sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5cdddc6fa4baeb357b2b294d1e39560644517c0714f0d4ec3e68e6166ae23853 | Python | 4,511 | 121 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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 config_path import LOG_PAT... |
3ecf0c6204faf498c1e9b26f90688df5d97979b48c332d04ba2d54f47052dc27 | Python | 4,513 | 121 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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 config_path import LOG_PAT... |
a0e94064bb58747593f721e2081a74d3d029b95dbc970490dd7c395dc8cc67d0 | Python | 4,525 | 175 | # Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors.
import os
import pytest
from numpy import allclose, array_equal
from openff.toolkit.topology import Molecule
from openff.toolkit.typing.engines.smirnoff import (
ElectrostaticsHandler,
ForceField,
LibraryChargeHandler,
vdW... |
d0779928bf712cfb5a78e4a10697387b69aa812bfa44644f1d28d79ff99f2e49 | Python | 4,526 | 156 | import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateModel
from gradient_accumulator.layers import AccumBatchNormalization
import random as python_random
import numpy as np
import os
def normalize_img(image, label)... |
d48056ebc271b7ebed8e652341ad66b2b1144877192b12c63c1428f1fb42455f | Python | 4,532 | 123 | # -*- coding: utf-8 -*-
"""
Created on Mon Oct 18 21:50:08 2021
@author: fhu14
"""
import json, os
from typing import Dict
def assert_unique_keys(d: Dict) -> bool:
r"""Checks that for a settings file, all the dictionary keys are unique under
each section (no duplicates).
Arguments:
d (D... |
dbd50113bdf77d9bce76bf55186a0b4ef5a616ce6178b5dd10988fc11917c1ee | Python | 4,534 | 107 | import torch
from torch import nn
try:
from fedml_core.trainer.model_trainer import ModelTrainer
except ImportError:
from FedML.fedml_core.trainer.model_trainer import ModelTrainer
class MyModelTrainer(ModelTrainer):
def get_model_params(self):
return self.model.cpu().state_dict()
def set_mo... |
f1de7da6625200866343cf8487c37100f53e8866f49e6869ca43a33cc1646f91 | Python | 4,539 | 116 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseMaskHead(BaseModule, metaclass=ABCMeta):
"""Base class for mask heads used in One-Stage Instance Segmentation."""
def __init__(self, init_cfg):
super(BaseMaskHead, sel... |
8a6017910fdbeea7eb3a2c73211d460dfa85c987958bead99428513281ce9957 | Python | 4,545 | 172 | import errno
import os
from neuron import h
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def voltageout(foldername, recordlist):
for vec in recordlist... |
592d18c0f2982d746aae39cbcc6311018fe2b79bfcd70a647f373e0ac617abe9 | Python | 4,547 | 151 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 16:02
@project: MalariaDetection
@description:
"""
from keras.layers import * #For adding convolutional layer
from keras.layers import Dense, ZeroPadding2D, BatchNormalization, Activati... |
5316c32c5b33d37ded51e9f72bca4649c2d86a51220cfa1eaff90caaa204379e | Python | 4,552 | 134 | import os
import shutil
import glob
# import collections
# import random
import pickle
# import logging
from skimage.transform import resize
# from collections import OrderedDict
import numpy as np
import torch
def transform_list_to_tensor(model_params_list):
for net in model_params_list.keys():
for k i... |
026d1c8a0ee9bc63dc02436a47965dcd7422be9bc1c1e3445fefe12d7b335d5f | Python | 4,553 | 145 |
import numpy as np
import argparse
import importlib
import os
from datetime import datetime
import json
import glob
import sys
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import classification_report
import pandas as pd
import matplotlib as mpl
mpl.use... |
63a9f61043c716740578af64b3de1492288aa4eebec53057c4d6993e8ca384c8 | Python | 4,554 | 138 |
import subprocess
def run(cmd, max_minutes = 6000):
import time
sys.stderr.write("Running cmd: %s\n"%cmd)
p = subprocess.Popen(cmd ,
shell=True,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subproc... |
10e791d859cfa2ce33a2b8e7e3cb406e905175e813c740692fd119ed569e2ebd | Python | 4,556 | 117 | import os
import shutil
import sys
from pathlib import Path
import matplotlib.pyplot as plt
import numpy
import numpy as np
from keract import get_activations
from tensorflow.keras import Input
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.layers import Dense, Dropout, LSTM
from tensorflow.kera... |
bdd4ddc3dc18332c1ffab6306f9d6d818b6fb9194b97381981d188d6af58ef16 | Python | 4,561 | 155 | import argparse
import logging
import os
import time
from pathlib import Path
from typing import List, Union
import numpy as np
import pandas as pd
from tools.utils import calculate_design_score
os.makedirs('logs', exist_ok=True)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s',
datefm... |
d51eb310a783dd383dcc1609bd5b21269a349d8ff33007c3797ae9ca4f4d4c87 | Python | 4,566 | 118 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaSepBNHead(AnchorHead):
""""RetinaHead with separate BN.
In RetinaHead, ... |
bc9949c477af91bf2ee1e3dc85b7e6a0afdb2238410c23a08b79d0596042495c | Python | 4,570 | 122 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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 config_path import LOG_PAT... |
c80376a01e1a903b295737b83ad6fdee16826df184e8453c04d3e6516d161d58 | Python | 4,570 | 136 | from .MUSIC_block import MUSIC
import numpy as np
from scipy.linalg import svdvals
from scipy import linalg
class TOPS(MUSIC):
"""
Class to apply Test of Orthogonality of Projected Subspaces [TOPS]_ for
Direction of Arrival (DoA) estimation.
.. note:: Run locate_source() to apply the TOPS algorithm.
... |
1f16eabae5e8de6964bc7c3ce76215f1daafcdb52daeb1987d7d38abf1cbe6f6 | Python | 4,574 | 130 | import torch.nn as nn
import torch.nn.functional as F
class ConvLayer(nn.Module):
def __init__(self, c_in):
super(ConvLayer, self).__init__()
self.downConv = nn.Conv1d(in_channels=c_in,
out_channels=c_in,
kernel_size=3,
... |
2e5717dc3af5f65a41af8201bd1302118e56839aa2d141c2de6b65eccc7b4aee | Python | 4,577 | 104 | import os
import os.path as osp
import sys
import time
import argparse
import pandas as pd
from tqdm import tqdm
import torch
from graphrepr.explain.gnnexplainer import GNNExplainer
from graphrepr.explain.utils import get_configs, get_data_and_parts, get_all_subfolders, make_directory
from graphrepr.explain.torchutil... |
0a3dd93f118a400712f7f02d26c184056e9647585edf072bdf12e40a86177754 | Python | 4,590 | 95 |
random_seed = 234
from utils.rnd import set_random_seeds
set_random_seeds(random_seed = random_seed)
from pipeline.crossvalidation_pipeline import CrossvalidationPipeline
from pipeline.one_split import OneSplitPipeline
from utils.logs import set_logging
import logging
import datetime
import imp
import os
import sys
... |
49968a90eb88c69cfed0a89a6d0cf5276c22aac297ecfcc842971fbf4690b197 | Python | 4,590 | 116 | # 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 torchmdnet.priors.base import BasePrior
from typing import Optional, Dict
import torch
from torch import nn, Tenso... |
60f9188ad679dce7a4419442cb3384b2554ad7b30bfa3780bc53ca97ea0c5cc2 | Python | 4,590 | 139 | import numpy as np
import cv2
import torch
import timm
from torchvision import transforms
from PIL import Image
class FeatureExtractor:
def __init__(self, device=None):
print("Loading feature extractor (prov-gigapath)...")
# --- Device ---
if device is None:
self.device = tor... |
19d3fac6802f9e9318a93cbee1166c7171545012658ec04a3f0d0a46ba0218c8 | Python | 4,591 | 143 |
import math
import torch
from torch import nn
from torchvision import transforms
from pl_bolts.transforms.dataset_normalizations import (
imagenet_normalization
)
import monai
from monai.transforms import (
EnsureChannelFirst,
Compose,
RandFlip,
RandRotate,
SpatialPad,
RandSpatia... |
1cb0af197dea150702731033f6b43c1d51e50b2a148a6a4cdf4ed9fb8545970c | Python | 4,591 | 114 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmdnet.models.energy_correction.functional import softplus_inverse
class ZBLRepulsionEnergy(nn.Module):
"""
Short-range repulsive potential with learnable parameters inspired by the
Ziegler-Biersack-Littmark (ZBL) potential descri... |
a02dfb850022d14303f52a5bea65f87f7ff8bd3ad4766d06aa42eb128af1b255 | Python | 4,591 | 91 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
a4bbd21109429fd0e0dcb9768495abc3d72ca55eeeba1eab449a1bf8a0db9d34 | Python | 4,594 | 121 | import pandas as pd
import numpy as np
from sklearn import linear_model
import os
import sys
dir, filename = os.path.split(os.path.abspath(__file__))
HOME = os.path.split(dir)[0]
def _is_upper(string):
return string == string.upper()
def count_cancer(name, df=None):
if type(df) == pd.DataFrame:
tmp = ... |
b5871c5499deab68a8918393ad5a8367bd6913ae1cfeb910b6a38463db3140b3 | Python | 4,594 | 107 | import logging
import os
from .data_utility import count_samples
from fedml_api.data_preprocessing.exp2_path import my_transforms
from .base_data_loader import BaseDataLoader
from .Nuclei_seg_dataset import NucleiSegTrainDataset, NucleiSegTestDataset
logging.basicConfig()
logger = logging.getLogger()
logger.setLeve... |
fad58481e07c1e92bdc68cbc7c466bf1aeb265bcb28d98943f8a436a866a89ed | Python | 4,594 | 133 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 25 13:55:29 2021
@author: baris
"""
from neural_coding import load_spikes, rate_n_phase
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# ==============================================================... |
1f05252d94593d295b46c094886ce8ce2b79262a8d92c0c54420064ed86181a9 | Python | 4,595 | 156 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
6a662a0bbf3cf2bab8dbf92c26f4b042c2d7792164a1542ad6227ecd1616ceb4 | Python | 4,595 | 156 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
4ac56fd1f1d5e52a75972cba4cf49389fe4d1a1ed3116ee85896add968100f26 | Python | 4,597 | 137 | # __author__ = haitham elmarakeby
from file_browser import get_models
from os.path import join, exists
from config_path import TEST_RESULTS_PATH, PLOTS_PATH
from os import listdir
import pandas as pd
import numpy as np
from utils.evaluate import evalualte
# f1 maximization
def expect_f1(y_prob, thres):
idxs = np... |
5f6462f1a2cb5c8cad8d769c72f02a760fcf90bdd40ced4da46703aa1e0affce | Python | 4,597 | 120 | # -*- coding: utf-8 -*-
"""
Created on Tue Jun 28 14:20:24 2022
@author: fhu14
"""
#%% Imports, definitions
import pickle
import os
from typing import List
from functools import reduce
#%% Code behind
def infer_skf_elements(direc: str) -> List[str]:
r"""Finds out the elements of an skf set
Arguments... |
aacdde666e81bb773b99283a4bd473ce36885d7880a376d40df3b18ed5f34dff | Python | 4,600 | 139 | """
Cross-validated Pearson correlation between a JOINT model
(SSS + INDIVIDUAL interpretable features) and high-gamma ECoG, across layers.
"""
#### 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_RO... |
cb634b4fb46a03b3df6c87c23237d830ab125fb6b06e3c3bacde7a0e9469be0e | Python | 4,600 | 130 | import json
import pandas as pd
import numpy as np
import datetime
from pingouin import partial_corr
from typing import Union, Dict, Iterable, Tuple, List
from scipy import interpolate
from statsmodels.stats.multitest import multipletests
__all__ = ['load_json',
'upper_and_lower_quartiles',
'deabbreviate_parcellation'... |
e59d34113f8c4de5e31325535fbf15ff2cd53cce070ba4e0ecae68b492d325fc | Python | 4,600 | 150 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.backbones.tcformer import TCFormer
from mmpose.models.necks.tcformer_mta_neck import MTA
def test_mta():
in_channels = [8, 16, 32, 64]
out_channels = 8
# end_le... |
86efc6f6cfa1563faa3e8e0633c63805a5ea666833c644137fdd709fb48bcf28 | Python | 4,603 | 129 | # 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 numpy as np
import os
import torch as pt
from torch_geometric.data import Data, download_url
from torchmdnet.dat... |
8e04c1a9cb1666073d23c425be519afd6bff470f02046f7fe431efc394f46210 | Python | 4,603 | 126 | # Copyright (c) OpenMMLab. All rights reserved.
from itertools import groupby
from typing import Dict, List, Optional, Union
import cv2
import numpy as np
from ...utils import get_eye_keypoint_ids
from ..base_visualizer_node import BaseVisualizerNode
from ..registry import NODES
@NODES.register_module()
class Bigey... |
547785b4ca28e29760d41ec87cb3f63290c95602707f09e1bdc70b9fbd0aa533 | Python | 4,604 | 130 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
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_... |
69278b125f427bbc4e89fa618cf8d8c663895f3ff4f5457638d279fc0014e7a9 | Python | 4,605 | 121 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import GFLHead, LDHead
def test_ld_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,
'... |
08e12fb845e33020d064d7ccbbcab107b313854a44317fc83c1d6e807b333bdc | Python | 4,609 | 91 | import os
import sys
# os.system("module purge")
cmd = "../../install/x86_64/bin/nrnivmodl ../../src/all_mechanisms"
if not os.path.isdir("./x86_64"):
os.system(cmd)
# generate binary dataset file for DeepDendrite
# @param1: original dataset file (*.npz)
# @param2: fold to save binary dataset file
# stim_img_trai... |
36b1719abc22c17a3ff3b975b7ba3159c864f605de61555a3034d4a093e7adc4 | Python | 4,616 | 116 | """
Does the unique neural variance of the semantic QA features survive a lexical
predictability control? (Reviewer 1, comment 1.3)
The manuscript isolates semantics against a non-semantic control set of
{speech presence, mel-spectrogram, GBFB, phonetic, syntactic}. The reviewer
observes that this omits word frequency... |
33049508778e14f093e0b362bdfc1e27632c10f2fec6ace90c624a662f4042d3 | Python | 4,620 | 156 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
5189dab8a123d393e3f75f9a7e91e18359cb33c04b8ae8c0901177c65f32f044 | Python | 4,620 | 156 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
e7aa36470d479066b7fec4cf57fd659ff48e275474a9cc6ed21fa558ed033f34 | Python | 4,620 | 143 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmpose.models.necks import PoseWarperNeck
def test_posewarper_neck():
"""Test PoseWarperNeck."""
with pytest.raises(AssertionError):
# test value of trans_conv_kernel
_ = PoseWarperNeck(
... |
083937650cb437fcc7937e504e76a29467409800fb90aa40e4db5b0db153776a | Python | 4,625 | 118 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from .camera_base import CAMERAS, SingleCameraBase
@CAMERAS.register_module()
class SimpleCameraTorch(SingleCameraBase):
"""Camera model to calculate coordinate transformation with given
intrinsic/extrinsic camera parameters.
Notes:
Th... |
2251a730f7fac060a089580686f5d6e2a6b3c25aaf4a2f07d10f81e067e4a5e6 | Python | 4,625 | 165 | # Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Mel-spectrogram loss modules."""
from distutils.version import LooseVersion
import librosa
import torch
import torch.nn.functional as F
is_pytorch_17plus = LooseVersion(torch.__version__) >= LooseVersion("1.7")
class MelSpectr... |
151823099abc7b1bd3586a6f0ab8fcf57f4d75d24f26fa203160d5bc416ba645 | Python | 4,627 | 149 | _base_ = ['../../../../_base_/default_runtime.py']
use_adversarial_train = True
optimizer = dict(
generator=dict(type='Adam', lr=2.5e-4),
discriminator=dict(type='Adam', lr=1e-4))
optimizer_config = None
lr_config = dict(policy='Fixed', by_epoch=False)
total_epochs = 100
img_res = 224
# model settings
mode... |
22661a04bf60dce1547202d8a9baf659506038e604a11c8296c5ee5dc9c3f926 | Python | 4,629 | 116 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmdet.models.builder import HEADS
from ...core import bbox_cxcywh_to_xyxy
@HEADS.register_module()
class EmbeddingRPNHead(BaseModule):
"""RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/... |
fdf2da690a6826c3d81e1b1db0347c2e1265cbebaa9aa8e4c17e6b72430435df | Python | 4,631 | 108 | import torch
from torch import nn
try:
from fedml_core.trainer.model_trainer import ModelTrainer
except ImportError:
from FedML.fedml_core.trainer.model_trainer import ModelTrainer
class MyModelTrainer(ModelTrainer):
def get_model_params(self):
return self.model.cpu().state_dict()
def set_mo... |
4a9edbde11bcb7c618236735fe8dace220eef0d2c1d01120ec9cd94f1c4628bf | Python | 4,632 | 136 | import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
"""
For info, these are the probability counts by categories we use for styling
cls dry normal wet
0.4 169,180 50,329 163,768
0.5 99,752 6,733 106,723
0.6 22,184 366 35,627
0.7 4,743 24 13,077
0.8 900 3... |
7662b234998896e4a206763dbc45dea3f4ea581fba0b940ff95b3347f9391dc4 | Python | 4,632 | 134 | import argparse
import typing
import numpy as np
import pandas as pd
from osgeo import gdal_array
import argparse_def
# noinspection PyUnresolvedReferences
import matplotlib_setup
import models.abstract
import sentinel_data_utils
import reference_data_utils
from osgeo_setup import *
def parse_args() -> argparse.Nam... |
10eef764abcb47fc0888507be3949951968d87f13e3e4849dbc072761b0108ac | Python | 4,634 | 122 | # Author: Fabi Prezja <faprezja@fairn.fi>
# Copyright (C) 2023 Fabi Prezja
# License: MIT License (see LICENSE.txt for details)
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(model, generator, generator_name, target_type)... |
c991ce3bf460aa9f19d5d2b7f58a497909e5ce269af1ca88e5d9f11344b005b2 | Python | 4,635 | 146 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor)... |
74de9900cc2706b2366b71fe95b13cfc9d51ef2a48b3393f5b13758f2c2c2fac | Python | 4,639 | 120 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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
# training_splits = [0,1,4,6,7... |
ef80f5fd179c631d9d35ce47bee5f9b17477d83d36cb5691fe4c56faf9e89dcb | Python | 4,639 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
9b27cdf095430f609f5bbc300994cde07faf53d9f838981c74f5845e4ae67fa5 | Python | 4,641 | 129 | import os
import gc
import ot
import pickle
import anndata
import scanpy as sc
import pandas as pd
import numpy as np
from scipy import sparse
from scipy.stats import spearmanr, pearsonr
from scipy.spatial import distance_matrix
import scipy.sparse as sp
import matplotlib.pyplot as plt
import matplotlib as mpl
from mat... |
42f7551ee31ad7664ea2fc130fd4f1e75767791ebcd6e63d26c88bd64cccfbe6 | Python | 4,645 | 125 | # -*- coding: UTF-8 -*
import os
import time
import tensorflow as tf
from coarsen import create_coarse_graph
from utils import normalized, graph_to_adj
from numpy import *
from sklearn.decomposition import PCA
from sklearn.cluster import MiniBatchKMeans
import json
import pickle
def print_coarsen_info(ctrl, g):
c... |
2cffcc43395604b5617c2752fbc05dd5ee6cac91c3d42ae74d72b47b665ba2e0 | Python | 4,646 | 134 | import logging
from keras import Input
from keras.engine import Model
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.regularizers import l1, l2
from model.model_utils import apply_m... |
e4c7ca36d9d367159cccfded79bc45fc6e157db7493d6e8429b3336ac947c955 | Python | 4,646 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
1606b477c5be0404fda237dbed42182e41cdec8342b8996e745113bd8d76f6cb | Python | 4,648 | 149 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/interhand2d.py'
]
checkpoint_config = dict(interval=5)
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learni... |
1e663d7eeb3c23a0e1098b96f8d77043a576ac4e7df937226bf49f9b71b27af6 | Python | 4,648 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
31e80a0ac7d622442a64e430a9c488a2e644c5347889d8b41f88027fa24d0ec0 | Python | 4,655 | 147 | import glob
import os.path as osp
import re
import shutil
from argparse import ArgumentParser
from functools import wraps
from multiprocessing.dummy import Pool
def bundle_args(func):
@wraps(func)
def wrapped(args):
return func(*args)
return wrapped
@bundle_args
def update_ceph_config(filename... |
93d001f294c43ed0154ddbe4c31d8c9bb8d6366e022303a4ae476bb7255bc54f | Python | 4,655 | 95 | #!/usr/bin/env python
import unittest
import pandas as pd
from os import path
import os
from picrust2.util import make_output_dir, TemporaryDirectory
from picrust2.default import (default_ref_dir_bac, default_ref_dir_arc,
default_tables_bac, default_tables_arc)
from picrust2.place_seqs im... |
37b72f1080f04dc3bf61842c887c65ab75b5f93059dc5f98ba1fd7ac7b5ab037 | Python | 4,656 | 151 | import pytest
from pathlib import Path
from clipkit.clipkit import execute
from clipkit.files import FileFormat
from clipkit.modes import TrimmingMode
from clipkit.settings import DEFAULT_NT_GAP_CHARS
here = Path(__file__)
@pytest.mark.integration
class TestOutLog(object):
def test_simple(self):
"""
... |
ab048c5fc55b20e6cff953fdae9a59c501c9e89fba3cb483bdb418dd7967674b | Python | 4,656 | 150 | # 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 contextlib import nullcontext
from math import sqrt
from typing import Tuple
import torch
from torch import Tensor
from sbi.sbi_types import Propos... |
a460906db16f3c52df7f994b4be00f75538f84aabfb3f051e6c1e921b7e76de2 | Python | 4,658 | 141 | """
Variance partition for:
1) Phonetic vs Speech
2) Phonetic vs (Speech + SSS), per SSS layer
"""
#### 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"... |
cbc328b801f75f3e6a2c298c4944575cf8c992e394800671bf42964cd53ef972 | Python | 4,658 | 141 | # Copyright (c) OpenMMLab. All rights reserved.
import tempfile
import torch
from mmcv import Config
from mmpose.datasets import DATASETS, build_dataloader
from mmpose.datasets.dataset_info import DatasetInfo
from mmpose.models import builder
def test_voxelpose_forward():
dataset = 'Body3DMviewDirectPanopticDat... |
73e5757ff518e759a3e0d127ef330df9e50d5b5f9c3974a6702299346708c097 | Python | 4,659 | 124 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 2 08:39:39 2021
@author: baris
"""
import matplotlib.pyplot as plt
import numpy as np
import grid_model as grid
from neo.core import AnalogSignal
import quantities as pq
from elephant import spike_train_generation as stg
from scipy import ndimage
... |
aad1b49ac0fc4c7ed2412f240797cbb744726e846a208b4c010bd66ddbcc49fd | Python | 4,661 | 154 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_hand.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
... |
3c04013f9981634d5ef114f0693fd3ed6444875c6909e4e02e6e1aaba9d11e38 | Python | 4,667 | 142 | #!/usr/bin/env python
# Copyright [1999-2017] EMBL-European Bioinformatics Institute
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... |
90d28dc6103df914eae3af8b6b30804202fba873fbd34c3a347df013bfd78e52 | Python | 4,673 | 133 | # -*- coding: utf-8 -*-
"""
Created on Mon Oct 31 23:01:40 2022
@author: Alfred
"""
import pandas as pd
from functions import *
from model_functions import *
from the_model import *
matplotlib.rcParams['axes.prop_cycle'] = matplotlib.cycler(color=["#F4511E", "#FB8C00", "#FFB300", "#FDD835", "#C0CA33", "#7C... |
e0ec770461e282716b022ada7d5c0a1124e3c9e8c2dfe2172f3a2af6f6aed3b7 | Python | 4,673 | 200 | import re
# parse vcf file lines
class vcf:
# varian type
UNKNOWN=0
SNV=1
DEL=2
INS=3
MULTI=4
# vcf file columns
CHROM=0
POS=1
ID=2
REF=3
ALT=4
QUAL=5
FILTER=6
INFO=7
FORMAT=8
@staticmethod
def get_chrom (E): return E[vcf.CHROM]
@staticmethod
def get_pos (E): return E[vcf.POS]
@staticmethod
... |
9906fd5bd2c9935de2edd92cd147eaefa0349138325a1de7cfa041cdcb1dfb2e | Python | 4,676 | 90 | """Public optimized dense E/F consistency across a molecular separation scan."""
import pytest
import torch
from aimnet.calculators import AIMNet2Calculator
pytestmark = [pytest.mark.gpu, pytest.mark.weights]
def _water_dimers(distance: float):
# Coordinates are angstroms. Both rows contain two actual water mo... |
cebe69bf036ede642e5a3ce6bc9bb47ba082b70ee1bf1e1d04c2162a1dbdbbb7 | Python | 4,677 | 144 | # -*-coding:utf-8-*-
import logging
import time
import uuid
from typing import List
import paho.mqtt.client as mqtt
from ..base_com_manager import BaseCommunicationManager
from ..message import Message
from ..observer import Observer
class MqttCommManager(BaseCommunicationManager):
def __init__(self, host, port... |
bce2ba98750f6d071fc0074df91d2708825eaafc8814695fbf4467df7e7405d2 | Python | 4,678 | 108 | import numpy as np
import scipy.special as sp
from importlib import reload
import sys
sys.path.insert(2,'support')
import functions
reload(functions)
import functions as f
import parameters
reload(parameters)
import parameters as p
direc = sys.argv[0].split('script')[0]+'data/'
plasticity_type = sys.argv[1]
n_simulati... |
5552a014bf86bfefd8b23b29ddffa8e213bcdb7279fa0172ee9d209083501c07 | Python | 4,680 | 112 | import os
from typing import List, Any
import numpy as np
import codecs
import json
from glob import glob
import cv2
import shutil
from sklearn.model_selection import train_test_split
# 1.标签路径
labelme_path = "LabelmeData_test/" # 原始labelme标注数据路径
saved_path = "tmp/VOCdevkit/VOC2007/" # 保存路径
isUseTest=False... |
b7a3e3acce5e619e0337439b9c4487bdd2e01f06b19ebf000fa74b15c00d9578 | Python | 4,687 | 130 | import numpy as np
class MUSIC(object):
def __init__(self, freq_bins, mod_vecotr):
self.num_freq = freq_bins.shape[0]
self.freq_bins = freq_bins
self.mode_vec = mod_vecotr
assert self.mode_vec.shape[0] == self.num_freq
self.n_points = mod_vecotr.shape[-1]
self.nu... |
936521fbbc5fac8d53e04ee9998c261bdbb5492c49bd1aa982bb84b7d6559de6 | Python | 4,693 | 131 | import torch
from sklearn.base import BaseEstimator
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.utils.extmath import softmax
from torch import optim, nn
from torch.utils.data import DataLoader
import time
import logging
# from barbar import Bar
class TextDataset(torch.utils.data.Datase... |
d4b9392bc7a75936e607e91560a893192e7eececc9ae93262e9c1a63892972ec | Python | 4,693 | 93 | import logging
import os
import sys
from .message_define import MyMessage
from .utils import transform_tensor_to_list
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.com... |
876626c8eda422f62a8490599ce2f7a93df3d548c282038732a94acea9cc0973 | Python | 4,696 | 149 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/interhand2d.py'
]
checkpoint_config = dict(interval=5)
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learni... |
9051a0ba21cb49e24ffb9b733cfc0e536cffbc3dc6b9d7e724003986c75166fa | Python | 4,696 | 151 | """
The main training script for training on synthetic data
"""
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from .network import normalize_input, unnormalize_input
from asteroid.metrics import get_metrics
def compute_metrics(orig: torch.Tensor,
e... |
a2ee556e90ba3e6217ae84daac2ef7f6e48759de0340dc8b3bd17b720dbad4d6 | Python | 4,696 | 128 | import argparse
import json
import numpy as np
import os
import torch
from sep.helpers.eval_utils import si_sdr
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
import sep.helpers.utils as utils
import scipy.stats as st
import glob
def draw_cdf(arr, fig_id = 0):
plt.figure(fig_id)... |
d62a491bcca9f354aff5018097675bba53e96f380460623de1856924e719b760 | Python | 4,700 | 132 | import pathlib
import shutil
import psycopg2
from psycopg2 import sql
from configparser import ConfigParser
def initialize_database(configuration_file):
parser = ConfigParser()
parser.read(configuration_file)
# connect to default postgres database, create exsclaim user
conn = psycopg2.connect(**parser[... |
16c828bace5f455772c460299b7fc7ce8f32d30805affc0213ebf478e8c96a67 | Python | 4,701 | 114 | """Resolve graph opt-in and its runtime requirements without importing the optional execution backend."""
import importlib.metadata
import os
import re
import sys
import torch
# Minimum versions for CUDA-graph capture. nvalchemi-toolkit-ops 0.5.0 orders its Warp neighbor kernels on
# PyTorch's current stream and res... |
54c4e36866bfdfd86da6ffe0ac9f5bb2acd2ecdc61677f8d5a00cd15d88a8dff | Python | 4,702 | 93 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
ca9eeb526cabe1e42ed24e912cb8ba2b02888bb6330a663cb4bbaf2210790cd7 | Python | 4,705 | 128 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import VGG
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..necks import ssd_neck
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detec... |
63f7d3bcfcbbc84dddfd5bd5020d427c4570d9094754e130c59a6d7d1460a6ec | Python | 4,706 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
425643659e21463c58981f54bea30bf920cc414c4bd3af582c64f301d62947e7 | Python | 4,707 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='... |
9d8a88bd06019da63d18b846402c8575ce670837c9f7b281d266bf7bb44fd822 | Python | 4,708 | 149 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/interhand2d.py'
]
checkpoint_config = dict(interval=5)
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learni... |
ba47802f9b30fd35ad5457aa5a74645cedcccdfb133c42278100a9e0cfb282e3 | Python | 4,708 | 114 | # Imports #
import kivy
import zipimport
import sys
import os
import configparser
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.uix.button import Button
from kivy.uix.image import Image
from kivy.uix.label import Label
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.gridlayout import ... |
b65967d5f82e560c7214fc9925ca38220ad17c8abdc0837a0da4c8a2eb165bb5 | Python | 4,711 | 126 | import os
import json
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image
from data.utils import pre_caption
class coco_karpathy_train(Dataset):
def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):
'''
im... |
c65940eb72148c3fa663c0441b309aafc4c1bd7eefb80d521f972deb1ae7b73b | Python | 4,711 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=2e-2,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
efd49495792238d4e26a6032451c3452955fc8260a89e6a88de68741ac8ee50e | Python | 4,715 | 108 | # supplement file for classifier's survival plot, will overlay on original image
# Created: 11/2/2021
# Status: in progress
# CUBLAS_WORKSPACE_CONFIG=:4096:8 python survival_plot.py
import numpy as np
import pandas as pd
import os
from collections import defaultdict
# from typing import Union, Dict, Tuple, List
# from... |
a03b9beaf4b42a298c3afc73dd598e0682b296dbbdf9cc863278dea6c9e86c13 | Python | 4,716 | 111 | import tkinter as tk
from tkinter import messagebox
from tkinter import filedialog
class GUI1:
def __init__(self):
super().__init__()
self.root = tk.Tk()
self.wait = self.root.wait_visibility()
self.title = self.root.title("Inputs")
self.geometry = self.root.geom... |
538028940325ee3e42e10ba29199bc803700b6a2aa80b674d6f88ccb3aaa6115 | Python | 4,722 | 121 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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
# training_splits = [0,1,4,6,7... |
7448ee1113ced888f396471e95a1ef6e127a7cabfd434d31468b79913ce3cdbd | Python | 4,723 | 118 | import torch
import torch.utils.data as data
from PIL import Image
import h5py
import numpy as np
def get_train_imgs_list(h5_filepath):
img_list = []
with h5py.File(h5_filepath, 'r') as h5_file:
img_filenames = list(h5_file['images'].keys())
label_filenames = list(h5_file['labels_ternary'].ke... |
cdbfee7504e8d39ca86f0ffad6af50a1b2be89bb057c44106c4fd9d19928e785 | Python | 4,726 | 93 | import logging
import os
import sys
from .message_define import MyMessage
from .utils import transform_tensor_to_list
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.com... |
80cb40124489aa215ed932e91e5172a56543fb9f77cf8db83f75e0471f7e5db3 | Python | 4,728 | 120 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_head
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>... |
33f50c0ba6e5e5c33ea4ff088dfcdceee5b3b654defa48cc98ed829dd5c3f8fb | Python | 4,731 | 120 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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
training_splits = [0,1,4,6,7,9... |
d6154c22205030034a3e9dfa8295ab976487ea82640230a6c5f6ba7f0e2e1f27 | Python | 4,731 | 120 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
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
training_splits = [0,1,4,6,7,9... |
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