sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2209874e9b53ae0d1fb330ed1affeba07220c6bc2b05ec75da39823446f8d6ce | Python | 4,377 | 116 | 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... |
99d9caf7146e07d89371611c6208b3198966126984285211c278f5a2ff77ca46 | Python | 4,378 | 116 | 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... |
a85e21b434367945c2c59d7ae61aceb72763ea161f1b4b76089430ff092070ea | Python | 4,378 | 116 | 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... |
c5a008f774ed02c7d0d08d858da381d04cf99d1156a7e8589d393318ae7fa537 | Python | 4,378 | 116 | 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... |
4e8894aa3cbaae046412f2251f6f0c10929963264bf8e0af1540e631184bb39b | Python | 4,379 | 116 | 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... |
7e9846613e49ca78cce6a07c04b3e08f2c616c2b4f0b544624ae32643cb207f8 | Python | 4,379 | 120 | # 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 contextlib
from typing import Callable, List, Optional
import joblib
import torch
from joblib import Parallel, delayed
from torch import Tensor
f... |
d1b200f76389613c9aba02c36ec6193bc7d963077bb61009514c68e7087d2839 | Python | 4,379 | 139 | # test_cvxUI_dataPs.py
# This file compares admUI with cvxopt on the distributions
# in dataPs.mat (see directory /data/).
# The results are used in Figure 3 of the paper.
#
# Distributed under the terms of the GNU General Public License v3
from admUI import computeQUI
import cvxopt_solve
import dit
import time
import... |
71f676857d2477c742a5c6463c64607d440b920f845669543972a29b680af553 | Python | 4,380 | 116 | 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... |
e1e30c5fcd050e44eccadccda4b903a53566a6f5b76ea5bba1a05b3f263132df | Python | 4,380 | 116 | 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... |
f84580931df4445090b4e15980f2fc623ad8c759627a376eec29a345502e0c84 | Python | 4,380 | 116 | 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... |
4c568d3d0ff699b22b4c1e7de7c177dc77a8d2b43249aca079785c1ac92c1c4f | Python | 4,381 | 135 | # -*- coding: utf-8 -*-
"""
Created on Tue Mar 1 12:54:57 2022
@author: Anandita
"""
import datetime, sys, os, time
from collections import defaultdict
import numpy as np
from scipy import linalg as sla
import matplotlib.pyplot as plt
import pickle
gen_fn_dir = os.path.abspath('..') + '/shared_s... |
cc70b89ee1ca285957a0663b0714bbc4ca5b16500c4b7b89e05c6006829fee7e | Python | 4,381 | 137 | __all__ = [
"parse_time_from_stack_name",
"extract_and_save_planes_from_one_stack",
"stack_images_with_same_name",
"skew_correct",
"skew_correct_one"
]
import subprocess
import os
import pycroscopy3D as pycro
from tifffile import imwrite, imread
from glob import glob
import numpy as np
from tqdm im... |
ab81642ac5ad65e3c20b516a550ec301f5785288e43bfa207d77342c0c08d173 | Python | 4,382 | 99 | import torch
import torch.nn as nn
from transformers import GPT2Model, GPT2Tokenizer, GPT2Config
from models_gpt2_source import GPT2Model as CustomGPT2Model
'''
The code of GPT-2 model in models_gpt2_source.py is slightly modified, for the purpose of multi-modal learning in ICON.
1, the positional embedding is remov... |
34a97b44e93adfdf315ead5d3d7d4ec222294b16e626d455339a7bc90bb42332 | Python | 4,384 | 132 | """
Cross-validated variance explained R^2 calculation using himalaya MultipleKernelRidgeCV.
"""
import random
import numpy as np
import torch
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
fro... |
361d2d903a8658c9d3d5ab0216102a6ae7a31ff722551eb39a92c97e7bb4a943 | Python | 4,385 | 107 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcv import ConfigDict
from mmdet.models.dense_heads import CenterNetHead
def test_center_head_loss():
"""Tests center head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3)... |
4e3c25a486e260ff7a3ac20a012f70588948b69828c1c86445a3677db455e38e | Python | 4,385 | 101 | from django.db.models import query
from django.shortcuts import render
from django.db.models.query import QuerySet
from django.http import HttpResponse
from django.views.generic import ListView
from django.db.models import Q
import os.path
import sys
from . import models
class SearchResultViews(ListView):
model = ... |
b0d583891c310c3a09b2e77a2c5d1ae3906912e52c128103d94e207d2aa51b11 | Python | 4,386 | 139 | import numpy as np
import h5py
import importlib
import stringcase
from fedml_api.data_preprocessing.my_transforms.compose import Compose
def init_transform(transforms, transforms_args):
transform_instances = []
for module_name in transforms:
transform = importlib.import_module(f"fedml_api.data_prepro... |
c3817c6dabc9b1813c5b7c267e4cbc634861e97b9efc43334b5ee1fbefd9d902 | Python | 4,386 | 116 | 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... |
0f107ba0dfa423ca4aded8f2b3b5b506a08b09d9eed8fb45b2187690299b93e5 | Python | 4,387 | 116 | 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... |
943596ed0153a90a42f89a4a4d5e64863e5ec476575ca9c90a05b7e369415a45 | Python | 4,387 | 116 | 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... |
9de017b84b76e264b03ea332ad3a03d0f485faca14154db92e5dbcabccf0eebb | Python | 4,387 | 116 | 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... |
4d477d8e08690595c4cc08e041c892060654c8cbdccb783aacfc143ca83110a5 | Python | 4,388 | 116 | 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... |
07cca2f5ef0bf80dbc1452d2d3ca57de772cc0ea53a98090f16e1cc39b82b98b | Python | 4,389 | 116 | 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... |
dc1d4f482382f4dd64c3844201dc7b09d740c8c3adff084e373d093e61a32202 | Python | 4,389 | 116 | 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... |
ff4384097745c884aa8177823ace55b2da96369be323b6c52e83f4dec13fc9aa | Python | 4,389 | 116 | 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... |
3764a5984b8b0086d6da9af0e3c23edf62cee0e38c8c2e82f8dbdbd8d52dffaa | Python | 4,395 | 116 | 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... |
0847cc3bb7ea4a84f0ebe51e84d5ac7db4c3ef37c4297d08b5e332a7c80ccf93 | Python | 4,396 | 158 | """
Video writing and watching utilities
"""
import glob
import os
import subprocess
import shutil
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
def write_mpl(mov_list, filename, fr=30, horizontal=True, titles=None):
... |
1821df96fe53528fadb0704a12fca22475ac38a4cfae26b16f5b95815f2595d9 | Python | 4,396 | 116 | 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... |
23b0721fb10c99119771a5fddafb884411ae9f1ef5e3b72024549706baf00ad3 | Python | 4,396 | 116 | 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... |
5ee74090e7ed70553b838589c611a719289c908b233401a8a161db02b52b0d66 | Python | 4,396 | 116 | 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... |
9c915987a376ee331b394eb99e5b15a3142c796fcd52d073b8f1f40c671e7a56 | Python | 4,396 | 117 | 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... |
5cb0f25afee8844e5b62c0b28749d364e5c021339a5eae788c470205459a64de | Python | 4,398 | 103 | from models.med import BertConfig
from models.nlvr_encoder import BertModel
from models.vit import interpolate_pos_embed
from models.blip import create_vit, init_tokenizer, is_url
from timm.models.hub import download_cached_file
import torch
from torch import nn
import torch.nn.functional as F
from transformers impor... |
010d68c788de6c88b1d3b35a19b4857102568197b278ec742dd9f65f7b30e056 | Python | 4,399 | 127 | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def... |
218b378234d7cb4cf2257fffafcf784cc866a8100b71974a1f569c36c3f2e400 | Python | 4,400 | 136 | # -*- coding: utf-8 -*-
"""
Created on Tue Jun 8 17:23:31 2021
@author: fhu14
This class is only used during the precompute process
"""
#%% Imports, definitions
from typing import List, Dict
import random
#%% Code behind
class data_loader:
def __init__(self, dataset: List[Dict], batch_size: int, shuffle: bool... |
842ee3b9f5687a2266e7b867835417ecb5e8671a1c4faa5b2e0eff6f4be723f6 | Python | 4,401 | 134 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmpose.models.backbones.pvt import (PVTEncoderLayer,
PyramidVisionTransformer,
PyramidVisionTransformerV2)
def test_pvt_block():
# test PVT structure a... |
adfec3bfb3647abce5c3b0269872a13ef34d73742447893caea7b1032149ec76 | Python | 4,401 | 124 | """
Variance partition of brain responses between Melspec and SSS representations.
"""
#### 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") # set to your... |
d393de3156f5d3653cdfdeb92853c63dda6ffd6065198f284d8112bac78556ec | Python | 4,401 | 149 |
import sys
import numpy as np
class ChromStats(object):
def __init__(self):
self.n = 0
self.n_nan = 0
self.sum = 0
self.min = None
self.max = None
def mean(self):
"""Calculates mean of sites that are not nan
on this chromsome"""
n = self.n - s... |
d70c1a51f7f837ecac50e8421be465b4a900fd0e7771f9ae8d683c0e35767221 | Python | 4,405 | 157 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/h36m.py'
]
evaluation = dict(interval=10, metric=['mpjpe', 'p-mpjpe'], save_best='MPJPE')
# optimizer settings
optimizer = dict(
type='Adam',
lr=1e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
deaec16ce0ff9fe510a4296ee61bc5eb3bb348253f4315663c2002f7555aaddf | Python | 4,405 | 129 | #!/usr/bin/env python3
"""
A simple example that demonstrates how to run Expectation over Transformation
coupled with any attack, on a Resnet-18 PyTorch model.
"""
from typing import Any
import torch
from torch import Tensor
import torchvision.models as models
import torchvision.transforms as transforms
import eagerpy... |
8ea2ee5e54faed5d1f3d4423ae8ce7617e4f6c4c88a08b14cd6b5749f82c0f62 | Python | 4,406 | 74 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
c4f884122059826afc429535f1e6fc0ae0a30d3fe86073b66fb6d523faea72c2 | Python | 4,406 | 111 | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class SparseRCNN(TwoStageDetector):
r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with
Learnable Proposals <https://arxiv.org/abs/2011.12450>`... |
267fa57a71cbd6e269191d61f9831669e7540f8d593b4b6f6226a841ba8451de | Python | 4,407 | 117 | import sys
sys.path.append('..')
import os
import torch
import numpy as np
from collections import OrderedDict
import gmm
import utils
import utils_mixture
import config_bayesian as cfg
def feedforward_and_save_mean_var(net, dataloader, task_no, num_ens=1):
cfg.mean_var_dir = "Mixtures/mean_vars/task-{}/".forma... |
6279c5b76f7948a44a6003e00b208c3579a08df9bef0cc73b16bdb782f7f8898 | Python | 4,407 | 117 | 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... |
8967cc3905aad5425acbb081b4d8f2b2b5444b1c18ae56ae0ac00ae0ef689418 | Python | 4,407 | 157 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/h36m.py'
]
evaluation = dict(interval=10, metric=['mpjpe', 'p-mpjpe'], save_best='MPJPE')
# optimizer settings
optimizer = dict(
type='Adam',
lr=1e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
86eee3ddc8449dc1233f3a44dfeea7914b22879ced18b29f42c6ebb11ed816bb | Python | 4,409 | 116 | 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... |
d66814f1dee6c8918811e6fb4ed6c4c35eaef376c982daa527a442f5d0bd1c04 | Python | 4,411 | 117 | 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... |
488ab54917a8e08b082a9e93cd79c25e1794ffadce443d13988665dcd69656ff | Python | 4,416 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
9a7e87a16870c8c5c48ebe17cba71c4ab4fd0d5095077283a08a3932b8d87e7b | Python | 4,416 | 116 | 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... |
c72ca65572345126270050bb73c9a4689e633dfcc4f58699daea72c661767101 | Python | 4,416 | 117 | 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... |
2aa64850ece6faf454d1cbd3884dd708d73e39b57204b1f149693fa293628a10 | Python | 4,418 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
31d4ed5df1d2bb6811aab239c1fb3ccbb182552f2b2ac63966db181547d4c499 | Python | 4,418 | 116 | 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... |
a286f3e179f42d4d23367251917582d0ea37e5d65f8583720f8980f8b59ee808 | Python | 4,418 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
a65f5d42ff5efde08660b3c96531e790cb76fd3d6e92aeab61a8c53c486bff9a | Python | 4,418 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
f4ce755f5ca2b8f300137b1a2ce69a4ab9a8a750f2ef2bde82db50a98edc6a9e | Python | 4,418 | 117 | 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... |
f85c458c088990f4a103e5aa1d21426c97524d740b676e8a24bbfb8c2c84107b | Python | 4,418 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
6d9df3fe3b0cd038759690759a0bbe3d06ee95057f4c24b1b76fe3fecfd02a20 | Python | 4,421 | 157 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_co... |
ec868808863decb504399e6fd28b081441bc6959f0473299e5f9c6d877eff24d | Python | 4,421 | 157 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_co... |
2aa9f5704702ec8b3f5d9badc1c00dc926bea9f755ac71600ecb4ff5cca76f50 | Python | 4,425 | 116 | 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... |
596a2c63839c2db138e100790a7aac2e04233113eaec16416ae2f8b7570b003b | Python | 4,426 | 159 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
f6addfbc524b8f98742237cd21309c8c2e1543f70ff722fc44ed41c0f17ea3de | Python | 4,427 | 117 | 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... |
95994166c0937414cf71ed28de215858d4c809478cd174c8741be5912b4b4de8 | Python | 4,429 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_co... |
44371bc9f4ce2b1007ecd015a3504c6f94cf2dee4e1877740fd93bdb01702a06 | Python | 4,430 | 127 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import visualization as vis
def test_color():
assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
assert vis.color_val_matplo... |
22c382577955d64872af216d89cbd1b4e3ad3b98b585e90869fcd0395097e638 | Python | 4,432 | 127 | #!/usr/bin/env python
"""
Simple test for parallel processing functions
"""
import numpy as np
import time
from multiprocessing import Pool
# Copy the helper function from msa.py
def _calculate_column_frequency_helper(args):
"""Helper function for parallel processing of column frequencies"""
column, gap_chars... |
79e9283b531287521254c29478f5e774e1851378d7bd62eb0e7ac4f132561516 | Python | 4,432 | 130 | import torch.nn as nn
import torch.nn.functional as F
import math
import torch
from torch_scatter import scatter
from .shifted_softplus import ShiftedSoftplus
from .swish import Swish
from typing import Optional
from typing import Dict, Union
class FeatureEmbedding(nn.Module):
def __init__(
self,
... |
19d898c8d01d59b10bfe435d108824d30eeec67820ed8261605bf06364f360c2 | Python | 4,434 | 138 |
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... |
99430c6e2704f923a1dbc853b55a645173e8f201a7af8f355cf3581133d19e58 | Python | 4,435 | 98 | import warnings
from nengo.config import SupportDefaultsMixin
from nengo.exceptions import ValidationError
from nengo.params import (
Default,
IntParam,
FrozenObject,
NumberParam,
Parameter,
Unconfigurable,
NdarrayParam,
)
from nengo.synapses import Lowpass, SynapseParam
from nengo.utils.nu... |
a62880440c57c4e63d58e99110dc46d978c3c03606e303fad655b806f5b33fc3 | Python | 4,437 | 169 | '''
Author: ----
Date: 2022-04-29 09:41:02
LastEditors: ----
LastEditTime: 2022-08-17 16:34:21
'''
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 SigmoidSurrogate(torch.autograd.Function):
@staticmethod
def forward(
... |
bf91c86ef05e5b5aebd4b3388d155944431bebbbf68557e9797637e14ddf7307 | Python | 4,437 | 94 | import os
import torch
import matplotlib.pyplot as plt
import numpy as np
import cv2
from torchvision import transforms
from scipy.ndimage.filters import gaussian_filter
from PIL import Image
from FOD.FocusOnDepth import FocusOnDepth
from FOD.utils import create_dir
from FOD.dataset import show
class Predictor(obje... |
43b5ad86d368478c1703bb5a34d4cab2d0255b7abad37fb5a89dfb847ed86842 | Python | 4,438 | 131 |
import torch
from torch import nn
import logging
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class RNN_Over_BERT(nn.Module):
def __init__(self, bert, nhid,n_layers, output_dim, bidirectional, dropout):
super(RNN_Over_BERT, self).__init__()
self.bert = bert
... |
863996d0e629d2f2bfcdc73bcfb1a7bde717fdcae59977507896e617b635a61a | Python | 4,440 | 103 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 24 15:47:29 2020
@author: bariskuru
"""
import numpy as np
from scipy.stats import skewnorm
from skimage.measure import profile_line
def grid_maker(spacing, orientation, pos_peak, arr_size, sizexy, max_rate):
#define the params from input her... |
15918b3cb367ff7c16be210707a8f81389d310848648ab47c03d1028ca16e4ff | Python | 4,442 | 160 | # Copyright (C) 2024-2025 ETH Zurich, Niels Maeder and other DASH contributors.
"""Test serenityff.charge.utils.serenityff_charge_handler.py."""
from pathlib import Path
import pytest
from numpy import allclose
from openff.toolkit.topology import Molecule
from openff.toolkit.typing.engines.smirnoff import (
Elect... |
f4474e5bd6a0bf30f532cae46b32399506fdbce1e5b5dd9420b0172bc1fd9956 | Python | 4,443 | 115 | 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... |
11b2be07f73a312d5870d442eb5096c145b8d3762f6775d06594370e685c70c4 | Python | 4,445 | 158 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config ... |
bcd48b89897078b8bba20108dd3edebf352223d189f1510620abe858a70804dd | Python | 4,448 | 157 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
checkpoint_config = dict(interval=50)
evaluation = dict(interval=50, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.0015,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_co... |
10226db2c31346170ec69d079d652fc83cb402bd0c4062da8f311c54083fcf9b | Python | 4,450 | 108 |
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.gridspec as gridspec
import jax
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GP... |
818a1b0c093c175ce624705e3a16d4c4901497ae2db2cd556155497791774ee7 | Python | 4,450 | 118 | 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... |
e9559a456e677651f2be66c173656b8472f3fac516b0ed739a54238a50a846ce | Python | 4,451 | 153 | import pickle
from pathlib import Path
import numpy as np
import torch
from sbi.inference import SNPE, simulate_for_sbi
from consbi.simulators import (
RuleSimulator,
default_rule,
default_rule_constrained_two_param,
default_rule_linear,
default_rule_linear_constrained_2p,
dso_linear_two_param... |
0c1af58bf0454303933fc671db8f90249afa5e7a08fca55535de0ce8d74ee972 | Python | 4,455 | 97 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 31 10:20:34 2021
@author: baris
"""
from pydentate import net_tunedrev, neuron_tools
import numpy as np
import scipy.stats as stats
def granule_simulate(grid_spikes,
trajs,
dur_ms,
poi... |
4362cdd753001cdadf2d71b060af5f39ccf198100b6eb00770c93829b93cc069 | Python | 4,458 | 112 | # 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
from torchmdnet.priors.base import BasePrior
from torchmdnet.models.utils import OptimizedDistance, Cosine... |
b006078e6d9deae12d1256fb36a7d2165067036374d2327c308e0b27aff8eb17 | Python | 4,460 | 118 | 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... |
f8561ed7837a9640bddd808407f392d471797930737688da075708c90b807fa2 | Python | 4,462 | 118 | 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... |
13ff25bf0dbe0b6939e7d137d986bdc74f455859e7cad93178ff871680b70535 | Python | 4,466 | 121 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-b... |
e312776bad198e878519ea9c83c667ee2c20d5dbe0f236ca712964e1bf61a553 | Python | 4,472 | 167 | """
Django settings for exsclaim_gui project.
Generated by 'django-admin startproject' using Django 3.1.6.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from co... |
10cc930589db671bcd3470c5fc9653c1067df328650400537f68f7bde599e988 | Python | 4,474 | 118 | 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... |
b14cb11f896738214a50fd416a5d107c7c7fb5fd30caa396ad5600b01382a873 | Python | 4,474 | 126 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET
from mmdet.models.builder import HEADS
from .base_panoptic_fusion_head import BasePanopticFusionHead
@HEADS.register_module()
class HeuristicFusionHead(BasePanopticFusionHead):
"""Fusion Head wit... |
8a831b228033b4aa6f70d2d44c95c18933044c80c86be80c819ecb7bb00252d3 | Python | 4,477 | 130 | # 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/>
"""
Base classes for ODE solvers.
"""
from abc import abstractmethod
from typing import Any, Dict, Protocol
import torch.nn as nn
from torch import Size... |
7dfa9c6c612a16001fc3d70ea76d92454af64a115692e53d04f46f12f46488c0 | Python | 4,484 | 117 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
import warnings
from typing import Tuple
import torch
from joblib import Parallel, delayed
from torch import Tensor
from tqdm import tqdm
from sbi.infer... |
61409dc5cbdb5f08ec8db0015f98d87ba55e7410946bd1101708608c02774572 | Python | 4,495 | 81 | """
Holds information about a transgenic cre line.
"""
class CreLine():
def __init__(self, cre, reporter, layers, color, reporter_extra=None, cre_abbrev=None, abbrev=None, booster=None, is_excitatory=True):
self.cre = cre
self.cre_abbrev = cre[:cre.index("-")] if cre_abbrev is None else cre_abbrev
... |
1d3a001fddf681dcd16663111630045ac7f5e32cbaca8cf9ae8b3e8380845a12 | Python | 4,498 | 131 | 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 in model_... |
a6239388ff507e047cd8ac51e843872e239f9c414da5f71031659c51b3ca6257 | Python | 4,499 | 153 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 15 10:51:38 2021
@author: fhu14
Test suite for different functionalities present in SKF writer.
"""
#%% Imports, definitions
import os
import pickle
import shutil
import Auorg_1_1
import DFTBrepulsive # SKF, SKFSet, SKFBlockCreator
from DFTBPlus import run_organics
fr... |
ae3e8b59d70d640f79977963fc51d746e948165264206a375fb57c8073b40406 | Python | 4,505 | 169 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 10 10:36:14 2020
@author: bariskuru
"""
import seaborn as sns, numpy as np
import os
from grid_poiss_input_gen import inhom_poiss
from gridcell_traj_activ_pf import grid_population, draw_traj
import matplotlib.pyplot as plt
'''Poiss noise differen... |
e15df2031d49a9e3291380a9c75aec436e7352a02ed9d5bb0d6e3d8ba54f65a5 | Python | 4,505 | 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... |
6bf37f5339aaed45f0c35aca503fd95d64701b82de62d1104b79aa3affa6d336 | Python | 4,507 | 119 | 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... |
99a97bd7cadbf726a1ecf60d7a746581114086fc0c5464aafa938f349c61da52 | Python | 4,507 | 123 | # Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors.
from typing import List, Optional, Sequence
import numpy as np
import torch as pt
from rdkit import Chem
from serenityff.charge.gnn.utils import CustomData, MolGraphConvFeaturizer
from serenityff.charge.utils import Molecule
def mols_f... |
a7c604ad631dac82b7d31394f85ba490d7072b4d08a6268afd844c086242f021 | Python | 4,507 | 122 | # dataloader.py: Prepare the dataloader needed for the neural networks
# Created: 5/21/2021
# Status: OK
import numpy as np
from sklearn.model_selection import StratifiedKFold
import glob
import torch
import torch.functional as F
from torch.utils.data import Dataset, DataLoader
from .utils import read_csv_cox2, read_c... |
dcc20d40be4c42b5a84b3439745b470c3f7d464be007aa67090cfd8610168028 | Python | 4,507 | 131 | # -*- coding: utf-8 -*-
"""
Created on Wed May 11 13:02:04 2022
@author: Anandita
"""
import datetime, sys, os, time
from collections import defaultdict
import numpy as np
from scipy import linalg as sla
import matplotlib.pyplot as plt
import pickle
gen_fn_dir = os.path.abspath('..') + '/shared_s... |
baa5387163fa344234d6da59042c04970aa4835a5cc923c37861d2ebedd32576 | Python | 4,508 | 131 | 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... |
1318302d18651f5aaf744c81240f9d7acd7fc1c73ad6c13acdf8ec7085a2443a | Python | 4,509 | 130 | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import re
from glob import glob
from titlecase import titlecase
os.makedirs('topics', exist_ok=True)
os.makedirs('papers', exist_ok=True)
def _parse_task(task):
"""Parse task name.
Data modality is represe... |
ea22ee68a7dda18f70d6336541d2b55114f2e6e12b5bdb4a6d6c8c385c800cad | Python | 4,509 | 111 | from torch_geometric.data import InMemoryDataset, Data
import torch
import numpy as np
import os
import requests
import zipfile
import re
def create_numpy_arrays(file_path):
with open(file_path, "r") as file:
num_atoms = int(file.readline().strip())
file.seek(0)
num_conformations = sum(1 f... |
bb0de836b0e2960ffd472f1877a1956f760c869c1e136baeb02f7edace124b1d | Python | 4,510 | 118 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class InstaBoost:
r"""Data augmentation method in `InstaBoost: Boosting Instance
Segmentation Via Probability Map Guided Copy-Pasting
<https://arxiv.org/abs/1908.07801>`_.
... |
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