sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3880ca41020726d3bef8f001098ece141b3c7466bc36045f3e05d5e820e6c4c4 | Python | 4,907 | 149 | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Pseudo QMF modules."""
import numpy as np
import torch
import torch.nn.functional as F
from scipy.signal import kaiser
def design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0):
"""Design pr... |
8f905aeab87e5aadecc29f180c41bfec93377a1ccde1debb7bd5a8d44089439a | Python | 4,907 | 162 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/300w.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
ad0c4337ebf82f3223b8b77f8750f8441766d26210beaa1eb00bb8521eafd758 | Python | 4,911 | 171 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=20)
evaluation = dict(interval=20, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.001,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
da1bcacae95417364fdc43c2a892877f7a6de89af513e8b1576cf9c0da3b7acf | Python | 4,911 | 171 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
checkpoint_config = dict(interval=20)
evaluation = dict(interval=20, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=0.001,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
bcd2268df157d6d7225909e4e899d25d94d4f5f02465ab7148746048e8d6baef | Python | 4,912 | 166 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
'abs... |
e7953e8093d9354d5f56ebb9241fbef41d6ca360968e954bbb03ee89558b434f | Python | 4,913 | 166 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
'abs... |
9f3ebcf43b4494090573d4b0eab427d86caaeb066dbc6230fc2d3551f32a74e5 | Python | 4,915 | 136 | 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... |
cfa752f935f41522a7aea5549ee152ab2a7742d18a76419a077a64a854bdb7f4 | Python | 4,915 | 143 | from typing import Union
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from pytorch_lightning import Trainer
from sklearn.base import BaseEstimator, ClassifierMixin
from binn import BINN, Network, SuperLogger
class BINNClassifier(BaseEstimator, ClassifierMixin):
"""
A... |
67049791cbe58d3184d88e19d7d844536223c8e929d8083377e9ab0fec467a86 | Python | 4,917 | 136 | from typing import TYPE_CHECKING, Union
from tempfile import NamedTemporaryFile
from .clipkit import run
from .files import FileFormat
from .helpers import SeqType, write_msa
from .logger import logger
from .modes import AmbiguityHandling, StopCodonMode, TrimmingMode
from .plot_report import write_trim_plot_report
if... |
6eac2d15d8baffd1ee48e89101a339d3663959eb2200d152d29d64b9cd9dee26 | Python | 4,921 | 120 | def download_micrographs(empiar_id, data_download_location, data_link):
# For Downloading Data Files
data_download_path = os.path.join(data_download_location, str(empiar_id), 'micrographs')
if not os.path.exists(data_download_path):
os.makedirs(data_download_path)
df = pd.read_excel('... |
8d178c46d9fbbf7a14f34e0ee4ba1ad98ed7738cc6d6a86e32e6859129a5c03e | Python | 4,921 | 158 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import numpy as np
import pytest
import torch
from mmpose.core.optimizers import build_optimizers
from mmpose.models.detectors.mesh import ParametricMesh
from tests.utils.mesh_utils import generate_smpl_weight_file
def test_parame... |
8976ae05774665e91bc3877f2a6e538ad0065297ca34510c8613abb80c667edf | Python | 4,922 | 138 | # -*- coding: utf-8 -*-
# Copyright 2020 MINH ANH (@dathudeptrai)
# MIT License (https://opensource.org/licenses/MIT)
"""Tensorflow MelGAN modules complatible with pytorch."""
import numpy as np
import tensorflow as tf
from parallel_wavegan.layers.tf_layers import (
TFConvTranspose1d,
TFReflectionPad1d,
... |
11d6d3d797028f80ff52f6621fb82b665593227c2e1b1ba9598144c8ee70a645 | Python | 4,928 | 136 | """Real display-free report generation and scientific map contracts."""
import html
import json
from pathlib import Path
import re
import subprocess
import sys
import numpy as np
import pytest
from brainspace.mesh.mesh_creation import build_polydata
from brainspace.mesh.mesh_operations import combine_surfaces
from b... |
cd648892194ceb28471a53be2eab06c78848b9225669e8815001ad10683a7a99 | Python | 4,930 | 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-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
1ba392cc32355b8cdee3190fe89fa0ba3bc2e336170bc4965af02192f2800d1d | Python | 4,931 | 124 | from typing import BinaryIO, Tuple, Dict
from pydapsys.binaryreader import DapsysBinaryReader
from pydapsys.page import DataPage, PageType, TextPage, WaveformPage
from pydapsys.toc.display import DisplayProperties, PlotType, LatencyPlotUnit, PointStyle, RGBA8
from pydapsys.toc.entry import Entry, EntryType, Folder, Ro... |
82e469f5090896fde91db862890bdf0cf0f78aad226712defd0954c6071cec5c | Python | 4,932 | 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-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
d1b59369195141c48ef65a5a013482def9a6bebfaecd5aa979f6b1b9335b7c79 | Python | 4,932 | 157 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
from numpy import *
from matplotlib import *
from matplotlib.pyplot import *
def grid_search(
pargrid,
evaluate,
verbose=True,
):
'''
Grid search hyperparameter optimization
Parameters
----------
pargrid: list of arrays
A l... |
711b6b0da2c81e5f623db0413df2c3f9021a0bd8e380753db11b0c70ac298f3b | Python | 4,934 | 137 | 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... |
4db5e2077725539fc21f42dcabfbd8b39f8880ab426b79d521c816e8bd01180b | Python | 4,935 | 109 | from my_tools import *
import matplotlib.pyplot as plt
import numpy as np
import argparse
import matplotlib.lines as mlines
import pandas as pd
def bimodal_connectivity(bimodal, VTA_name, destination_folder=False, sorting=False, scaling=False):
# define 4 color-blind friendly colors from https://gist.github.com/t... |
02db82e0ce007e2f155434bc5ccd781a77c7e64908f8dbb4b25780fec02af091 | Python | 4,936 | 137 | 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... |
907f5e3ff4bbc71e775f80895f0a4584ef2e619723d5cd54d6b050d0b804f28c | Python | 4,936 | 137 | 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... |
36fe2b4bd74f6c7773d96c06a9e674c3fb3da89d0a4aa3609cc01ffbc78180ae | Python | 4,937 | 137 | 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... |
b8e77dcb27afe54a05ae7e9146340c40f37d70d76f08e55ec8444662d883585a | Python | 4,938 | 137 | 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... |
f7c171eaf62984cddc68cac1f7bb757b2bdd036956aacc94239090936aa715a9 | Python | 4,938 | 145 | import numpy
def computeQUI_numpy(PXgSa, PYgSa, PS, eps=1e-7, IPmethod="GIS",
maxiter=100000, maxiter2=100000, DEBUG=False):
'''
Compute an optimizer Q
PXgSa : The conditional distribution of X given S (as a numpy.array).
PYgSa : The conditional distribution of Y given S (as... |
9174d6525d86ef1b3cfc3a72ec73dc1280ad7d3f515dbbf04f66183e4cc7d849 | Python | 4,939 | 137 | 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... |
ccbf4ef2f52f1de8a47a6faeaf947628ea78f46eed5d35de111080989461e420 | Python | 4,939 | 137 | 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... |
f06a059ad2358597b9d610367cd4ca18fd94bc161f17a12f5b4e9dea1ba5fab2 | Python | 4,939 | 137 | 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... |
788f5cd1a3ac411b55ebcc8a7c0d52385a78f1438f8844aa7ed9337cea0a2c95 | Python | 4,940 | 155 | '''
Author: ----
Date: 2022-04-07 20:48:43
LastEditors: GhMa
LastEditTime: 2022-10-02 19:29:44
'''
import torch
import torchvision
import torchvision.transforms as transforms
import pandas as pd
import os
import random
import numpy as np
from tqdm import tqdm
class Cutout(object):
r"""
Forked from https://g... |
dbb9e3e4b5c992731cd89fe43564b330ec3c7c6b9f583b876ebfbc393a87fc6d | Python | 4,941 | 142 | import logging
import os.path
import sys
from .helpers import SeqType
from .exceptions import StopCodonValidationError
from .modes import AmbiguityHandling, StopCodonMode, TrimmingMode
from .settings import DEFAULT_AA_GAP_CHARS
logger = logging.getLogger(__name__)
def process_args(args) -> dict:
"""
Process... |
54aae872260a09a48ac38c0dc988403f6e339c575bc14d92f4c98769af20100c | Python | 4,942 | 119 | import os
import numpy as np
import pandas as pd
import itertools
from scipy.stats import pearsonr
import seaborn as sns
import matplotlib.pyplot as plt
import glob
import pathlib
from collections import OrderedDict
import json
from havoc_clustering_v2.general_utility import unique_colors
# from general_utility import... |
f0913ee7afe821c7d40a4d3fc851a180954ea6d222456ac9a94d22dcbc904caf | Python | 4,942 | 126 | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import os.path as osp
import mmcv
import torch.distributed as dist
from mmcv.runner import DistEvalHook as BaseDistEvalHook
from mmcv.runner import EvalHook as BaseEvalHook
from torch.nn.modules.batchnorm import _BatchNorm
def _calc_dynamic_intervals(star... |
0cba1091cad0ee0b3d863468149f4c6dc113ae9408f30fb3eb2cbb902095429b | Python | 4,944 | 152 | import pandas as pd
import xml.etree.ElementTree as ET
import json
import numpy as np
import re
import io
def write_QuickNII_XML(df: pd.DataFrame, filename: str, aligner: str) -> None:
"""
Converts a pandas DataFrame to a quickNII compatible XML
"""
df_temp = df.copy()
if "nr" not in df... |
0abafaaff894185eb6409a513a9b9fce40a2206efb69dbe4495a9b4e15ed421f | Python | 4,949 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
1e2643b83b148cf03e9d57d837aabf23e8884e4660fe98a4248d7f332b922b20 | Python | 4,949 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
4c78191343c760a9b2e5418e83542f70e94e59d8207d46c1c3eda2da43ae6694 | Python | 4,949 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
65e256bbfae4460f3de876a0a6db393e43e62d488937a69a3d55ccc2917e769a | Python | 4,949 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
c9b31e13769ae4450ae31abd80a10af7a3fb95c5d81fb4e22433b835c6e6d571 | Python | 4,949 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
f256b2c331372155bf86003f2aff2a6afa28cbebed133265ae060fc2330076c5 | Python | 4,949 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
885b7d9a856335389a2f4a667b1ee508819f4eb6b98417638c56deb158b262d0 | Python | 4,952 | 147 | """Generate colored noise."""
from numpy import sqrt, newaxis, integer
from numpy.fft import irfft, rfftfreq
from numpy.random import default_rng, Generator, RandomState
from numpy import sum as npsum
def powerlaw_psd_gaussian(exponent, size, fmin=0, random_state=None):
"""Gaussian (1/f)**beta noise.
Based ... |
4129bd5ce6723031eaae90ba53ba93144df254be5ad3d3360cbe91272a928414 | Python | 4,954 | 147 | import os
from typing import Dict, Tuple
import multiprocessing as mp
import pandas as pd
import json
from process_parcellations.cat12reader import Cat12Reader
from process_parcellations.make_imaging_sheet import uniq_rids
with open("process_parcellations/parcellation_config.json") as fi:
CONFIG = json.loads(fi.re... |
ef09216973cd91c130bf523cd0f4f58012209ecdfcf9b6b5a5421edc57904ea7 | Python | 4,954 | 127 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 22:10
@project: MalariaDetection
@description:
"""
import tensorflow as tf
import os
from tensorflow.python.keras.layers import Layer
from tensorflow.python.keras import backend as K
c... |
0d58fd2efa5129739d93d9e7bf7a70d4dd76862f2b45d46a9ade1325ab6d00c2 | Python | 4,959 | 131 | import numpy as np
import torch
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing, global_mean_pool, GCNConv, BatchNorm
from torch_geometric.utils import add_self_loops
from typing import Optional
class GraphConvNetwork(torch.nn.Module):
def __init__(self, conv_layers_num: int, dense_... |
33dfd6a96017cf9bd1d4024a8a4e5ac04dcd06d9bc11a1c228defadeeeb29eef | Python | 4,959 | 131 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import torch
import torch.distributed as dist
import torch.nn as nn
class BasePose(nn.Module, metaclass=ABCMeta):
"""Base class for pose detectors.
All recognizers should subclass it.
... |
3d3c90f749080e20726ebc418daa0c184819a9e75f5e6abdcd8ba257ead58bd8 | Python | 4,959 | 124 | import pytest
from pathlib import Path
from Bio import AlignIO
from clipkit.ecomp.reader import HEADER_MAGIC
from clipkit.exceptions import InvalidInputFileFormat
from clipkit.files import get_alignment_and_format, FileFormat
from clipkit.files import get_custom_sites_to_trim
here = Path(__file__)
SAMPLES_DIR = here.... |
09fca28d3728fd7fc1d7f18a1e7e9c3761f52379b62d0e2d0d0e79932c72aeb0 | Python | 4,964 | 137 | # Copyright (c) OpenMMLab. All rights reserved.
# This script consists of several convert functions which
# can modify the weights of model in original repo to be
# pre-trained weights.
from collections import OrderedDict
import torch
def pvt_convert(ckpt):
new_ckpt = OrderedDict()
# Process the concat bet... |
c58d54032f7c9edbc0c2edf8cf8df03fce6fb10cf7c084831799ab7a9f8fa82f | Python | 4,970 | 125 | # 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, scatte... |
d6467c59341da9c8d6ed4523661f6b1286ffb8884d8c3f93122dcd2ddbe218ee | Python | 4,971 | 122 | # This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries
# pip install --upgrade google-cloud-storage
# from google.cloud import storage
import os
import platform
import subprocess
import time
from pathlib import Path
import torch
def gsutil_getsize(url=''):
# gs://bucket/fil... |
2080a10e6797af3904fd97c36e29279b7705a6016da638e9b50a77dc8b14202d | Python | 4,973 | 142 | r"""
This test file contains tests that involve fitting the xmin value for different
distributions.
Synthetic data is generated that has a flat probability distribution up
until some xmin value, which then scales with the specified distribution.
Fitting is performed to find this xmin value, and compared to the value
f... |
141b0ef3664f19e3bf9fe9a48a93c8725f023f4cdad86244ef9a37c34165dfb4 | Python | 4,978 | 117 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 19:03:43 2021
@author: fhu14
"""
#%% Imports, definitions
from typing import List, Dict
import numpy as np
Array = np.ndarray
import torch
Tensor = torch.Tensor
from copy import deepcopy
#%% Code behind
class Reference_energy:
def __init__(self, allowed_Zs: List... |
558377a3bc7c5e21f4a8aac55a3125428ea34eab54ee489cad6a36db0313e528 | Python | 4,988 | 125 | """
Fig 5e with the lexical predictability control (Reviewer 1, comment 1.3).
Fig 5e reports how much of the unique semantic neural variance in IFG-Tri is
absorbed when SFM representations are added to the control set:
delta = (ref - low) / ref
where `ref` is the mean IFG-Tri unique semantic R^2 against the non-... |
95e1003a69eb45b02a46a2187d70b6c5bfee7f7c2b2ab58ed75b0f06ae271836 | Python | 4,990 | 90 | import argparse
import torch
import pickle as pkl
import numpy as np
import random
import os
from bayes_design.decode import decode_order_dict, decode_algorithm_dict
from bayes_design.model import model_dict
from bayes_design.utils import get_protein, get_fixed_position_mask
parser = argparse.ArgumentParser()
parser... |
7bad7daae1ac2478467ec0c5fe4554c048247cdcbe6c2dc6f974fa2a263e444c | Python | 4,993 | 131 | # 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/>
"""
Zuko ODE solver.
"""
import functools
import torch.nn as nn
from torch import Size, Tensor
from torch.distributions import Distribution
from zuko.di... |
b340e6c13adec874f102a429e733b45e50aa63611f3024721a18e6d0c2e465b5 | Python | 4,997 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/halpe.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='... |
eb3bd6a00a8179f442f938f86ef53827d104efec2b89fea7d60c29b1f3d41596 | Python | 5,001 | 163 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_face.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning pol... |
d88dc6f329d249d981837a5ad1bc2bf770fd01c24900d93291b0493b94b0df6b | Python | 5,003 | 171 | _base_ = ['../../../../_base_/datasets/coco_wholebody.py']
log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
checkpoint_config = dict(interval=10)
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='AdamW',
lr=5e... |
69fad2b64e45d671a05882ee84764a6f21f842a1c9967bfbdc5da6704ab5fd51 | Python | 5,004 | 103 | import logging
import numpy as np
def non_iid_partition_with_dirichlet_distribution(label_list,
client_num,
classes,
alpha,
... |
66e82b6f9c590b0d190e887d1299af7ad0d22da6bcb8fbdd902281d082e48d43 | Python | 5,006 | 176 | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import functools as func
import glob
import re
from os.path import basename, splitext
import numpy as np
import titlecase
def anchor(name):
return re.sub(r'-+', '-', re.sub(r'[^a-zA-Z0-9]', '-',
name.strip(... |
7238396dccea8e807e31f1bcdb71774c69a8619b63b166a9e362ecf8dbc79fbe | Python | 5,006 | 166 | import timeit
import numpy as np
import os
import os.path as osp
import shutil
import copy
import torch
import torch.nn as nn
import torch.distributed as dist
from .cfg_holder import cfg_unique_holder as cfguh
from . import sync
print_console_local_rank0_only = True
def print_log(*console_info):
local_rank = sync... |
9acfaea867ce57b5b8d5d4f970c0aa2bf4222ca7226cb801b96b4b028e25d752 | Python | 5,008 | 131 | 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... |
cd6b235f692cfe04f382a238f1bf785d078365cce766b09c24fb01bed6fd9a44 | Python | 5,008 | 131 | 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... |
d330e06c8037dbb7a04fb81ffc3b984a39a05da6bed15a2202d17da5b21e549f | Python | 5,011 | 131 | 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... |
2869acdabd69ad77c31d17414e2d04dc380e5e1cc882e5d1a43a8d92f765b5ae | Python | 5,012 | 111 | import os
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
from pathlib import Path
from timm.models import create_model
from models.model_lngrowth import LNGrowthNet
from datasets import build_dataset
from engine import evaluate_h
def get_... |
95df6a44c7f364ae4e0f64f1e43921b8d37573008ba05aeddfee44fbd1183458 | Python | 5,014 | 131 | 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... |
d0b3c4bd19c3bba2c516a527d694add319473723903ce3acbaa00af6c0229bf8 | Python | 5,014 | 130 | import torch
import torch.nn as nn
import torch.nn.functional as F
##https://github.com/RaffaeleGalliera/pytorch-cnn-text-classification/blob/master/model.py
class CNN1D(nn.Module):
def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim,
dropout, hidden_dims):
s... |
47e248685efaf70add1b787415699126648d351501874680f68098ab59e21965 | Python | 5,015 | 131 | 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... |
715ceefa71aa7e8947a493e1c786ee8860a7dd1420542a8e893e4bda499aed67 | Python | 5,016 | 131 | 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... |
e1a23547fba8974824d1a7afd94eff1e4a46868275d41d7ba94a370e35b159a3 | Python | 5,016 | 131 | 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... |
c37b1c390dcc059e1929bfe85354ddc75c1edffb96866d4f7c07326922eecd87 | Python | 5,019 | 118 | import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
"""Pytorch implementation of group normalization in https://arxiv.org/abs/1803.08494 (Following the PyTorch Style)"""
def group_norm(input, group, running_mean, running_var, weight=None, bias=None,
use_input_stats=True, ... |
f73182a541084104c1c6548b00a1222c05ca89f5ded8f5e6acfd8e51fd1a81a4 | Python | 5,020 | 176 | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import functools as func
import glob
import re
from os.path import basename, splitext
import numpy as np
import titlecase
def anchor(name):
return re.sub(r'-+', '-', re.sub(r'[^a-zA-Z0-9]', '-',
name.strip(... |
59e7e4b4947e470efab345dcb4aac2f0c79da4a860793b2f48d44fe9a02b33b8 | Python | 5,021 | 153 | # coding: utf-8
import os
import re
import numpy as np
import pandas as pd
import ujson as json
patient_ids = []
for filename in os.listdir('./raw'):
# the patient data in PhysioNet contains 6-digits
match = re.search('\d{6}', filename)
if match:
id_ = match.group()
patient_ids.append(id_... |
18c3c4d66348601ea9f45dff99f4c3079d72aa5fe76d18dc4ce2309ad151f3f8 | Python | 5,023 | 128 | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union
import cv2
import numpy as np
from mmcv import color_val
from ...utils import FrameMessage
from ..base_visualizer_node import BaseVisualizerNode
from ..registry import NODES
@NODES.register_module()
class NoticeBoardNode... |
6aa562229982918c87d3afc159e5c87f30a1170730b1ad7c7694cf8349e827fe | Python | 5,023 | 113 | import os
import numpy
from setuptools import setup, Extension, find_packages
from Cython.Build import cythonize
from Cython.Distutils import build_ext
# -------------------- DEPS, COMPILER ARGS, LIBS, & LOCS ---------------------#
# Additional (Optional) Packages May Be Needed To MAke Videos In Demos
DEPENDENCIES ... |
6408ed3d478811257ae49e7adcd70c635c08899cbe318f13ca1a0d616340b3dd | Python | 5,024 | 130 | import os
import numpy as np
from os.path import join, exists
from sklearn import metrics
from config_path import PLOTS_PATH
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix
from paper_analysis_revision2.plot_utils import plot_auc_all, plot_auc_bootstrap, plot_prc_all, plot_confusion_m... |
9136630561c5d9c1fe6e84916c2c3e356bcfc834dfbdde559ad30947f4aed026 | Python | 5,024 | 169 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/custom.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=... |
5a4dbf9ae7d0e0a2f7d2a31c3681b2fcbfe4d4ebf07d7d592c147ad0c1fd4b64 | Python | 5,025 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/rhd2d.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(
policy='st... |
abc569d652ec495b32fd7d10180ddcc4956305b5c5bdb6f96999d46ca5222f7f | Python | 5,026 | 163 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_face.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning pol... |
32a41754fd203137a3ef2f5d8929b623624cb2787da749df298e1886550a1c04 | Python | 5,027 | 179 |
##########
#sort
def sort_list(x,y):
return cmp(x,y)
##########
#code to calculate reciprocal overlap
def reciprocal_overlap(s_1,e_1,s_2,e_2):
if s_2 > e_1 or s_1 > e_2:
return [0,0]
else:
#get the smaller start
if s_2 >=s_1:
o_start = s_2
else:
o_start = s_1
#get the smaller end
if e_2 ... |
f984fb3d41d66d52fd4560ca9e506831e4d9787a502cd27161ae5ecaf2499c03 | Python | 5,031 | 131 | 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... |
a8f8314a86755bc8918a15d695d7e5c122cbf0b0a367d9d426465ad2dab412e4 | Python | 5,034 | 138 | import numpy as np
import pandas as pd
import os
import pdb
def str2int(str_time):
res = 0
for s in str_time:
if s == ',':
continue
res = res * 10 + int(s)
return res
def get_runtime(filename, kernel_type):
f = open(filename, 'r')
run_time = 0
if kernel_type == 0:
... |
ef64713e54b6a6fd1b28bad78f4faa04d84592b9984addb4f1990c44bb0ffb97 | Python | 5,036 | 152 | """Compare per-torch-version GPU observables against a same-run baseline.
Pure stdlib. The orchestrator writes one ``<label>.json`` per torch version
(plus a ``status.json``); this module diffs every version's energies/forces
against the baseline version and renders a verdict matrix.
"""
from __future__ import annota... |
1e2fd6b530a020f838faf066c48da84f67e8ad704347e14edaa28afc7dd41203 | Python | 5,038 | 133 | import json
import os
import pathlib
import shutil
import unittest
import responses
from deepdiff import DeepDiff
from exsclaim.pipeline import Pipeline
class TestNatureFull(unittest.TestCase):
def setUp(self):
"""Instantiates a test search query and Exsclaim pipeline to test"""
# Set paths
... |
1f5f43e06af74a82f0f38a6f3bd82eb416b22c984dae5a137112f3f14eb6855c | Python | 5,038 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
742bd5adec1f361f65be9c5749e2f897c6a6be97ef58b27145f8abb46139462f | Python | 5,038 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
ca2f7135ddf5ec8e2ffd913ecd632a9575099987f44cddaca53c21186a2c4058 | Python | 5,038 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
8108b746f6dc4d55141f12edac611daeb3c36c20b929ec147ae748635509f818 | Python | 5,040 | 172 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_lower.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',... |
f371d6e69b7b2915a0733e0a38f492a0a9a87fc379f0371aaa504daa12f88ea1 | Python | 5,040 | 82 | """General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), e... |
689ce985d2773f9304ef237535fd52ff9fbcdca587facb962d7606419a3deb15 | Python | 5,050 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/rhd2d.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(
policy='st... |
dcb9f464db7a4cf3754f880cd6c6c3bc2914e225b2eb8694c5509bcd4b3fc60a | Python | 5,052 | 135 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import constant_init, xavier_init
from mmcv.runner import BaseModule, ModuleList
from ..builder import NECKS, build_backbone
from .fpn import FPN
class ASPP(BaseModule):
"""ASPP (Atrou... |
8daa88143a9cd73d2fbbf6d800d5f102388c3d39281f1e5e087d1e3e92c7ad0c | Python | 5,056 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/freihand2d.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(
polic... |
06a6be20477a11948b2ced2af8e748059663c49b3de0a09ec89cee6f876012af | Python | 5,057 | 173 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_lower.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',... |
8fe570f8c3f8ee696a305d7d1f4781aec74f815830a86b1bd03986d6b6459428 | Python | 5,057 | 176 | #!/usr/bin/env python3
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Test code for StyleMelGAN modules."""
import logging
import numpy as np
import pytest
import torch
from test_parallel_wavegan import make_mutli_reso_stft_loss_args
from parallel_wavegan.losses import (
... |
6acc5f9655999e97f9419c1c786c80697bb66b9343edaf109f7d29fa0e9715e9 | Python | 5,060 | 159 | import argparse
import logging
import os
import pickle
from pathlib import Path
from typing import List
import matplotlib.pyplot as plt
import pandas as pd
import shap
os.makedirs('logs', exist_ok=True)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%d.%m.%Y %I:%M:%S',
f... |
6543de450f03583fb251824d68dff74fc0309acb65297f93ddcbc0a7cc2a573c | Python | 5,061 | 168 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
743b9287a5a6266f161e7799da86bf785a2c618b5d91d5e9af2b30a5ccc5c793 | Python | 5,062 | 152 | from rdkit import Chem
from tqdm import tqdm
import numpy as np
import pandas as pd
import pickle
from serenityff.charge.tree.tree import Tree
from serenityff.charge.tree_develop.tree_constructor import Tree_constructor
data_file = "./combined.csv" # "../test133_explain/combined.csv"
# sdf_suply="../test131_psi4_res... |
0b965b3e7a29c9db7b9dbc97dd893c94d6559fc23c761ed9af692b44230610be | Python | 5,063 | 139 | from copy import copy, deepcopy
from transformers import TrainingArguments, IntervalStrategy, EarlyStoppingCallback
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_... |
41bef54fb549e6a38bb7a8c7511923dc05ed3c2a71f812269ea26b677761015c | Python | 5,063 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
evaluation = dict(interval=10, metric='mAP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
... |
4c33498ec893f107b44e7913c47a3b4a5f6b0137255450d7170e64b926f50beb | Python | 5,063 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
5378c5923f2d790cf1a9da017d219b2fba01ab8d7649fcf65b41e684513ff0d3 | Python | 5,063 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
55561df7bd261c6ddcac66998b0e7eadecc5f0a2ed5ab458f194c3a8365b42a4 | Python | 5,063 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
5d641de031a71e34fb4cbf54b7068254f0a007daf2300e386b8d43357861d3ea | Python | 5,063 | 167 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/crowdpose.py'
]
evaluation = dict(interval=10, metric='mAP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
... |
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