sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5e770737273f3f727e76882495e4087f1b49e589e8ce9e4fb863daa81703248f | Python | 2,362 | 70 | """Module for handling bounding box coordinates
Throughout the models and data formats used in exsclaim, bounding
boxes are sometimes stored as x1,y1,x2,y2 or 'coords' and sometimes
as [{"x": x1, "y": y1}, ...] or 'labelbox'
In addition, we often want to check the relation of two bounding
boxes and their properites (... |
8d7dbf3b2a56c1f699e8d938da28b58fb8f21b3fc9627f1e66873fbfbc7a7a50 | Python | 2,369 | 62 | import numpy as np
class CropPadding:
def __init__(self, load_size, training=True):
"""
Padding the image: C x H x W, if H or W less than load_size, and will keep the overflow side.
"""
# TODO: Not just support static filling of the padding, but also mirror, etc...
if isin... |
8e053af9f02f98929036cd16ebdf17c381cbb2e35736409513fa9783d836ff0c | Python | 2,370 | 69 | from fontTools.ttLib import TTFont
from fontTools.ttLib.tables._c_m_a_p import CmapSubtable
import argparse
# Default PUAs
SOURCE_PUA_START = 0xEA00
SOURCE_PUA_END = 0x100F2
TARGET_PUA_START = 0xF0000
def move_pua_glyphs(input_font_path, output_font_path):
font = TTFont(input_font_path)
cmap_table = font['cm... |
a4d20544c50b738c42f698925de7eb3b8ddeec779a882b2bc7fa0cfb6a56f0eb | Python | 2,371 | 52 | import numpy as np
from pytictoc import TicToc
from skimage.transform import resize
class Resize:
def __init__(self, load_size, 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: neares... |
6b92e008ec6af0724dead55d43a1c348532ce4280c696f17094fff071e864a1c | Python | 2,373 | 83 | import numpy as np
import scipy.signal as signal
import scipy.ndimage as ndimage
def butter_highpass(signals, cutoff, fs, order=6, axis=-1):
""" Forward-backward filter inpute signals with butterworth kernel
Parameter:
signals: singnal matrix (number of channels by frames)
cutoff: cutoff freq... |
0e10586cdb70d4cda71350be3bbfff82a47cd80371484d651f05ec380529ee51 | Python | 2,376 | 67 | """test_malloc.py
This file is part of the test suite for keras2c
Implements tests for dynamic memory allocation
"""
#!/usr/bin/env python3
import unittest
import tensorflow.keras as keras
from keras2c import keras2c_main
import subprocess
import time
import os
from test_core_layers import build_and_run
import tensor... |
ed434da0c6068e301aa934ec37a34320bfd62921ab0c08461917ae604a6e799b | Python | 2,378 | 62 | #!/usr/bin/env python
"""
Nipype workflow derived from preprocessing_synthseg.py, swapping the
synthseg node by a datagrabber that get the precomputed synthseg parc
"""
import os
from nipype.pipeline.engine import Node, Workflow
from shivai.interfaces.datasource import Direct_File_Provider
from shivai.workflows... |
b457c07a62a4636f83aae2c514e1ad1b67d4d23f91bef5c0f9edc6a6e888f3a0 | Python | 2,380 | 90 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math
import utils
import argparse
import data_loader
import rits_i
from sklearn import metrics
from ipdb import set_trace
SEQ_LEN = 36
... |
83f8f0610fdb439572e3b387a60d7187891a0b35cdd507e48fefeb0ad94fc74e | Python | 2,383 | 90 | # -*- coding: utf-8 -*-
"""Logger, taken from Torch WC book"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
import logging.handlers
import datetime
import time
import sys
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
# Some libraries attempt to add their own root logger handlers. This is
... |
9296dc11daba4f42c2b0506a290495d5968375330d07d9023123585d25b6c415 | Python | 2,389 | 64 | """
dataset.py
Provides PyTorch Dataset implementations for drug-target interaction data,
integrating on-the-fly tokenization and pre-computed protein vector mapping.
"""
import torch
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
from transformers import AutoTokenizer
class DTIDataset(D... |
06d69d4313f25f489e3aa3945cad0710583a2cafa6e35315eb72f07d6d56db33 | Python | 2,391 | 68 | import sys
import numpy as np
import sklearn.linear_model as skl
import pickle
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]
train_path = 'data/pro... |
a94373ff64b08dc69ea742867dcdb2ad059565668db24c8a5186d9cfd3e7babe | Python | 2,392 | 91 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math
import utils
import argparse
import data_loader
import rits
from sklearn import metrics
from ipdb import set_trace
SEQ_LEN = 48
RNN... |
b8526b1303ac7e36173744a69f64afe8221e6f9dc315aba0f0b21cd6162c42bb | Python | 2,394 | 67 | import pandas as pd
import numpy as np
from sklearn.feature_selection import (
mutual_info_classif,
mutual_info_regression)
def getLearningProb(rank: int, population_size: int, min_prob: float = 0.05, max_prob: float = 0.5) -> float:
""" Calculate the learning probability (based on the Conprehensive Learn... |
773d9feac34f1ec905989f125f479fd35f52e39ea28cd5204e9bb1493c1e48a1 | Python | 2,406 | 64 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import FCOSHead
def test_fcos_head_loss():
"""Tests fcos head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sh... |
9fa8b52325a5351e9097f19625bc9cf86f8ded55eee4d03dd6b4c4bedbbad48d | Python | 2,406 | 76 | from math import *
import random, time
class MyOVBox(OVBox):
def __init__(self):
OVBox.__init__(self)
self.nChannel = 0
self.sampling = 0
self.epochSampleCount = 0
self.startTime = 0
self.endTime = 0
self.dimensionSizes = list()
self.dimensionLabels = list()
self.timeBuffer = list()
self.signalBuf... |
e1ef24fe119be8755a32cf9b76bd71bf016df0380515e4ec13ae9eb4f233447a | Python | 2,407 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 7 12:36:35 2022
@author: baris
"""
import numpy as np
import os
import shelve
def load_spikes(path, cell_type, trajectories, n_samples):
if not os.path.exists(path+'.dir'):
print(path)
raise Exception('Path does not exist!')
... |
508de4a7df81b0ce9164aef84ea93a2af58b48e3b1f8f209175036a18046889e | Python | 2,409 | 66 | import math
import time
import parmap
import pickle
import multiprocessing
from imagen_post_hoc_helper import *
import matplotlib.pyplot as plt
import seaborn as sns
num_cores = multiprocessing.cpu_count()
print(f'Available CPU cores: {num_cores}')
num_cores = math.floor(num_cores/3)
print(f'Set CPU cores: {num_cores}... |
b63fae46a3aa5722f504b4827803532d5b9ffa3d8c44e327a607c0e37898848a | Python | 2,410 | 78 | from __future__ import annotations
import re
from typing import ItemsView, KeysView, List, ValuesView
from .consts import ALIAS2TARGET
class Target:
def __init__(self, target: str) -> None:
r"""Convert target string in natural language into a dictionary"""
self.tt = target
self.target = ... |
7fe82639ea02fe6d33e508c12c2b7696639eca9ea1c359a9b6afed4981a62a6f | Python | 2,411 | 76 | import numpy as np
import cv2
class DenseSIFT(object):
def __init__(self):
self.sift = cv2.xfeatures2d.SIFT_create(nfeatures=1)
def detectAndCompute(self, image, step_size=12, window_size=(10, 10)): # 12 10
if window_size is None:
winH, winW = image.shape[:2]
window_size = (winW // 4, winH // 4)
if ima... |
06df6a3c62655ec7936a8bdeee3d4581abbc02550944ad681fbb365a0e5d68f4 | Python | 2,412 | 76 | """
Predict 32x21 mel-spectrogram features from SSS encoder embeddings
and report cross-validated R2 per mel bin.
"""
#### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for the content.
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/... |
70688afef5e3d31c0417b3ec12d15a26d728836669aa8e9e1cb4f332335b9f3c | Python | 2,412 | 77 | from abc import ABC, abstractmethod
from typing import TypeVar
import eagerpy as ep
from .devutils import flatten
from .devutils import atleast_kd
T = TypeVar("T")
class Distance(ABC):
@abstractmethod
def __call__(self, reference: T, perturbed: T) -> T:
...
@abstractmethod
def clip_perturb... |
ee949b7bc5a5011588bb67e5492d5930aad27fc4ea68385a4a280f71b72ecd6b | Python | 2,415 | 78 | # -*- coding: utf-8 -*-
"""
This file is part of the cerebellar-task-dependent repository (Xie et al. 2023)
Copyright (C) 2023 Marjorie Xie and Ashok Litwin-Kumar
See README for more information
Library of functions for generating activity in the granule cell layer of the model and for
learning the readout weights.
... |
5c834fd0fea2b10a330521c8624a50a6e95ecd50a16ac74e987787f3a8cd5b65 | Python | 2,417 | 87 | import copy
import torch
from daart.models import Segmenter
from daart.models.temporalmlp import TemporalMLP
def test_temporal_mlp(hparams, data_generator, check_batch):
hp = copy.deepcopy(hparams)
hp['backbone'] = 'temporal-mlp'
hp['variational'] = False
model = Segmenter(hp)
model.to(hp['devi... |
70514935248297235f378b2ee0f2ed7e627624155783a98bcb94a0e808ef6555 | Python | 2,417 | 67 | import pandas as pd
import os
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', 1000)
# fn1 = r'V:\foodsec\Projects\SNYF\SIDv\TN\_SF_test\ObsAsSF\Tuning_data\TNWinter_STATS_cleaned90.csv'
# fn2 = r'V:\foodsec\Projects\SNYF\SIDv\TN\_SF_test\NO_SF_baseline... |
70c5fbe3c5c95f5c3a60c9ef40ed7182f07a86744236e05bca8d4931ac3d5053 | Python | 2,423 | 69 | import pytest
import matlab.engine
import numpy as np
import scipy
from numpy.testing import assert_allclose
from pystoi.utils import thirdoct, stft, remove_silent_frames
from pystoi.stoi import FS, N_FRAME, NFFT, NUMBAND, MINFREQ, N, BETA, DYN_RANGE, OBM
ATOL = 1e-5
eng = matlab.engine.start_matlab()
eng.cd('matlab/... |
29a9caf61b00ee8051a6e8404b7bdbfef283be33c0aad116fdb0593b5d53aebe | Python | 2,428 | 71 | import sys
import numpy as np
import sklearn.linear_model as skl
import pickle
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]
train_path = 'data/pro... |
75e98066188eec443204e2292fe78c37fb6ec36415483277eb34a1d17c44cfaf | Python | 2,428 | 67 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from typing import Callable, Dict, Union
import numpy as np
import torch
import torch.distributions.transforms as torch_tf
from torch import Tensor
from... |
8574d4a906049de56c9d0accfa9b9a64dc157e49fdc1a7b8fad04a53bcfc720e | Python | 2,432 | 93 | import xarray as xr
import numpy as np
import rasterio
from rasterio.transform import from_bounds
import os
nc_file = r"V:\asap\asap_analysis\ERA5 from C3S\b96dade6843f6dd9d9a0fef09ecb5256.nc"
output_tif = r"V:\asap\asap_analysis\ERA5 from C3S\2025_era5_dek18_fix_mm2.tif"
outdir = r"V:\asap\asap_analysis\ERA5 from C3S... |
7fc0b8bfeac3887d9455af6b523f1d4e177095ebc8c245d858db1531033a679e | Python | 2,433 | 62 | from ..builder import BBOX_CODERS
from ..transforms import bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class DistancePointBBoxCoder(BaseBBoxCoder):
"""Distance Point BBox coder.
This coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,
... |
959bd7d8b4570a822ff2cce51b7a8ed1282fdfe45c11c38dcba80a7049cc2da0 | Python | 2,433 | 62 | # import setuptools
from setuptools import Extension, setup, find_packages
from Cython.Build import cythonize
import numpy
from glob import glob
VERSION_MAJOR = 0
VERSION_MINOR = 1
VERSION_MICRO = 6
with open("README.txt", "r") as fh:
long_description = fh.read()
# just build the module with a previously compil... |
236f8c43ccb4f2187d7f8c641a2472d79b6a3134d649287473089f526714f8a6 | Python | 2,435 | 75 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 31 10:16:37 2021
@author: baris
"""
from pydentate import net_tunedrev, neuron_tools
from grid_model import grid_simulate
import shelve
import copy
import time
import numpy as np
import scipy.stats as stats
from pydentate_integrate import granule_s... |
6b425763a170a0a68da93d2bc11fd207983755829636f104803608d986d9e959 | Python | 2,446 | 73 | import argparse
import logging
import os
from pathlib import Path
import numpy as np
import pandas as pd
from tools.metrics import calculate_all_metrics
os.makedirs('../logs', exist_ok=True)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%d.%m.%Y %I:%M:%S',
filename='lo... |
5cf1a376d2f9a5aa7181761809a3c0e5a78fbe267edbdb6e5acd96201fdc610b | Python | 2,449 | 73 | import jax
import jax.numpy as jnp
import weno_scheme
import weno_roll
import matplotlib.pyplot as plt
import numpy as np
import utils
@jax.jit
def euler_eqn(u, gamma):
rho = u[...,0]
rhv = u[...,1]
ene = u[...,2]
v = rhv/rho
p = (gamma-1)*(ene - 0.5 * rhv * v)
f1 = rhv
f2 = rhv * v + p
f3 = v ... |
fbf2cbaec6169bedfe64211fa7912ca18624c16924db90e339f3efd81e253494 | Python | 2,449 | 79 | from typing import Any
import sys
import pytest
import foolbox as fbn
from foolbox.zoo.model_loader import ModelLoader
@pytest.fixture(autouse=True)
def unload_foolbox_model_module() -> None:
# reload foolbox_model from scratch for every run
# to ensure atomic tests without side effects
module_names = ["... |
3f66212f617329d00681c131b70a7984b1c7e991625af6b43d813cd323e599bd | Python | 2,450 | 82 | import numpy as np
from abc import ABCMeta, abstractmethod
from typing import List
from .particle import Particle
class Callback(object):
""" Abstract class used to identify callbacks"""
__metaclass__ = ABCMeta
def __init__(self, name: str):
self._name = name
@abstractmethod
def evaluat... |
876690a2a406cf5fc5c906af16c15bcd90be2f059d57612c94e7234ae37335d4 | Python | 2,454 | 55 | import torch
import torch.nn as nn
from layers.Transformer_EncDec import Encoder, EncoderLayer
from layers.SelfAttention_Family import ReformerLayer
from layers.Embed import DataEmbedding
class Model(nn.Module):
"""
Reformer with O(LlogL) complexity
- It is notable that Reformer is not proposed for time s... |
d465a8df0b0dd62cbd788e3795dd6db9cba54f6760ede3adfcd2b9e45545c29c | Python | 2,454 | 66 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 9 13:40:21 2021
@author: fhu14
"""
#%% Imports, definitions
from typing import List, Dict
from DFTBLayer import DFTBList
import os
from .h5handler import per_molec_h5handler, per_batch_h5handler
import pickle
#%% Code behind
def save_feed_h5(feeds: List[Dict], dftb_ls... |
90dff0669e5ce64ddac80e89a01a51b8c4bd5206f47fa09ab7a619f677afef7e | Python | 2,456 | 67 | """
date: 20/12/2021
author: mathildelpx
input: dataset with calcium activity of V2a in paralysed larvae, during MLR single shot stimulation.
output: traces of each individually identified V2a in pontine and retropontine regions.
"""
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
imp... |
ab20ee9051c1d9f8d8fe40a4aa4f9d2fd60fa42735bbef92b543d7f6938f03ad | Python | 2,456 | 70 | from p4.p4exceptions import P4Error
import p4.pf as pf
import p4.func
from p4.var import var
class Part:
def __del__(self, freePart=pf.freePart):
self.alignment = None
# print "Part.__del__() here. cPart=%s" % self.cPart
if self.cPart:
# print "Part.__del__() about to free ... |
87bef5e0b87d69666582fe2718ba6e8f000e0ed91450606437b54fb5581608fd | Python | 2,457 | 56 | # 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... |
deff902ec36d495a32b84bea2d992ecc96a2131a933e57226b74c71861f0023e | Python | 2,458 | 61 | import numpy as np
import random
class ExpCentralCrop3d:
"""
Crop the 3D numpy [c, h, w, d] and mask [d, 2] in a sample
The d should be randomly sampled
"""
def __init__(self, output_size, padding=0, include_lax_tensor=False, training=True):
assert isinstance(output_size, (int, list, tupl... |
2142d2144aca6f750c8f83cd0e2e7c84ff27edb1ceaddda54884fe665206a887 | Python | 2,459 | 71 | #!/usr/bin/env python3
"""
A simple example that demonstrates how to run a single attack against
a PyTorch ResNet-18 model for different epsilons and how to then report
the robust accuracy.
"""
import torchvision.models as models
import eagerpy as ep
from foolbox import PyTorchModel, accuracy, samples
from foolbox.atta... |
ef78edefdc6ea9ad66377a5e974b13bd43a29c097e1fa8267fd1aacb335cce02 | Python | 2,460 | 56 | import logging
from fedml_api.distributed.turboaggregate.message_define import MyMessage
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communication.message import Message
class TA_DecentralizedWorkerManager(ClientManager):
def __init__(self, args, comm, rank,... |
36ca2a0c3b1cd7f846efe7714b863c9e8d3366653c7e08856009403453560642 | Python | 2,461 | 64 | # Copyright (c) 2020, Jonas Rauber
#
# Licensed under the BSD 3-Clause License
#
# Last changed:
# * 2020-07-15
# * 2020-01-08
# * 2019-04-18
import eagerpy as ep
def l2_clipping_aware_rescaling(x, delta, eps: float, a: float = 0.0, b: float = 1.0): # type: ignore
"""Calculates eta such that norm(clip(x + eta *... |
b83b273418d90d51569091a384fb4b6d75d592262c20945425c1bb57b058f96a | Python | 2,461 | 59 | 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, inchi should be ... |
9c253d3dad82b82b30802755c33ab3712e6f020ba20a6d09102761503bd24214 | Python | 2,463 | 85 | import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateModel
# get current tf minor version
tf_version = int(tf.version.VERSION.split(".")[1])
def normalize_img(image, label):
"""Normalizes i... |
63828a85e3099153c6089574a943da6615fbdb5053ed0d2219c79baf698906f4 | Python | 2,466 | 56 | import logging
from fedml_api.distributed.decentralized_framework.message_define import MyMessage
from fedml_core.distributed.client.client_manager import ClientManager
from fedml_core.distributed.communication.message import Message
class DecentralizedWorkerManager(ClientManager):
def __init__(self, args, comm,... |
3b07abffc74008480826b88ecec31a03fa2dbd801b42637df9d9cbfede16e03d | Python | 2,471 | 82 | """Internal module for attacks that support logging to TensorBoard"""
from typing import Union, Callable, TypeVar, Any, cast
from typing_extensions import Literal
import eagerpy as ep
from functools import wraps
FuncType = Callable[..., None]
F = TypeVar("F", bound=FuncType)
def maybenoop(f: F) -> F:
@wraps(f)
... |
a00b44c308b62489b8f406a5ad6dfed70eb683179b492efc7b02ef67bcd55561 | Python | 2,472 | 50 | import re
import numpy as np
from sklearn.decomposition import PCA
def getPCA(self, feature_names, X):
# Perform PCA data reduction ON SELECTED FEATURES: Only on NDVI, RAD, Temp (precipitation is excluded)
# Note: SF are escluded because the feature2PCA is taken from feature_groups
# first: - no request of... |
7a17bf6576e4307aa310e9f638cbaaa327087f3d363e4be755f85de5feb5b001 | Python | 2,474 | 96 | from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
vis_pose_result, process_mmdet_results)
import matplotlib.pyplot as plt
import json
import cv2
import numpy as np
img_dir = "data/NECK/images/"
index = 0
with open("data/NECK/val.json", "r", encoding="utf-8") as f:
c... |
2e0ac04782d721dcfd0e4119675a79ec3931ef79341e925fb3b6f5960c86f6fa | Python | 2,479 | 80 | # -*- coding: utf-8 -*-
"""
Base
=====
This module contains the base extraction class
author: Damian Wilary
email: dmw51@cam.ac.uk
"""
from abc import abstractmethod
import logging
from typing import List
log = logging.getLogger(__name__)
class BaseExtractor:
"""Base for all extractor classes. All subclasses sho... |
90d9f23d49deff636a985840c86c164fad3cd564a68967fe25a5fb97443d643b | Python | 2,480 | 87 | import os
import struct
import sys
import numpy as np
import getopt
def read_data_from_file(file_name):
f = open(file_name, "rb")
line = f.readline()
line = line.split()
nparam = int(line[0])
rec_len = int(line[1])
nrec = int(line[2])
nlast = int(line[3])
print(nparam, rec_len, nrec, nl... |
483a943b7cb607fc7acd847fec9532c32e871479206fdafe48f0162784cc54a3 | Python | 2,481 | 75 | import numpy
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.datasets import imdb
from tensorflow.keras.layers import Dense, Dropout, Embedding, LSTM
from tensorflow.keras.preprocessing import sequence
from attention import Attention
de... |
b9fa0b5c78f729cadd08854565144f805884bad08bf6297aa1e5155b01710162 | Python | 2,483 | 70 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class DETR(SingleStageDetector):
r"""Implementation of `DETR: End-to-End Object Detection with
Transformers <https://arxiv.o... |
f9f59ec1b91044a09fad1856615387c264609ffa8344b0ddc7f19cecfea9f455 | Python | 2,483 | 54 | # 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 torch.distributions import Normal
from torch.special import log_ndtr
from sbi.utils.vector_field_utils import UniformNorm... |
5e5c344094c080b740f6e29bf9139071eb92b0f884d356c1f0c33ec7f49bc28b | Python | 2,485 | 70 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 8 16:26:50 2021
@author: fhu14
This is a dry run of the total machine learning model. The loss numbers
should agree well with established benchmarks. This will be using the
same settings as was used for the skf_8020_100knot run.
"""
#%% Imports, definitions
import os
... |
e2caee1772a01c23b952476e09d31ce8b3bcef431ec59e9939f480cae202b517 | Python | 2,492 | 74 | # -*- coding: utf-8 -*-
"""
Recognise
========
This module contains optical chemical structure recognition tools and routines.
author: Damian Wilary
email: dmw51@cam.ac.uk
Recognition is achieved using OSRA and performed via a pyOsra wrapper.
"""
import os
import itertools
import logging
from PIL import Image
impor... |
13a698a3c2ca04f29186ec220c9bd0709397ee09b0ee1e274f9b06aa6bb81b5a | Python | 2,495 | 63 | import logging
from data.manual_labels.data_reader import ManualLabels
from data.updated_labels.data_reader import UpdatedLabels
# from data.medical_notes.data_reader import ClinicalData
import pandas as pd
import numpy as np
class Data():
def __init__(self, type, id, params):
self.data_type = type
... |
3f6b329b943daa248f09409262220f0cb4573d6a79189136d9402f1f24b09693 | Python | 2,500 | 76 | import copy
import warnings
import numpy as np
import torch
import torch.nn as nn
from torch_geometric.data import Data, Batch
def clip_norm(vec, limit, p=2):
norm = torch.norm(vec, dim=-1, p=2, keepdim=True)
denom = torch.where(norm > limit, limit / norm, torch.ones_like(norm))
return vec * denom
d... |
5280960f9339950676dbb43094bb88898c91f31ebd191539c1d43e80a21fb292 | Python | 2,501 | 81 | """
Cross-validated Pearson correlation between SSS embeddings
and high-gamma ECoG activity
"""
#### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for the content.
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # se... |
d0b924e8ea205938a35358ba427c700179a32e03b5f01bceb0c02cce8bb669c5 | Python | 2,501 | 79 | from typing import Tuple
import numpy as np
import pandas as pd
from scipy import interpolate
def inter(preds_raw: np.ndarray, time: np.ndarray):
bins = [0, 24, 48, 108]
interp = interpolate.PchipInterpolator(bins, preds_raw, axis=1)
return interp(time)
def pred_reversions(ds) -> Tuple[np.ndarray, np.n... |
4977b35e697ef4cac644d253437ea8d7fc96e2f7d99415a4018d0e52508892a6 | Python | 2,505 | 69 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
# Change:
from .distance import SquaredL2
import numpy as np
def neighbor_graph(X, metric=SquaredL2, k=None, epsilon=None, symmetrize=True):
'''Construct an adj matrix from a matrix of points (one per row)'''
assert (k is None) ^ (epsilon is N... |
a27e7163b61e2daa9b3b8b6feb21414e33be01c5f0742cf33f2217eb80bf7dfc | Python | 2,508 | 60 | import numpy as np
from skimage.transform import resize
class Scale:
def __init__(self, load_size: int, interp="bilinear", training=True):
"""
Scale the image:
Input format: image: C x H x W mask: H x W
The output size will be matched smaller edge of the image.
If height >... |
c77479ce944e4cf53e9f5517c6d27d2306b376bb4db90accbd344b2f5bccb7c8 | Python | 2,509 | 65 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
# Load data from CSV file
# Create a new column after the first column in the generated features.csv file to provide the labels of your... |
2e58f0f908b801339d7f2badf606c9f3ae297daaf518656912f9b1bacc926aba | Python | 2,514 | 106 | from multiprocessing import Process, Event
import time
import pygame
from pygame.locals import *
import math
from random import randint, randrange
import sys
sys.argv = ['openvibe']
class OVProcess(Process):
def __init__(self):
Process.__init__(self)
self.initialized = Event()
self.stop_asked = Event()
def i... |
5215606b9d427dbd10d9e8031d2c5e2737ec092f4bd43555f6dea70a66110067 | Python | 2,514 | 93 | from foolbox.zoo import fetch_weights
from foolbox.zoo.common import home_directory_path, sha256_hash
from foolbox.zoo.weights_fetcher import FOLDER
import os
import pytest
import shutil
import responses
import io
import zipfile
@responses.activate
def test_fetch_weights_unzipped() -> None:
weights_uri = "http:... |
c33f2df745783fc6a18a74b1920495dbee8c3935751e7af1a6d7d511b448d949 | Python | 2,515 | 114 | import sys
def sumf(arr):
s = 0
for i in arr:
s += i
return s
def meanf(arr):
return sum(arr)/len(arr)
def variancef(arr):
sq = 0.0
m = meanf(arr)
sumsq = 0
for item in arr:
sumsq += item**2
return (sumsq/len(arr) - m*m)
def stdevf(arr):
return variancef(arr) ** 0.5
def msef(arr):
result = 0
mean =... |
ec49109343d31f8f22c5f797cc5f45ffd157cccbbc3cbb3a4705d661c0dcc320 | Python | 2,515 | 60 | import argparse
from fontTools.ttLib import TTFont
import os
def unicode_to_utf8_escape(codepoint):
return ''.join([f'\\x{b:02x}' for b in chr(codepoint).encode('utf-8')])
def format_macro_name(prefix, glyph_name):
# Convert names like 'repo-forked' -> 'ICON_VS_REPO_FORKED'
return "ICON_" + prefix + "_" +... |
ae5cf8f7349732716279a7c88cd925dcf258576791d167aa32b81aa9f990e707 | Python | 2,516 | 82 | import math
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from .constants import SI_SNR_POWER_THRESHOLD
import librosa
import mir_eval
MIN_ERR = 1e-8
def si_sdr(estimated_signal, reference_signals, scaling=True):
"""
This is a scale invariant SDR. See https://arxiv.org/pdf/1811.0... |
36380277ac09cc3291254bb013ec7fc1daa514c66c27f97d6687a1b08acd747b | Python | 2,517 | 58 | from copy import deepcopy
import pandas as pd
from data.data_access import Data
from model.model_factory import get_model
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from transformers import Train... |
cbd38873368c48e952072f9365a72885fc3201b18cff13908cb9f25baa3cc3dc | Python | 2,520 | 74 | from typing import Iterable, List
import numpy as np
from matplotlib import pyplot as plt
from .tfspline import spline_linear_model, spline_vals
# TODO: integrate tfspline.py
class BSpline:
def __init__(self, xknots: np.ndarray, bconds: Iterable,
deg: int, maxder: int, coef: np.ndarray = None) ... |
2ee06be99bc06c893ddaaaf514daf6b63479492eb9626d8c3ce45586da055d99 | Python | 2,522 | 75 | # Simulate very hetero data
# Below you specify the dataType, length, and relRate of each data
# partition (and symbols, if it is standard dataType). There is one
# part per alignment, in this example. You also specify the number of
# taxa in the tree
import random
import math
def randomNumbersThatSumTo1(length, m... |
486233289e0e574a35de4afd967a7ee3e29bd5c065f36161642052e8b4f40b72 | Python | 2,523 | 77 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
b5ef218de55800f21c403dc5736f2a5c6219509f7f51b40c05f6deec93661cc6 | Python | 2,524 | 33 |
from bayes_design.protein_mpnn.training import main as run_training
import argparse
argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
argparser.add_argument("--path_for_training_data", type=str, default="my_path/pdb_2021aug02", help="path for loading training data")
argpar... |
65f483adbfb5109dea981dcb10ee3b6f50a6b1d7f1ecdaa87f526a467d2fd7da | Python | 2,526 | 74 | # training surv-based network without pretrained model
# Modified: 5/21/2021
# Status: OK
# Consider merge into 1 main file
import torch.nn as nn
import sys
sys.path.insert(1, '../plot/')
from plot import report_table,roc_plot_perfrom_table
from networks import CNN_Surv_Wrapper
from utils import read_json, cross_set
i... |
5ac537f7b227034e9f9e8dbe42a562cc5bd99f936781998e9a4d732d8cd25a45 | Python | 2,528 | 75 | import pytest
import torch
from mmdet.core.post_processing import mask_matrix_nms
def _create_mask(N, h, w):
masks = torch.rand((N, h, w)) > 0.5
labels = torch.rand(N)
scores = torch.rand(N)
return masks, labels, scores
def test_nms_input_errors():
with pytest.raises(AssertionError):
ma... |
830f72aacb369ac75342004d89948b2c7128f0982caf73dccdd83e71bec0175a | Python | 2,530 | 61 | import numpy as np
class RandomCrop3d:
"""
Crop the 3D numpy [c, h, w, d] and mask [h, w, d] in a sample
"""
def __init__(self, output_size, padding=0, training=True):
assert isinstance(output_size, (int, list, tuple))
if isinstance(output_size, int):
self.output_size = (o... |
c4a5d20beedb3872de18f713647c38aa49c9322303c700076cd064549b981eb2 | Python | 2,530 | 96 |
# 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... |
3a5a08bf7b7cfcdf57e40170b71b5864434f4bc9732310b1a03a332f29f2ce76 | Python | 2,534 | 67 | import logging
from abc import abstractmethod
from mpi4py import MPI
from ..communication.message import Message
from ..communication.mpi.com_manager import MpiCommunicationManager
from ..communication.mqtt.mqtt_comm_manager import MqttCommManager
from ..communication.observer import Observer
class ClientManager(Ob... |
8c8fa386220e1ce56d2cd25707c8745cfae2f2efe73f4f04c51c5d1e00139be5 | Python | 2,534 | 73 | import pytest
from binn import BINNExplainer, BINN, Network
import torch
import pandas as pd
@pytest.fixture
def binn_explainer():
input_data = pd.DataFrame({"Protein": ["a", "b", "c"]})
pathways = pd.DataFrame(
{"source": ["A", "B", "C"], "target": ["path1", "path1", "path2"]}
)
translation =... |
80be0878b25374069c0d82b2229f3620cf519da4c59247e4a509f8f2dc6f59ea | Python | 2,538 | 77 | import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from torch.autograd import Variable
from PIL import Image
# import numpy as np
import torch.nn.functional as F
class ResnetSimilarity():
def __init__(self):
self.train_on_gpu = torch.cuda.is... |
842fa2d0eba56ff63aede6e516dcd4c21821885490297cb83e1e824b96bf1c76 | Python | 2,538 | 72 | import tensorflow as tf
# implementation from: https://github.com/sayakpaul/Adaptive-Gradient-Clipping/blob/main/agc.py
def compute_norm(x, axis, keepdims):
"""
Computes the euclidean norm of a tensor :math:`x`.
Args:
x: input tensor.
axis: which axis to compute norm across.
keepd... |
cdd9316a66a8086e64a654c4b7cec39bf6cb37f675400f45c01e456439c1641b | Python | 2,540 | 99 |
import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
J = 10
NB_EPOCH = 1000
BATCH_SIZE = 128
DROPOUT_RATIO = 0.5
# F(t) = 1 - exp(-(t-g)^m/s)
def weibull_cdf(parameters: torch.Tensor):
m = parameters[:, 0]
s = torch.clamp(parameters[:, 1], min=0.001)
output_list = []
... |
3725f9b8b0a5fca22bfa3610601389da4bb4dd360a607d9da3707fe8711d7791 | Python | 2,541 | 81 | from typing import Optional, Tuple
import torch as pt
from NNPOps.CFConv import CFConv
from NNPOps.CFConvNeighbors import CFConvNeighbors
from .models.model import TorchMD_Net
from .models.torchmd_gn import TorchMD_GN
class TorchMD_GN_optimized(pt.nn.Module):
def __init__(self, model):
if model.rbf_type... |
8f30ba4ad9554a0bade2a684d0c554d7ff3d3f59163fae815aa181489f25589e | Python | 2,542 | 58 | #!/usr/bin/env python
"""
Nipype workflow derived from preprocessing_synthseg.py, swapping the
synthseg node by a datagrabber that get the precomputed synthseg parc
"""
import os
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.io import DataGrabber
from shivai.workflows.preprocessing_s... |
0f27f496cab23383fcf0d359a8ad2c5b0fca7263fd1f21ae53ca25777ecbd3ab | Python | 2,543 | 77 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmcv import Config
from ...builder import DATASETS
from .topdown_coco_dataset import TopDownCocoDataset
@DATASETS.register_module()
class TopDownHalpeDataset(TopDownCocoDataset):
"""HalpeDataset for top-down pose estimation.
'https://gith... |
dae5fe164198d045d2e804e15f8b0b4115711c109053a2da5fcf201ef23e23b2 | Python | 2,543 | 92 | # 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)
# Configuration file for the Sphinx documentation builder.
# -- Project information
project = "TorchMD-Net"
author = "... |
56bd4b1511495e7b857a426fdf9de840a7a2f166ca13fc2112091a0677c56013 | Python | 2,545 | 72 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
def images_to_levels(target, num_levels):
"""Convert targets by image to targets by feature level.
[target_img0, target_img1] -> [target_level0, target_level1, ...]
"""
target = torch.stack(target, 0)
level_targets = []
start = 0
... |
056f45ad80cbe210d97f6c27b13069795eeebff3e4600ec7af4d7c04061ea9d5 | Python | 2,548 | 70 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import AnchorHead
def test_anchor_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,
'... |
e97687c9f4d6c8dda6133c889a7b2b607a18b779cd2e4c0a59351d7563478140 | Python | 2,554 | 89 | import numpy as np
import collections
import os
DEFAULT_WORD_COUNT_FILE = 'stackoverflow.word_count'
word_count_file_path = None
word_dict = None
word_list = None
_pad = '<pad>'
_bos = '<bos>'
_eos = '<eos>'
'''
This code follows the steps of preprocessing in tff stackoverflow dataset:
https://github.com/google-resea... |
8f9bcb8dd0d8116d3478d1940514a65dfbf45bb1b589c97cc34ba5967ef99698 | Python | 2,558 | 85 | import pandas as pd
import pytest
import networkx as nx
from binn import ImportanceNetwork
@pytest.fixture
def importance_df():
df = pd.DataFrame(
{
"source name": ["A", "B", "C", "D", "E", "F"],
"source": [1, 2, 3, 4, 5, 6],
"target": [4, 5, 6, 0, 0, 0],
"v... |
f2a5b6a29877231a6b9e1807bf5b6d91691c92ce57ce55588085cbed2777a7d8 | Python | 2,559 | 60 | import numpy as np
import tensorflow as tf
tf.config.set_visible_devices([], device_type='GPU')
import os
from utils import load_json
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
def split_equation(equation, lead_n=4):
'''equation is a string, e.g. 'ode_auto_linear1_forward_0.806_0.047'''
# Split th... |
d3ecb54f9585527ae203da037300f932a211c9532b4441f07687b459bcaa0b73 | Python | 2,560 | 71 | # Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties ... |
03592a5b8aa6ddbe36a64d41fc70cf9eb94324ab57b2bc095e95a2444e01dc69 | Python | 2,561 | 63 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import VFNetHead
def test_vfnet_head_loss():
"""Tests vfnet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad... |
3522d526bc8ad64a70d1aefc14a8a6ec0101f99c755bfcf47a4c9cb2cd003156 | Python | 2,561 | 42 |
def fill_caption_number(caption_template, identifier, params, format_str = '{:.3g}'):
if "ode_auto_const" in identifier:
caption = caption_template.replace('[0.001]', format_str.format(params[0])) \
.replace('[0.002]', format_str.format(params[1]))
elif "ode_auto_linear1" in ident... |
9cf0cfba52db37ff4dfb933c7a6a15fd8c4c7e7f38ff0b4f2b1152359597d343 | Python | 2,573 | 54 | from data.manual_labels.data_reader import ManualLabels
# data = ManualLabels(outcome = 'any_cancer', text='IMPRESS')
data =[{'id':'any_cancer_IMPRESS','type': 'manual_label' , 'params': {'outcome': 'any_cancer', 'text': 'NARR+IMPRESS', 'training_split':9}}]
# data = ManualLabels(outcome = 'any_cancer')
data = Manual... |
f365f8d83b222ff626f6185fe1cbc0133103f060656f2f01f182ba2f56e68245 | Python | 2,573 | 103 | '''
Author: ----
Date: 2022-06-30 11:56:44
LastEditors: GhMa
LastEditTime: 2022-09-20 14:35:33
'''
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.Function):
... |
5f77649d5c501b1e59d6045440f3ca3432a7b2c19fd4f7d3a781df3ef8489074 | Python | 2,574 | 100 | import os
import shutil
import numpy as np
import torch
from torch.autograd import Variable
class AvgrageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.sum += val * n
... |
5774e0c625508ea6f846e2be9b17c4f97a9f1e3cc8eccdfd5abb10fe318a7c24 | Python | 2,577 | 87 | # ref: https://github.com/cprogrammer1994/ModernGL/blob/master/examples/julia_fractal.py
import moderngl
import numpy as np
class SquareProgram:
def __init__(self, screen):
# save settings
self.screen = screen
# initialize settings
self.color = 1.0
self.toggle = True
... |
16c19af20cf8eda1172965c7e48a2da0a239355458fc92a50905b8d9e12292fb | Python | 2,578 | 83 | import numpy as np
from .methods import intersect
class LinearWall:
def __init__(self, name, bias, slope, t=[0, 1]):
"""
line-segment: u+tv
"""
self.name = name
self.bias = bias
self.slope = slope
self.t = t
self.intersects = {}
@property
... |
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