sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f9d12c483a041dfe2dd7e37c8f730bba36b9cfd55657be3d791496752e184185 | Python | 2,935 | 108 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 27 11:01:35 2021
@author: baris
"""
import torch
import torch.nn as nn
from torch import optim
import numpy as np
# labels maker
# output for training the network, 5 for each trajectory
def _label(n_poiss):
a = np.tile([1, 0], (n_poiss, 1))... |
0cc20d1a231ea2192bd08b344d11824876b585c8cdfd500ee4bf524e20d07836 | Python | 2,940 | 79 | from dtor.logconf import logging
import numpy as np
from sklearn.metrics import f1_score
log = logging.getLogger(__name__)
# Used for computeBatchLoss and logMetrics to index into metrics_t/metrics_a
METRICS_LABEL_NDX = 0
METRICS_PRED_NDX = 1
METRICS_LOSS_NDX = 2
METRICS_SIZE = 3
def process_metrics(metrics_t, class... |
10ff86fcedb6bd837ee6a2a0c3f3fe934f76d3858c45f978abea3dace7204225 | Python | 2,940 | 77 | import os
import gzip
from typing import Callable
from tqdm import tqdm
from Bio import AlignIO
from .shrinked_force_permutations import ShrinkedForcePermutationsDataset
from ._utils import get_family_ids, _download
splits = ['train', 'val', 'test']
polymers = {'rna': 'Rfam', 'protein': 'Pfam'}
modes = ['seed', 'en... |
b08c9ac1b0c6894f780cef60f40acebba23061bef864031a3497c879a9720748 | Python | 2,940 | 100 | # Module containing the elements necessary to serialise objects.
#
# Author: Fernando García Gutiérrez
# Email: fgarcia@fundacioace.org
#
import pickle
import os
import traceback
from datetime import datetime
from tqdm import tqdm
from .wrapper import FeatureSelectionGA, MultiObjFeatureSelectionNSGA2
from ... |
7e77d7c5990fa33029b971ad301d579acb7bfc1013895ec93db8288449b6cb84 | Python | 2,944 | 104 | import fastReducedRF
import numpy
import pyublas # not explicitly used--but makes converters available
nTax = 7
import string
tNames = list(string.ascii_uppercase[:nTax])
# Here is a supertree, the first of many.
var.warnReadNoFile = False
read("(((D, E), A), B, (C, (F, G)));")
bigT = var.trees.pop()
bigT.taxNames = ... |
b83700c0ba730004923eeb5295d5d09a0a230cae9348decee951cc91a80b00cd | Python | 2,944 | 97 | '''
assumes that the user has already generated .json file(s) containing data
'''
import argparse
import json
import os
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
parser = argparse.ArgumentParser()
parser.add_argument('--name',
help='name of dataset to parse; defa... |
1c0268d1e30429f6ec5553e87e85f7ea9cce4728d1e7549c792847d031813a71 | Python | 2,945 | 73 | """Download model ZIPs after establishing a Synology sharing session."""
import argparse
import requests
from pathlib import Path
import tkinter as tk
from tkinter import filedialog
import zipfile
LATEST_MODELS = {
'PVS': 'https://cloud.efixia.com/fsdownload/Zvbj1CpkM/T1-PVS.zip', # V4
'WMH': 'https://cloud.... |
c16f9cfc3266c2c8d680873cbb4f9c41eb31d0543794bf4835e8779ce53a2436 | Python | 2,947 | 92 | import pandas as pd
import matplotlib.pyplot as plt
import sys
import re
import math
"""
need to check
if there is atrophy there should be no nan values
"""
# start from mri_pet working directory
csv = pd.read_csv("./metadata/data_raw/MCIADSubtypeAssessment.csv")
# Need to create a tree of a loop
lobes = [
"me... |
20b6be07086c6413364473aecf1dcbd5f8d154a50aa31093dc9e2408df21ffee | Python | 2,949 | 77 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import ATSSHead
def test_atss_head_loss():
"""Tests atss head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sh... |
915a56f76110eb374684daa0bc7bf5860fb4dab6c89d57bd8ea88a6179420a98 | Python | 2,949 | 72 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.dense_heads import YOLOXHead
def test_yolox_head_loss():
"""Tests yolox head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'... |
79f02897cbb51714e6afed335a1dfff80d683b59934b691af5e36d1cd9520ec4 | Python | 2,950 | 69 | import argparse
import os
import pandas as pd
def parse_excel(filepath):
# Given an excel file, returns the relevant assessment and stimulation information in visit_dict
# relying on thei constant position
df = pd.read_excel(filepath)
visit_dict = {}
visit_dict['date'] = df.iloc[3, 4] #string form... |
485bd23f3cc156f7fce7af53e303f981c975dde870e0a061b1f7222eece8e737 | Python | 2,954 | 49 | # Copyright (c) OpenMMLab. All rights reserved.
from ...deprecated import (TopDownFreiHandDataset, TopDownOneHand10KDataset,
TopDownPanopticDataset)
from .animal import (AnimalATRWDataset, AnimalFlyDataset, AnimalHorse10Dataset,
AnimalLocustDataset, AnimalMacaqueDataset,
... |
07c85a9ac924e6c07abf05bf3baefcb410b475e09c5bdfba2ecbf4a2c6630fdd | Python | 2,957 | 75 | import argparse
import collections
import importlib
import stringcase
import numpy as np
import torch
import json
from parse_config import ConfigParser
# fix random seeds for reproducibility
SEED = 123
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.se... |
475595d178961824f36d5f4bef1459a719f657d0f76464373be697bf2e8aaaee | Python | 2,959 | 81 | # -*- coding: UTF-8 -*
from collections import defaultdict
from graph import Graph
import numpy as np
from utils import cmap2C ,graph2nx
import networkx as nx
def create_coarse_graph(stru_comms,graph):
in_comm = stru_comms
NewGraph=create_NewGraph(in_comm,graph)
return NewGraph,in_comm
#$$$$$... |
c26d606ba1db3621c638437b0a9d806648575e7e3703f17ad47304f920f15bcf | Python | 2,961 | 74 | import torch
from torch import Tensor
from sbi import utils as utils
class ExamplePosterior:
"""
Class that builds a density with broad marginals and narrow conditionals.
This is used only for the tutorial on conditional correlations.
It has the key function that a `NeuralPosterior` object would al... |
fe37093b73ed65ce61f0a0c0f6969cef387448ec6cd7cdc383cfda6b2f04cbd5 | Python | 2,961 | 111 | import sys, signal
import numpy as np
import moderngl
from PyQt5 import QtWidgets
from PyQt5.QtWidgets import QOpenGLWidget
from PyQt5.QtGui import QSurfaceFormat
def make_qt_format():
fmt = QSurfaceFormat()
fmt.setVersion(3, 3)
fmt.setProfile(QSurfaceFormat.CoreProfile)
return fmt
class StimDisplay(... |
ab5113a0343a5a8fde701f944c882eda63f7b4a4101363d8b95f75b84fa5f1af | Python | 2,965 | 53 | #! /usr/bin/env python
# ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source ... |
34a74286c8f70bd843f1e0a9abd75866fb5d83cdcc5217849029b6f886dec1dd | Python | 2,968 | 56 | # Author: Fabi Prezja <faprezja@fairn.fi>
# Copyright (C) 2023 Fabi Prezja
# License: MIT License (see LICENSE.txt for details)
import os
from tensorflow.keras.callbacks import Callback
class CustomSaveWeights(Callback):
"""
Custom Keras callback to save model weights during training based on a specified metr... |
4a75b0fc187ac333f6ac594251827cb3ad608fbfc4a2455393d0b43a4068e181 | Python | 2,972 | 83 | import pytest
from clipkit.parser import create_parser
@pytest.fixture
def parser():
return create_parser()
class TestParser(object):
def test_required_only(self, parser):
input_path = "my/input/file.fa"
parsed = parser.parse_args([input_path])
assert parsed.input == input_path
... |
b9731e33953bab8a0bbaf4b277557f1c82bd27203822a7f16c593900a38965c7 | Python | 2,972 | 97 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Setup Parallel WaveGAN libarary."""
import os
import pip
import sys
from distutils.version import LooseVersion
from setuptools import find_packages
from setuptools import setup
if LooseVersion(sys.version) < LooseVersion("3.7"):
raise RuntimeError(
"para... |
dc93e46b4248019731916da750ff03b152ed49a3feef67f0c3cda29aa1295cdb | Python | 2,975 | 105 | import os
from pathlib import Path
from typing import Any
import numpy as np
import torch
import yaml
from typeguard import typechecked
from daart.data import DataGenerator
from daart.models.base import Segmenter
from daart.transforms import ZScore
@typechecked
class Model:
"""High-level API wrapper for daart m... |
adbc8dd45504fefa1c5d6addc11ce4680188583b5308e6bb544706303795daf9 | Python | 2,979 | 77 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import SABLHead
from .utils import _dummy_bbox_sampling
def test_sabl_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = SABLHead... |
23550f64a5f4f10dc32b2e557e0ebe54617e106e4877a5af3f3f6d03ddc28b73 | Python | 2,980 | 123 | # coding: utf-8
import json
import fancyimpute
import numpy as np
import pandas as pd
X = []
Y = []
Z = []
for ctx in open('json/json'):
z = json.loads(ctx)['label']
ctx = json.loads(ctx)['forward']
x = np.asarray(ctx['values'])
y = np.asarray(ctx['evals'])
x_mask = np.asarray(ctx['masks']).asty... |
43a952a30f140aef0120d3cbe3dceae0a3d42265396ee1bd18f96f19f9ad1927 | Python | 2,980 | 74 | import argparse
import typing
import argparse_types
from models.abstract import ModelClassMetaData, input_data
from models.keras.geography_weighted_model import GeographyWeightedKerasModelMeta, GeographyWeightedKerasModel
# Import bez importu :)
if False:
import tensorflow.keras as keras
class GeographyWeighted... |
7c74ab598f5e0321dad43508d3be04e57a330999b0e5d06e108b2d1d5104fddf | Python | 2,982 | 83 | # 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 warnings
from typing import Optional
from torch import Tensor, nn, tensor, unique
from sbi.neural_nets.estimators import (
CategoricalMADE,
... |
090be5a5c61422994e7e0b03fe6104ca9d9a764c5a0f12a37e4d55d7d79f547f | Python | 2,984 | 98 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# 2023 Jiatong Shi
# Adapated from ESPnet Fastspeech LengthRegulator
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Length regulator related modules."""
import logging
import torch
def pad_list(xs, pad_value)... |
604d03048feb08630b19fc1901c0fceec84be0acf916f5c8f43d3564a2a1848c | Python | 2,984 | 75 | import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_updates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('de... |
8ddb0f5cef38ccbc2fbb9ceae48c458a67e03393580e74fa80af40a7a9dbe126 | Python | 2,988 | 54 | import torch
from torchmdnet.priors.base import BasePrior
from torchmdnet.models.utils import Distance, CosineCutoff
class ZBL(BasePrior):
"""This class implements the Ziegler-Biersack-Littmark (ZBL) potential for screened nuclear repulsion.
Is is described in https://doi.org/10.1007/978-3-642-68779-2_5 (equat... |
cf98a2434d228151b7f46b77325732f1a0f967e8de3d48ba297e291ceff0d759 | Python | 2,988 | 70 | import torch
import torch.nn as nn
from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer
from layers.SelfAttention_Family import FullAttention, AttentionLayer
from layers.Embed import DataEmbedding
class Model(nn.Module):
"""
Vanilla Transformer with O(L^2) complexity
"""
... |
4ba0c8d194c293da7d1470891ade781da2bf52367af4f6fa6496bd9f80552103 | Python | 2,990 | 79 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
@mmcv.jit(coderize=True)
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
targe... |
28a38957508aab35815234a7af7eda733ba7ba8bcdbb476ed8218541731b5528 | Python | 2,994 | 93 | import csv
import json
import os
import numpy as np
import time
import argparse
import yaml
class LogWriter(object):
# kind of inspired form openai.baselines.bench.monitor
# We can add here an optional Tensorboard logger as well
def __init__(self, path, keys, header="", name="monitor.csv"):
self.k... |
25ded6d8c4a446b444a5f1e37d4364c2a964972e93eef5b8e70ad9ae833f6430 | Python | 2,995 | 97 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import numpy as np
import torch
from mmcv import Config, DictAction
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_ar... |
7ba7cd0f40fc04b864b89d85f2c21b10f487dffb58cb9fae4c6cac4bd83ee0c3 | Python | 2,996 | 87 | from logging import getLogger
import numpy as np
from base.results.sleep_stage_tracker import SleepStageResultTracker
logger = getLogger(__name__)
class AnySleepNChannelsSSResultTracker(SleepStageResultTracker):
"""
Result tracker that handles duplicate recording IDs in AnySleep evaluation.
When evalu... |
d49cf6dc20aadedeeed18d51af27ea8d5a6a08edc176b76889d731bf52db7b9d | Python | 2,996 | 113 | # coding:utf-8
import argparse
import os
import numpy as np
import glob
from PIL import Image
import cv2
import math
import random
from keras.callbacks import ModelCheckpoint, EarlyStopping
import pyexcel as pe
import pandas as pd
import shutil
def check_file(filename):
if os.path.exists(filename):
... |
4c1f662aa48660a0d7521c9782dda7b3ac3c867539aed0077440083589480385 | Python | 2,998 | 88 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import CONV_LAYERS
from .builder import LINEAR_LAYERS
@LINEAR_LAYERS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
... |
da73366bf87c4aab6b08d414b47b9e4d37197d1800b52f72e0dfdea9272b2652 | Python | 2,998 | 98 | # 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_... |
ff11380e330e351e8ede48dfdb350a3ed06396267b59f9f03bb68772148eb7c8 | Python | 2,998 | 63 | import time
import os
import json
import pandas as pd
from D_modelling import d100_modeller
# to avoid all warnings (GPR was triggering ConvergenceWarnings)
os.environ["PYTHONWARNINGS"] = "ignore::UserWarning"
def fit_and_validate_single_model(fn, config, runType, run2get_mres_only=False, debug=False):
# when ... |
3d96459546f11b202d8c8dd90ad00e64f3f017f1360787c78b9a084784c41f5d | Python | 3,000 | 106 | import tensorflow.keras as keras
import typing
from tensorflow.keras import backend as K
class WeightLayer(keras.layers.Layer):
def __init__(self, x: float, y: float, r: float,*args, **kwargs):
super().__init__(*args,**kwargs)
self._x = x
self._y = y
self._r = r
def get_confi... |
2bf7d27bba80d58b3a6fc9503ad7c52b1c0a7b84594e75c888a66aae07f634f1 | Python | 3,002 | 88 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv import ops
from mmcv.runner import BaseModule
class BaseRoIExtractor(BaseModule, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specify R... |
8f28371294a93a72fb6ba3c169e7887fa7e6146e6a50d801c869a9f95e501ab5 | Python | 3,004 | 77 | """
model.py
Defines the Multi-View Linear Manifold (MVLM) architecture and the
Hybrid Geometric Loss function for drug-target interaction prediction.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiViewLinearManifold(nn.Module):
"""
Projects pre-trained molecular and prote... |
b51cbcd0642cf60fc9023e44a3b2ca67df2788889a1c9d9e3d12d65a864ff984 | Python | 3,012 | 63 | import argparse
from create_patient_instance import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 17})
def functional_profile(functional_df,destination_folder,vta,day):
os.makedirs(os.path.join(destination_folder,'functional','profiles'),exist_ok=Tru... |
046e29ff1292b1aa4f9cae7b4d8c2548e15fab2a2f9bd98988ab359884753b6b | Python | 3,025 | 100 | import argparse
import os
import librosa
import numpy as np
UTT_PREFIX = "kising"
DEV_LIST = ["435_all.wav"]
TEST_LIST = ["434_all.wav"]
def pack_zero(string, size=20):
if len(string) < size:
string = "0" * (size - len(string)) + string
return string
def train_check(song):
return (song not in ... |
5421b0975e41c7fd7648224a34160565ccd41342f29dc05079770498665bbd03 | Python | 3,027 | 97 | #!/usr/bin/env python3
import argparse
import os
import random
import shutil
import sys
from shutil import copyfile
UTT_PREFIX = "kiritan"
DEV_LIST = ["13", "14", "26", "28", "39"]
TEST_LIST = ["01", "16", "17", "27", "44"]
def train_check(song):
return (song not in DEV_LIST) and (song not in TEST_LIST)
def de... |
e3dc340299cde78d7294ea00e9c8f6f5e3215d1af7ec0f138567ad4a245cddc1 | Python | 3,027 | 87 | #!/usr/bin/env python
# Author: Heng Li and Aaron Quinlan
# License: MIT/X11
import sys
from ctypes import *
from ctypes.util import find_library
import glob, platform
def load_shared_library(lib, _path='.', ver='*'):
"""Search for and load the tabix library. The
expectation is that the library is located in... |
e860893173f218a6a2c5da116a257745e6199d474adbe123e9d9aff426015253 | Python | 3,031 | 79 | import argparse
import collections
import json
import torch
import numpy as np
import data_loader.data_loaders as module_data
import loss.loss as module_loss
import metric.metric_nuclei_seg as module_metric
# import model.unet as module_arch
# import optimizer.optimizer as module_optimizer
# import optimizer.lr_schedu... |
c00ca2d9a3b297f9468c5815089fcb0b4f91f35ae79a18716bae72087c62d27e | Python | 3,033 | 107 | #!/usr/bin/env python3
import argparse
import math
import os
import sys
class LabelInfo(object):
def __init__(self, start, end, label_id):
self.label_id = label_id
self.start = start
self.end = end
class SegInfo(object):
def __init__(self):
self.segs = []
self.start =... |
c9f8f8dd30d952f23cb026d08291200a67cf3290ff58f293eca38082856238d4 | Python | 3,033 | 71 | ############################################
# imports
############################################
from oligo_designer_toolsuite.database import OligoDatabase
from oligo_designer_toolsuite.oligo_specificity_filter import BaseSpecificityFilter
from oligo_designer_toolsuite.utils import check_if_key_in_database
#####... |
3805af5f6fca09e6b298e19918d65bff7783d23477884a7bfd2a29db0c7e08b8 | Python | 3,034 | 96 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 2 20:34:10 2021
@author: fhu14
Utilities related to analyzing inter-atomic distances in all molecules of
a dataset
"""
#%% Imports, definitions
from itertools import combinations
from typing import List, Dict
from scipy.spatial.distance import pdist
from functools impo... |
ff66d64a694ee98f9a120868679f34805d5b01415c54951b3b64aff0d0f4d2fc | Python | 3,040 | 40 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
956cd782d7b800a409a69b262a24f7f0349a21cf5d2a98ab8e9a83639e221dbf | Python | 3,053 | 69 | import sys
import glob
import shutil
import os
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__':
"""
Thi... |
cf3843525b9d37d172d77992c6171b1f00b4ff55a3bb07bca76d89d1a9441b7f | Python | 3,054 | 78 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.core.mask import BitmapMasks, PolygonMasks
def _check_fields(results, pipeline_results, keys):
"""Check data in fields from two results are same."""
for key in keys:
if isinstance(results[key], (BitmapMasks, PolygonMasks)):... |
04922d3a09be038d60a46bd47a0d67c135c4bb808fdb2ccd670077d42ace691c | Python | 3,058 | 77 | import pandas as pd
import numpy as np
from utils.functions_ZZ_extraction import load_ZZ_output
def load_clustering_results(path):
output = pd.read_pickle(path)
return output['dfParam'], output['pca_result']
def reassign_bout_cluster(id, df_bout_all, df_clustering):
start, end = df_bout_all.BoutStart[id... |
0ac74b93952782db2eb0a8a158ae786a729be50448a8b09f29cbf51246b2cb81 | Python | 3,061 | 72 | # Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
import torch
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import distributed as dist
@HOOKS.register_module()
class SyncRandomSizeHook(Hook):
"""Change and synchronize the random image size... |
b510f26c270a8b04377223d1c2081652a11ec2755593f11b1f171005b0aa4398 | Python | 3,061 | 106 | import numpy as np
import plotly.graph_objects as go
def _sensitivity_contour_plot(
x,
y,
contour_values,
unadjusted_value,
scenario_x,
scenario_y,
scenario_value,
include_scenario,
rho,
benchmarks=None,
fill=True,
):
if fill:
text_col = "white"
contours... |
a937a04a1c475321ccfe324df35e2e883085201bc0dc8fafedae48012d1d2877 | Python | 3,065 | 72 | from torchvision import datasets, transforms
from base import BaseDataLoader
from data_loader.mura_dataset import MuraDataset
from data_loader.BraTS_seg_dataset import BraTSSegDataset
import data_loader.my_transforms as my_transforms
class MnistDataLoader(BaseDataLoader):
"""
MNIST data loading demo using Ba... |
42092531939b1ccfdc524c5ffe5e39b3c9c02f9086929dc098edab766acea93d | Python | 3,066 | 81 | import pathlib
import setuptools
# build with python setup.py bdist_wheel
# upload to testpypi w/ python3 -m twine upload --repository testpypi dist/*
here = pathlib.Path(__file__).parent.resolve()
with open(here / "README.md", "r") as fh:
long_description = fh.read()
with open(here / "requirements.txt", "r"... |
ace2fd69950306406aff4e6399a6daf6c6a712d2557cce09ee33859895f626d5 | Python | 3,071 | 82 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of po... |
333145dcc554155cc8968c545adc5c9fbd3708e3327052e1324f4fb2b9f00149 | Python | 3,073 | 64 | """
General Data Loader and General Dataset
General purpose data loader and dataset.
## GPL License
## Author: Qi Chang<qc58@cs.rutgers.edu>
"""
import h5py
import math
import numpy as np
from data_loader.general_data_loader import GeneralDataset
from skimage.util import view_as_windows
from torch.utils import data
fro... |
ea84b07582157c938681951b2a182a4507f778e58c19aa06d83bb8396feb74c8 | Python | 3,073 | 85 | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Residual stack module in MelGAN."""
import torch
from parallel_wavegan.layers import CausalConv1d
class ResidualStack(torch.nn.Module):
"""Residual stack module introduced in MelGAN."""
def __i... |
987c3ee455dbe31734619ed7542bbce8b09a72bd9664dd3d5413d9800c7190da | Python | 3,076 | 80 | # Copyright (C) 2023-2025 ETH Zurich, Marc Lehner and other DASH contributors.
from rdkit import Chem
from serenityff.charge.tree.atom_features import AtomFeatures
from serenityff.charge.utils.rdkit_typing import Molecule
def new_neighbors(neighbor_dict, connected_atoms) -> tuple:
connected_atoms_set = set(conn... |
5ea0c78c5fe285cb94ec120fa52d821348ec39fc4b6f71044f384f670bcc1316 | Python | 3,080 | 76 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import SABLRetinaHead
def test_sabl_retina_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
... |
547e65837675860f27282ebdac9496aa5946222332e36fa8a3dd766b28116405 | Python | 3,081 | 83 | # -*- coding: utf-8 -*-
"""
Created on Tue Jun 28 16:27:04 2022
@author: fhu14
Quick functions for energy analysis
"""
#%% Imports, definitions
import os, pickle
from typing import List, Dict
from DatasetGeneration import test_strict_molecule_set_equivalence
#%% Code behind
def determine_nelectrons(mol: Dict) -> ... |
a3e9ef2ab44a6abbfd643cf0d16daf48b51825099113cb5db6d0a5dcf90bde5a | Python | 3,081 | 97 | r"""
This test file contains tests that involve generating synthetic data and
then fitting to make sure we get the expected result for power law
distributions.
We generate power law data using inverse sampling transform; so essentially
the same process as is used internally, but the idea here is to compare
to a genera... |
8954064265e4f17711b9f30e95bd3816ba94db292109ccf86c6ea032bb0c5deb | Python | 3,083 | 87 | from collections import Counter
import numpy as np
from sklearn.linear_model import LinearRegression
from .consts import SYM2ATOM
from .dataset import Dataset
class Shifter:
def __init__(self, fit_intercept=True) -> None:
self.fit_intercept = fit_intercept
self.shifter = LinearRegression(fit_int... |
de0a6f356c5baf8bb6181b3d6aa9c4f0c6993a7c273c099ef57a241da0dc7c47 | Python | 3,084 | 63 | import logging
from fedml_api.distributed.fedgkt.message_def import MyMessage
from fedml_core.distributed.communication.message import Message
from fedml_core.distributed.server.server_manager import ServerManager
class GKTServerMananger(ServerManager):
def __init__(self, args, server_trainer, comm=None, rank=0,... |
59983a3a4f4648ce4a667b46047834b3574999a4e296d7f60814709480743c16 | Python | 3,087 | 98 | # -*- 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... |
e47fe5ad4718755e2b576410e22ab358c909d4e7e66dcb69f99474be3256ad59 | Python | 3,087 | 104 | import multiprocessing as mp
def run_experiment():
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras import mixed_precision
from gradient_accumulator import GradientAccumulateModel
import os
def normalize_img(image, label):
"""Normalizes images: `uint8`... |
77e2ac0077fe39c63bc8b43314d582da10fe625af55b7977c32c8d7ddf33283b | Python | 3,088 | 99 | import os
import subprocess
import pkg_resources
import logging
import numpy as np
import multipagetiff as mtif
logging.basicConfig(level=logging.DEBUG)
log = logging.getLogger(__name__)
log.setLevel(logging.WARNING)
matlab_script_path = pkg_resources.resource_filename(
"pycroscopy3D", 'Matlab/skew_correct.m')
... |
09c1f89839f6a19f68372ef970adb8803e2815f00305d7280e1970d0e75f60d5 | Python | 3,089 | 100 | # Copyright (c) OpenMMLab. All rights reserved.
import multiprocessing as mp
import os
import platform
import time
import cv2
import mmcv
import torch
import torchvision
from mmcv import Config
import mmpose
from mmpose.utils import StopWatch, collect_env, setup_multi_processes
def test_collect_env():
env_info ... |
8f5518f95efaca7406614547b08afdb8019712fa5531da5bc975fd93f583ec01 | Python | 3,095 | 89 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 9 18:17:58 2021
@author: fhu14
Used to generate a parameter dictionary from a set of SKF files. This
circumvents the need to copy and paste/depend on external file structures.
"""
#%% Imports, definitions
import pickle, os
from DFTBrepulsive import SKFSet
from SKF impo... |
6239507b2e332dbb1743810f236fa2f2f9648a8bca4a113ff71eb18252868efd | Python | 3,097 | 82 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import FSAFHead
def test_fsaf_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_... |
2a790f978cb68be91d35313140b12544c9f58b4acfb00710b97a2a73ccf15e4b | Python | 3,100 | 91 | from collections import namedtuple
Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
PRIMITIVES = [
'none',
'max_pool_3x3',
'avg_pool_3x3',
'skip_connect',
'sep_conv_3x3',
'sep_conv_5x5',
'dil_conv_3x3',
'dil_conv_5x5'
]
NASNet = Genotype(
normal=[
... |
2b7c7a2eaafd9ef0384b52c980b65817de59d65e7a8b2f022a9d39cba22626af | Python | 3,103 | 66 | #! /usr/bin/env python
# ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source ... |
fc1558665cbc847772653c73cc100ce2386d0107af0473e3b72e677707f514c3 | Python | 3,103 | 101 | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... |
274253fbc8fd516b893b751aff8035002cca5087699b8c64fcb6b6f9e8d87233 | Python | 3,104 | 146 | # %%
import pycroscopy3D as pycro
import os
from glob import glob
from tqdm import tqdm
import pipeline_tools as pt
# %% folders preparation
run = '220127'
root_path_in = f"/media/mathilde.lapoix/T7 Touch/SCAPE_new/"
def relative_in(path): return os.path.join(root_path_in, path)
root_path_out = f"/media/mathilde.l... |
604713f61f3ee34f799f229efebf2ae60adf4d6ca48ce3aa11758db9b1082506 | Python | 3,108 | 74 | import logging
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../../FedML")))
try:
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communicat... |
37e0ff869e6e80309abe04fea29803bb799ac94c2141481be5fb8ffafe12af34 | Python | 3,109 | 75 | import logging
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../../FedML")))
try:
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communicat... |
cb8d252bf23c715964debf0cc88cb23f8bf503839e40687663788a0ea4b74041 | Python | 3,109 | 101 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import mmcv
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert images to coco format without annotations')
parser.add_argument('img_path', help='The root path of images')
parser.a... |
64bf863ad95d3a2f86e66a89fbe2900e031a3b7f252522f994ba1dcd8a7731ce | Python | 3,110 | 80 | #######################################################################
# 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
... |
769ae3effebf96ece6f8c68b423f13d81c97678e0570d7520bb92c1c7e28c65b | Python | 3,110 | 132 |
import h5py
import numpy as np
from sklearn.svm import SVC, LinearSVC
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, roc_auc_score
train_set_n = 300
data = np.load('closet_train_4ch_300_feat.npz')
X... |
80888699bfb1bd5059d787ca9e2799e7001e78a7c7716362142dd28566e77f06 | Python | 3,112 | 111 | import getpass
import ntpath
import os
from datetime import datetime
def path_leaf(path):
head, tail = ntpath.split(path)
return tail or ntpath.basename(head)
def log_parameters_to_file(to_log, script_path):
"""Write script parameters to file.
Writes a log of the experiment with parameters used for... |
b6500b7222f058943185998d6f713001d94ff552d79ddc563293d0d59365a5a7 | Python | 3,112 | 116 | from typing import Optional, Tuple, Any
import eagerpy as ep
import warnings
import os
import numpy as np
from .types import Bounds
from .models import Model
def accuracy(fmodel: Model, inputs: Any, labels: Any) -> float:
inputs_, labels_ = ep.astensors(inputs, labels)
del inputs, labels
predictions = f... |
78ebd5c2a8bff6ec07f26997eaad56c606d3ff1e7e4639876a1118c46f02182b | Python | 3,113 | 96 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class FFN(nn.Module):
def __init__(self, in_dim, dim_hidden, out_dim, hidden_layers):
super().__init__()
# self.dropout = dropout
self.hidden_layers = hidden_layers
self.midlin = nn.ModuleList()
... |
d63f86bff28e101b675270eb705f27756ecdc6035e8d063e90dffe0db332bdf0 | Python | 3,114 | 107 | import re
from Bio import SeqIO
from Bio.Align import MultipleSeqAlignment
import numpy as np
from typing import Optional
from .ambiguity import is_nucleotide_alphabet
from .msa import MSA
from .modes import AmbiguityHandling, TrimmingMode
from .settings import DEFAULT_AA_GAP_CHARS, DEFAULT_NT_GAP_CHARS
from .files i... |
539083ec1218f70c19175af6aba69dc8b1a871f1567b9a25d231462562a4ced5 | Python | 3,116 | 115 | #---------------------------------------
# Compute all section areas and lengths
# 1 Feb 2023
#---------------------------------------
import os
import json
import pandas as pd
import numpy as np
import shapely
from shapely import affinity
from shapely.geometry import Polygon, MultiPolygon
data = pd.read_csv("../data... |
7441c07e82c932e745ba675f6d6ba4d1ace45bc21091a85f2126654159f07203 | Python | 3,116 | 99 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from datetime import datetime
import mmcv
from mmcv import Config
from mmpose.apis.inference import init_pose_model
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade m... |
02b65c52400998cd0e6002ea7903c6c73f588d02efc9c9ae672d6522081d97d8 | Python | 3,120 | 120 | '''
Author: ----
Date: 2022-06-30 11:56:44
LastEditors: GhMa
LastEditTime: 2022-09-20 14:35:29
'''
import torch
import torch.nn as nn
import torch.distributions as distrib
import numpy as np
import math
def heaviside(x: torch.Tensor):
return (x >= 0.0).to(x)
def triang(x, a):
fc = 0.5
mask = (x ... |
c8da9ba6a7482af71104ccdabdb6ec8a53e202864ce7b6ef9d828181fdaeafcc | Python | 3,120 | 103 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from functools import partial
import torch
from mmpose.apis.inference import init_pose_model
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parse... |
3eb2fe2a31f8cbe37c4fddc9efb31e8511f482a2734d1521c627258bae198353 | Python | 3,123 | 90 | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _allreduc... |
0eeb72e78f2eeaafdbd0d241f8eff978f882f6c8d01b0f984ef0434a44e477d3 | Python | 3,124 | 93 | # 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 typing import Optional, Tuple
import torch as pt
from .models.model import TorchMD_Net
from .models.torchmd_gn imp... |
80908222f41615b1e1a998713bfb872572e45701b4eb0f432da10ec8600e690b | Python | 3,124 | 98 | import math
from torch import nn as nn
from dtor.logconf import logging
log = logging.getLogger(__name__)
# log.setLevel(logging.WARN)
# log.setLevel(logging.INFO)
log.setLevel(logging.DEBUG)
log = logging.getLogger(__name__)
# log.setLevel(logging.WARN)
# log.setLevel(logging.INFO)
log.setLevel(logging.DEBUG)
clas... |
3122d43b275f330a7170320704ab9e08e627898b02cb9fb8f729fbfa071c5403 | Python | 3,128 | 79 | from abc import ABC, abstractmethod
import numpy as np
class DatasetError(Exception):
pass
class Dataset(ABC):
"""
Abstract base class representing a dataset.
All datasets should subclass from this baseclass and need to implement the
`load` function. Initializing of the dataset and a... |
c7a0ce139a0895a82f216f2dbcec19e221ae2d7bf1c7bd8e30ce7534633577b2 | Python | 3,130 | 91 | #
# The MIT License
#
# Copyright (c) 2011 Seoul National University.
#
# 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 restriction, including
# without limitation the rights to use, ... |
4b6008180255d3a05164a1f4ec616105ef6960ed5d479bc7dd0895026cdb4b6d | Python | 3,131 | 65 | import numpy as np
from skimage.transform import resize
class RandomResize:
def __init__(self, scale, interp="bilinear", training=True):
"""
Resize the image: C x H x W to the C x load_size[0] x load_size[1]
order will apply to image and mask separately. 0: nearest-neighbor, 1: bi-linear(d... |
9983f5ef859c958b0c8326d606e3861e4eb46e3e06c31c07d3997902854d1a60 | Python | 3,132 | 96 | import numpy as np
def crop(img, top, left, new_h, new_w, padding=(0, 0)):
if len(img.shape) == 2:
img = img[top: top + new_h, left: left + new_w]
if padding != (0, 0):
img = img[padding[0]:(img.shape[0] - padding[0]),
padding[1]:(img.shape[1] - padding[1])].copy(... |
da87ee93bc0ecae95776661731113ad0f0e41498e445f81b9188366cfa605ccf | Python | 3,133 | 111 | #!/bin/python
import mne
from mne.preprocessing import ICA
import numpy as np
import scipy.stats
import statistics
import json
import sys
def isIn(intervals, start, stop):
for begin, end in intervals:
if begin < start < end or begin < stop < end:
return True
return False
#mne.set_... |
dc75b11e40c6c60d54183e0390e3728335c0407d68fea87cfafaeabb3519b11a | Python | 3,133 | 97 | import argparse
import os
import shutil
UTT_PREFIX = "oniku"
DEV_LIST = ["chatsumi", "goin_home", "aoimeno_ningyou", "momiji", "tetsudou_shouka"]
TEST_LIST = ["usagito_kame", "sousyunfu", "romance_anonimo", "momotarou", "furusato"]
def train_check(song):
return (song not in DEV_LIST) and (song not in TEST_LIST)
... |
f3b98c64874273112f4f1bcf3b38cff58d2c57b1d550fb944612fe0cc3531c05 | Python | 3,134 | 98 | import math
from torch import nn as nn
from dtor.logconf import logging
log = logging.getLogger(__name__)
# log.setLevel(logging.WARN)
# log.setLevel(logging.INFO)
log.setLevel(logging.DEBUG)
log = logging.getLogger(__name__)
# log.setLevel(logging.WARN)
# log.setLevel(logging.INFO)
log.setLevel(logging.DEBUG)
clas... |
96d142c75a933d80ff1dc1f847dd0a2765c7cafc3bc98cda4bbd39ff3562d25f | Python | 3,135 | 94 | from configparser import ConfigParser
from distutils.util import strtobool
from tpot import TPOTRegressor, TPOTClassifier
utils_section = "UTILS"
csv_section = "CSV"
metrics_section = "METRICS"
force_classification_metrics_section = "FORCE_CLASSIFICATION_METRICS"
def str_to_bool(val):
# distutils.util.strtobool... |
fac2ecea2b9ca42b04dc79e0cd654c43673b09ce2c37dd98b165201e0fe2eef4 | Python | 3,137 | 84 | import logging
import torch
from torch import nn
try:
from fedml_core.trainer.model_trainer import ModelTrainer
from fedml_api.standalone.fedopt.optrepo import OptRepo
except ImportError:
from FedML.fedml_core.trainer.model_trainer import ModelTrainer
from FedML.fedml_api.standalone.fedopt.optrepo imp... |
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