sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
620f66feab0b1213bd1cc10d7a126f663ef18b7c441eed659e3ba30020325e7f | Python | 4,007 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_mpii_256x256-418ffc88_20200812.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_... |
773db07b4a4c235135b1123dfacb79f47fb6ce23442f8e42e2c2995c577dc477 | Python | 4,007 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_mpii_256x256-418ffc88_20200812.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_... |
4e347fd1fec3540cb91214ebd59be955de688cb79ff900c33736f77e9fb66fc6 | Python | 4,012 | 133 | _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(
... |
fe394cfa1599a2ce86190bd0c8e527685910c97f4e576d0549b5b10b5c3b1a8c | Python | 4,015 | 117 | from typing import Optional, Any, Union
import eagerpy as ep
from ..criteria import Misclassification
from ..distances import l2
from ..devutils import flatten
from ..devutils import atleast_kd
from .base import MinimizationAttack
from .base import get_is_adversarial
from .base import get_channel_axis
from ..model... |
da61aaeeb2446b9a1b3c9a490b4e878c89378cbb94d12c4f3526d337461b343f | Python | 4,016 | 93 | ############################################
# imports
############################################
from abc import abstractmethod
from joblib import Parallel, delayed
from joblib_progress import joblib_progress
from oligo_designer_toolsuite.database import OligoDatabase
############################################... |
a4cc4cd8b8732597094a5346efb5e856ae8cc8d4cbda66b0e30e17ad44191f79 | Python | 4,022 | 107 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from typing import Optional
import torch
from torch import Tensor, nn
from torch.distributions import Distribution
from sbi.neural_nets.estimators.mixtu... |
957614effb34e6a499dfc37e1dceda21a7295edae6f4b1ea4bd7bd24a6bc833e | Python | 4,023 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/locust.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='s... |
b700297abe5eb4315259532519148e875fe816edc112a4298a758c271a4e4be6 | Python | 4,025 | 95 |
# Copyright (c) 2020-present Joindbre.com
# Authors: Patricia Maraver.
#
# This program is free software: you can redistribute it and/or modify
# it under the GNU Affero General Public License version 3
# as published by the Free Software Foundation.
# You should have received a copy of the Server Side Public Li... |
5d59866a887dd9de39d965805d7196fa1524d737ad92c62f28e9b87945c61942 | Python | 4,026 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/locust.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='s... |
af8ece55000b8d7a80ff518d0f25b0e2df21c36d5ac57f59d55e6be3c65f53f0 | Python | 4,026 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/locust.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='s... |
88f9d62c5cff84f4d4959d56646447b36598ee75685691f68c999e2ca44a49e5 | Python | 4,028 | 127 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import warnings
from argparse import ArgumentParser
import mmcv
from mmpose.apis import (inference_bottom_up_pose_model, init_pose_model,
vis_pose_result)
from mmpose.datasets import DatasetInfo
def main():
... |
f9c37616e7886b14a841a56a6a467e49f49a95c58cb2c4d8cacce2c3422e5ae8 | Python | 4,028 | 66 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
a777fe6ca1eae205f89a9768b57a3f6b9e41cc113c2c587a638321e9c39943bb | Python | 4,030 | 76 | import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Activation, SimpleRNN, Bidirectional, Flatten, \
BatchNormalization, AvgPool1D
from tensorflow.keras.layers import Conv2D... |
40b37339d7341e31ea81015a90b040238e51a89124f619f091bdb85cc6c96468 | Python | 4,032 | 130 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser
import mmcv
from xtcocotools.coco import COCO
from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
vis_pose_result)
from mmpose.datasets import DatasetInfo
def m... |
57c6e31a7c6c2b7019f7544fc0623534c542b12131056ddd3afd9e8f4cda522b | Python | 4,032 | 105 | import numpy as np
import matplotlib.pyplot as plt
import pickle as pkl
from absl import flags, app
def get_error(file_name_list):
error_mean_list = []
error_std_list = []
for file_name in file_name_list:
file = np.load(file_name)
if len(file["label"].shape) == len(file["out"].shape):
label = file[... |
a5e68ee51d1f8ed86871453bd81dd9ca94ba0a7d494aeb089043fe2c32cee99e | Python | 4,032 | 114 | """Test-tube helper functions for use in fitting scripts."""
import os
import sys
from test_tube import HyperOptArgumentParser
import yaml
from daart.io import export_expt_info_to_csv
from daart.io import find_experiment
from daart.io import get_expt_dir, get_model_dir, get_subdirs
# to ignore imports for sphix-auto... |
f09f143957ee7d2c45901a7a15c1df71ed5235fad61b16d44ab45af419ef54d5 | Python | 4,032 | 91 | import numpy as np
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import roc_auc_score, accuracy_score
from fedml_api.standalone.classical_vertical_fl.vfl import VerticalMultiplePartyLogisticRegressionFederatedLearning
def compute_correct_prediction(*, y_targets, y_prob_preds, thres... |
111089c8a5858977e9c8232c0c3f3ff96cf306bde5763639dc64f6308aba4a42 | Python | 4,033 | 133 | _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',
... |
5bd7b28f5adfadca74fbd983edf3ed52737a0419ffc82a0d58e970019ea67184 | Python | 4,039 | 122 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 17 13:52:41 2022
@author: baris
"""
from neural_coding import load_spikes, rate_n_phase
from perceptron import run_perceptron
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pickle
import copy
sav... |
1d7efae1f48287488bb365821881400aad080b97d5c7ccee2e62b2548c568088 | Python | 4,041 | 134 | _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(
... |
5859c2276d18893996a037d05a4cad2f7bc3689c021a91f4954c1f5e0b09e718 | Python | 4,043 | 137 | #!/usr/bin/env python3
import argparse
import math
import os
import sys
class LabelInfo(object):
def __init__(self, start, end, label_id):
self.label_id = label_id
self.start = start
self.end = end
class SegInfo(object):
def __init__(self):
self.segs = []
self.start =... |
4ee2012f156c9eec803b3dde6e974817252eda58654dca4f88ecdff055a9c63f | Python | 4,045 | 136 | from typing import Union, Any, Optional
import eagerpy as ep
from ..devutils import atleast_kd
from ..models import Model
from ..criteria import Criterion
from ..distances import Distance
from .base import FlexibleDistanceMinimizationAttack
from .base import T
from .base import get_is_adversarial
from .base import... |
27ac5095d238f2a5fdb5a5b5ee96459f0adc6a974c6a48462eabf092c670e33e | Python | 4,048 | 119 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 17:17:15 2021
@author: fhu14
This file runs the test_join function originally implemented in tfspline
"""
#%% Imports, definitions
import matplotlib.pyplot as plt
import numpy as np
from Spline import (construct_joined_splines, maxabs, merge_splines,
... |
02fb51a2af6453d14ad3688dc5881f81562d20d40c6a472754011a7ead12159f | Python | 4,051 | 137 | _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',
... |
ad416d47dc61b6354769b8e9f759d35f35efe71cc3bc68356512133daacef484 | Python | 4,051 | 131 | import numpy as np
import torch
from torch.autograd import grad
from tqdm import tqdm
import torch.nn as nn
import os
from torch.nn.parameter import Parameter
import torch.nn.parallel
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
import time
class rational(nn.Module):
def __init__(self):
s... |
d6bb6e114c0d13eba42fbf8f6dea05f070458bf361c0fb51cfaf2d4e55e2786e | Python | 4,053 | 135 | import struct
import numpy as np
import scipy.misc
import h5py
import glob
import tqdm
import torch
from torch.utils.data import Dataset, DataLoader
import os
from scipy.io import loadmat
import sys
import cv2
train_filename = "DvsGesture/dvs_gestures_dense_train.pt"
test_filename = "DvsGesture/dvs_gestures_dense_tes... |
73881a65b9342d885f0545d57865b7b2ada7570ebce4c55e113fd1642091b482 | Python | 4,054 | 139 | _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',... |
a3196108aaada8f2ecc9421f02d31043f4a6c2865016bbcddf033edbbdf7096d | Python | 4,054 | 90 | from statsmodels.stats import multitest
from statistics.shap_viz.utils import sig
from scipy.stats import wilcoxon
import pandas as pd
import numpy as np
import itertools
__all__ = ['bootstrap_pvals']
def bootstrap_pvals(mod: str) -> pd.DataFrame:
if mod == 'mlp':
df = pd.read_csv('./metadata/data_proce... |
d5515c98e7bd7fbb6c7dad4767e0a682a89f468fe99ef914cba251fa3f3f4436 | Python | 4,054 | 139 | _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',... |
0fa4b700e1ffec815fbaa4966101e7cc928e3a5a957cc19ab2317107120d8086 | Python | 4,057 | 136 | _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',
... |
4f3409b0b616640b41d7f97d81fdb10ec53626bff812ef5bf3660a9a6fd87e93 | Python | 4,057 | 136 | _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',
... |
92a56f4cfef2a07cfa9aefca552b2610a3e45bb5e013fa705146193e38d6ca34 | Python | 4,059 | 115 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf>`_.
... |
98d0d07a92f4e365cb9c229c245b8a45e9bc3105af6bdc03f8a0be850907a72c | Python | 4,059 | 136 | _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',
... |
a1644021eecfbf4efa3cce1c5e5812b46004b091167035d1efb49ad092f3eb8e | Python | 4,059 | 136 | _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',
... |
a222aa1654a0e77704273be334a6e75eeac17a67f25adff74339326c49845ed9 | Python | 4,059 | 136 | _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',
... |
ac2779cded49dc54886f1d9888b8f4c1a21b23ed468541305a920fe365bcc0c2 | Python | 4,059 | 136 | _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',
... |
ef886371ebfa191fa6c69f462b0db6f8e3dfb7a10c55cddcf55bc7cf4196c544 | Python | 4,060 | 130 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
posterior.py: Subroutines for further analysis of the
posterior rate map returned from Gaussian process inference.
"""
import numpy as np
from pylab import *
from .data import bin_spikes
def findpeaks(q,height_thr=-inf,rclear=1):
'''
Find peaks in radius ``r`` n... |
de02b718acfc63de1cff696189127bc7407541c92c4850990edef976d736dab0 | Python | 4,063 | 114 | from transformers import PreTrainedTokenizer
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import tensorflow as tf
def get_tfds(
train_file: str,
eval_file: str,
test_file: str,
tokenizer: PreTrainedTokenizer,
label_column_id: in... |
8a6eac10768913d7d589c684e47dc772f8302be0339d71a441152155ec7c364b | Python | 4,064 | 103 | import unittest
import json
import pathlib
import responses
from bs4 import BeautifulSoup
import bs4
from exsclaim import journal
class TestNature(unittest.TestCase):
def setUp(self):
""" Instantiates a test search query and JournalFamily to test """
nature_json = pathlib.Path(__file__).parent /... |
7160757a1083417e4144f7c31596817ebafec83993a107c6c5c75b595a03aeda | Python | 4,066 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_upper.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',... |
c2e76246454aaf66092a9dff5684884047b0de76d713c617cf918ebb28f0c390 | Python | 4,066 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_upper.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',... |
cde4b5366a3d9c033690e42fc288cfeedc2d39c2d9758fe81ce6fee69c646686 | Python | 4,069 | 107 | from .dataset import Dataset, DatasetError
import os
import numpy as np
import scipy.io
class NST(Dataset):
"""An NST dataset.
An NST dataset usually consists of three files that are within a specific
subdirectory. The implementation follows this structuring, i.e. the user
needs to pass a ... |
29f692b843fd71f8a81ce6134f1eec497f246bb53277f194099eddc3adf88b06 | Python | 4,070 | 126 | import json
import os
import numpy as np
import torch
from .language_utils import word_to_indices, VOCAB_SIZE, \
letter_to_index
def read_data(train_data_dir, test_data_dir):
'''parses data in given train and test data directories
assumes:
- the data in the input directories are .json files with
... |
f901af3769a9e7976c26307df5f3577c1ba9186c1b2f7780ae537da6b5cd0f5b | Python | 4,070 | 150 | """
Timing offsets for aligning MASS arousal annotations with trimmed PSG signals.
Background:
-----------
The MASS (Montreal Archive of Sleep Studies) dataset PSG files were trimmed
during data preprocessing to align with hypnogram start times. This creates a
mismatch between:
- Original annotation files (arousal... |
2a1c47acd7a217600e2221a00a304388ec74341a495007b8c7cd43cff53d5697 | Python | 4,071 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_full.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',
... |
66d905f32c72977127ccfd1d1d9b5e6958c25a7b23b3f522dfd1413bf6e3c8ce | Python | 4,071 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_full.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',
... |
e6562162b294677ea613eea96c6e20376f7ac52dfe9daf215d854b74f3b3e1ff | Python | 4,071 | 98 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from typing import Literal, Optional, Union
from torch.distributions import Distribution
from torch.utils.tensorboard.writer import SummaryWriter
from s... |
61faab37e60e6a4d384262df69363225340626e527e5238470485aaa89c3cc8d | Python | 4,072 | 124 | # 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/>
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the d... |
d42ed737d7c267712b667229ffba1c54b4f5f7c0a820cb04e7bd6f1309b0ef09 | Python | 4,075 | 136 | from typing import Tuple, Union, List, Any
import eagerpy as ep
import foolbox as fbn
import foolbox.attacks as fa
from foolbox.devutils import flatten
from foolbox.attacks.brendel_bethge import BrendelBethgeAttack
import pytest
from conftest import ModeAndDataAndDescription
def get_attack_id(x: Tuple[BrendelBethge... |
389a6225e879f90d196b66336f4d5e7e8c8acb07ccd52aea264ac92b51927d70 | Python | 4,076 | 137 | _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',
... |
546337f03dff287bee822a26ac27f89b56fd7af98ef383a613bf9649289c2a0e | Python | 4,076 | 142 | _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',... |
96987b3726753b06f0bb5c4eebbc408a7f142639f0e9609ab39550801ce52f35 | Python | 4,076 | 133 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser
import cv2
import mmcv
from mmpose.apis import (inference_bottom_up_pose_model, init_pose_model,
vis_pose_result)
from mmpose.datasets import DatasetInfo
def main():
"""Visualiz... |
6dc3f68d76265eda2b818dc178a6c598b80b9e7dccabbc7f45ffe5873b7cb450 | Python | 4,078 | 142 | _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',... |
e0cb889e66e1455b7f567d18d605c3795c39c27ee68af0a938ac82c78a6e7437 | Python | 4,078 | 142 | _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',... |
34d9bc034afae0c9e526f8717317d5c09244c1c5f91959612cf9485b9c55906b | Python | 4,080 | 102 | import random
import torch
class ClientDSGD(object):
def __init__(self, model, model_cache, client_id, streaming_data, topology_manager, iteration_number,
learning_rate, batch_size, weight_decay, latency, b_symmetric):
# logging.info("streaming_data = %s" % streaming_data)
# Sin... |
20f202a15c279d13022d7ac05c3a19165f52cd77b4399c7b7b02cd0e62cc4a82 | Python | 4,082 | 136 | _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',
... |
33e7112e8707366d9acf73ae0b20a120d2ee98c2bb32c9f09e65a6891666f5c1 | Python | 4,082 | 136 | _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',
... |
24d0a64de85672d9590e04fdb6fcf91e157d767a778acfba069e2a91b328a5c7 | Python | 4,084 | 136 | _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',
... |
3c56962b45e86ad5dcd68b0d497fc866f91c966fbc1d56195d74fc1a78c34bab | Python | 4,084 | 136 | _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',
... |
5f74deb95a1d46dc93caee87844859145521a8a082d62ce37e3d83513cbfb430 | Python | 4,084 | 136 | _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',
... |
92d0889972e57f21ed4657fc70dbe90aa45814b4e454cbbcce7547294cedcdf1 | Python | 4,084 | 136 | _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',
... |
fa74fdc222da23fa34d2d5ae80a77105e1d25192259800a315af0230c02637a6 | Python | 4,084 | 136 | _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',
... |
08cd02fafd4295d0969768217fea5740eb8980d80c09711d8d10e01dc64d1406 | Python | 4,085 | 89 | """Fresh-output ConvSV masking must preserve higher derivatives and inputs."""
import pytest
import torch
from aimnet import nbops
from aimnet.modules.aev import AEVSV, ConvSV
def _mask_calls(monkeypatch):
calls = []
original = nbops.mask_i_
def observed(value, data, mask_value=0.0, inplace=True):
... |
927ef1a5e1da04a5921eed4a12e035a51060e5512422d65baec3aa0f2d63c2ba | Python | 4,085 | 150 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
import logging
import numpy as np
import pytest
import torch
from parallel_wavegan.layers import (
PQMF,
CausalConv1d,
CausalConvTranspose1d,
Conv1d,
Conv1d1x1,
... |
839d23707c860443e0d0e8d9b5562f85ea8e9f3174f16c31c75111183502651a | Python | 4,086 | 137 | _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... |
5d7ba62af2f1312666e0d91e29a52615239175b1b4ac0072731f352e37611517 | Python | 4,088 | 141 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from typing import List
from copy import deepcopy
from functools import partial
from collections import defaultdict
from .particle import Particle
class PSOReport(object):
VALID_STATS = {
'all': None,
'mean': np.... |
cf3b356700a8b3034fbc637471c6706b28a3f5ef59fa3fdeab75fbc3ea886a3c | Python | 4,088 | 137 | _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... |
f15a01aa99eef6b32a5a1fcab378e21d706f7a003e7ede69ef1dafa6e137de4c | Python | 4,088 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_upper.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',... |
079c9719fa87495e2ad389d7aee749648c45f0ac1edca7242472f83497b1418a | Python | 4,090 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_upper.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',... |
400ea52c585d59cbb99bbd7b33c0f230768e040d1010009d0c5d58a29f55d366 | Python | 4,090 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_upper.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',... |
aedf9ccfc90d1c7c2d7f8d9c41d10f37c9cc6891eca0fa4de4dbca81fbe2836e | Python | 4,091 | 118 | from osgeo import gdal
import rasterio
import numpy as np
# ------------------------------
# INPUTS
# ------------------------------
input_raster = r'X:\Active Projects\HOT SPOT SYSTEM\Pop layer GHSL\GHS_POP_E2025_GLOBE_R2023A_54009_100_V1_0\GHS_POP_E2025_GLOBE_R2023A_54009_100_V1_0.tif'
binary_raster = r'X:\Active Pr... |
2a2a971c700157ba230dc09bdeb46a37471190506bd4c94d1894ab60c303aed2 | Python | 4,093 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_full.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',
... |
2b5e43b1e087b43515402f5a90d61c6ac12484df41d3ffba907102cc6a8c4922 | Python | 4,093 | 139 | _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(
... |
764ffe8fd2c64058524582424133355dfc8c15a90a0c715d0954cc53125d2c65 | Python | 4,094 | 122 | r"""
This test file contains tests that involve generating synthetic data and
then fitting to make sure we get the expected result for lognormal
distributions.
We can easily generate lognormal distributed samples by performing a
multiplicative random walk:
X_N = \prod_i x_i
The sigma parameter for the lognorma... |
018fb2fc34680732919372f80f9d80a3e01ec851f47f0452c67a2ef55a924a2f | Python | 4,095 | 124 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Mod... |
1bf0d781fbe2cecac99f311e47266cbc90203ac570a9822430092da41ae9ab0d | Python | 4,095 | 135 | _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... |
32375dcd3261bdf0103ab97c74935b71921ee8db34f2d875c951c6a146b6cdbd | Python | 4,095 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_full.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',
... |
961ed8e3a8b6e8592ff8191d4b32d4415019b97293cce7f91c2fad3a1c25d094 | Python | 4,095 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_full.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',
... |
3f84012f1539312a022c59921cac067ce1a4672d50b170b085bd96ec6c809870 | Python | 4,097 | 135 | _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... |
5cf0df4073bd96811565868c9ee30145a6015a2b30beb0494f2bd87efa9de536 | Python | 4,097 | 135 | _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... |
110f95881529f866437a856352a74eb5b482c06a59e5f5463bd63fe4c0c0e596 | Python | 4,102 | 138 | _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... |
18f8a59f011f9d0f6e4108466a0b9928f4e40eaa4bb666f05f0c2183efcd7551 | Python | 4,102 | 138 | _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... |
d966b681aa3ac244a78f3337096968c063baeb6a0562419a5db80b939bf3f06b | Python | 4,105 | 138 | _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... |
488ae947937813b2b027446f5d26aba79779499763086e61bbcfea9fc12c4861 | Python | 4,106 | 121 | # model settings
model = dict(
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(ty... |
d21c6656d31e65881e8eb693d3f85aeef75e6c64ed443c40aac776d25c8ff6f0 | Python | 4,106 | 102 | # -*- coding: utf-8 -*-
"""
Labels
======
This module contains LabelExtractor and methods concerning labels assignment, as well as other classes
for RGroup resolution
author: Damian Wilary
email: dmw51@cam.ac.uk
"""
import logging
import string
from typing import List, Tuple
from matplotlib.patches import Rectangle... |
80b21a69ba241b908682a954f20a2a7eac1cfd81175f61b423301f8067daa771 | Python | 4,107 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.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... |
20f051ad6f0ed8f09d7040671ab45c38f48da88bc2e7c6f4baea3fcff2d3e90f | Python | 4,108 | 124 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 16:04
@project: MalariaDetection
@description:
"""
import glob
import os
import numpy as np
from PIL import Image
import random
class Dataset(object):
def __init__(self, path, data... |
6204981b56b0691c162d062e3d7719b8606e741e48d6728787321043f0a6cf12 | Python | 4,108 | 109 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from snf import cv
rs = np.random.RandomState(1234)
def test_compute_SNF(simdata):
# don't define cluster number (find using compute.get_n_clusters)
zaff, labels = cv.compute_SNF(simdata.data, metric='euclidean',
n_pe... |
24838eb9274d96730d48751af7dfa0de4d485a47d51d49b105d8181d03b0cf9d | Python | 4,109 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.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... |
e4c86a9deb5dc0afaa9084a75081846c631478473aa95390aeb7e6f5ab2aabdd | Python | 4,109 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.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... |
fa1393c6e2bf2c4337d45560cfcc33f2bbd27847f709baab64c843d11b3f1560 | Python | 4,110 | 100 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
import pytest
import torch
from torch.distributions import MultivariateNormal
from sbi.inference.posteriors import (
DirectPosterior,
EnsemblePos... |
4a54a6d137784b850118e231bba3fcbfe8fcd7f181ac357156d2ce12468632d9 | Python | 4,111 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_mpii_256x256-418ffc88_20200812.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_... |
7af23d52ca6329aca04d885c223755a6113c3526e7a12520669fd66983e10324 | Python | 4,111 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_mpii_256x256-418ffc88_20200812.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_... |
c315236da9f3be9fb74319eb7285e0a38df96675661ae2e0ea885458c96bf190 | Python | 4,111 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_mpii_256x256-418ffc88_20200812.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_... |
1b99135157fbb2dedc4003d8b56a7cfd58e7be7e76e908b95b80cd3b672cc87e | Python | 4,113 | 139 | _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... |
4420a39d4c83f2ae245551cbf19f60aa444504c9cac75da58fff2165da35104d | Python | 4,113 | 149 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from __future__ import annotations
import itertools as it
import pytest
import torch
import torch.nn as nn
from sbi.neural_nets.embedding_nets import C... |
70aa424b0aa0f30e912cad4312a8a925329a4f80f23a76f4e5438d23b5232482 | Python | 4,115 | 138 | _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... |
a7295a4503f3f39e92ff683dce522ffe17a1a28449ee70f5acb58a49cddcdae0 | Python | 4,115 | 102 | """The optional graph backend is selected explicitly and imported lazily."""
import inspect
import os
import subprocess
import sys
from pathlib import Path
from types import ModuleType
import pytest
import torch
import aimnet.calculators.calculator as calculator_module
from aimnet.calculators import AIMNet2Calculato... |
eab99fb77ecc753b81394992eee28b6d92422a04a25c8fac56dc87da7cf2f9a9 | Python | 4,115 | 138 | _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... |
f7d72dde0e703f908e92924daedacc65cff7a26eb6b07a4f3d2c62913664def8 | Python | 4,116 | 96 | # 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 torch
from torch import Tensor
def reshape_to_sample_batch_event(
theta_or_x: Tensor, event_shape: torch.Size, leading_is_sample: bool = Fals... |
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