sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e6faaf7e4240e56fab1e683904ec251eaa143fe46cb914f906fd0f95590ab1fc | Python | 2,578 | 68 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 27 17:56:04 2020
@author: bariskuru
"""
import numpy as np
from elephant import spike_train_generation as stg
from neo.core import AnalogSignal
import quantities as pq
from scipy import interpolate
def inhom_poiss(arr, n_traj, dt_s=0.0001, speed_c... |
2c642713aafb4f48d7c20e5702aa684ac7d408d883a8efa8b3e3b0ea3b45b5e6 | Python | 2,579 | 61 | # 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... |
ab8458a167a19b4bd5e1ab8ba8e1eafbdd59b89c04e6ef596e8395aab2758153 | Python | 2,581 | 68 | import numpy as np
import os
import pandas as pd
import pathlib
import SimpleITK as sitk
import argparse
from imutils import paths
from dtor.utilities.utils import image_search, bbox3d, crop3d
parser = argparse.ArgumentParser()
parser.add_argument("--labels", type=str, help="Location of labels", default="data/external... |
c7107008fd293efd986a54cd9d79e51768d2cd2cccb14278c63fefa2a6023f7a | Python | 2,587 | 80 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.backbones import ResNet
from mmdet.models.builder import SHARED_HEADS
from mmdet.models.utils import ResLayer as _ResLayer
@SHARED_HEADS.register_module()
class ResLa... |
b6f56ada2bf0e6a4ddad65924547bff94a7ae2f8e39a223a72b81f53c680b44f | Python | 2,592 | 77 | # Copyright (c) OpenMMLab. All rights reserved.
# Code is modified from `HRNet/DEKR <https://github.com/HRNet/DEKR>`.
import torch
from mmcv.runner import load_checkpoint
class DekrRescoreNet(torch.nn.Module):
"""Rescore net used to predict the OKS score of predicted pose. We use the
off-the-shelf rescore ne... |
1a555fa29414dfe6b7f33c8afdfa4483ba6b72132796b207634d64702f849224 | Python | 2,593 | 49 | import cv2
import xml.etree.ElementTree as ET
import os
from pathlib import Path
import numpy as np
import random
# 原图片、标签文件、裁剪图片路径
img_path = r'D:\Tuo\yolov5-cell\detect\output\image'
xml_path = r'D:\Tuo\yolov5-cell\detect\output\detect_xml'
obj_img_path = r'D:\Tuo\yolov5-cell\detect\output\split'
# 声明一个... |
9ac9a3521075d8ce598ac7a5f31663e27ec2be47f19c50f2090ef20dac589fe0 | Python | 2,595 | 69 | import os
import sys
import csv
import logging
import subprocess
import glob
import torch
import numpy as np
import pandas as pd
import importlib
from collections import OrderedDict
from timm.utils import get_state_dict
def save_best_model(model, model_ema, optimizer, lr_scheduler, epoch, loss_scaler, trained_epochs... |
9365ab7f42a88feba5a02717a456b380d59b178501fec34d8309b2b2c5e1780a | Python | 2,603 | 84 | import numpy as np
import torch
import matplotlib.pyplot as plt
plt.switch_backend('agg')
def adjust_learning_rate(optimizer, epoch, args):
# lr = args.learning_rate * (0.2 ** (epoch // 2))
if args.lradj == 'type1':
lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch - 1) // 1))}
elif args.l... |
9ac07178f2ce6cce97eefaf0b0f8ec3ab33c8d268ac58bd094331479f8fcb498 | Python | 2,604 | 87 | import pytest
import torch
import lightning as pl
from torchmdnet.models.model import create_model
from utils import create_example_batch
def _make_args_tensornet2(static_shapes):
return {
"model": "tensornet2",
"embedding_dimension": 128,
"num_layers": 2,
"num_rbf": 32,
"... |
b162b2b70ae5de813c65de65b3f7e5934d2a5d4490dd198e570abbf1e8a2e038 | Python | 2,608 | 83 | # Copyright (c) OpenMMLab. All rights reserved.
"""Tests for async interface."""
import asyncio
import os
import sys
import asynctest
import mmcv
import torch
from mmdet.apis import async_inference_detector, init_detector
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import concurrent
class ... |
e3b84655f8f3d210d941b2062a9a547aa323cbfc9a68394bb92dcace1f8d591c | Python | 2,612 | 50 | # 导入模块
import cv2
import xml.etree.ElementTree as ET
import os
from pathlib import Path
import numpy as np
import random
# 原图片、标签文件、裁剪图片路径
img_path = r'D:\Tuo\yolov5-cell\detect\output\image/'
xml_path = r'D:\Tuo\yolov5-cell\detect\output\detect_xml/'
obj_img_path = r'D:\Tuo\yolov5-cell\detect\output\split/... |
6be3e2863f49328127a160cd19c3c2f5093d287510afa76b0b74ccb467a6d5da | Python | 2,614 | 110 | #!/usr/bin/env python
import sys
from optparse import OptionParser
def interval_to_bedpe(size, call, sv_type, i):
half = size/2
chrom,interval = call.split(':')
start,end = [int(x) for x in interval.split('-')]
length = end-start
bedpe = '\t'.join([chrom,
str( max(1,start-hal... |
91c21fe6a99bc805338c4d53ac99af3ea6faae417931d1b4645f6705ac4df289 | Python | 2,614 | 69 | import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
import argparse
from utils import log_1px_transform, siren_model, scheduler
if __name__ == "__main__":
argparser = argparse.ArgumentParser(description="Train ML model with specific hparams")
argparser.add_argument("--lr", type=fl... |
b945089b8b98ce97fd7a86601c748ae2f539d55e22a637010aba4742ae2e78dc | Python | 2,620 | 69 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 27 17:56:04 2020
@author: bariskuru
"""
import numpy as np
from elephant import spike_train_generation as stg
from neo.core import AnalogSignal
import quantities as pq
from scipy import interpolate
def inhom_poiss(arr, n_traj, dt_s=0.0001, speed_c... |
a3c1decc0e363dfda193cb836a29b3c5b592314aabe7408533c0327a9c7f7100 | Python | 2,622 | 83 | import pytest
from binn import BINN, Network
import pandas as pd
import torch
@pytest.fixture
def binn_model():
input_data = pd.DataFrame({"Protein": ["a", "b", "c"]})
pathways = pd.DataFrame(
{"source": ["A", "B", "C"], "target": ["path1", "path1", "path2"]}
)
translation = pd.DataFrame(
... |
38d6b1fc864115ad89267d9c738e3c10b16211a13e9c48b13cbe77dfc1eb84c6 | Python | 2,623 | 40 | #! /usr/bin/env python
# ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source ... |
5cf0c342c0a958ffd04febc6f5cef096317913f0eb052a6529f9d0a1cbe02d3c | Python | 2,626 | 73 | from torch.nn import CrossEntropyLoss, MSELoss
from transformers import BertModel, PreTrainedModel, BertConfig, load_tf_weights_in_bert
from transformers.modeling_outputs import Seq2SeqSequenceClassifierOutput, SequenceClassifierOutput
import torch
from torch import nn
import torch.nn.functional as F
from model.bert_mo... |
028bb6ea32ef099f8d68cd286ec09d96bac75f60e08f75ca9d43819bc6487826 | Python | 2,631 | 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 sbi.neural_nets.factory import (
classifier_nn,
likelihood_nn,
marginal_nn,
posterior_flow_nn,
posterior_nn,
posterior_score_... |
c9f5cecedc7bb5e1bd6a80232912710d211def03ab2ba4415e3f37a2dfc6f421 | Python | 2,635 | 76 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.cnn import is_norm
from torch.nn.modules import GroupNorm
from mmdet.models.utils import InvertedResidual, SELayer
def test_inverted_residual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
Inv... |
cbb8dae088a7646f654fc6bda6f17bf7c72ad3a79716fd52c15c96e60004ee7d | Python | 2,635 | 74 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
11d54fd63871e58f80c9186487b250432768bb4d3104cff79cb7381a39621a27 | Python | 2,637 | 87 | #!/usr/bin/env python3
# Copyright 2020 Tomoki Hayashi
# 2023 Jiatong Shi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Variance predictor related modules."""
import torch
from typeguard import check_argument_types
from parallel_wavegan.layers.layer_norm import LayerNorm
class VarianceP... |
9b402a15f8ac8e3fd00b5478f80ed18dc97b81bea79af4c98d82c83c168379e6 | Python | 2,637 | 74 | from typing import Union
import torch
from torch import Tensor
from torch.distributions import Binomial, Distribution, InverseGamma
from sbi.inference.potentials.base_potential import BasePotential
from sbi.utils.torchutils import atleast_2d
class BinomialGammaPotential(BasePotential):
"""Binomial-Gamma potenti... |
ce254ef7ff02e3dcb55deb0107a7f6c44d9124ae15c88624fda45f30b30123b8 | Python | 2,638 | 91 | import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateModel
from gradient_accumulator import unitwise_norm
from tensorflow.keras import mixed_precision
import os
def normalize_img(image, label):
"""Normalizes im... |
ceacac97b527947bdb7adbe97e554491996433965bfb4750f4853b2ee1eb82d3 | Python | 2,641 | 70 | import plotly.graph_objects as go
import os
import colorcet as cc
import pandas as pd
import numpy as np
from statistics.utilities import CONFIG
from statistics.clustered_mlp_output_wrappers import load_adni_clusters, load_adni_ad_clusters
def sankey_plot(cluster_to_cluster_map, label):
fig = go.Figure(data=[go.Sa... |
375cb3f99e88efaf589e6057932e103ca54958fc5831461e69d5685972e597fd | Python | 2,643 | 77 | import pandas as pd
import numpy as np
from utils.tools_processing import compute_distance_to_midline
def calc_DFF(f, baseline):
f0 = np.nanmedian(f[baseline[0]:baseline[1]])
dff = [(i - f0) / f0 for i in range(len(f))]
return dff
class Cell:
dff = []
def __init__(self, exp, plane, cell, id):
... |
a64f51d4d5d4ee008bbda105b12c20db205a94673cdc5d349dd65403983c3c7c | Python | 2,643 | 66 | # This script shows how to use a TorchMD-Net model as a force field in OpenMM
# We will run some simulation steps with OpenMM on chignolin using a pretrained model.
try:
import openmm
import openmmtorch
except ImportError:
raise ImportError(
"Please install OpenMM and OpenMM-Torch (you can use cond... |
6f1e65d4935b7798737c37febae886b715c1adf8bd3ed5c870a329670817ae5a | Python | 2,646 | 91 | #! /usr/bin/env python3.5
"""
This is a sample script that programmatically controls OSC1Lite using the API
"""
import ok # The OpalKelly SDK, you may need to manually copy it to current folder
import osc1lite # The OSC1Lite python interface
import os
# Enable debug logging
import logging
logging.basicConfi... |
7bf6126c540fb42a8a1a933160e749105c21fa005cb62ca45150011441b81d8d | Python | 2,647 | 68 | import numpy as np
import random
from .common_func import flip
class RandomFlip:
def __init__(self, horizontal=True, vertical=True, training=True):
self.horizontal = horizontal
self.vertical = vertical
self.training = training
def __call__(self, sample):
"""
Randomly f... |
d6f6c14874038158c53dbc4e3116e93d9e43dc78751485085b74f65f96d132e1 | Python | 2,649 | 94 | # IMPORT STATEMENTS
import cv2
import re
import numpy as np
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 mpl_toolkits.axes_grid1.mpl_axes import Axes
import ma... |
600189ac30ac70604e8bf2d4ca746cb20a2925141263f0aecbb3a79b7472c788 | Python | 2,651 | 47 | from mpi4py import MPI
from fedml_api.distributed.fedavg_robust.FedAvgRobustAggregator import FedAvgRobustAggregator
from fedml_api.distributed.fedavg_robust.FedAvgRobustTrainer import FedAvgRobustTrainer
from fedml_api.distributed.fedavg_robust.FedAvgRobustClientManager import FedAvgRobustClientManager
from fedml_api... |
bab653b9fb3436b425bb2c401ec4679190037e952632c5540d6f9e71228c4c08 | Python | 2,653 | 103 | import pickle
from pathlib import Path
import torch
from sbi.inference import SNPE, simulate_for_sbi
from consbi.simulators import (
RuleSimulator,
default_rule,
)
from consbi.simulators.utils import get_batch_simulator, seed_all_backends
seed = 42
seed_all_backends(seed)
# set parameters
BASE_DIR = Path(__... |
dcba5061738c55956db2107be5da1e2064999c11d1dcd6d6adcf4946da2d1350 | Python | 2,653 | 67 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, ModuleList
class ConvUpsample(BaseModule):
"""ConvUpsample performs 2x upsampling after Conv.
There are several `ConvModule` layers. In the first few layers, ups... |
0f85be3ec372badee562eab0685634e4a7658f3eb1be95921581c562ed30efac | Python | 2,656 | 62 | import pandas as pd
from tqdm import tqdm
import numpy as np
import rdkit
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit import DataStructs
#Fingerprint generation
#(@ming if we want to use inchi as an input, inch... |
d72288ee347592a34880488c3541fdb1e626e3bc74ceaf1a9d10fd8048c63e3b | Python | 2,656 | 76 | import numpy as np
import os
import time as t
import nibabel as nib
from os.path import join, basename, dirname
import re
import matplotlib.pyplot as plt
import ants
import multiprocessing as mp
import pandas as pd
"""
This class PREP is created to preprocess raw MRI images by
1. Normalization with ANTs.
"""
def regis... |
f7226021526275014747e734788923abce8302e778d2272707aeeb26b725f067 | Python | 2,656 | 86 | # -*- coding: utf-8 -*-
"""Process the outputs and generate ROC"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
from dtor.utilities.utils_stats import stats_from_results
import numpy as np
import argparse
from dtor.utilities.utils import set_plt_config
from dtor.utilities.model_retriever import load_model
from dt... |
a2d523f04bdfbcc01d490a8f9d104194cfed83e843ff411a30eb1a1970d4be4d | Python | 2,658 | 67 | import h5py
import torch.utils.data as data
class StackOverflowDataset(data.Dataset):
"""StackOverflow dataset"""
__train_client_id_list = None
__test_client_id_list = None
def __init__(self, h5_path, client_idx, datast, preprocess=None):
"""
Args:
h5_path (string) : path... |
59164a294c9153447656a6d4f137e994d8a51aa26f328c1b02964646b040f027 | Python | 2,663 | 72 | """
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 ExpRandomShiftNaive:
"""
Subsample the 3D numpy [h, w, d] by slice max number.
"""
def __init__(self, random... |
744f5f536f640582849b4205c7b3cd477b8a8463411a93a89d9ce2a59557b277 | Python | 2,664 | 84 | import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateOptimizer
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., label
def test_opti... |
b2b82f2bfcc9f49e592500f635ca0240357be0913ad0f17db87b4c68af435e43 | Python | 2,664 | 101 | """
Methods for loading test datasets.
For information on test datasets, including sources (where available)
see the documentation.
One dataset, ``weblinks.hist`` is given as a histogram, not sampled data
so we can't analyze it with this library.
"""
import numpy as np
import os
import pathlib
from .plotting import... |
81d25c624c5f681fe90ee7133b4e4706eaab4f3ed947937d6d85d79fcbe4182e | Python | 2,665 | 74 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
4ff5343bd6ec4f4cbcc053cc2ae04713eb85854aad96b19f8add3ec1044eec85 | Python | 2,667 | 58 | import sys
import glob
import shutil
import os
import pandas as pd
from pathlib import Path
from F_post_processsing import F100_analyze_hindcast_output
from A_config import a10_config
import tuner
import threading
import manager_0_user_params as upar
from manager_20_tune import monitor_condor_q
if __name__ == '__main_... |
809e5a2b927e865f308e1e0ab055332d85b62ea893e280a8460a33e2f06f74d1 | Python | 2,673 | 45 | #! /usr/bin/env python
# ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source ... |
00bc8807d771cef9f02570f4edb63d3aa638447f785e3e713f47ba74385fe701 | Python | 2,678 | 58 | import pandas as pd
import numpy as np
import os
import shutil
"""
Split Nigeria (Wet, sorghum and maize) in two items: mono and bimodal areas. Prepare Tuning data. Config must be adapted manually
"""
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.expand_frame... |
4cdca7fd7862448a3303342b35ac45c2252df22e7fd2a132050ebba8730c53e7 | Python | 2,678 | 50 | # ***********************************************************************************
# * Copyright 2010-2017 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source and binary forms, w... |
f300772c6ec88cb38752d7c366b70173adc4fd68ebaafc63ac43b9b8bb8ab946 | Python | 2,679 | 86 | # Copyright (c) OpenMMLab. All rights reserved.
import tempfile
import numpy as np
import pytest
from mmpose.core import (imshow_bboxes, imshow_keypoints, imshow_keypoints_3d,
imshow_multiview_keypoints_3d)
def test_imshow_keypoints_2d():
img = np.zeros((100, 100, 3), dtype=np.uint8)
... |
723483dd9238a8912d2876e61c46360dfed0ed4198d69f81b3974440c5403044 | Python | 2,684 | 65 | import math
import torch
"""
IMPORTANT NOTE: Piecewise functions can be a bit tricky in pytorch:
If there are divisions by 0 in different branches of the piecewise definition,
this can lead to nan values in gradients when automatic differentiation is used
(even if the problematic branch should in theory not be evaluat... |
bb72f40e80ff73367bf1c6ca17e7d41c40b63f17a25b92608784f5474366012c | Python | 2,684 | 75 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
dda9cad5dbf52d4178d997bbced540817b2ac3e9e7675994326be3f494eb4540 | Python | 2,684 | 54 | import logging
from fedml_api.distributed.fedgkt.message_def import MyMessage
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communication.message import Message
class GKTClientMananger(ClientManager):
def __init__(self, args, trainer, comm=None, rank=0, size=0... |
543045dc18b824c978968790bce632bd61cc5a7649a7e38a63e972b43a89014f | Python | 2,686 | 77 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
a741a6075a1cca17519acfce05c1e61e9b4a7a5a793363dc0e99eccce0be9c2d | Python | 2,688 | 81 | 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
import matplotlib
import matplotlib.pyplot as plt
direc_data = sys.argv[0].split('script')[0]+'data/'
direc_fig = ... |
0130868ded86d21bbd89106da78f1d0ac9531ad145f0fcb19c6cddec2ebffeb0 | Python | 2,691 | 97 | import os.path as osp
import numpy as np
import numpy.random as npr
import PIL
import cv2
import torch
import torchvision
import xml.etree.ElementTree as ET
import json
import copy
from ...cfg_holder import cfg_unique_holder as cfguh
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
... |
d43307f290a6ca776b8f5dbdedadec4a84888f08637ce026f3f0a3b4dee04750 | Python | 2,694 | 76 | # 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 pytest
import torch
from sbi.analysis import ActiveSubspace, conditional_corrcoeff, conditional_pairplot
from sbi.inference import NPE
from sbi.ut... |
f2fbf21a972913f48a2fb09252fbea8f887fd40473eeb01d9233d876b1c78e7a | Python | 2,694 | 78 |
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be usef... |
14ea81310913a5fead7bfd51743ea75569535752c35cea7eb99453c2e2a22869 | Python | 2,695 | 78 | #######################################################################
# Software License Agreement (AGPL-3 License)
#
# OpenViBE SDK Test Software
# Based on OpenViBE V1.1.0, Copyright (C) Inria, 2006-2015
# Copyright (C) Inria, 2015-2017,V1.0
#
# This program is free software: you can redistribute it and/or modify
... |
8a85c30fd3bb663e77af6931a5dc3c95985ff4d531e7b7e3a0d554769b4b1a73 | Python | 2,697 | 75 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
939dcd354b64b30986f5beadfda8c1d7fecb384466bb703bf04415b8829417e7 | Python | 2,697 | 83 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmpose.models.backbones.swin import SwinBlock, SwinTransformer
def test_swin_block():
# test SwinBlock structure and forward
block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
assert block.ffn.embed_dims ... |
43f9b05c8e69bc79d22c228431e5201f303f62bad55cd72e14e74624c42d3824 | Python | 2,698 | 73 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without res... |
40732a6b7b65f094108923b4608976ff282b3545102259d5c33d54ce6b40a5fa | Python | 2,702 | 72 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 20:09:22 2021
@author: fhu14
"""
import numpy as np
from scipy.spatial.distance import pdist, squareform
from .util import TriuToSymm
class Repulsion(object):
def __init__(self, atomList, parDict):
self._atomList = atomList
self.__parDict = ... |
baaa9a43bc1c85171c9efc9a58b0e8dc196cdd65a250b3b35d7a90dda0d74194 | Python | 2,703 | 62 | import torch
import torch.nn as nn
import torch.nn.functional as F
##https://github.com/RaffaeleGalliera/pytorch-cnn-text-classification/blob/master/model.py
class CNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim,
dropout, hidden_dims):
sup... |
054ca0f016bd4c5ea5cf3e186538e85e9ef36d06872c2502576ccdc04d3b8b7c | Python | 2,705 | 88 | """
Generate per-recording confusion matrices for detailed error analysis. Used to create Figure 1a in our manuscript.
This script runs inference on a dataset and computes a confusion matrix for
each recording and channel combination, enabling fine-grained analysis of
model performance across different subjects and ch... |
00d3a6aac9d291bc051011f8d437538ed6325fc586bc9f72c9592c5fd67fe4c3 | Python | 2,712 | 57 | # Copyright (C) 2023-2025 ETH Zurich, Marc Lehner and other DASH contributors.
import pickle
import os
from typing import Sequence
import argparse
from serenityff.charge.utils import command_to_shell_file
from serenityff.charge.tree.tree_utils import (
get_DASH_tree_from_DEV_tree,
)
from serenityff.charge.tree_de... |
bb37ae62ca73d2102f3d6fafe4c4d9d9f97423593fc8c43f9c11cbd27151e1f3 | Python | 2,716 | 76 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import YOLOFHead
def test_yolof_head_loss():
"""Tests yolof head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad... |
b1750c5302a690ac57b8962c7545a9116d4a57b3e9b262c39eff4863a679316d | Python | 2,718 | 83 | import numpy as np
import numpy.testing as testing
import xgboost as xgb
from selbstaufsicht.models.xgb import xgb_utils
def test_sigmoid():
x = np.array([-np.log(42), 0, np.log(42)])
y = xgb_utils.sigmoid(x)
y_ref = np.array([1/43, 0.5, 1 - 1/43])
testing.assert_allclose(y, y_ref)
def te... |
b8d2859198f6f7985cd9b66a359a75dac5c365424739a16773e6b42e99a8e6a2 | Python | 2,718 | 104 | # Copyright (C) 2021 Grzegorz Chojnowski
# EMBL-Hamburg
import os,sys
from setuptools import setup
import setuptools
import subprocess
import distutils.log
import distutils.command.build
from distutils.command.install import install as DistutilsInstall
from pathlib import Path
from setuptools.command.build import build... |
31e3f8bcf21ed3239300fa0cb481555e70f27985f6b59db8df903e55792c3de0 | Python | 2,719 | 97 | #!/usr/bin/python
"""
Saving output files
"""
import numpy as np
import pandas as pd
from pathlib import Path
import shutil
# -----------------------------------------------------------------------
def save_best_model(path, pattern):
"""
"""
best_dir = path / 'best_model'
best_dir.mkdir(parents=True,... |
178cd50aa084c360a1d88a2af3084f86a378675298676fe4a28fb07bee2691db | Python | 2,720 | 112 | import pathlib
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import backend
from functions import *
###############
#signal2 --> data1 predictions / lo
#signal --> data2 ... |
7ad2daa722980233d804a3deb0c0b28907bbf7e047c226dd2a4b02dab172e59e | Python | 2,720 | 75 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
212469dd52e1b110839dfa35635968a870ea3b2f5c82ec850ccea91e9f71e543 | Python | 2,721 | 94 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 9 13:47:35 2021
@author: fhu14
Testing for the functionalities implemented in DataPrecompute
NOTE: This test does NOT guarantee correctness, only that the code can run through
the fold generation and precompute processes!
TODO: Incorporate gammas precompute in... |
31fc87548ee770e950c31dcb363d8bf598af8319247c094a369b451aa39ce707 | Python | 2,721 | 80 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor
def test_replace_ImageToTensor():
# with MultiScaleFlipAug
pipelines = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
... |
9de790a2ffeefce02dd91b1b2f7a6927aac5029207e00708d13b9ceb6f7800e2 | Python | 2,722 | 59 | # Author: Alfred Lim
# Location: Singapore
# Date: 2021-11-27
# Task: build example files for Lens
# Reference: Lim, O'Brien, & Luca (submitted)
## Import libraries
import numpy as np
import pandas as pd
import random
import math
import time
## Define function that write example files for Lens
def write... |
ae03abf44fb3cbfbe9af3a4d9fd7e989a09373a02d482f23e392ebe8dc83686f | Python | 2,722 | 78 | import os
import json
import random
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image
from data.utils import pre_caption
class nlvr_dataset(Dataset):
def __init__(self, transform, image_root, ann_root, split):
'''
image_root (string)... |
bb46e58968f732c315f5032d529ac34f1f9fd52d734447cb1c0d50fde33b79bc | Python | 2,723 | 69 | import pytest
from foolbox import accuracy
from foolbox.models import ThresholdingWrapper
from foolbox.devutils import flatten
from foolbox.attacks import BinarySearchContrastReductionAttack
from foolbox.attacks import BinarizationRefinementAttack
from conftest import ModeAndDataAndDescription
def test_binarization... |
e4c208bcdee9afd8df904ffc7f64c8954e5b2f55d5f8f82e38185a802849ef42 | Python | 2,725 | 96 | import logging
import numpy as np
import torch.utils.data as data
from PIL import Image
from torchvision.datasets import CIFAR100
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')
def accimag... |
80d3e9220463b2e3e828a7bc4895abc5aa67be26324b7a8b5916f2b9f9ab0e15 | Python | 2,729 | 46 | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .dataset_info import DatasetInfo
from .pipelines import Compose
from .samplers import DistributedSampler
from .datasets import ( # isort:skip
AnimalATRWDataset, AnimalFlyDataset, AnimalHo... |
8407d294a8d3d70f3d15eff1d383b7e81321672a4be5ee0e85d1317d986e3f57 | Python | 2,729 | 82 | #!/usr/bin/env python3
"""
test_advanced_activation_layers.py
This file is part of the test suite for keras2c
Implements tests for advanced activation layers
"""
import unittest
from tensorflow import keras
from keras2c import keras2c_main
import time
from test_core_layers import build_and_run
__author__ = "Rory Con... |
f5494dfa05ded809476d8e6db0acf1cf89f7ea179367a2d083bf4b42edaf34c8 | Python | 2,729 | 63 | """
SI figure for Reviewer 1, comment 1: per-electrode encoding correlation r of
each SFM at its best layer, plotted on the template brain (same plotting
recipe as interpretable_unique_r2_on_brain.ipynb).
"""
#### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for th... |
435f1adcb592383a7ea1af395fa314455837654228a5c167d4ebde721a95518c | Python | 2,730 | 69 | import os
from pathlib import Path
import numpy as np
import scipy.io as sio
from PIL import Image
from torch.utils.data import Dataset
class PascalVocDataset(Dataset):
def __init__(self, root_dir, split='train', transform=None, dataidxs=None):
self.images_dir = Path('{}/dataset/img'.format(root_dir))
... |
ba5586616a1496e064a1042df27d6293ed5e258ec40f82882e08d260240714ef | Python | 2,730 | 81 | # 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
import pytest
import torch
from torch import eye, zeros
from torch.distributions import MultivariateNormal
from sbi.n... |
18ad3c8980b4eedbb2cb90e52935021fe360425785dcc36c6142c40e62a841de | Python | 2,731 | 75 | from jax.config import config
config.update("jax_enable_x64", True)
import jax.numpy as jnp
import jax
import weno_scheme
import numpy as np
import weno_roll
import matplotlib.pyplot as plt
import tabulate
def test_reconstruct(N, u_fn, u_integral_fn, get_w, get_u_roll):
dx = 1/N
x = jnp.linspace(0, 1, N+1)
x_lef... |
5f0734ae6c1dd025c6882ffb609fbdd4000c9211fb9f44c82d32552511aa86a5 | Python | 2,733 | 51 | from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import numpy as np
import pandas as pd
import geopandas as gpd
import os
import pathlib
import calendar
from D_modelling import d090_model_wrapper, d140_modelStats
from D_modelling import d140_modelStats
from E_viz import e50... |
a154958d2b20a7708345b49a4ed5ff5b0b8b450a9bbbd4124dcbcf4ac97efe06 | Python | 2,733 | 75 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
68b2b730fee1e2275d5d5703611078e45380156646b5992a9f18138c36150eef | Python | 2,734 | 85 | import os
import numpy as np
import json
from glob import glob
import cv2
from sklearn.model_selection import train_test_split
from os import getcwd
classes = ["cell"]
# 1.标签路径
labelme_path = "LabelmeData/"
isUseTest = True # 是否创建test集
if isUseTest:
#labelme_path = "LabelmeData/"
labelme_path=".... |
9f73be088b9304c594ffed261f7beef45cc5a8fa3aeec1e14a7ae62d443360df | Python | 2,735 | 76 | import numpy as np
from scipy.integrate import odeint
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/'
sim_idx = int(sys.argv[2])
plast... |
509ec2898a8e7ffdbefa57203fbd9d1f0073cc15ffc2231693e36fffc5000865 | Python | 2,736 | 86 | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 21 19:15:47 2021
@author: fhu14
Test module for testing construction of gammas from gammas saved per fold
"""
#%% Imports, definitions
import os
import pickle
import numpy as np
from DataManager import load_config_tracker_per_fold, load_gammas_per_fold
from InputLayer ... |
b3ee8f422c29b321addb552a88bf344f8c8bcd6e2b060fcc0a8fce1939fb6b0d | Python | 2,736 | 79 | import sys
sys.path.append("..")
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import utils
from metrics import calculate_kl as KL_DIV
import config_bayesian as cfg
from ..misc import ModuleWrapper
class BBBLinear(ModuleWrapper):
def __init__(self... |
d12affddc83ea5b337742015c057ff71b68a973f66c67d61bce63ae425e3a30c | Python | 2,736 | 90 | '''
assumes that the user has already generated .json file(s) containing data
'''
import argparse
import json
import os
import numpy as np
from scipy import stats
parser = argparse.ArgumentParser()
parser.add_argument('--name',
help='name of dataset to parse; default: sent140;',
... |
3145fb8de965f19d134a95f933928818c497c20dc3074d2220df444c5697dcb2 | Python | 2,738 | 76 | import sys
import os
import requests
from zipfile import ZipFile
def __convert_size(size_in_bytes, unit):
"""
Converts the bytes to human readable size format.
Args:
size_in_bytes (int): The number of bytes to convert
unit (str): The unit to convert to.
"""
if unit == 'GB':
return '{:.... |
285666a601fe1dda3e263e5757f692c914bd4fb08d437b4cb8195c2e29697217 | Python | 2,739 | 76 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Test utilities based on Octave"""
import numpy as np
from numpy.testing import assert_allclose
from oct2py import octave
import pytest
from pystoi.stoi import DYN_RANGE, FS, MINFREQ, N_FRAME, NFFT, NUMBAND, OBM
from pystoi.utils import remove_silent_frames, stft, thirdo... |
f509ed50afbc936d7b7ae6e767525ee74deff06f9b69a4bd44e9cb86ac3d3857 | Python | 2,739 | 101 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
import scipy.stats as st
def get_path(file):
steps = file.split("_")
path = ' '.join((steps[-2], steps[-1]))
return path.split('.csv')[0]
def col(x, dico):
return plt.get_cmap("nipy_spectral")(x/... |
46ebea4bcb52a36aee08062f777aec7f33e674ae8d0c1da61ade63d0529ef19c | Python | 2,740 | 105 | import sys, signal
import numpy as np
import moderngl
from PyQt5 import QtWidgets
from PyQt5.QtOpenGL import QGLWidget
from PyQt5.QtOpenGL import QGLFormat
def make_qt_format():
fmt = QGLFormat()
fmt.setVersion(3, 3)
fmt.setProfile(QGLFormat.CoreProfile)
return fmt
class StimDisplay(QGLWidget):
d... |
d218697deb7fe7718f69636abaf29dd84820eac971974b335b8579c1a8d57846 | Python | 2,742 | 83 |
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be usef... |
ec14ad594e9908d37cb71c141c24c0b77861ecde72e795c45a001362a258dd8f | Python | 2,743 | 84 | # test_dit_dataPs.py
# This file compares admUI with the algorithm implemented in dit
# on the distributions in dataPs.mat (see directory /data/).
# The results are used in Figure 3 of the paper.
#
# Distributed under the terms of the GNU General Public License v3
from admUI import computeQUI
import dit
import time
imp... |
924a8c6445df7f1776dbe27a9bfd0ebd3d73f6ee110953b6b3a786ad8b9fcfae | Python | 2,745 | 73 | import numpy as np
def non_max_suppression_malisiewicz(boxes, overlap_threshold):
""" Eliminates redundant boxes using NMS adapted from Malisiewicz et al.
Args:
boxes (np.ndarray): A >=5 by N array of bounding boxes where each
of the N rows represents a bounding box with format of
... |
e4ff6a1f56c754ef56e6c5954064a2e74255cf2b8c7514a37cdda106bab53b0b | Python | 2,749 | 95 | import json
import random
import numpy as np
from tqdm import trange
NUM_USER = 30
def softmax(x):
ex = np.exp(x)
sum_ex = np.sum(np.exp(x))
return ex / sum_ex
def generate_synthetic(alpha, beta, iid):
dimension = 60
NUM_CLASS = 10
np.random.seed(0)
samples_per_user = np.random.lognorm... |
25eae53cc28d0606c657e1edddbc3ae9b4e7e94311c5d6f3cd719ec552b48b4c | Python | 2,753 | 92 | #!/usr/bin/env python
"""
Test script for parallel processing implementation in ClipKIT
"""
import time
import sys
from clipkit.clipkit import execute
from clipkit.files import FileFormat
from clipkit.modes import TrimmingMode
def test_parallel_processing():
"""Test that parallel processing works correctly"""
... |
ae57d3bf74d8213dd49dfc887ecd4275cabec2188338f278fdf3f2334fda2dbf | Python | 2,753 | 97 | # coding=utf-8
from typing import Callable, Union
from torch_geometric.typing import OptPairTensor, Adj, OptTensor, Size
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_mean, scatter_add
class MeanReadout(nn.Module):
"""Mean readout operator over graphs with v... |
e7af36291bd9f6af992669501a3ad7c99b46ae900e3f9590f55bf2bcd2c176ae | Python | 2,755 | 49 | #! /usr/bin/env python
# ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source ... |
f41c21abf8f37a3b327d1b2f0be8409bda1326e3cb648154430a6e0019948d38 | Python | 2,755 | 74 | 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
training_splits = [0,1,4,6,7,9]
number_reports = [11182,7382,2897,1214, 865, 453 ]
number_patients= [884,592,214,103,68, 35 ]
filename = os.path.basenam... |
0ef7b6c30dabbe550bf660ca1b97e7fb0967c41350f787d803e4b7f00f5f55f0 | Python | 2,761 | 76 | import numpy as np
def non_max_suppression_malisiewicz(boxes, overlap_threshold):
"""Eliminates redundant boxes using NMS adapted from Malisiewicz et al.
Args:
boxes (np.ndarray): A >=5 by N array of bounding boxes where each
of the N rows represents a bounding box with format of
... |
69a138bab2d3cf59124a96398b1892ef102b4e32ea4ed6a40bdad98c14177214 | Python | 2,761 | 95 | import numpy as np
import pytest
import nengo
from nengo.utils.stdlib import Timer
from nengo_extras import Conv2d, Pool2d
@pytest.mark.parametrize('local', [False, True])
def test_conv2d(local, Simulator, rng):
f = 4
c = 2
ni, nj = 30, 32
si, sj = 5, 3
# f = 64
# c = 64
# ni, nj = 32, ... |
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