reload(parameters)
import parameters as p
direc = sys.argv[0].split('script')[0]+'data/'
nsim = int(sys.argv[1])
all_seeds = np.arange(nsim)
mean_bias = n... |
15925852cb8a111894dd0820647e5a2c10e132726a0395a33cc343954b586172 | Python | 3,765 | 100 | import re
from multiprocessing import Pool
from os import listdir
from os.path import join, exists
import numpy as np
import pandas as pd
from h5py import File
def dftb_parse(opts):
res_dir = opts['res_dir']
save_path = opts['save_path']
n_worker = opts.get('n_worker', 1)
# Parse in parallel
mol... |
b5198d981367734508736a135b11d8e917e41ec9f3ca9bd0b18977ee3f20753c | Python | 3,765 | 120 | from email.policy import strict
import torch
import torchvision.models
import os.path as osp
import copy
from ...log_service import print_log
from .utils import \
get_total_param, get_total_param_sum, \
get_unit
# def load_state_dict(net, model_path):
# if isinstance(net, dict):
# for ni, neti in ... |
07d064d0840651e2f74516b53ad44f19be7fb9d2b065f4b7b40b5b4bd45488af | Python | 3,766 | 114 | # -*- coding: utf-8 -*-
"""Process our data choice"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
from dtor.datasets.dataset_nominal import CTImageDataset
from dtor.datasets.dataset_mnist import MNIST3DDataset
from torchvision import transforms
from torchvision.transforms import transforms
import torch
import to... |
4971a1d5793bf297bb21f7e1e11ad6c1619492d3543e034d7dfde783d6a51bdd | Python | 3,767 | 99 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from typing import Callable, Union
import torch
from pyro.distributions import InverseGamma
from torch import Tensor
from torch.distributions import Beta... |
3054712f0fb8d79e077155504c29231325949bf86295f9839904f2a64220bd25 | Python | 3,768 | 116 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
import collections
import logging
from dataclasses import asdict, is_dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Union
import numpy... |
b98b5a6ad6bef8d9fbe4185ccdcd3471b7a2d4c25430005af1ce524f8701e4c7 | Python | 3,770 | 122 | import json
import os
import re
from pathlib import Path
from typing import List, Tuple, Union
import numpy as np
def calculate_lumen_score(
lumen_abs: float,
) -> float:
lumen_score = lumen_abs
return lumen_score
def calculate_stress_score(
stress_abs: float,
uts: float = 8.9,
) -> float:
... |
0b7d4f4a1ba7c179d2bcdba3fd054c7dd7571a7f20bef504b9e8980fee8e1839 | Python | 3,772 | 99 | # Imports #
import kivy
import zipimport
import sys
import os
import configparser
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.uix.button import Button
from kivy.uix.image import Image
from kivy.uix.label import Label
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.gridlayout import ... |
840a4249c8679206f06a799d6a2cccd6b81bfb7f2c061afbaaab0c8652f12d63 | Python | 3,772 | 99 | # Imports #
import kivy
import zipimport
import sys
import os
import configparser
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.uix.button import Button
from kivy.uix.image import Image
from kivy.uix.label import Label
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.gridlayout import ... |
397848869894ea260a7f8fbbb7b7f88ec799784ff3ca2af637f5e6c73aad3ca6 | Python | 3,773 | 91 | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam,
LoadMultiChannelImageFromFiles)
class TestLoading:
@classmethod
def setup_clas... |
5f6ae381d13a7f2e3e0759b6b6227cace4f6f7cc58e7fdfc00b53b941b194667 | Python | 3,773 | 85 | import os, torch, numpy as np, pandas as pd, torch.nn as nn, torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel, logging
from rdkit import Chem
import warnings
warnings.filterwarnings('ignore')
logging.set_verbosity_error()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
... |
b6baa79716de01d98e89b2bacf35dcc1b0240cfad06fced46ef9d9320fa99e8e | Python | 3,773 | 92 | from deprecated.driver_v0 import *
if __name__ == '__main__':
"""
Precision error occurs under certain cases.
model.get_targets_from_dataset() was found to give different outputs
when in_memory was set to True/False. The difference of the outputs
reached (+/-) 2^-36. This can be reproduced by:
... |
68ca5a35bae2e37eff1d26f15adc7fff052a4d9f747fefec81d80402ec733532 | Python | 3,774 | 106 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
training_splits = [0,1,4,6,7,9]
number_r... |
ea90f66a84b6e9e7bd10a96699845b6781d1bc0419b99f481380276dca1a499f | Python | 3,774 | 101 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
@HEADS.register_module()
class GlobalContextHead(BaseMod... |
ffc1a33c981e77ea1b1340af01967bf96228aeef90a8ccc2582ec0749386a6c6 | Python | 3,777 | 102 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import NonLocal2d
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class BFP(BaseModule):
"""BFP (Balanced Feature Pyramids)
BFP takes m... |
40655633cc32a423e1a8b124c9a38c3cb0ceab45f47b6453cc428631f61bd1f4 | Python | 3,778 | 120 | from email.policy import strict
import torch
import torchvision.models
import os.path as osp
import copy
from ...log_service import print_log
from .utils import \
get_total_param, get_total_param_sum, \
get_unit
# def load_state_dict(net, model_path):
# if isinstance(net, dict):
# for ni, neti in ... |
974eecd6c32a816a682b49faba9df53fd2a1a2bbed12674667588fb2628eab5c | Python | 3,778 | 99 | # Imports #
import kivy
import zipimport
import sys
import os
import configparser
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.uix.button import Button
from kivy.uix.image import Image
from kivy.uix.label import Label
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.gridlayout import ... |
3c9d343046203eecf055375d5fd375ad78d9132c69d9d1dc4a2e2cf608665f03 | Python | 3,779 | 111 | import pandas as pd
from os.path import dirname, join
import numpy as np
from transformers import AutoModelForMaskedLM, AutoTokenizer
from datasets import load_dataset, Dataset
from transformers import DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
import torch
torch.manual_seed(0... |
c8fcf13839f93625efd8d5c2a35eee072917480c594630aee66f2c62e3d83050 | Python | 3,781 | 82 | # fusion.py
# Train a model that fuses the output of 3 FCNs together for AD status prediction
# classifier baselines: applying the previous cnn (vanilla cnn, with our tuned parameters) for baseline estimation
import torch.nn as nn
import sys
sys.path.insert(1, './plot/')
from plot import report_table,roc_plot_perfrom... |
c439bde228fa07d98b4ef4c262f4ef675d219350dcdaeb040bf4c9ac44af6eec | Python | 3,782 | 83 | import torch as th
import numpy as np
import scanpy as sc
import scipy as scp
from sklearn.model_selection import train_test_split
from latentvelo.utils import normalize, sparse_mx_to_torch_sparse_tensor
class Dataset(th.utils.data.Dataset):
def __init__(self, adata, batch_size, shuffle=True, test=0.1, random_se... |
675a8aea9ae137b30d2237e51346c2535085b8f1c18089fa77007adbe5643d00 | Python | 3,783 | 134 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/nvgesture.py'
]
checkpoint_config = dict(interval=5)
evaluation = dict(interval=5, metric='AP', save_best='AP_rgb')
optimizer = dict(
type='SGD',
lr=1e-2,
momentum=0.9,
)
optimizer_config = dict(grad_clip=None)
# lear... |
13a0650a6eca2673ecd140bc0bf685709c02148e7cc7599b2a2c112ebda30923 | Python | 3,784 | 107 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
training_splits = [0,1,4,6,7,9]
number_r... |
91d0c5b08f7cccf199cdcafbf81639e2d835ca94c471bedaa811a588fcecf2da | Python | 3,784 | 108 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from numpy.testing import assert_array_almost_equal
from mmpose.core import (affine_transform, flip_back, fliplr_joints,
fliplr_regression, get_affine_transform, rotate_point,
transform_preds)
def tes... |
a93216c7a8d507023d1bf1bc6da22d459f3561dc559849b254a1c7feea86f67e | Python | 3,790 | 106 | import unittest
import wave
from memory_profiler import memory_usage
import webrtcvad
class WebRtcVadTests(unittest.TestCase):
@staticmethod
def _load_wave(file_name):
fp = wave.open(file_name, 'rb')
try:
assert fp.getnchannels() == 1, (
'{0}: sound format is incor... |
fab80360c3a3464970c4b8ba9adb5ac7872d3f0b1fe4931e7fe2e60608f41885 | Python | 3,790 | 100 | import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
import wandb
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
from fedml_api.model.linear.lr import LogisticRegression
from fedml_api.standalone.decentralized.decentralized_fl_api import FedML_... |
5deead261ce01c3f2310abaa6602cb444401177eceb7ced28b60f5e4108b6fc8 | Python | 3,791 | 104 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
from math import pi, radians
import matplotlib.pyplot as plt
from flystim.draw import draw_screens
from time import sleep
def get_subscreen(dir):
# ... |
e0c40dc23ebb0ceb7bc3d27a4613fe9403a3fb643d0e958a7bc112fdf2920d40 | Python | 3,791 | 96 | # -*- coding: utf-8 -*-
"""
This file is part of the cerebellar-task-dependent repository (Xie et al. 2023)
Copyright (C) 2023 Marjorie Xie and Ashok Litwin-Kumar
See README for more information
Script that walks through how to compute generalization error analytically
for a given kernel and Gaussian process target f... |
5a3f80c4bc176e27c4bdab07dd3ea7d397b82a64331c9061d480e18c85ea33ba | Python | 3,795 | 113 | #!/usr/bin/python
"""
Some functions to read and compute spectral features on SITS
"""
import sys, os
import numpy as np
import pandas as pd
import math
import random
import itertools
import dill as pickle
from tensorflow.keras.utils import to_categorical
import csv
# ------------------------------------------... |
5e8afc45f16fc235f3619691e65bd3a87c2eaa9f8cc64a40fa626dddf6fd63f0 | Python | 3,795 | 76 | import os
import sys
from sklearn.utils import shuffle
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
from fedml_api.data_preprocessing.lending_club_loan.lending_club_dataset import loan_load_two_party_data
from fedml_api.standalone.classical_vertical_fl.vfl_fixture import FederatedLearni... |
f03129f71017c2696ad8e0874ad9f9d75695dab0d07a4e196027d3e9fe0f34d0 | Python | 3,796 | 98 | """
Download the weights in ./checkpoints beforehand for fast inference
wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth
wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth
wget https://storage.googleapis.com/sfr-vision-language... |
7e02a4a94b8725996222bb1a909d00b94fa308cba87e69270a2c91ccd2f057ff | Python | 3,797 | 116 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the di... |
b888fd39e596a42086976baa4b1d497b2163b128fd3c6b3a1005cdac3bea4327 | Python | 3,797 | 100 | import json
import pathlib
import unittest
import bs4
import responses
from bs4 import BeautifulSoup
from exsclaim import journal
class TestNature(unittest.TestCase):
def setUp(self):
"""Instantiates a test search query and JournalFamily to test"""
nature_json = pathlib.Path(__file__).parent / "... |
f1a561861fd8c151a2f4b9efe5688f7d3e99251fc560b3b0cbd4f08d16a16604 | Python | 3,801 | 86 | from lifelines.datasets import load_rossi
from lifelines import CoxPHFitter
import sys
sys.path.insert(0, "/home/mfromano/Research/mri-surv-dev/mri_surv")
from simple_mlps.datas import ParcellationDataVentriclesNacc, ParcellationDataVentricles
import pandas as pd
import numpy as np
import functools
# ignore warnings
# ... |
5a039fbc1e1ccfe222227ab2722b6a9254af299172187ea447d4787ae6e298fd | Python | 3,805 | 75 | from mpi4py import MPI
from .FedOptAggregator import FedOptAggregator
from .FedOptClientManager import FedOptClientManager
from .FedOptServerManager import FedOptServerManager
from .FedOptTrainer import FedOptTrainer
from ...standalone.fedopt.my_model_trainer_classification import MyModelTrainer as MyModelTrainerCLS
f... |
42fc6a6cadd397ccf02615382b894469c0f72f5bcea8d211d3e5c2dc9bf6b8fa | Python | 3,809 | 101 | import networkx as nx
import numpy as np
from .base_topology_manager import BaseTopologyManager
class SymmetricTopologyManager(BaseTopologyManager):
"""
The topology definition is determined by this initialization method.
Arguments:
n (int): number of nodes in the topology.
neighbor_num ... |
06846626b591025f0c20c873e5f2ec4765cbcd45fb9476dec46fa82d5a8dfe3e | Python | 3,810 | 71 | import os
import sys
from sklearn.utils import shuffle
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
from fedml_api.data_preprocessing.NUS_WIDE.nus_wide_dataset import NUS_WIDE_load_three_party_data
from fedml_api.model.finance.vfl_models_standalone import LocalModel, DenseModel
from fed... |
f4bc0013e04174e969372929bf95a10188b8fd5754231ef30ea18e70a889f5e4 | Python | 3,810 | 76 | import logging
import json
import numpy as np
import torch
import torch.utils.data as data
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
DEFAULT_BATCH_SIZE = 16
train_file_path = '../../../data/synthetic_1_1/train/mytrain.json'
test_file_path = '../../../data/synthetic_1_1/test/my... |
7b27c624b2925a83fbe1cdde2380b91ac3fc89c74980b4cee67d97d3b5acc59f | Python | 3,813 | 86 | from gensim.models import Word2Vec
import numpy as np
import multiprocess as mp
# For dynamic loading: name of this method should be the same as name of this python FILE.
def deepwalk(ctrl, graph):
'''Use DeepWalk as the base embedding method. This is a wrapper method and used by MILE.'''
args = DeepWalkSetti... |
20538acca9e229f9a966fe70aa91ccd0f1c3a9b1b16128ad65db9d729a98073d | Python | 3,816 | 75 | from mpi4py import MPI
from .FedAVGAggregator import FedAVGAggregator
from .FedAVGTrainer import FedAVGTrainer
from .FedAvgClientManager import FedAVGClientManager
from .FedAvgServerManager import FedAVGServerManager
from ...standalone.fedavg.my_model_trainer_classification import MyModelTrainer as MyModelTrainerCLS
... |
bb20b20d5eff5fd32305d1c43d3f411cdb264f230282abc7a691fe56224e393f | Python | 3,819 | 105 | import shutil
import sys
import urllib
from functools import partial
from urllib.request import urlopen
import genomad
from genomad import utils
from rich.progress import (
BarColumn,
DownloadColumn,
Progress,
TextColumn,
TimeRemainingColumn,
TransferSpeedColumn,
)
class DatabaseDownloader:
... |
099f3738feae55a7bc59ccb370a3c06ed1289fc26bfc7e95c33b4e9b6dde9ab3 | Python | 3,820 | 119 | # -*- coding: utf-8 -*-
"""
Created on Mon Jan 17 10:09:58 2022
@author: daniel
"""
import shelve
import os
from tempotron.main import Tempotron
import numpy as np
import matplotlib.pyplot as plt
import sqlite3
import sys
import multiprocessing
import argparse
import uuid
"""Parse Command Line inputs"""
pr = argpars... |
72b04c84a231d8ec91e63c30c0adb7512132932467db31a608fe854c567580ad | Python | 3,820 | 116 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# 2023 Jiatong Shi
# Adapted from ESPnet fastspeech duration predictor
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Duration predictor related modules."""
import torch
from parallel_wavegan.layers.layer_norm i... |
73af9dd2465e3abaa1adf2673b646de6be5fffa1f7e85b2c276540dc2910fdc8 | Python | 3,820 | 108 | # %%
from pathlib import Path
import datetime
import shelve
import pandas as pd
from sklearn.metrics import make_scorer, roc_auc_score
from eegml import model as mod
from eegml import toolkit as tk
config = tk.parse_config("config.yaml")
models = mod.parse_models("models.yaml")
clinical_data = Path(config["data"]["... |
70af340841adea48adc66219f45cfcba985d253c16a419409700fe3e7b989f66 | Python | 3,822 | 117 | """
Cross-validated Pearson correlation between a JOINT model
(SSS + interpretable features) and high-gamma ECoG, across layers.
"""
#### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for the content.
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engra... |
96c41d7192958289faad4981b265b3c4b3bb6b9535f2d8ae32830d97a72b6983 | Python | 3,825 | 109 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PA... |
fa3fef6dac6032f949bc7c8f0b706208a7b5a0f1dc4439394dae629f7a65bcd2 | Python | 3,825 | 75 | import logging
from fedml_api.distributed.fedavg_robust.message_define import MyMessage
from fedml_api.distributed.fedavg.utils import transform_tensor_to_list
from fedml_core.distributed.communication.message import Message
from fedml_core.distributed.server.server_manager import ServerManager
class FedAvgRobustSer... |
775f54c2f07ccb1409c7d9d2a8b063aa79a71c4f7fca344373be5c1e3c3bdd18 | Python | 3,826 | 103 | from batchgenerators.utilities.file_and_folder_operations import *
import numpy as np
import SimpleITK as sitk
from statistics import fmean,median
from scipy.stats import entropy
from evaluator import make_bootstrap
import glob
import os
from tqdm import tqdm
import argparse
import matplotlib.pyplot as plt
def remov... |
0820c819f24787a51c71f2119973a89e4a9a2ec810a37707c0444fe26c1ffdb9 | Python | 3,829 | 129 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.backbones import HRNet
from mmpose.models.backbones.hrnet import HRModule
from mmpose.models.backbones.resnet import BasicBlock, Bottleneck
def is_block(modules):
"""Check if is HRMod... |
9ada6f190ccf1aa0b2eef0fd1a3e8ed1bf963b320a0045a3ec1f32aacdc90022 | Python | 3,829 | 92 | regions = {
"Orbital": {
"labels": [6],
"classifier": r"K:\workstation\code\shared\img_processing\NeuronCounting\classifiers\200316auditory\short1.classifier".replace('\\', '/'),
"volume_bar": 20,
"volume_avgbar": 100
},
"PrimarySomatosensory": {
"labels": [16],
... |
4e87f5c930901fd246a2e39b8d27a1763a269f25ff89f6877fda7a8213c2dc98 | Python | 3,831 | 81 | #!/usr/bin/env python
# Compare a list of CNV calls to a list of repetitive and low-complexity regions (RLCRs)
# Determine how many bases of each CNV overlap with these unclean regions, what percentage overlap,
# and what types of regions overlap
# Usage: compare_with_RLCR_definition.py RLCR_definition_filename CNV_f... |
e556c42fe413e9a741aa52c466bd9d07e3bf7cc54bcfe9414eaa45202d8daec3 | Python | 3,832 | 83 | from typing import Tuple
import torch
from ..utils import rna2index
class ShrinkedForcePermutationsDataset(torch.utils.data.Dataset):
"""
Dataset that supports dataset shrinking and forced jigsaw permutations by MSA duplications.
"""
def __init__(self, num_data_samples=-1, jigsaw_force_permutations... |
837b260f44e6f1bc45cb53c462250eb85f71bf4e6dff1e7a1bff8610d383f3a1 | Python | 3,836 | 129 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_face.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning pol... |
e4855ad27924dd005c3fdb3b5fb5abc8dd9a9a814ee11b40713a8b952c644eca | Python | 3,836 | 93 | from collections import OrderedDict
from logging import Logger
from typing import List
from tqdm import tqdm
from .data_data import MoleculeDatapoint, MoleculeDataset, make_mols
def filter_invalid_smiles(data: MoleculeDataset) -> MoleculeDataset:
"""
Filters out invalid SMILES.
:param data: A :class:`~... |
58362f80c5ae07180bd6670196037e84e5d3faf71f23d885fd81dea59acc32da | Python | 3,839 | 111 | import logging
import queue
import time
from typing import List
from ..base_com_manager import BaseCommunicationManager
from ..message import Message
from .mpi_receive_thread import MPIReceiveThread
from .mpi_send_thread import MPISendThread
from ..observer import Observer
class MpiCommunicationManager(BaseCommunica... |
aea870d1d9af6c876e4cb4841c41bab1f18c845e226bdf96f8e47d1639031587 | Python | 3,840 | 86 | from fedml_api.distributed.split_nn.message_define import MyMessage
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communication.message import Message
import logging
class SplitNNClientManager(ClientManager):
def __init__(self, arg_dict, trainer, backend="MPI... |
c74c729326cd0c995d6d840155166e6d8e7ffefd10b10ac847da4cf4c08a9700 | Python | 3,840 | 60 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
f301ad0356e3e1b963857fae844c054eb18329eb6667815ee0981f1058d5c237 | Python | 3,846 | 97 | import math
import re
import subprocess
import tempfile
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from genomad import sequence, utils
class Aragorn:
def __init__(
self, input_file: Path, aragorn_output: Path, score_threshold: float = 1.05
) -> None:
self.input... |
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