repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/models/cifar/resnet_model.py | '''
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of the implementations on the web is copy-paste from
torchvision's resnet and has w... | 5,001 | 30.459119 | 120 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/models/cifar/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['wrn']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplac... | 4,080 | 41.510417 | 116 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/models/celeba/celeb_id_class_model.py | import torch
import torch.nn.functional as F
import torch.utils.model_zoo
# It was 93.5940, 104.7624, 129.1863 before dividing by 255
MEAN_RGB = [
0.367035294117647,
0.41083294117647057,
0.5066129411764705
]
def vggface(pretrained=False, **kwargs):
"""VGGFace model.
Args:
pretrained (b... | 3,372 | 27.108333 | 69 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/autograd_lib/autograd_lib.py | from contextlib import contextmanager
from typing import List, Optional, Callable, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import util as u
class Settings(object):
forward_hooks: List[Callable] # forward subhooks called by the global hook
backward_hooks: List[Callab... | 7,234 | 39.875706 | 219 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/autograd_lib/util.py | # Take simple example, plot per-layer stats over time
import random
from typing import List
import numpy as np
import torch
import torch.nn as nn
import torchvision.datasets as datasets
from PIL import Image
_pytorch_floating_point_types = (torch.float16, torch.float32, torch.float64)
_numpy_type_map = {
'float6... | 9,474 | 33.580292 | 127 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/setup.py | import numpy as np
import os.path as osp
from setuptools import setup, find_packages
from distutils.extension import Extension
from Cython.Build import cythonize
def readme():
with open('README.rst') as f:
content = f.read()
return content
def find_version():
version_file = 'torchreid/__init__.p... | 1,504 | 24.948276 | 78 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/tools/visualize_actmap.py | """Visualizes CNN activation maps to see where the CNN focuses on to extract features.
Reference:
- Zagoruyko and Komodakis. Paying more attention to attention: Improving the
performance of convolutional neural networks via attention transfer. ICLR, 2017
- Zhou et al. Omni-Scale Feature Learning for Pers... | 5,959 | 33.252874 | 92 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/tools/parse_test_res.py | """
This script aims to automate the process of calculating average results
stored in the test.log files over multiple splits.
How to use:
For example, you have done evaluation over 20 splits on VIPeR, leading to
the following file structure
log/
eval_viper/
split_0/
test.log-xxxx
spli... | 2,976 | 27.625 | 82 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/tools/compute_mean_std.py | """
Compute channel-wise mean and standard deviation of a dataset.
Usage:
$ python compute_mean_std.py DATASET_ROOT DATASET_KEY
- The first argument points to the root path where you put the datasets.
- The second argument means the specific dataset key.
For instance, your datasets are put under $DATA and you wanna
... | 1,509 | 24.166667 | 72 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/__init__.py | from __future__ import print_function, absolute_import
from torchreid import data, optim, utils, engine, losses, models, metrics
__version__ = '1.4.0'
__author__ = 'Kaiyang Zhou'
__homepage__ = 'https://kaiyangzhou.github.io/'
__description__ = 'Deep learning person re-identification in PyTorch'
__url__ = 'https://gi... | 359 | 35 | 73 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/shufflenetv2.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = [
'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
'shufflenet_v2_x2_0'
]
model_urls = {
'shufflene... | 8,011 | 29.463878 | 103 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/mudeep.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
__all__ = ['MuDeep']
class ConvBlock(nn.Module):
"""Basic convolutional block.
convolution + batch normalization + relu.
Args:
in_c (int): number of input channels.
... | 6,297 | 29.425121 | 80 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/inceptionv4.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['inceptionv4']
"""
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""
pretrained_settings = {
'inceptionv4': {
'imagenet': {
'url':
... | 11,271 | 28.507853 | 86 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/inceptionresnetv2.py | """
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""
from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['inceptionresnetv2']
pretrained_settings = {
'inceptionresnetv2': {
'imagenet': {
... | 11,194 | 29.925414 | 89 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/resnet.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
]
model_urls ... | 15,281 | 27.617978 | 106 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/mobilenetv2.py | from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['mobilenetv2_x1_0', 'mobilenetv2_x1_4']
model_urls = {
# 1.0: top-1 71.3
'mobilenetv2_x1_0':
'https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NU... | 8,515 | 29.523297 | 99 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/squeezenet.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512']
model_urls = {
'squeezenet1_0':
'https://download.pytorch.org... | 7,617 | 31.14346 | 99 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/mlfn.py | from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['mlfn']
model_urls = {
# training epoch = 5, top1 = 51.6
'imagenet':
'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAd... | 8,661 | 29.935714 | 86 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/osnet_ain.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
__all__ = [
'osnet_ain_x1_0', 'osnet_ain_x0_75', 'osnet_ain_x0_5', 'osnet_ain_x0_25'
]
pretrained_urls = {
'osnet_ain_x1_0':
'https://drive.google.com/uc?id=1-CaioD9NaqbH... | 17,731 | 28.068852 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/densenet.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import re
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils import model_zoo
__all__ = [
'densenet121', 'densenet169', 'densenet201', 'd... | 11,627 | 29.519685 | 104 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/senet.py | from __future__ import division, absolute_import
import math
from collections import OrderedDict
import torch.nn as nn
from torch.utils import model_zoo
__all__ = [
'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnet50_fc512'
]
"""
Code imported fr... | 20,684 | 29.021771 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/shufflenet.py | from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['shufflenet']
model_urls = {
# training epoch = 90, top1 = 61.8
'imagenet':
'https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrg... | 6,264 | 30.482412 | 86 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/nasnet.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['nasnetamobile']
"""
NASNet Mobile
Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!
--------------------... | 36,186 | 30.967314 | 137 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/__init__.py | from __future__ import absolute_import
import torch
from .pcb import *
from .mlfn import *
from .hacnn import *
from .osnet import *
from .senet import *
from .mudeep import *
from .nasnet import *
from .resnet import *
from .densenet import *
from .xception import *
from .osnet_ain import *
from .resnetmid import *
f... | 3,642 | 28.617886 | 81 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/xception.py | from __future__ import division, absolute_import
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['xception']
pretrained_settings = {
'xception': {
'imagenet': {
'url':
'http://data.lip6.fr/cadene/pretrainedmodels/xception-4... | 9,687 | 27.081159 | 124 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/resnet_ibn_a.py | """
Credit to https://github.com/XingangPan/IBN-Net.
"""
from __future__ import division, absolute_import
import math
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet50_ibn_a']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
... | 8,598 | 28.651724 | 99 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/resnet_ibn_b.py | """
Credit to https://github.com/XingangPan/IBN-Net.
"""
from __future__ import division, absolute_import
import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet50_ibn_b']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101'... | 8,261 | 29.043636 | 99 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/osnet.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
__all__ = [
'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
]
pretrained_urls = {
'osnet_x1_0':
'https://drive.google.com/uc?id=1LaG1EJpHrxdAxK... | 17,037 | 27.444073 | 108 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/resnetmid.py | from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = ['resnet50mid']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth'... | 9,165 | 28.75974 | 99 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/pcb.py | from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import functional as F
__all__ = ['pcb_p6', 'pcb_p4']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/... | 9,125 | 27.971429 | 86 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/models/hacnn.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
__all__ = ['HACNN']
class ConvBlock(nn.Module):
"""Basic convolutional block.
convolution + batch normalization + relu.
Args:
in_c (int): number of input channels.
... | 13,761 | 32.161446 | 105 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/optim/lr_scheduler.py | from __future__ import print_function, absolute_import
import torch
AVAI_SCH = ['single_step', 'multi_step', 'cosine']
def build_lr_scheduler(
optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1
):
"""A function wrapper for building a learning rate scheduler.
Args:
optimize... | 2,461 | 34.681159 | 97 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/optim/radam.py | """
Imported from: https://github.com/LiyuanLucasLiu/RAdam
Paper: https://arxiv.org/abs/1908.03265
@article{liu2019radam,
title={On the Variance of the Adaptive Learning Rate and Beyond},
author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei}... | 11,626 | 34.126888 | 129 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/optim/optimizer.py | from __future__ import print_function, absolute_import
import warnings
import torch
import torch.nn as nn
from .radam import RAdam
AVAI_OPTIMS = ['adam', 'amsgrad', 'sgd', 'rmsprop', 'radam']
def build_optimizer(
model,
optim='adam',
lr=0.0003,
weight_decay=5e-04,
momentum=0.9,
sgd_dampening... | 5,307 | 32.594937 | 97 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/metrics/rank.py | from __future__ import division, print_function, absolute_import
import numpy as np
import warnings
from collections import defaultdict
try:
from torchreid.metrics.rank_cylib.rank_cy import evaluate_cy
IS_CYTHON_AVAI = True
except ImportError:
IS_CYTHON_AVAI = False
warnings.warn(
'Cython evalu... | 6,955 | 32.442308 | 112 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/metrics/accuracy.py | from __future__ import division, print_function, absolute_import
def accuracy(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for
the specified values of k.
Args:
output (torch.Tensor): prediction matrix with shape (batch_size, num_classes).
target (torch.... | 1,134 | 28.868421 | 86 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/metrics/distance.py | from __future__ import division, print_function, absolute_import
import torch
from torch.nn import functional as F
def compute_distance_matrix(input1, input2, metric='euclidean'):
"""A wrapper function for computing distance matrix.
Args:
input1 (torch.Tensor): 2-D feature matrix.
input2 (tor... | 2,446 | 29.209877 | 73 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/metrics/rank_cylib/test_cython.py | from __future__ import print_function
import sys
import numpy as np
import timeit
import os.path as osp
from torchreid import metrics
sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
"""
Test the speed of cython-based evaluation code. The speed improvements
can be much bigger when using the real r... | 2,746 | 31.702381 | 125 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/engine/engine.py | from __future__ import division, print_function, absolute_import
import time
import numpy as np
import os.path as osp
import datetime
from collections import OrderedDict
import torch
from torch.nn import functional as F
from torch.utils.tensorboard import SummaryWriter
from torchreid import metrics
from torchreid.util... | 18,705 | 34.973077 | 120 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/engine/video/softmax.py | from __future__ import division, print_function, absolute_import
import torch
from torchreid.engine.image import ImageSoftmaxEngine
class VideoSoftmaxEngine(ImageSoftmaxEngine):
"""Softmax-loss engine for video-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``... | 3,479 | 30.636364 | 93 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/engine/video/triplet.py | from __future__ import division, print_function, absolute_import
import torch
from torchreid.engine.image import ImageTripletEngine
class VideoTripletEngine(ImageTripletEngine):
"""Triplet-loss engine for video-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``... | 4,038 | 31.837398 | 93 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/engine/image/softmax.py | from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.losses import CrossEntropyLoss
from ..engine import Engine
class ImageSoftmaxEngine(Engine):
r"""Softmax-loss engine for image-reid.
Args:
datamanager (DataManager): an instance of ``torchr... | 2,860 | 28.193878 | 93 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/engine/image/triplet.py | from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.losses import TripletLoss, CrossEntropyLoss
from ..engine import Engine
class ImageTripletEngine(Engine):
r"""Triplet-loss engine for image-reid.
Args:
datamanager (DataManager): an instanc... | 3,877 | 30.528455 | 93 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/torchtools.py | from __future__ import division, print_function, absolute_import
import pickle
import shutil
import os.path as osp
import warnings
from functools import partial
from collections import OrderedDict
import torch
import torch.nn as nn
from .tools import mkdir_if_missing
__all__ = [
'save_checkpoint', 'load_checkpoin... | 9,672 | 29.904153 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/avgmeter.py | from __future__ import division, absolute_import
from collections import defaultdict
import torch
__all__ = ['AverageMeter', 'MetricMeter']
class AverageMeter(object):
"""Computes and stores the average and current value.
Examples::
>>> # Initialize a meter to record loss
>>> losses = Averag... | 1,983 | 25.810811 | 71 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/feature_extractor.py | from __future__ import absolute_import
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchreid.utils import (
check_isfile, load_pretrained_weights, compute_model_complexity
)
from torchreid.models import build_model
class FeatureExtractor(object):
"""A simple ... | 4,564 | 28.836601 | 83 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/tools.py | from __future__ import division, print_function, absolute_import
import os
import sys
import json
import time
import errno
import numpy as np
import random
import os.path as osp
import warnings
import PIL
import torch
from PIL import Image
__all__ = [
'mkdir_if_missing', 'check_isfile', 'read_json', 'write_json',
... | 3,532 | 23.534722 | 90 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/loggers.py | from __future__ import absolute_import
import os
import sys
import os.path as osp
from .tools import mkdir_if_missing
__all__ = ['Logger', 'RankLogger']
class Logger(object):
"""Writes console output to external text file.
Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py>... | 4,373 | 28.755102 | 90 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/__init__.py | from __future__ import absolute_import
from .tools import *
from .rerank import re_ranking
from .loggers import *
from .avgmeter import *
from .reidtools import *
from .torchtools import *
from .model_complexity import compute_model_complexity
from .feature_extractor import FeatureExtractor
| 293 | 25.727273 | 54 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/model_complexity.py | from __future__ import division, print_function, absolute_import
import math
import numpy as np
from itertools import repeat
from collections import namedtuple, defaultdict
import torch
__all__ = ['compute_model_complexity']
"""
Utility
"""
def _ntuple(n):
def parse(x):
if isinstance(x, int):
... | 9,470 | 25.019231 | 101 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/main.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,998 | 26.383562 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/utils.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 3,691 | 25.753623 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/gnn_reranking.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,804 | 29.083333 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/extension/propagation/setup.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,200 | 31.459459 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/utils/GPU-Re-Ranking/extension/adjacency_matrix/setup.py | """
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng Zhang, Minyue Jiang, Zhedong Zheng, Xiao Tan, Errui Ding, Yi Yang
Project Page : https://github.com/Xuanmeng-Zhang/gnn-re-ranking
Paper: https://arxiv.org/abs/2012.07620v2
==================================... | 1,236 | 32.432432 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/losses/hard_mine_triplet_loss.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
class TripletLoss(nn.Module):
"""Triplet loss with hard positive/negative mining.
Reference:
Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
Imported from `<h... | 1,770 | 35.142857 | 101 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/torchreid/losses/cross_entropy_loss.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
class CrossEntropyLoss(nn.Module):
r"""Cross entropy loss with label smoothing regularizer.
Reference:
Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
With label smoothing... | 1,923 | 36.72549 | 92 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/OSNet_AIN/main.py | import os
import sys
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, compute_model_complexity
)
import osnet_search as osnet_models
from softmax_... | 4,193 | 27.726027 | 79 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/OSNet_AIN/softmax_nas.py | from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.engine import Engine
from torchreid.losses import CrossEntropyLoss
class ImageSoftmaxNASEngine(Engine):
def __init__(
self,
datamanager,
model,
optimizer,
schedul... | 2,108 | 27.5 | 73 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/OSNet_AIN/osnet_search.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
EPS = 1e-12
NORM_AFFINE = False # enable affine transformations for normalization layer
##########
# Basic layers
##########
class IBN(nn.Module):
"""Instance + Batch Normalization."""
def... | 18,586 | 30.77265 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/OSNet_AIN/osnet_child.py | from __future__ import division, absolute_import
from torch import nn
from torch.nn import functional as F
##########
# Basic layers
##########
class ConvLayer(nn.Module):
"""Convolution layer (conv + bn + relu)."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
... | 16,436 | 29.666045 | 91 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/attribute_recognition/main.py | from __future__ import division, print_function
import sys
import copy
import time
import numpy as np
import os.path as osp
import datetime
import warnings
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, AverageMeter, check_isfile, open_all_layers, save_checkpoint,
set... | 12,430 | 30.0775 | 79 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/attribute_recognition/models/osnet.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
__all__ = ['osnet_avgpool', 'osnet_maxpool']
##########
# Basic layers
##########
class ConvLayer(nn.Module):
"""Convolution layer."""
def __init__(
self,
in_channels,
... | 11,484 | 26.674699 | 80 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/attribute_recognition/datasets/dataset.py | from __future__ import division, print_function, absolute_import
import os.path as osp
from torchreid.utils import read_image
class Dataset(object):
def __init__(
self,
train,
val,
test,
attr_dict,
transform=None,
mode='train',
verbose=True,
... | 2,563 | 28.136364 | 69 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/DML/main.py | import sys
import copy
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
)
from dml import ImageD... | 4,789 | 27.682635 | 79 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/projects/DML/dml.py | from __future__ import division, print_function, absolute_import
import torch
from torch.nn import functional as F
from torchreid.utils import open_all_layers, open_specified_layers
from torchreid.engine import Engine
from torchreid.losses import TripletLoss, CrossEntropyLoss
class ImageDMLEngine(Engine):
def _... | 4,430 | 28.54 | 72 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/scripts/main.py | import sys
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
)
from default_config import (
i... | 5,871 | 29.583333 | 81 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/scrub/deep-person-reid-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,646 | 30.027473 | 79 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/tester.py | import argparse
import numpy as np
import pandas as pd
import json
import os
import torch
import torchvision
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor
import torchvision.transf... | 14,898 | 45.41433 | 174 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/test.py | """
Implements RevGrad:
Unsupervised Domain Adaptation by Backpropagation, Ganin & Lemptsky (2014)
Domain-adversarial training of neural networks, Ganin et al. (2016)
"""
import argparse
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data impor... | 5,893 | 36.069182 | 155 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/utils.py | import torch
from torch.autograd import Function
class GradientReversalFunction(Function):
"""
Gradient Reversal Layer from:
Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
Forward pass is the identity function. In the backward pass,
the upstream gradients are multiplied by -lambda (... | 815 | 23 | 77 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/dataset.py | from __future__ import print_function
import os
import os.path
import numpy as np
import pandas as pd
import sys
from scipy import ndimage as nd
import torch
import torch.utils.data as data
from PIL import Image
class OurCelebA(data.Dataset):
"""
Args:
root (string): Root directory of dataset where dir... | 3,880 | 36.317308 | 197 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/spureg_train.py | import argparse
import numpy as np
import pandas as pd
import json
import os
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor
import torchvision.transforms as transforms
... | 14,527 | 44.974684 | 174 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/models.py | from torch import nn
from utils import GradientReversal
class Net(nn.Module):
def __init__(self):
super().__init__()
self.feature_extractor = nn.Sequential(
nn.Conv2d(3, 20, kernel_size=3),
# nn.MaxPool2d(2),
# nn.ReLU(),
nn.Conv2d(20, 30, kernel_size=3),
# nn.MaxPool2d(2),
# nn.ReLU(),
nn.Co... | 1,454 | 21.045455 | 46 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/spurious/train_feature_ext.py | import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision.datasets import MNIST
from torchvision.datasets import CelebA
from torchvision.transforms import Compose, ToTensor
import torchvision.transforms as transform... | 3,133 | 35.44186 | 123 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/roc_gen.py | import numpy as np
import pandas as pd
import time
import h5py
import pickle
import json
import torch
import os
import sys
sys.path.append("../pycit-master/")
sys.path.append("../../")
from codec import codec2, codec3, foci
import argparse
from pycit import *
from bullseye import bullseye_network, get_ci_dict
from ... | 6,767 | 29.763636 | 154 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/run_bullseye3d.py | import numpy as np
import h5py
import pickle
import json
import torch
import os
import sys
sys.path.append("../pycit-master/")
import argparse
from pycit import *
from bullseye import bullseye_network, get_ci_dict
from mapping import ModelManager
# from graph_synth import createFOCIGraph
parser = argparse.Argumen... | 4,803 | 25.988764 | 104 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model_manager.py | import glob
import os
import json
import argparse
import torch
import numpy as np
import matplotlib.pyplot as plt
import pickle
from .utils import Logger
from . import model as module_model
from . import data_loader as module_data
from .evaluation import loss as module_loss
from .evaluation import metric as module_me... | 3,631 | 34.607843 | 116 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/trainer/trainer_lasso.py | import numpy as np
import torch
from torchvision.utils import make_grid
from ..base import BaseTrainer
import time
import os
import torch.nn.functional as F
from torch.nn.utils import clip_grad_norm_
from torch.autograd.gradcheck import zero_gradients
from torch.autograd import Variable
import scipy.interpolate
impor... | 6,575 | 31.078049 | 109 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/trainer/trainer_pushpull.py | import numpy as np
import torch
from torchvision.utils import make_grid
from ..base import BaseTrainer
import time
import os
import torch.nn.functional as F
from torch.nn.utils import clip_grad_norm_
from torch.autograd.gradcheck import zero_gradients
from torch.autograd import Variable
import scipy.interpolate
impor... | 9,531 | 33.164875 | 109 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/evaluation/loss.py | import torch
def mse_loss(output, target):
"""
Inputs have shape: (N,d)
"""
if output.dim() == 1 or target.dim() == 1:
return torch.mean((output.view(-1) - target.view(-1))**2)
else:
return torch.mean(torch.sum((output - target)**2, dim=1))
def gaussian_loss(output, target, offse... | 859 | 28.655172 | 75 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/evaluation/metric.py | import torch
def detection_rate(output, target, thresh=0.5):
output = output.view(-1)
target = target.view(-1)
correct = 0
n_one = 0
with torch.no_grad():
pred = output >= thresh
assert pred.shape[0] == len(target)
correct += torch.sum((pred==1)*(target==1)).item()
... | 1,780 | 28.683333 | 90 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/data_loader/np_supervised.py | import numpy as np
import torch
from torch.utils import data
from torchvision import datasets, transforms
from ..base import BaseDataLoader
class NpSupervisedDataset(data.Dataset):
"""docstring for SupervisedDataset"""
def __init__(self, x_path, y_path, selected_features=None, bit16=False):
x_data = ... | 1,542 | 35.738095 | 113 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/data_loader/h5_supervised.py | from torchvision import datasets, transforms
from ..base import BaseDataLoader
from torch.utils import data
import h5py
import numpy as np
import torch
class SupervisedDataset(data.Dataset):
"""docstring for SupervisedDataset"""
def __init__(self, h5_path, selected_features=None):
df = h5py.File(h5_pa... | 1,691 | 33.530612 | 113 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/data_loader/h5_timeseries.py | from torchvision import datasets, transforms
from ..base import BaseDataLoader
from torch.utils import data
import h5py
import numpy as np
import torch
class TimeseriesDataset(data.Dataset):
"""docstring for BackBlazeDataset"""
def __init__(self, h5_path, feat_list=None, time_trimmed=0, tia=0):
df = h... | 2,148 | 32.578125 | 82 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/base/base_model.py | import logging
import torch.nn as nn
import numpy as np
class BaseModel(nn.Module):
"""
Base class for all models
"""
def __init__(self):
super(BaseModel, self).__init__()
self.logger = logging.getLogger(self.__class__.__name__)
def forward(self, *input):
"""
Forwa... | 1,075 | 27.315789 | 93 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/base/base_trainer.py | import os
import math
import json
import logging
import datetime
import torch
from ..utils.util import ensure_dir
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, model, loss, metrics, optimizer, resume, config, train_logger=None):
self.config = config
self.log... | 8,107 | 40.367347 | 157 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/base/base_data_loader.py | import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.data.sampler import SubsetRandomSampler
class BaseDataLoader(DataLoader):
"""
Base class for all data loaders
"""
def __init__(self, dataset, batch_size, shuffle, validat... | 2,434 | 32.356164 | 132 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/delta_dropout.py | """ Drop out random number of features from 1 to delta+1 """
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
class DeltaDropout(nn.Module):
"""
Inverted dropout, except drops out entire dimensions with same probability
"""
def __init__(self, delta):
super().__i... | 959 | 26.428571 | 82 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/jeffreys.py | """ Jeffreys divergences """
import torch
def jeffreys_normal(mu1, lv1, mu2, lv2):
mu1, lv1 = mu1.view(mu1.shape[0], -1), lv1.view(lv1.shape[0], -1)
mu2, lv2 = mu2.view(mu2.shape[0], -1), lv2.view(lv2.shape[0], -1)
return (0.25*((-lv1).exp() + (-lv2).exp())*(mu1-mu2)**2 + 0.25*((lv1-lv2).exp() + (lv2-lv1)... | 551 | 35.8 | 120 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/mapping_feedforward_gaussian.py | import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
from collections import OrderedDict
from ..base import BaseModel
from .delta_dropout import DeltaDropout
from .diagonal_linear import DiagonalLinear
from .jeffreys import *
class MappingFFGaussia... | 2,946 | 32.488636 | 126 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/input_weighting.py | import torch
import torch.nn as nn
class InputWeighting(nn.Module):
"""
Input weighting for lasso-type feature selection
"""
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.weight = nn.Parameter(torch.randn(num_features))
... | 638 | 24.56 | 61 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/mapping_sequence_binary.py | import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
from collections import OrderedDict
from ..base import BaseModel
from .jeffreys import *
from .delta_dropout import DeltaDropout
class MappingRNNBinary(BaseModel):
"""
Data has di... | 3,330 | 33.697917 | 121 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/lasso_recurrent_binary.py | import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
from collections import OrderedDict
from ..base import BaseModel
from .input_weighting import *
class LassoRNNBinary(BaseModel):
"""
Data has dimension (N, T, M)
- N samples
... | 1,192 | 30.394737 | 125 | py |
LCODEC-deep-unlearning | LCODEC-deep-unlearning-main/bullseye/model-augmented-mutual-information-master/mapping/model/diagonal_linear.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class DiagonalLinear(nn.Module):
"""
keep track of nonzero parameters separately
Flow:
self.optimizer.zero_grad()
output = self.model(data)
loss = self.loss(o... | 1,967 | 29.75 | 104 | py |
splade | splade-main/splade/train.py | import os
import hydra
import torch
from omegaconf import DictConfig, open_dict
from torch.utils import data
from conf.CONFIG_CHOICE import CONFIG_NAME, CONFIG_PATH
from .datasets.dataloaders import CollectionDataLoader, SiamesePairsDataLoader, DistilSiamesePairsDataLoader
from .datasets.datasets import PairsDatasetP... | 11,507 | 58.015385 | 119 | py |
splade | splade-main/splade/models/transformer_rep.py | from abc import ABC
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModel
from ..tasks.amp import NullContextManager
from ..utils.utils import generate_bow, normalize
"""
we provide abstraction classes from which we can easily derive representation-based models with transformers like S... | 8,892 | 44.605128 | 120 | py |
splade | splade-main/splade/datasets/dataloaders.py | """
custom dataloaders (for dynamic batching)
"""
import torch
from torch.utils.data.dataloader import DataLoader
from transformers import AutoTokenizer
from ..utils.utils import rename_keys
class DataLoaderWrapper(DataLoader):
def __init__(self, tokenizer_type, max_length, **kwargs):
self.max_length = ... | 5,633 | 44.804878 | 104 | py |
splade | splade-main/splade/datasets/datasets.py | import gzip
import json
import os
import pickle
import random
from torch.utils.data import Dataset
from tqdm.auto import tqdm
class PairsDatasetPreLoad(Dataset):
"""
dataset to iterate over a collection of pairs, format per line: q \t d_pos \t d_neg
we preload everything in memory at init
"""
de... | 6,073 | 36.263804 | 108 | py |
splade | splade-main/splade/utils/utils.py | import os
import random
import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from ..losses.pairwise import DistilKLLoss, PairwiseNLL, DistilMarginMSE, InBatchPairwiseNLL
from ..losses.pointwise import BCEWithLogitsLoss
def parse(d, name):
return {k.replace(name + "_", ""): v for k, v in d... | 4,722 | 30.278146 | 115 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.