sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
67ef7148191b2a31df4c5b2d5099f3e038b4fc642dcd6cde0e1db414b74654e9 | Python | 5,733 | 178 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, ModuleList
from mmdet.models.backbones.resnet import Bottleneck
from mmdet.models.builder import HEADS
from .bbox_head import BBoxHead
class BasicResBlock(BaseModule):
"""Basi... |
bb6d310c39380754ddbaadd88d2ea0f171f2dbb739a9b76ec9af813b7af88198 | Python | 5,737 | 111 | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.expand_frame_repr', False)
csv_in = r'V:\foodsec\Projects\SNYF\stable_input_data\SO\Gu\Tuning_data\SOGu_STATS'
# csv_in = r'V:\foodsec\P... |
a6c36194c25ff9123f042cbbb056d666f128acd6f10985e2746b53cee62752f3 | Python | 5,742 | 160 | # 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)
import os
import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_... |
11db8a9d16ed054168418624b77431f2ba5c1c39191689b48b2e392bb1f42b7d | Python | 5,743 | 241 | """Tranform classes to process data.
Data generator objects can apply these transforms to data upon loading.
"""
import numpy as np
__all__ = [
'Compose',
'Transform',
'BlockShuffle',
'MakeOneHot',
'MotionEnergy',
'Unitize',
'ZScore'
]
class Compose(object):
"""Composes several tra... |
49ec69a1b9893c065598c81797f31ebd8e56ec9d26b05daa6c58948a45215db2 | Python | 5,743 | 155 | # -*- coding: utf-8 -*-
"""
Scheme Extractor
======
This module contains SchemeExtractor - the main extractor used to process chemical reaction schemes.
for RGroup resolution
author: Damian Wilary
email: dmw51@cam.ac.uk
"""
from pathlib import Path
import matplotlib.pyplot as plt
from utils.vectorised import estima... |
b23d97eef38dc119524b8707c7c3175e1b6b1095113b8204f55ef57f073d572e | Python | 5,745 | 196 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=20)
evaluation = dict(interval=20, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.001,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
2d94d50ea93e5b90da21464331acb0b8f4b92ce941f5054bcb6805021ed4773a | Python | 5,746 | 196 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=20)
evaluation = dict(interval=20, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.001,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
367ad9dd2c1934eb2f77a3bf834894496ad5e44a06542b0f7bde389c6826e00a | Python | 5,748 | 151 | import numpy as np
import torch
import torch.nn.functional as F
from collections import OrderedDict
from torch import nn
from torch import Tensor
from einops import rearrange
from einops import reduce
from torch.utils.data import Dataset
import torch
import random
from rdkit import Chem
from rdkit.Chem import AllC... |
d38ffb7a3d39cff271c5ae06c512b46927306ca1279309021f5927bab2baccb4 | Python | 5,750 | 195 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
831a066d793d583cba49e27fb7deb83c05e118136f2e25ee785b67e393559ca8 | Python | 5,754 | 195 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
checkpoint_config = dict(interval=20)
evaluation = dict(interval=20, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.001,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_con... |
b7a60771f905c2c3e99de1f2e8c3196be93de3b9eb9bd2905411d854337d2e6a | Python | 5,756 | 141 | import cv2
import os
import numpy as np
import xml.dom.minidom
from xml.dom.minidom import Document
import sys
stdi, stdo, stde = sys.stdin, sys.stdout, sys.stderr
def custombasename(fullname):
return os.path.basename(os.path.splitext(fullname)[0])
def GetFileFromThisRootDir(dir, ext=None... |
c1b8d74f70a87700dc763cfc2c7009627f989121a11890f58ad82e5ec7eb57fb | Python | 5,756 | 205 | from PyQt5 import QtOpenGL, QtWidgets, QtCore
import sys
import signal
import moderngl
import numpy as np
import os.path
from argparse import ArgumentParser
from flystim.square import SquareProgram
class SpotProgram:
def __init__(self, color=1.0):
self.color = color
def initialize(self, ctx):
... |
9a3a7531289e38cba15c9f676ee0c2b3a12d47235bbe1d1673b10181c47b6d87 | Python | 5,758 | 142 | # training surv-based network with resnet model
# Created: 5/21/2021
# Status: OK
# Consider merge into 1 main file
# CUBLAS_WORKSPACE_CONFIG=:4096:8 python res_main.py
# import torch.nn as nn
import sys
import torch
import numpy as np
import pandas as pd
sys.path.insert(1, '../plot/')
from networks import CNN_Surv_Wr... |
ece4ab617cc88099993f7f24b829a852bf0da5895213e1dbf9fed75af026656b | Python | 5,760 | 195 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
checkpoint_config = dict(interval=20)
evaluation = dict(interval=20, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.001,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_con... |
157b74a38a3d9fb5720e29a3e92938af8bdb7c78d80363526540543ec8f731c7 | Python | 5,764 | 191 | # -*- coding: utf-8 -*-
from __future__ import print_function
import os
import os.path
import torch.utils.data as data
import torchvision.transforms as transforms
import h5py
import numpy as np
class DatasetHDF5(data.Dataset):
def __init__(self, hdf5fn, t, transform=None, target_transform=None):
"""
... |
91d65570ec284a433b8fb4cb240cc44833385f01a56a6577ac2b27efb30f7c01 | Python | 5,767 | 115 | import logging
import traceback
import time
from fedml_api.distributed.fedseg.message_define import MyMessage
from fedml_api.distributed.fedseg.utils import transform_tensor_to_list
from fedml_core.distributed.communication.message import Message
from fedml_core.distributed.server.server_manager import ServerManager
... |
a77c3d957bf9bb6d5a9761ebb65eb1fe2e8fa71e3eb95960ea3b52525e3be399 | Python | 5,767 | 166 | # Acquired from https://github.com/pytorch/vision/tree/master/references/detection
import math
import pathlib
import sys
import time
import numpy as np
import torch
import torchvision.models.detection.mask_rcnn
from . import process, utils
from .coco_eval import CocoEvaluator
from .coco_utils import get_coco_api_from... |
8b025eb3c4552f843c50eb16d9647b5409b21a1e080d5a3274b24fb1f5319477 | Python | 5,776 | 125 | """Static dense input preparation and CUDA replay of the shared calculator computation."""
from __future__ import annotations
import weakref
import torch
from torch import nn
from ._capture import warm_up
def check_dense_model(model):
if not hasattr(model, "forward_prepared"):
raise NotImplementedErro... |
41fd2540c4a6e17431db1d2e9fd630773077a617f2ae3fb5769cf508a8089859 | Python | 5,779 | 127 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, DistancePointBBoxCoder,
TBLRBBoxCoder, YOLOBBoxCoder)
def test_yolo_bbox_coder():
coder = YOLOBBoxCoder()
bboxes = torch.Tensor([[-42., -29., 74... |
ca7de7d62c3ddd0697dea91cc57328cd7eaa7df3023e3aa8310d60d353701f88 | Python | 5,790 | 154 | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
import numpy as np
import torch
from mmcv import ConfigDict
from mmcv.ops import nms
from ..bbox import bbox_mapping_back
def merge_aug_proposals(aug_proposals, img_metas, cfg):
"""Merge augmented proposals (multiscale, flip, etc.)
... |
907eabbc1dc6f6fc101c73669871926a3cc0aaf8f02e5e109568fb4924e1792c | Python | 5,791 | 159 | """Temporal MLP model implemented in PyTorch."""
import numpy as np
from sklearn.metrics import accuracy_score
import torch
from torch import nn
from daart.models.base import BaseModel, get_activation_func_from_str
# to ignore imports for sphix-autoapidoc
__all__ = ['TemporalMLP']
class TemporalMLP(BaseModel):
... |
2879079a0840a598ae39d74a9134bdf0a4773f66200ba678d3d7e0d5f50f235b | Python | 5,796 | 156 | # 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 typing import Callable, Union
import pytest
import torch
from torch.distributions import (
Categorical,
... |
ae9254dd954a3c099a69eb79cb623ba86a6dbc38c56f20909c9934dd55f8e7e3 | Python | 5,803 | 193 | import gzip
import textwrap
from pathlib import Path
from typing import List
from genomad import utils
from numba import njit
class Sequence:
def __init__(self, header: str, seq: str, compress: bool = False) -> None:
self._compress = compress
self._header = header
if self._compress:
... |
5d1f5956032cc3c9419733da4d78b5151e0d68f5f06ce6971598ec96b2b07bb8 | Python | 5,822 | 147 | # -*- coding: utf-8 -*-
"""
Created on Mon May 30 17:01:07 2022
@author: fhu14
"""
"""
Module that handles the required precomputations for the different
datasets that have been generated
"""
#%% Imports, definitions
from InputParser import parse_input_dictionaries, collapse_to_master_settings, inflate_to_dict
impo... |
9e68ad81704c7d029d97eb2fdda8dfde44cf67572a4450d663d81a5e80988a18 | Python | 5,822 | 158 | """test_recurrent_layers.py
This file is part of the test suite for keras2c
Implements tests for recurrent layers
"""
#!/usr/bin/env python3
import unittest
import tensorflow.keras as keras
from keras2c import keras2c_main
import subprocess
import time
import os
from test_core_layers import build_and_run
import tenso... |
7237e3ba052e7b3b6db846c02e2f776d7e82ac08e68c7ea7b819ed30a71f863e | Python | 5,823 | 198 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
d9b1d9078801e239dab257169b940ffcdd4c4611af364c77596ae6b8721ca952 | Python | 5,825 | 165 | # 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)
import h5py
import numpy as np
import os
import torch as pt
from torch_geometric.data import Data
from torchmdnet.datas... |
1ecbab4f1b4ea471a84961cf37d10c0a8f3e75cb10d43543376d9eddfb072757 | Python | 5,827 | 216 | import numpy as np
import scipy as sp
def reshape_dims(M,dims=None):
num_dim = np.ndim(M)
if num_dim ==3:
M1r= M.reshape((np.prod(dims[:2]),dims[2]),order='F')
elif num_dim ==2:
M1r = M.reshape(dims,order='F')
return M1r
def remove_trend(Y_rm,detrend_option='linear'):
... |
2bb4abf9d6768aed862e75e3ebf353ea5eaf8618c579aa69d0be6a45db1d4af2 | Python | 5,839 | 133 | from jax.config import config
config.update("jax_enable_x64", True)
import jax.numpy as jnp
import jax
import numpy as np
import matplotlib.pyplot as plt
from weno_solver import generate_weno_scalar_sol, generate_weno_euler_sol
import utils
def test_weno_burgers(b):
xs = jnp.linspace(0.0, 1.0, 100, endpoint=False)
... |
76944c73211ecae36b0d08f37e91713685dc1c031525febe0e1345025e1e575a | Python | 5,856 | 191 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 10 17:13:01 2020
@author: baris
"""
import torch
from torch.autograd import Variable
import torch.nn as nn
from torch import optim
import seaborn as sns, numpy as np
import matplotlib.pyplot as plt
import os
from rate_n_phase_codes import phase_co... |
ce2def6905943cc5f7d0da2a48bfeefa83ed3403e6b2307b27eacc2601e3b3a2 | Python | 5,857 | 152 | from os.path import join
from tqdm import tqdm
import torch
from torch.utils.data import Subset
from torch_geometric.loader import DataLoader
from pytorch_lightning import LightningDataModule
from pytorch_lightning.utilities import rank_zero_warn
from torchmdnet import datasets
from torchmdnet.utils import make_splits,... |
dbc056e41f3dd61a4cf4a8c36d52313c63e3dbc872057fe3a49f6463fb524865 | Python | 5,859 | 107 | import sklearn.metrics as metrics
import numpy as np
def mean_error_nan(y_true, y_pred):
y_true = np.array(y_true)
y_pred = np.array(y_pred)
nas = np.logical_or(np.isnan(y_true), np.isnan(y_pred)) #2021-4-30 nan treatement added
return np.mean(np.array(y_pred[~nas]) - np.array(y_true[~nas]))
... |
b0453a48725da5934667a4b05b1347639d805681cf6de702636d55c7b1e8e3c7 | Python | 5,863 | 177 | import glob
import numpy as np
import scipy
import torch
from scipy.signal import resample_poly
from sklearn.preprocessing import RobustScaler
from anysleep_no_hydra import AnySleep
def load_mat_dir(mat_dir: str, channels_to_include, resample_rate):
mat_glob = glob.glob(f"{mat_dir}/*.mat")
total_dict = {}
... |
05317bcabdf56d03c150b342b61cc58857188368e4edca7e1f3e40087d5c7df2 | Python | 5,865 | 173 | # -*- coding: utf-8 -*-
from matplotlib import pyplot as plt
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.decomposition import PCA
import numpy as np
import pandas as pd
import pickle
from PolyBox import PolyBox
import warnings
warnings.filterwarnings("ignore")
flag_3D = Tr... |
d625f20b3e259261616b3c7b0da599cac938ff3e05b6e014053e66aafc084515 | Python | 5,870 | 190 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math
import utils
import argparse
import data_loader
from ipdb import set_trace
from sklearn import metrics
SEQ_LEN = 48
def binary_cros... |
9c51115c5c63834cab4d6c42e95cd4a3f8e3e35e7d8517d63d9117260ed2a120 | Python | 5,873 | 135 | # -*- coding: utf-8 -*-
"""
Created on Tue Aug 31 14:29:32 2021
@author: Anandita
"""
import numpy as np
''' This file contains:
Different tuning curves for neurons whose response can be described by a 1d '
latent variable.
Neural responses which can be described by a product of 1d respon... |
84346b65163f3615cf67a6d3d654533498beeb7faf6b64cce4037269a07979c4 | Python | 5,874 | 107 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2021/1/9 15:28
@project: MalariaDetection
@description:
"""
from keras.layers import * #For adding convolutional layer
from keras.layers import Dense, ZeroPadding2D, BatchNormalization, Activation ... |
dd57f1c97583c131e709d8989b2fcdd720c99f58adf74fa45c4ecadcdf2c82cd | Python | 5,874 | 163 | import copy
import torch
from torch_geometric.data import Data
from torch_geometric.utils import to_dense_adj, dense_to_sparse
from torch_sparse import coalesce
from torchmdnet.datasets.geom_utils import utils
class AddHigherOrderEdges(object):
def __init__(self, order, num_types=len(utils.BOND_TYPES)):
... |
1c62fab48cad05191003fbcc93dfd30cf66ce48adb4cc1146c7a9749228d9ef8 | Python | 5,885 | 159 | "Modified from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py"
import torch
import torch.nn as nn
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
class VGG(nn.Module):
def __init__(
self,
features: nn.Mod... |
2e82e06be83cebae8d6dd3bc0ebbe02a90696ff5ea1498574b5f4858e12f23bb | Python | 5,885 | 135 | import os, torch, warnings, numpy as np, pandas as pd
import torch.nn as nn, torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModel, logging
from sklearn.metrics import roc_auc_score, average_precision_score
from sklearn.model_selection import train_t... |
c49d17e5f7c7281370e9c2bade155cc1e80a6ae16fdd17917973d565ecbc1cbb | Python | 5,886 | 153 | ## Acquired from https://github.com/pytorch/vision/tree/master/references/detection
import math
import sys
import time
import torch
import numpy as np
import torchvision.models.detection.mask_rcnn
from .coco_utils import get_coco_api_from_dataset
from . import utils
from .coco_eval import CocoEvaluator
from . import p... |
4bfeb7f3fb1c28df868c22e1a8c545f45abdad2a00f20bdf389da8942f235a80 | Python | 5,888 | 155 | import os
import os.path as osp
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from torch_geometric.data import Data, DataLoader
from ..graphconv import GraphConvNetwork, predict
from ..config import utils_section, params_section
from ..data import load_data_from_smiles
# u... |
1818acddb0ecb6fea48ddf4fa8961a46c6312cbe34c8458244c72409b7328df9 | Python | 5,890 | 143 | import numpy as np
from .plane_alignment_functions import plane_alignment
from .depth_estimation import calculate_brain_center_depths
def calculate_brain_center_coordinate(section, atlas_shape, axis):
"""
Calculates the coordinate closest to the middle of the section as defined by the two
dimensio... |
b9ea78f3acf2f736a48db00b8721b6429a25d34391e353dabcbdcb0dfd0e7b4b | Python | 5,890 | 162 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import (center_of_mass, filter_scores_and_topk,
flip_tensor, mask2... |
64ac60bb99c7d596137dd668c2c0f7b852f81a78fdf312b2dc99a2b3c15a5c58 | Python | 5,894 | 206 | """io_parsing.py
This file is part of keras2c
Copyright 2020 Rory Conlin
Licensed under LGPLv3
https://github.com/f0uriest/keras2c
Helper functions to get input and output names for each layer etc.
"""
from .backend import keras
__author__ = "Rory Conlin"
__copyright__ = "Copyright 2020, Rory Conlin"
__license__ = "M... |
a91eae177c600801c8ddba692cee6091d223e8e68d246ced91c1de0f9a18edfc | Python | 5,894 | 140 | # Author: Akshara Balachandra / Michael Romano
# Date: 02-19-23
# Description: Revisions #2
import os
from typing import Dict, Tuple
import multiprocessing as mp
import pandas as pd
import json
from process_parcellations.cat12reader import Cat12Reader
from process_parcellations.make_imaging_sheet import uniq_rids
wi... |
4c25c0d5c31efe9c7e51465dd2ec7c1c8541432292d48b47f71baed13c77937a | Python | 5,900 | 136 | import argparse
import math
import os
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from nets.classification import CleftNet, CleftSegNet
from loaders.cleft_dataset import CleftDataset, CleftSegDataset
from transforms.volumetric import CleftEvalTransforms, CleftSegEvalT... |
cad44e7ab553108c48fc3321504bed4c3f0912a4d6804b80977086cbae4054f1 | Python | 5,906 | 200 |
# coding: utf-8
# In[2]:
# IMPORT STATEMENTS
import cv2
import re
import numpy as np
from scipy import stats
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
from mpl_toolkits.axes_grid1 import SubplotDivider, Size
from mp... |
77e2eaa055655d62da5bbbf4f245ea8e09040c79a40c1b93d2cb42eee1e067a2 | Python | 5,907 | 115 |
import os
import pwinput
import girder_client
# Names of the environment variables read by GirderSink to authenticate. The
# actual secret values are never passed around as nipype traits (which would
# get pickled to disk in node caches / crash files): they are only ever read
# from the current process' environment... |
87d4750f4da167bca84aba3348646d177ee2948a72a7c8cf8a9d54c88d96daf9 | Python | 5,908 | 143 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.parallel import MODULE_WRAPPERS as MMCV_MODULE_WRAPPERS
from mmcv.parallel import MMDistributedDataParallel
from mmcv.parallel.scatter_gather import scatter_kwargs
from mmcv.utils import Registry
from torch.cuda._utils import _... |
8c2d78065dd627af42116d4d51a8cdc54a90dbcab4ec1420baab98033de7d77b | Python | 5,909 | 167 | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Union
import cv2
import numpy as np
from mmcv import color_val
from ..node import Node
from ..registry import NODES
try:
import psutil
psutil_proc = psutil.Process()
except (ImportError, ModuleNotFoundError):
psutil_... |
ef23e12bedaed3bac715c1c7171a386329e8cfdb1c76323a82bdea1e7e96b404 | Python | 5,920 | 176 | import glob
from pathlib import Path
import mne
import numpy as np
import torch
from scipy.signal import resample_poly
from sklearn.preprocessing import RobustScaler
from anysleep_no_hydra import AnySleep
def load_edf(input_file: str, channels_to_include, resample_rate):
"""
Load and preprocess an EDF file ... |
aa2b73e9f0520b8f41884d2c68d317c0787910cf7b7c1be7670ccb2e6447cb40 | Python | 5,922 | 143 | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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... |
b6cc80c04dcc290f1ed31996d70dd0294f2d78b97bef192b0a1a5e774c156846 | Python | 5,941 | 165 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
import numpy as np
import torch
from mmpose.apis import init_pose_model
try:
import onnx
import onnxruntime as rt
except ImportError as e:
raise ImportError(f'Please install onnx and onnxruntime first. {e}')
try:
from mm... |
2a77c0dd9c8c4b674b69e38ece4e16f663babcdd8f8dda3998c61d6ba1065403 | Python | 5,948 | 200 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
import os
import sys
from collections import OrderedDict
# -- Path setup -... |
a1971f051ea9287dfe665dfba6d2e527e5bd3a1f3da58e911e60e3b183ee8ee1 | Python | 5,949 | 152 | import torch
import torch.nn as nn
import copy
import numpy as np
from scipy.io import mmread
from scipy.io import mmwrite
from sys import getsizeof
# user specificed dataset name vector used to train autoencoder and impute
datasets = ['jurkat', 'monocyte','mbrain','pbmc','lymphoma','293t','bmmc','human_mix',... |
c3c01739c27b1202f74149654d870c7a4a44ab336a511e10add935b4a96fa17c | Python | 5,952 | 144 | import argparse
import logging
import os
import sys
import numpy as np
import setproctitle
import torch
import torch.nn as nn
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
from fedml_api.data_preprocessing.cifar100.data_loader import load_partition_data_distributed_cifar100
from fedml_a... |
26174c27abf8d9cb475a822148568629a4c6bedae5aed15c59b635d0309ae105 | Python | 5,959 | 123 | import nengo
import nengo.spa as spa
import numpy as np
from nengo.spa.module import Module
from nengo.spa.compare import Compare
class CompareAccumulator(Compare):
"""A module for computing the dot product of two inputs, now including:
a) cleanup memories before comparison
b) accumulator to judge outcome... |
bfe0c4f207213e11567f0e4c48ab6d4846bab1ab91875a7f76b51097f24ddff8 | Python | 5,959 | 132 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 18:58:12 2021
@author: fhu14
"""
#%% Imports, definitions
from MasterConstants import Model, RawData
from typing import List, Dict
import numpy as np
Array = np.ndarray
import torch
Tensor = torch.Tensor
from Spline import get_dftb_vals
#%% Code behind
class Input_la... |
11f29b79ed61bf668b40294847d15d1aea883f8290197a5eeaacc44136fc6823 | Python | 5,970 | 144 | import csv
import numpy as np
from sklearn.cluster import KMeans
class DataLoader(object):
def __init__(self, data_name, data_path, client_list, sample_num_in_total, beta):
# SUSY, Room Occupancy;
self.data_name = data_name
self.data_path = data_path
self.client_list = client_list... |
3a468bbd14592e594f4807818bb8496944aa8e5c242f554b3f9d5ba8e8a14419 | Python | 5,973 | 196 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={'relative_positi... |
6d792d375e022fd713b62ea87fa9c7a9ecd52b7b4ba7e635d60cc18ccca7147d | Python | 5,973 | 196 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={'relative_positi... |
29d722c36a660e691982c1bac27518529862ac917cfbe05f84935c12deac3f9f | Python | 5,974 | 196 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={'relative_positi... |
74256d5f803dfcdd4dfe34fea45430179bdb1ed26374f9c32f6aea96a84a0b02 | Python | 5,974 | 196 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={'relative_positi... |
2aa41e9ec10de7abb43aeb1adcaaa905cfe0a5394e1259c5ca9693427f49dba6 | Python | 5,981 | 178 | import glob
from pathlib import Path
import mne
import numpy as np
import torch
from scipy.signal import resample_poly
from sklearn.preprocessing import RobustScaler
from anysleep_no_hydra import AnySleep
def load_edf(input_file: str, channels_to_include, resample_rate):
"""
Load and preprocess an EDF file ... |
9e6b8b41e80fd9512fac04224b11b2995ffa43f5af413adcbc87b2b998122600 | Python | 5,982 | 228 | import numpy as np
from scipy import stats as spstats
def syn_current(duration=120, dt=0.01, t_on=10, curr_level=5e-4, seed=None):
duration = duration
t_off = duration - t_on
t = np.arange(0, duration + dt, dt)
# external current
A_soma = np.pi * ((70.0 * 1e-4) ** 2) # cm2
I_inj = np.zeros_l... |
554be0e59cbba3fce9d41fcf7f9f052fb2c765eaa7d4a2a0eda783186dfa1279 | Python | 5,986 | 124 | import sys
import os
# runtype is overwritten when called by manager_50_ope or manager_30
runType = 'fast_tuning' # ['tuning', 'fast_tuning', 'opeForecast']
# Minutes to wait for asking condor_q
time_step_check = 60
# metric for best model selection
metric = 'RMSE_val'
# ml models to rerun after fust tuning (obsrvati... |
fa0ca4e2fb8a9ee6aa6acc8a1f3f7017f94c9b75b9afe687e170ebcc0933ad3e | Python | 5,989 | 143 | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
from unittest import TestCase
import numpy as np
from mmcv import is_list_of
from mmpose.core.post_processing.smoother import Smoother
class TestSmoother(TestCase):
def build_smoother(self):
smoother = Smoother(
... |
39862b276ae6be21a181772dfd2ec2623244adb7beb637ab97c3f6c2827242b1 | Python | 5,994 | 146 | import argparse
import os
from pathlib import Path
# from typing import Dict, List
from typing import List
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
import xgboost as xgb
from selbstaufsicht.models.xgb import xgb_utils
def save_contact_maps(preds: np.ndarray, msa_mapping... |
a2d7a468f7cc4d5a2fefd05f67bc83ffe7582a52b7ad18886fe77b22bdc5692a | Python | 5,995 | 159 | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
import mmcv
import numpy as np
import torch
from mmcv.runner import load_checkpoint
def generate_inputs_and_wrap_model(config_path,
checkpoint_path,
input_config,
... |
bb36c03dd658f02cc424f593db26075fc3750a9bc13a38270810b95176802548 | Python | 5,995 | 134 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class PointAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each point.
... |
6b34e363edc14925f09188864100e813e6fde4597e5446fd01b3a510ba297640 | Python | 6,005 | 140 | import os
import sys
import time
import signal
import logging
import numpy as np
from sklearn.metrics import confusion_matrix
import metstab_shap.grid as grid
from metstab_shap.config import utils_section, csv_section, metrics_section, force_classification_metrics_section
from metstab_shap.config import parse_model_co... |
c0e648ed4c985af5df8a69a94c27b58dccf0139e06582848a71979d70fa807a3 | Python | 6,005 | 155 | import os
import re
import time
from multiprocessing import cpu_count
import numpy as np
from h5py import File
from consts import CUTOFFS, HARTREE
from dataset import Dataset
from dftb_calc import dftb_calc
from dftb_parse import dftb_parse
from model import RepulsiveModel
from options import Options
from skf import ... |
faa7eaeb17220d292d9302106843370c124ca9f9187296f7ddd0d4d7e8a8973f | Python | 6,009 | 254 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 27 17:13:07 2020
@author: baris
"""
gra_spikes = gra_spikes_diff[0]
f=10
n_phase_bins=360
dur_ms=500
dur_s = dur_ms/1000
T = 1/f
time_bin_size = T
times = np.arange(0, dur_s+time_bin_size, time_bin_size)
n_time_bins = int(dur_s/T)
def mean_phase(s... |
0ffd24a521f85dd66639e273cdcde97020caf2482dfb2f8104881fd4ac296e4b | Python | 6,017 | 161 | """Test cluster-based sample splitting for scalar PLR models."""
import numpy as np
import pytest
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from doubleml import DoubleMLData
from doubleml.plm.datasets import make_plr_CCDDHNR2018
from doubleml.plm.plr_scalar i... |
f1bd3451bb4e6d575b9f42aaa95faaf50a995325e85db8b443fc3ced1e598a49 | Python | 6,018 | 137 | from torch.utils.data import Dataset, DataLoader
import pandas as pd
import numpy as np
import SimpleITK as sitk
from PIL import Image
import nrrd
import os
import sys
import torch
from torch import nn
from torch.nn.utils.rnn import pad_sequence
import pytorch_lightning as pl
class CleftDataset(Dataset):
def __i... |
03509b1e900caa458584ec9d71a49304e1dde70e2f8f56b3f44eb4afdc499764 | Python | 6,019 | 157 | # 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... |
1866ab1407ba8c0756d06f8da40063160817b3fa9f017859c6abec0ea36edbc8 | Python | 6,020 | 190 | import numpy as np
import pytest
import random
from Bio.Align import MultipleSeqAlignment
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from clipkit.modes import StopCodonMode
from clipkit.msa import MSA
from clipkit.settings import DEFAULT_NT_GAP_CHARS
def make_msa(*sequences: str) -> MSA:
alignme... |
f87f4f85f5bd9be6dcdc4bd99d1488b52617e86a7ff47a3b2ef80eb9b15d3c76 | Python | 6,022 | 157 | import logging
import torch.utils.data as data
import numpy as np
import h5py
import os
from .data_utility import init_transform, count_samples, build_pairs
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def get_transforms():
transforms_train = ["Resize", "RandomCrop", "Rando... |
2e6307f2d9a024e3987030cf987a7e2167f45ff9f3bf0048076a5af7702e46de | Python | 6,026 | 201 | # 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 Optional
from warnings import warn
import nflows.nn.nde.made as made
import numpy as np
import torch
import torch.nn.functional as F
f... |
433d6a1e5ac641de7c1fd696453ebd95d2417be9319551e6b80e735e5b48ebf6 | Python | 6,028 | 173 | from enum import Enum
import re
import pathlib
from collections import namedtuple
from datetime import datetime
import openslide
from PIL import Image, ImageFile
Image.MAX_IMAGE_PIXELS = 100000000000
ImageFile.LOAD_TRUNCATED_IMAGES = True
class ImageType(Enum):
# our supported image types
SVS = '.svs'
JP... |
50e6885fbfd329ced6e3f2a2a2320f77f2935fad323d74fef76f56fda42b4219 | Python | 6,040 | 157 | import logging
import torch.utils.data as data
import numpy as np
import h5py
import os
from .data_utility import init_transform, count_samples, build_pairs
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def get_transforms():
transforms_train = ["Resize", "RandomCrop", "Rando... |
bdabee1ffd6cfa4196cbc7f3d40c114bbd8ac7664e91fbbc194e066aa2795107 | Python | 6,040 | 167 | # ------------------------------------------------------------------------------
# Copyright and License Information
# https://github.com/microsoft/voxelpose-pytorch/blob/main/lib/models
# Original Licence: MIT License
# ------------------------------------------------------------------------------
import torch
import... |
0941a74cb2dcd13dca51eca447edb5e5c714fffb0ec419d8250713a13668bda3 | Python | 6,044 | 168 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmpose.apis import (get_track_id, inference_bottom_up_pose_model,
inference_top_down_pose_model, init_pose_model,
vis_pose_tracking_result)
from mmpose.datasets.dataset_info import DatasetInfo
def tes... |
664c7c09a5a60fae3d773ebda706a632701c8c363a8c403ce7d5c1659cafad0b | Python | 6,047 | 168 | #!/usr/bin/env python3
if 1:
# This is needed for the SE calculation
import rpy2
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector
from rpy2.robjects import r
importr('coda')
import statistics
import math
import numpy
# from scipy.stats import beta
import sys
imp... |
08c54f9887f37af05b010f560b3c83f34373f105c429b440d2d010153706493e | Python | 6,049 | 184 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 12 12:31:03 2022
@author: baris
"""
import matplotlib.pyplot as plt
import numpy as np
import grid_model
import matplotlib.gridspec as gridspec
from neo.core import AnalogSignal
import quantities as pq
from elephant import spike_train_generation a... |
628d60dbbdce617c0b185d3e76ffbfe3c65ee07a54db29626511cf3962e19682 | Python | 6,049 | 129 | import logging
import torch
from torch import nn, optim
from fedml_api.distributed.fedgkt import utils
class GKTClientTrainer(object):
def __init__(self, client_index, local_training_data, local_test_data, local_sample_number, device,
client_model, args):
self.client_index = client_inde... |
16aa10521d476589b345d6334d70faf7ca3a0e98e87d4ff106b7724362b0342a | Python | 6,056 | 184 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser
import cv2
import mmcv
from mmpose.apis import (get_track_id, inference_bottom_up_pose_model,
init_pose_model, vis_pose_tracking_result)
from mmpose.core import Smoother
from mmpose.d... |
96e4ab101ee1b152e2800a1996e4db0b25cac49ca95254d105c6acf839ef08bb | Python | 6,061 | 245 | #program for cnn
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.image import imread
import cv2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img... |
61360765ed19db0477592748d59559bcc553b85337fa930afda927b543eb5215 | Python | 6,064 | 149 | import logging
import os
import h5py
import numpy as np
import torch
import torch.utils.data as data
from . import utils
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
client_ids_train = None
client_ids_test = None
DEFAULT_TRAIN_CLIENTS_NUM = 500
DEFAULT_TEST_CLIENTS_NUM = 100
DEFA... |
76683563e4279cef43598a99867038de66393690a57b6f376c3ecce484f241e3 | Python | 6,066 | 155 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
@LOSSES.register_module()
class JointsMSELoss(nn.Module):
"""MSE loss for heatmaps.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may h... |
62f4bc50b5f7b5934a680b5910755d61800d97d74f46f35bf99296c0d1e74098 | Python | 6,073 | 157 | import os
import logging
from pathlib import Path
from functools import reduce, partial
from operator import getitem
from datetime import datetime
from tmp.logger import setup_logging
from tmp.utils import read_json, write_json
class ConfigParser:
def __init__(self, config, resume=None, modification=None, run_id=... |
b51c63940547104b9f3d73c3aed9f5f43ad769d4ddf9af7046bc34e423e8fd9b | Python | 6,074 | 149 | """Capture-time reference ordering and reuse of the validated graph output."""
import numpy as np
import pytest
import torch
from conftest import CAFFEINE_FILE, load_mol
from aimnet.calculators import AIMNet2Calculator
from aimnet.calculators.cuda_graph import dense
pytestmark = [pytest.mark.gpu, pytest.mark.weights... |
a1ac8e49ac80960c98a511c7c19c21c2157fdb59f140ea890d8d03485f1d7cb4 | Python | 6,079 | 131 | import time
from glob import glob
import numpy as np
import os
from run_type import RunType
from predict import predict_accuracy
from sklearn.model_selection import train_test_split
from tensorflow.keras import backend as K
from tensorflow.keras import callbacks
from tensorflow.keras.losses import binary_cross... |
b9e59e09962fc2d63211bb4435d34b964fae477abb02a5cbbe0783d17858ddbc | Python | 6,080 | 140 | import pandas as pd
import numpy as np
import sys
from Bio import SeqIO
from multiprocess import Process, Manager
import os
import subprocess
from tqdm import tqdm
from Web_genome import download2
def renames_gff(df, original_domains):
"""
Esta función convierte los nombres de los dominios del gf... |
5fb7f5117e758f6a646f88da9aba5b376cb771d55c3f93077ac384b3409828b3 | Python | 6,087 | 191 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.necks import FPN
def test_fpn():
"""Tests fpn."""
s = 64
in_channels = [8, 16, 32, 64]
feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8]
out_... |
8883c8fc117da61478e4dac3a79b2a11cf1356d1f4ddbb10f9eb31ac62f0e317 | Python | 6,087 | 222 | import pathlib
import mne
from mne.io import Raw
from mne.io.edf.edf import RawEDF
from mne.io.base import BaseRaw
try:
import autoreject
except ModuleNotFoundError:
import h5io
import sys
sys.modules["mne.externals.h5io"] = h5io
import autoreject
import numpy as np
import pandas as pd
from toolz ... |
1955c9f91913097e4e19b3e1d5de14a4459de0cb3e6677760591306e78f26699 | Python | 6,089 | 129 | """
Shivai worflow for swomed with a dcm2nii first step and synthseg
"""
from nipype import Node, Workflow, IdentityInterface, DataGrabber
from nipype.interfaces.dcm2nii import Dcm2niix
from shivai.interfaces.shiva import (Shivai_Contained, SynthsegContained)
from shivai.utils.container_config import configure_contai... |
cbfec266141cf9aa94be2e107c36c130bc00e8e84a9ade13e58dd3ac95f80985 | Python | 6,094 | 160 | import seaborn as sns, numpy as np
import matplotlib.pyplot as plt
import glob
import pandas as pd
data = np.load("rate_n_phase_codes_perceptron_2000ms_net-seeds_410-419.npz", allow_pickle=True)
n_seeds = 10
th_grid = data['grid_th_cross'].T.reshape(6*n_seeds)
th_gra = data['gra_th_cross'].T.reshape(6*n_seeds)
speed... |
76fb769a4d9c85b02b9602f30c75580f504806c7870ae6ff3ed78f191ada5723 | Python | 6,110 | 203 | # -*- coding: utf-8 -*-
# File Name : PolyBox.py
# Created By : Yannis Bendi-Ouis
from StimulationsCodes import *
from PolyStimulations import *
from openvibe import *
import sys, traceback, collections
from io import StringIO
import re
def get_label_from_stim(stim):
stims_1 = list(OpenViBE_stimulation.items())
st... |
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