sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
81575f69bbca297d9650d80986abe7fa86312900b1b8bd8ac3fc675e7add28fa | Python | 2,762 | 81 | """
Configuration singleton for global access to Hydra configuration.
This module provides a singleton pattern implementation for managing the application's
configuration throughout the codebase. The configuration is typically initialized
once at the start of training/evaluation scripts with a Hydra configuration obje... |
d3095a56f3a121b9656cc6b89ea6654747a5f0fdfde2763ec299abae698aac8a | Python | 2,769 | 80 | # 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/>
# Script to strip outputs of Jupyter notebooks except for plots and prints so that there
# are displayed nicely on the documentation website.
import os
... |
c8bc41c8332b13285b426bcbbf8dcbcda4b9acf039676d731ad70466bc146c11 | Python | 2,770 | 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/>
"""Progress bar helpers shared by the samplers.
Samplers that draw from a proposal, e.g. `accept_reject_sample` or SIR, show a
progress bar of their own.... |
19a210e263e733dc39bb293806a8ecfb2ec6d0e6940172f906c387eca378d4fe | Python | 2,771 | 68 | import torch
import torch.testing as testing
# from selbstaufsicht.models.self_supervised.msa.modules import InpaintingHead, JigsawHead, ContrastiveHead
from selbstaufsicht.models.self_supervised.msa.modules import InpaintingHead, JigsawHead
def test_inpainting_head():
num_classes = 4
bs, S, L, D = 2, 3, 4, ... |
2a130ba4c44d9dfe39e315879abd4b85ea0184a6f386abaa9a0f1f010fd2cdd3 | Python | 2,772 | 96 | import logging
import numpy as np
import torch.utils.data as data
from PIL import Image
from torchvision.datasets import CIFAR10
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')
def accimage... |
d46db3471214ce6fc2b2bb3624dc92549d12408057225aeeec9d0dea62b469b0 | Python | 2,773 | 64 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source... |
45b704443bf9adefe380c0485d89d9a775d76d84194905581e7fb8ca89165a28 | Python | 2,777 | 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 ExpDtToSeg:
"""
Select Short axis image and convert the distance transform to binary mask.
"""
def __init__(... |
949f423127c05c66addee7b1bf8d4b8b0e8c024879eed7edd0cd066a177d0488 | Python | 2,777 | 76 | 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... |
1f5e2dbed2808e705bee4fd0dbd3a28336fb1ea72ac18f09922f362ed330122e | Python | 2,778 | 26 |
label_map = {
"mfc_gparam_hj_forward11": {'legend': "MFC $g$-parameter 1D -> 1D", 'linestyle': '-', 'marker': 'o', 'xlabel': r"$x$"},
"mfc_gparam_hj_forward12": {'legend': "MFC $g$-parameter 1D -> 2D", 'linestyle': '--', 'marker': 'o', 'xlabel': r"$x$"},
"mfc_gparam_hj_forward22": {'legend': "MFC $g$-para... |
083424259abd8a4ea77a200e35eddaba1d1017f637b68af7073a52a88d13c94b | Python | 2,782 | 81 | import utils
import xml.etree.ElementTree
import random
import math
# gate prospector rpc url
prospector_url = 'http://192.168.100.101:8080/gwt/gate.prospector.rpc.ProspectorRpc/rpc'
# Mimir Web Service
mimir_service_url = 'http://192.168.100.101:8080/dcf68f8f-14b1-49e9-ab44-3380eec0a22f/search'
mimir_ns = {'m': 'htt... |
f0c20f6f3aebbfb8e5fef915811a0f489b5cf25a09862c78a1d2243ea8c1f732 | Python | 2,783 | 53 | #! /usr/bin/env python
# ***********************************************************************************
# * Copyright 2010 - 2016 Paulo A. Herrera. All rights reserved. *
# * *
# * Redistribution and use in source ... |
e4af6b7e7af405c281a94a0ee7cad0afd5181834b31109db6f9c43671b301df8 | Python | 2,786 | 74 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import GFLHead
def test_gfl_head_loss():
"""Tests gfl head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape... |
164cb570031d1d8d5f37dc527fa79788bf69595c80e3ff298180a917245f621c | Python | 2,788 | 81 | import os
import sys
import numpy as np
import h5py
import scipy.io as spio
import nibabel as nib
import argparse
parser = argparse.ArgumentParser(description='Argument Parser')
parser.add_argument("-sub", "--sub",help="Subject Number",default=1)
args = parser.parse_args()
sub=int(args.sub)
assert sub in [1,2,5,7]
r... |
9c170bc23b5a4db0782430756c597271b4a0e5105ec37c66c9711965bb4960b9 | Python | 2,789 | 73 | import os
import json
import torch
import sep.helpers.utils as utils
import mir_eval
import numpy as np
from asteroid.metrics import get_metrics
def get_items(curr_dir, denoise_gt=False):
"""
This is a modified version of the SpatialAudioDataset DataLoader
"""
# Load metadata
with open(os.path.jo... |
23a9b1be71c1bc0feefc7e2b287186f35a83d16e534be05b6bfafc362f3885f4 | Python | 2,790 | 105 | 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(... |
ca3f61d981945c25dfd502f79fdf1b19d10b17f6d4c0721cb472c1967af6bd04 | Python | 2,792 | 65 | import logging
from fedml_api.distributed.fedavg_robust.message_define import MyMessage
from fedml_api.distributed.fedavg.utils import transform_list_to_tensor
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communication.message import Message
class FedAvgRobustCli... |
18c0744b8ef0c3476785692f18d22ed0e247b954f026cd673a861a5dcc8d1e27 | Python | 2,794 | 71 | # Same structure as pre-trained network for transfer learning | standard learning
# Created: 5/22/2021
# Status: OK
# CUBLAS_WORKSPACE_CONFIG=:4096:8 python cnn_main.py
# Consider merge into 1 main file
import sys
import torch
sys.path.insert(1, '../plot/')
from networks import CNN_Wrapper
from utils import read_json
... |
92452154808d18b18140e6fe5a3df323f5f74c8b05865e4549d61c9478e07d89 | Python | 2,794 | 94 | # Copyright (c) OpenMMLab. All rights reserved.
import os
from argparse import ArgumentParser
import mmcv
from xtcocotools.coco import COCO
from mmpose.apis import (inference_mesh_model, init_pose_model,
vis_3d_mesh_result)
def main():
"""Visualize the demo images.
Require the json... |
b7d2e26908ace10735a63f9dda7b79cd2e741e1e7ff440ba9dc9091c35784f0b | Python | 2,795 | 111 | import numpy as np
import pandas as pd
import datetime
import time
from model import Net1
import torch.optim as optim
from scipy import io
import argparse
import os # nn.BatchNorm2d(2,affine=False),
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import h5py
impor... |
305d873050377630fde4c54f1c1292f0d0a186a1ace8399b4eb93b2ff6237832 | Python | 2,797 | 71 | import pycroscopy3D as pycro
import multipagetiff as mtif
import logging
import argparse
import os
import sys
from . import utils
defaults = dict(size_tolerance=0.7, expected_size=[-1,-1,-1])
def main(*args, **kwargs):
parser = argparse.ArgumentParser(description="Calculate an average psf from \
a stack c... |
e2b8ab22e74ceaf06848660c59944881eea0c258e387238b90f8af565842e897 | Python | 2,799 | 74 | import sklearn.model_selection
import numpy as np
from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit, cross_val_score, StratifiedKFold
def normal(X, labels, test_size):
"""Split a dataset into training and test parts.
Args:
X (numpy.ndarray): 2D features matrix
... |
f84c0746453fd12d53eb1262dd52e73ed5a497db7caee2a01912bed766983172 | Python | 2,801 | 88 | import sys
sys.path.append('versatile_diffusion')
import os
import PIL
from PIL import Image
import numpy as np
import torch
from lib.cfg_helper import model_cfg_bank
from lib.model_zoo import get_model
from lib.experiments.sd_default import color_adjust, auto_merge_imlist
from torch.utils.data import DataLoader, Data... |
0e57ab268d39459d7d28d756cff8162652410592a5ca2d3eb903daa9f6e628c4 | Python | 2,805 | 76 | 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... |
22d25ef418f49e72b522b5e7a8a1703a9ee021e7405278b3208333c77c68cea2 | Python | 2,805 | 76 | 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... |
68629245f6c758931f91439d57f79f3450f3c546a4bd578e195e79a39539f0af | Python | 2,805 | 100 | from .gradient_descent_base import L1BaseGradientDescent
from .gradient_descent_base import L2BaseGradientDescent
from .gradient_descent_base import LinfBaseGradientDescent
from ..models.base import Model
from ..criteria import Misclassification, TargetedMisclassification
from .base import T
from typing import Union, A... |
07b3ef2a37760de04c39d17b1723c6d3f4f2d2954fe36a85e55e66510d9d6670 | Python | 2,806 | 84 | """test_advanced_activation_layers.py
This file is part of the test suite for keras2c
Implements tests for advanced activation 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_... |
d3c400bd984c292c570e1ccf74d2c2dbdb45ebcc0526a8ecd937cdcdc1a808a4 | Python | 2,807 | 85 | import torch
import numpy as np
from graphrepr.config import csv_section, optimizer_section
from graphrepr.data import load_data_from_df
from graphrepr.chemprop.data_utils import filter_invalid_smiles
from graphrepr.chemprop.data_data import MoleculeDatapoint, construct_molecule_batch, MoleculeDataLoader
def load_d... |
918dfb287961e1d28d89d61dacfe9eb02b7aa696c9140213bcf6a90e301f0123 | Python | 2,812 | 76 | 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... |
f7c6e9f679fd6ca9ba7d9da951a447210554807f2bb0b094bfb6ff8601325967 | Python | 2,812 | 80 | """IUPAC ambiguity definitions and helpers used during alignment analysis."""
from collections.abc import Iterable
NT_RESOLVED_STATES = frozenset("ACGTU")
AA_RESOLVED_STATES = frozenset("ACDEFGHIKLMNPQRSTVWYOU")
# Nucleotide ambiguity definitions use T internally. For RNA-only alignments,
# ``ambiguity_map`` substit... |
a3f7291d1127b306e3420c59fba8ce0cd93063ba3a49861724f0265c36ca98e3 | Python | 2,813 | 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... |
c6ae0618e906977248703201b689765e1a3b6a225a2f6441d68b5c38c42ced81 | Python | 2,814 | 64 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
import numpy as np
from flystim.draw import draw_screens
from time import sleep
def main():
subscreens = [SubScreen(pa=(-0.1, 0.2, -0.1), pb=(0.1, 0.2, -... |
31a9fa318109e9d4dfdd1d573bb16e10bb76b6eec4def3f0db8169fa896ded4a | Python | 2,819 | 66 | # -*- coding: utf-8 -*-
"""
Created on Fri Jun 11 19:45:55 2021
@author: fhu14
Updates the targets of the underlying molecules based on the
output results from the DFTBlayer for a single feed.
These methods should be invoked during the training loop process
when information is being fed through the DFTB layer
"""
... |
3fa0fe7723a28243e7bace1fc89617b5e897e25cb5d6390f7a2abde62f13fd89 | Python | 2,819 | 82 | import numpy as np
import trefide.extras.noise_estimator as noise_estimator
#import trefide.extras.spatial_filtering as spatial_filtering
import trefide.extras.tool_grid as tool_grid
import time
#from trefide.utils.noise import estimate_noise
# ____________________________
# Wrapper to call denoisers
# see individ... |
bb462c405b08b89e5e204c6f3a1a67225711387505eef896fcb1572c32e8a6f3 | Python | 2,821 | 86 | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from abc import ABCMeta, abstractmethod
import numpy as np
from torch.utils.data import Dataset
from mmpose.datasets.pipelines import Compose
class GestureBaseDataset(Dataset, metaclass=ABCMeta):
"""Base class for gesture recognition datasets with Mult... |
ee1e5e442de3b7ab70c9758f74c89a653a7dd93f4efc42fe1c81caa7869148bb | Python | 2,822 | 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... |
3fec3a774b92e328df500fb8b02284a1b2a11cfd11923a042452bd17ffd0dbc9 | Python | 2,828 | 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... |
c5b18c9ef45ba7d35f3d53857ce7e3167144a05c37f753d615093fd063b0b02d | Python | 2,829 | 79 | 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 '{:.... |
bb77c23ea07373b94978fb909929cbec8df9ffb517782afc8883c931939520c0 | Python | 2,833 | 79 | 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... |
738e031582565b80c7bbecb2ebe6ee1705f8486fc9f66dafc63312a4baec48ca | Python | 2,834 | 79 | 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... |
f87f9c0b358d66958d23d92e952226abf603842bf7ea5b46c099e3e091981bb3 | Python | 2,834 | 79 | 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... |
9a85de67a617b4a2e0ee4e50c7796cf59b42238712a6fdea1a83458048fa7d8e | Python | 2,837 | 103 | import os
import cv2 as cv
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import peak_signal_noise_ratio as psnr
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(3) #2
img_ori_path = 'E:/Zhu/ETreg/paper/compareimg/synapse1206/myown/ori/'
img_fine_path = 'E:/Zhu/ETreg/... |
665cbb96842273e9af58afa29f4f16a69a3d4a563dbf8466d2977c5c18e140e6 | Python | 2,838 | 89 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from __future__ import annotations
from dataclasses import asdict
import pytest
from torch import Tensor, eye, zeros
from torch.distributions import Mul... |
2433ae32c7ca292fa71e6e8c7c13ce02c8ee67196852b304e99e88d37affb829 | Python | 2,840 | 76 | 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... |
4c17e9eb66c4e851b02f28bb7cbd56f8bcb0343df99931347bf0802d4de5bb05 | Python | 2,841 | 82 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
from math import pi, radians
import matplotlib.pyplot as plt
from flystim.draw import draw_screens
from time import sleep
def get_subscreen(dir):
'''
... |
85a29de198b5567955cb5df43900c3f235306f0540b559d14d0e6cbe24b06248 | Python | 2,841 | 79 | 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... |
ed7f679fb4bd5242b08451cd65adf1199ac20e26fd4b8ae7aa903e3b69359f6c | Python | 2,841 | 82 | import numpy as np
import pandas as pd
import requests
from ftplib import *
import ftplib
import wget
import os
import time
import glob
df = pd.read_excel('Download_List.xlsx')
empiar_ids = df['EMPIAR_ID']
data_link = df['Data_Link']
gain_link = df['Gain_Link']
for i in range(len(empiar_ids)):
#running for indiv... |
242aa35c6e6692f53e6b7a438ad9eef6ba09994936ee35a0f3c38d12b6d5e2ef | Python | 2,846 | 79 | 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... |
64d2aa6bfd973508aa2742f69970f1a295e35258afe1c65c71aeaf9bb0795f2c | Python | 2,846 | 105 | import numpy as np
from daart import transforms
def test_compose():
# motion energy -> zscore
t = transforms.Compose([transforms.MotionEnergy(), transforms.ZScore()])
T = 10
D = 4
signal = np.random.randn(T, D)
s = t(signal)
assert s.shape == (T, D)
assert np.allclose(np.mean(s, axis=... |
64be843e74aed9dc91d5421db2cc7def889cd7f1898cc28e62838b1d0a3d958b | Python | 2,847 | 79 | 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... |
a06bd537d1f630e59c5eb504a7be5b6e5c9dd9cc9c33c9506bc4bda71f9fc77a | Python | 2,847 | 76 | 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
from metrics import calculate_kl as KL_DIV
from ..misc import ModuleWrapper
class BBBLinear(ModuleWrapper):
def __init__(self, in_features, out_features, bias=True, prio... |
83c741fa9348045d9b580ca3349be2421e013205d973da05daa590f332c52a08 | Python | 2,848 | 85 | #format CNVnator output
##CNV_type coordinates CNV_size normalized_RD e-val1 e-val2 e-val3 e-val4 q0
#deletion 10:43698001-43698700 700 0.302088 0.0409004 5.72382e-13 1 1 0
import re
import sys
import os
if len(sys.argv) != 3:
print "Usage: <cnvnator ou... |
8311720eb034b959e1cc84c9052bdae098d79bcb05ae398f8671880cd25a4322 | Python | 2,849 | 86 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch.nn.functional as F
from mmcv.runner import BaseModule, force_fp32
from ..builder import build_loss
from ..utils import interpolate_as
class BaseSemanticHead(BaseModule, metaclass=ABCMeta):
"""Base module of Sema... |
49795691f60ff52467772822e86493fec109619d71ebac9ce6dbc34057749b90 | Python | 2,851 | 78 | import os
import os.path as osp
from ..config import parse_data_config, parse_model_config, parse_representation_config
from ..config import utils_section, data_section, csv_section
from ..data import load_dataset
def get_all_subfolders(path, extend=False):
subfolders = [folder for folder in os.listdir(path) if ... |
a58074fc298d4d5cba9fb0f6021b97d25a96aa146f4bdd3dea5d3e04c76fca09 | Python | 2,860 | 116 | '''
Author: ----
Date: 2022-04-29 09:43:08
LastEditors: GhMa
LastEditTime: 2022-09-20 14:35:41
'''
import torch
import torch.nn as nn
import numpy as np
import math
def heaviside(x: torch.Tensor):
return (x >= 0.0).to(x)
class StochasticST(torch.autograd.Function):
r"""
default noise type: gauss... |
a8b5db657454992c1d196d3fa2dce37a908391e7bbccbba638f3f71ac3998f63 | Python | 2,860 | 106 | import pytest
import numpy as np
from daart import transforms
def test_compose():
# motion energy -> zscore
t = transforms.Compose([transforms.MotionEnergy(), transforms.ZScore()])
T = 10
D = 4
signal = np.random.randn(T, D)
s = t(signal)
assert s.shape == (T, D)
assert np.allclose(np... |
cebbe14cf35881c930017283dd5e015556fd675feb9a8ae3e60251bd53c53e46 | Python | 2,868 | 70 | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .faster_rcnn import FasterRCNN
@DETECTORS.register_module()
class TridentFasterRCNN(FasterRCNN):
"""Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
def __init__(self,
backbone,
... |
925bb8d403c44ad8bab0b9a7de86b940a8165f4b824a1b8beda9cf772fe38b26 | Python | 2,869 | 86 | import math
import torch
from torch import nn
from pybrid import utils
class Layer(nn.Module):
def __init__(
self, in_size, out_size, act_fn, use_bias=False, kaiming_init=False, is_amortised=False
):
super().__init__()
self.in_size = in_size
self.out_size = out_size
sel... |
b998a62423d135ecc53fda5503c17dc65dfd5dc4c1ceedc0dd2e5c66de80c4be | Python | 2,871 | 65 | from pathlib import Path
import sys
root_dir = Path(r'')
git_dir = Path(r'')
for i in [str(root_dir), str(git_dir)]:
sys.path.insert(0, i)
class Project(object):
def __init__(self, root, raw_data_dir):
self.root_dir = root
self.rdata_dir = raw_data_dir
self.data_dir = root / 'data'
... |
1fd1733b3c3175097c5e1f512cde642120f1137786d9d74a7f9fa0b0d4144ba9 | Python | 2,875 | 79 | # 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 torch as pt
from tempfile import TemporaryDirectory
from torch_geometric.loader import DataLoader
from torchmdne... |
42da9cce4103c98c90b3d63c7bc58df001dc726c0331c6ab790e956f9d769066 | Python | 2,875 | 79 | 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... |
cf02277d77ca79ea9ea34f41d78167e7ab02b296fadadef2fd12224a5c51ce18 | Python | 2,875 | 97 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 16 15:35:32 2020
@author: bariskuru
"""
'''poisson noise for variable max firing rate for grid cells'''
import seaborn as sns, numpy as np
import os
from grid_poiss_input_gen import inhom_poiss
from grid_trajs import grid_population, draw_traj
imp... |
dd4bdf99576281caeabbf5185f8b56e2b850e614ffaa11dfeb0354830ed9ac07 | Python | 2,875 | 138 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
``sg.py``: Routines for Savitsky-Golay filtering
"""
import numpy as np
def SGOrd(m,fc,fs):
'''
Compute polynomial order for Savitsky-Golay filter
Fc = (N+1)/(3.2M-4.6)
For fixed M, Fc
N = Fc*(3.2M-4.6)-1
Parameters
----------
m ... |
5ce618266cc11e035543820ed11b8e5a9f1ce7fa57d697e55665f1a6f73e2fb2 | Python | 2,877 | 111 | '''
Author: ----
Date: 2022-06-30 11:22:52
LastEditors: GhMa
LastEditTime: 2022-09-20 14:35:24
'''
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)
class StochasticST(torch.autograd.Fun... |
9eb4f0cf80f679586071c5a02ccdce7179c40f98fbfec9cf1cb0edead6b8668c | Python | 2,879 | 70 | # 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... |
1267c1e664cb63bbf5dc570ceae3ba40f17a5314f4130ed4a9427a5870bfc6bb | Python | 2,884 | 79 | from numpy import linalg as la
from sklearn.preprocessing import normalize
from utils import graph_to_adj
import math
import numpy as np
import scipy.sparse as sp
# For dynamic loading: name of this method should be the same as name of this python FILE.
def grarep(ctrl, graph):
'''Use GraRep as the base embedding... |
e4bdfce6f8e385e4d91dc2d7f1b769ae7fce82da62fac2f4347d12d1b69139c1 | Python | 2,884 | 88 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
def bbox_xyxy2xywh(bbox_xyxy):
"""Transform the bbox format from x1y1x2y2 to xywh.
Args:
bbox_xyxy (np.ndarray): Bounding boxes (with scores), shaped (n, 4) or
(n, 5). (left, top, right, bottom, [score])
Returns:
... |
75f66c875c1e9e6065f8bbdf8d17c01108181bcb23346c33eb1cbeba2f8910b4 | Python | 2,885 | 68 | import opuslib
import torch
import numpy as np
class OpusCodec():
"""
Runs Opus compression with the same parameters used on each of the robots
"""
def __init__(self, channels, sr, frame_width=0.02) -> None:
self.channels = channels
# Initialize encoder
self.encoder = ... |
b8ed57faf63bf433cbeb1ab020baf9691fb13dc598b0d6278f5583afa659c853 | Python | 2,886 | 68 | import logging
import torch
from torch import nn
from fedml_api.distributed.turboaggregate.utils import transform_tensor_to_list
class TA_Trainer(object):
def __init__(self, client_index, train_data_local_dict, train_data_local_num_dict, train_data_num, device, model,
args):
self.client... |
e761e4a0c4e5e13291d4ed77e6aa852da9c319c500f5f6e5b0a6d4db6348f609 | Python | 2,889 | 69 | import setuptools
import pathlib
## 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... |
4f184a4a33f3772f357def1372196a423edc98836f0d5f156dd659f40894d577 | Python | 2,890 | 91 | # -*- 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
import torch
from dtor.utilities.utils import set_plt_config
from dtor.utilities.model_retriever import load_... |
0f1fcabd04a66855c0e0cbe402bc7f36e32b5f8a470ae3d5cb53844e1af31e1a | Python | 2,891 | 104 | import copy
import torch
from daart.models import Segmenter
from daart.models.tcn import DilatedTCN
def test_tcn(hparams, data_generator, check_batch):
hp = copy.deepcopy(hparams)
hp['backbone'] = 'dtcn'
hp['variational'] = False
model = Segmenter(hp)
model.to(hp['device'])
# load the corr... |
96f8b5cbf9456a05c7ac492a423369f3e1c605b2cd56effaa13afc7ac17e2a9a | Python | 2,891 | 99 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.backbones import ViPNAS_MobileNetV3
from mmpose.models.backbones.utils import InvertedResidual
def is_norm(modules):
"""Check if i... |
565d3b0a2ce05e50dca78c1479bf742f887676b41911f9a067083bfcc238fe5f | Python | 2,892 | 83 | """
Converts dictionaries from h5 files and back.
"""
import numpy as np
import h5py
import os
class h5_handler(object):
@classmethod
def save_dict_new_h5(cls, dic: dict, dest: str):
r"""Entry point to turning a dictionary to an h5 file
Arguments:
dic: The dictionary to be sa... |
2070001c6733645a0a559b3cb2dedd5d2b532af5816745c655f420a4182a441b | Python | 2,895 | 91 | import subprocess
import sys
from pathlib import Path
import pytest
from clipkit.clipkit import main
def _run_clipkit(*args, **kwargs):
cmd = [sys.executable, "-m", "clipkit"] + list(args)
return subprocess.run(cmd, **kwargs)
class TestEntrypoint(object):
def test_help(self):
result = _run_cli... |
6b2000b2291e449ebf34a2926a9f154defdf0534fa77920495a9d5eee89bb67f | Python | 2,898 | 81 | # These are to prevent bad matrix exponentiations
var.PIVEC_MIN = 1.e-7
var.RATE_MIN = 1.e-7
var.BRLEN_MIN = 1.e-6
var.GAMMA_SHAPE_MIN = 0.1
# You may be doing more than one run, and if so you will want to set
# the runNum arg when you instantiate the Mcmc object below. I have
# runNum=rNum below. If you are using t... |
b86c98d08d36038dba34d6f926fa51b2bae0b3f9af262f1d13f602790a39bbd1 | Python | 2,899 | 111 | '''
Author: ----
Date: 2022-06-30 11:22:52
LastEditors: GhMa
LastEditTime: 2022-09-20 14:35:24
'''
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)
class StochasticST(torch.autograd.Fun... |
d2c4e5dfec83838035ccf13989777669a73f1dad83a10638f2b39bee5a9f2dfd | Python | 2,906 | 76 | '''
Script for plotting predicitons
plot all 5 folds of complete training data and validation
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
'''
import numpy as np
import pandas as pd
import csv
import scipy
import pickle
from sklearn.ensemble import RandomForestR... |
1abbbe4535052cd89b644b0871fa3bc9a139063ecee8e92c8c15d7efcd0a70ab | Python | 2,908 | 80 | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmdet.models.dense_heads.fcos_head import FCOSHead
from ..builder import HEADS
@HEADS.register_module()
class NASFCOSHead(FCOSHead):
"""Anchor-free head used in `NASFCOS <https://arxiv.o... |
d22bfe8ce41d005f355e6ee26067c29974953bbb03b259da368a79fd8d436a62 | Python | 2,911 | 111 | 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... |
ad5f6a3b220c646c70ad9d683131c2fde7ab5a705e3c9471b8e95c470361ae70 | Python | 2,912 | 85 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 16 15:35:32 2020
@author: bariskuru
"""
'''poisson noise for variable cell number for grid cells'''
import seaborn as sns, numpy as np
import os
from grid_poiss_input_gen import inhom_poiss
from grid_trajs import grid_population, draw_traj
import ... |
ae7f94bc7c60ca2cfb30536308916ef6b5c6340ede3b33da8f999d2ef5ec8d41 | Python | 2,912 | 88 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def knowledge_distillation_kl_div_loss(pred,
so... |
919f1951c4c9af04e9751cf0e680246e96c138242fcaf492be8eac7444441863 | Python | 2,915 | 88 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
Matplotlib and numpy configuration
"""
# Relative import regardless of file nesting depth
import os, sys
path = os.getcwd().split(os.sep)
while len(path) and path[-1]!='notebooks': path = path[:-1]
sys.path.append(os.sep.join(path[:-1]))
datadir = os.sep.join(path[:-2]+['e... |
58b6c5b12c19f6361a3eb8950d96212549cd91e056a59addfbda4773848c0737 | Python | 2,917 | 67 | import argparse
def main(args):
import json
import torch
from .dataset import worker_init_fn, get_pdbs, loader_pdb, build_training_clusters, PDB_dataset
data_path = args.path_for_training_data
params = {
"LIST" : f"{data_path}/list.csv",
"VAL" : f"{data_path}/valid_clust... |
754f2aae629b43d80870cb0eff2c5e0ca8124d79521ed102aa75372639432411 | Python | 2,917 | 78 | 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... |
af2089053ba675c7fa42eefa4d191ec7859a5a63be49c2e7bf74e574caa2fe3a | Python | 2,917 | 78 | 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... |
8b90d6d7e33e1ffdc7bb1998b1fbfe4cf3ab0d5a05e6f44a45003624dc6cab30 | Python | 2,919 | 80 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
... |
aac6b52d8c9b83d17faa7e9b72aa47fb3fcf1e1940422fe8e6c616e1d48a0c7c | Python | 2,920 | 102 | import numpy as np
"""
Adapted from UniTVelo (Gao et al. 2022, bioRxiv)
Selects a set of genes to regularize in gene-space
"""
def get_weight(x, y=None, perc=95):
from scipy.sparse import issparse
xy_norm = np.array(x.A if issparse(x) else x)
if y is not None:
if issparse(y):
y = y.A... |
d635f19c66fc1816e4d6c7f1aa10e8042ad0ad1978ed76e474c681e3ead2277e | Python | 2,920 | 72 | import utils
import sqldbutils as dutil
import json
# query concept sql
autoimmune_concepts_sql = """
select distinct concept_name from [SQLCRIS_User].[Kconnect].[ulms_concept_mapping]
"""
# query patient sql
patients_sql = """
select brcid, primary_diag, diagnosis_date, dob, gender_id, ethnicitycleaned from [SQLCRIS... |
b76e448b692efc7e43f9440fc377baa9e890dcd92549c152fc7edf37d126b1e9 | Python | 2,921 | 102 | import math
import os
import re
import subprocess
import tempfile
from concurrent.futures import ThreadPoolExecutor
from shutil import which
def count_sequences(file):
n = 0
with open(file) as fin:
for line in fin:
if line.startswith(">"):
n += 1
return n
def run_prod... |
417be3ffdd4ccd85b7d8d5180f975bb48afada0821bdf3729e4b931e77d57d16 | Python | 2,922 | 103 | import math
import os
import re
import subprocess
import tempfile
from concurrent.futures import ThreadPoolExecutor
from shutil import which
def count_sequences(file):
n = 0
with open(file) as fin:
for line in fin:
if line.startswith(">"):
n += 1
return n
def run_prod... |
aaffa5725da6e370bbe771765357aef8697bdd9e976182f7d030eeac22e7c051 | Python | 2,922 | 76 | 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... |
617b4b70cfc5458e4ff4a3f12b35e26a6ab049a8af7f987be525da6ae4f191a1 | Python | 2,925 | 81 | import numpy as np
import random
from scipy import ndimage, misc
class RotationTransformBase(SpatialTransformBase):
"""
Rotation transformation base class.
"""
@staticmethod
def get_rotation_transform(dim, angles):
"""
Returns the sitk transform based on the given parameters.
... |
687985d7aa93217e8f89f307a405009eb821b656b122695677fab48b042444d7 | Python | 2,925 | 85 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import PLUGIN_LAYERS
eps = 1e-6
@PLUGIN_LAYERS.register_module()
class DropBlock(nn.Module):
"""Randomly drop some regions of feature maps.
Please refer to the method proposed in... |
1f5f32b3517253c133dac46e359277a779a65a872ee74af476ed9708bf0123c8 | Python | 2,927 | 91 | import numpy as np
import pytest
from daart.eval import get_precision_recall, run_lengths
def test_precision_recall():
# basic binary test: perfect predictions
T = 20
true = np.zeros(T,)
pred = np.zeros_like(true)
pr = get_precision_recall(true, pred, background=None)
assert pr['precision'] ... |
90fcf6f9e8ee7d4b4e34aa056b0119382f50bd7f835940406b6c775f3b91352a | Python | 2,930 | 93 | import argparse
import os
import shutil
UTT_PREFIX = "natsume"
DEV_LIST = ["9", "22", "38", "43", "44"]
TEST_LIST = ["2", "13", "24", "25", "27"]
def train_check(song):
return (song not in DEV_LIST) and (song not in TEST_LIST)
def dev_check(song):
return song in DEV_LIST
def test_check(song):
return ... |
20194066e7b9680e6adff8d828cdf529f6fa6945b4cb924aff4fae6ffa2a1fc7 | Python | 2,931 | 93 | from unittest.mock import patch
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.utils import AdaptiveAvgPool2d, adaptive_avg_pool2d
if torch.__version__ != 'parrots':
torch_version = '1.7'
else:
torch_version = 'parrots'
@patch('torch.__version__', torch_version)
def te... |
61443af230a6d385544226ec22222fa8e988aa87e91c788d01c14c3bc388a490 | Python | 2,931 | 71 | import os
import sys
import time
import json
import shutil
import logging
def save_history(train_loss, valid_loss, fold_subdirectory):
"""saves model history in fold_subdirectory"""
timestamp = time.strftime('%Y-%m-%d-%H-%M')
# casting to float because float32 is not JSON-serializable
hist = {'train':... |
7f58b23ebb29d8877ae7e9b4d19b298a8c8c8625fc12cf29336dbfa6eef799d9 | Python | 2,933 | 72 | import numpy as np
import cv2
from havoc_clustering_v2.general_utility.tile_image_utils import TileUtils
# from general_utility.tile_image_utils import TileUtils
class ImageCreator:
def __init__(self, height, width, scale_factor=1, channels=3):
'''
Creates an image of the specified size except ... |
6727009735bbcd9c3de78b67e72cdbebea5451096719bd06289061ea43357b32 | Python | 2,935 | 72 | import json
import os
from torchvision import datasets, transforms
from torchvision.datasets.folder import ImageFolder, default_loader
# for imagenet
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from med_dataset.dataset_lngrowth import LnGrowthDataset
# #for fundus
# IMAGENET_DEFAULT_M... |
90d8d39104994784140c3372850bdb710712ad735a099b89395f7d89a98303d2 | Python | 2,935 | 92 | """
Generate per-channel sleep stage logits (raw model outputs).
This script runs inference on a dataset and saves the raw logits (pre-softmax
outputs) for each recording and channel combination. Unlike predict-high-freq.py,
this preserves the full probability distribution rather than just the predicted class.
Usage:... |
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