repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
finer | finer-main/models/bilstm.py | import tensorflow as tf
import numpy as np
from tf2crf import CRF
class BiLSTM(tf.keras.Model):
def __init__(
self,
n_classes,
n_layers=1,
n_units=128,
dropout_rate=0.1,
crf=False,
word2vectors_weights=None,
subword_po... | 4,319 | 31 | 110 | py |
finer | finer-main/models/transformer.py | import tensorflow as tf
import numpy as np
from transformers import AutoTokenizer, TFAutoModel
from tf2crf import CRF
class Transformer(tf.keras.Model):
def __init__(
self,
model_name,
n_classes,
dropout_rate=0.1,
crf=False,
tokenizer=None,
... | 4,291 | 29.013986 | 112 | py |
finer | finer-main/models/__init__.py | from models.bilstm import BiLSTM
from models.transformer import Transformer
from models.transformer_bilstm import TransformerBiLSTM
| 132 | 32.25 | 55 | py |
finer | finer-main/configurations/configuration.py | import json
import os
import logging
from shutil import copy2
from time import strftime, gmtime
from configurations import CONFIG_DIR
from data import EXPERIMENTS_RUNS_DIR, VECTORS_DIR
parameters = {}
class ParameterStore(type):
def __getitem__(cls, key: str):
global parameters
return parameter... | 5,368 | 39.368421 | 116 | py |
finer | finer-main/configurations/__init__.py | import os
CONFIG_DIR = os.path.dirname(os.path.realpath(__file__))
from configurations.configuration import Configuration
| 122 | 29.75 | 56 | py |
finer | finer-main/data/__init__.py | import os
DATA_DIR = os.path.dirname(os.path.realpath(__file__))
EXPERIMENTS_RUNS_DIR = os.path.join(DATA_DIR, 'experiments_runs')
VECTORS_DIR = os.path.join(DATA_DIR, 'vectors')
| 179 | 35 | 65 | py |
TCPD | TCPD-master/build_tcpd.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect and verify all time series that are not packaged in the repository.
Author: Gertjan van den Burg
License: See LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import platform
import os
DATASET_DIR = "./datasets"
TARGETS = [
... | 3,081 | 25.568966 | 80 | py |
TCPD | TCPD-master/examples/python/load_dataset.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Example code for loading a dataset to a TimeSeries object.
Note that this code requires Pandas to be available.
Author: Gertjan van den Burg
Copyright: The Alan Turing Institute, 2019
License: See LICENSE file.
"""
import json
import numpy as np
import pandas as pd... | 2,654 | 22.289474 | 75 | py |
TCPD | TCPD-master/datasets/scanline_126007/get_scanline_126007.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the scanline_126007 dataset.
See the README file for more information.
Author: Gertjan van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import hashlib
import os
i... | 4,045 | 24.2875 | 181 | py |
TCPD | TCPD-master/datasets/shanghai_license/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import json
import argparse
import clevercsv
def reformat_time(mmmyy):
""" From MMM-YY to %Y-%m """
MONTHS = {
"Jan": 1,
"Feb": 2,
"Mar": 3,
"Apr": 4,
"May":... | 1,956 | 20.744444 | 75 | py |
TCPD | TCPD-master/datasets/bee_waggle_6/get_bee_waggle_6.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the bee_waggle_6 dataset.
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import hashlib
import json
imp... | 5,957 | 28.205882 | 114 | py |
TCPD | TCPD-master/datasets/construction/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import argparse
import json
import xlrd
MONTHS = {
"Jan": 1,
"Feb": 2,
"Mar": 3,
"Apr": 4,
"May": 5,
"Jun": 6,
"Jul": 7,
"Aug": 8,
"Sep": 9,
"Oct": 10,
"Nov": 11,
... | 2,316 | 19.147826 | 68 | py |
TCPD | TCPD-master/datasets/lga_passengers/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import json
import argparse
import clevercsv
def month2index(month):
return {
"Jan": "01",
"Feb": "02",
"Mar": "03",
"Apr": "04",
"May": "05",
"Jun": "06"... | 1,932 | 21.476744 | 78 | py |
TCPD | TCPD-master/datasets/unemployment_nl/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Author: Gertjan van den Burg
"""
import argparse
import clevercsv
import json
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", help="File to write to")
... | 1,974 | 23.6875 | 76 | py |
TCPD | TCPD-master/datasets/scanline_42049/get_scanline_42049.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the scanline_42049 dataset.
See the README file for more information.
Author: Gertjan van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import hashlib
import os
im... | 4,038 | 24.402516 | 180 | py |
TCPD | TCPD-master/datasets/us_population/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import json
import argparse
import clevercsv
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", he... | 1,428 | 20.984615 | 72 | py |
TCPD | TCPD-master/datasets/usd_isk/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Author: Gertjan van den Burg
"""
import clevercsv
import json
import sys
def format_month(ymm):
year, month = ymm.split("M")
return f"{year}-{month}"
def main(input_filename, output_filename):
with open(input_filename, "r", newline="", encoding="asci... | 2,059 | 22.678161 | 78 | py |
TCPD | TCPD-master/datasets/jfk_passengers/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import json
import argparse
import clevercsv
def month2index(month):
return {
"Jan": "01",
"Feb": "02",
"Mar": "03",
"Apr": "04",
"May": "05",
"Jun": "06"... | 1,927 | 21.16092 | 78 | py |
TCPD | TCPD-master/datasets/centralia/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import json
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"-s",
"--subsample",
help="Number of observations to skip during subsam... | 1,365 | 20.015385 | 68 | py |
TCPD | TCPD-master/datasets/iceland_tourism/get_iceland_tourism.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the iceland_tourism dataset
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import hashlib
import json
i... | 5,389 | 24.424528 | 177 | py |
TCPD | TCPD-master/datasets/global_co2/get_global_co2.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the global_co2 dataset
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import clevercsv
import hashlib
... | 4,971 | 24.761658 | 239 | py |
TCPD | TCPD-master/datasets/robocalls/get_robocalls.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the robocalls dataset
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import bs4
import hashlib
import ... | 5,907 | 25.375 | 89 | py |
TCPD | TCPD-master/datasets/ozone/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import argparse
import clevercsv
import json
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", hel... | 1,378 | 20.546875 | 73 | py |
TCPD | TCPD-master/datasets/homeruns/get_homeruns.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the homeruns dataset
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import clevercsv
import hashlib
imp... | 4,748 | 24.532258 | 181 | py |
TCPD | TCPD-master/datasets/run_log/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import argparse
import clevercsv
import json
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", he... | 1,458 | 20.776119 | 72 | py |
TCPD | TCPD-master/datasets/measles/get_measles.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the measles dataset
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import clevercsv
import hashlib
impo... | 4,225 | 24.305389 | 108 | py |
TCPD | TCPD-master/datasets/gdp_argentina/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import json
import argparse
import clevercsv
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", he... | 1,824 | 20.72619 | 76 | py |
TCPD | TCPD-master/datasets/brent_spot/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import argparse
import clevercsv
import json
SAMPLE = 10
def date_to_iso(datestr):
mm, dd, yyyy = list(map(int, datestr.split("/")))
return f"{yyyy}-{mm:02d}-{dd:02d}"
def parse_args():
parser... | 1,660 | 21.445946 | 76 | py |
TCPD | TCPD-master/datasets/bitcoin/get_bitcoin.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Retrieve the bitcoin dataset.
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import clevercsv
import hashlib
im... | 4,297 | 24.431953 | 129 | py |
TCPD | TCPD-master/datasets/occupancy/get_occupancy.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the occupancy dataset.
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import clevercsv
import hashlib
i... | 4,628 | 25.301136 | 148 | py |
TCPD | TCPD-master/datasets/ratner_stock/get_ratner_stock.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the ratner_stock dataset.
See the README file for more information.
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import clevercsv
import hashli... | 5,611 | 24.981481 | 88 | py |
TCPD | TCPD-master/datasets/gdp_croatia/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import json
import argparse
import clevercsv
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", he... | 1,806 | 20.511905 | 76 | py |
TCPD | TCPD-master/datasets/gdp_iran/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: Gertjan van den Burg
"""
import json
import argparse
import clevercsv
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", he... | 1,812 | 20.329412 | 76 | py |
TCPD | TCPD-master/datasets/businv/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import argparse
import json
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", help="File to write ... | 1,812 | 22.545455 | 76 | py |
TCPD | TCPD-master/datasets/well_log/convert.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Dataset conversion script
Author: G.J.J. van den Burg
"""
import json
import argparse
SAMPLE = 6
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file", help="File to convert")
parser.add_argument("output_file", help="Fi... | 1,038 | 18.603774 | 63 | py |
TCPD | TCPD-master/datasets/apple/get_apple.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Collect the apple dataset.
This script uses the yfinance package to download the data from Yahoo Finance
and subsequently reformats it to a JSON file that adheres to our dataset
schema. See the README file for more information on the dataset.
Author: G.J.J. van den... | 13,677 | 39.111437 | 88 | py |
TCPD | TCPD-master/utils/plot_dataset.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Utility script to plot datasets and annotations.
Author: G.J.J. van den Burg
Copyright (c) 2020 - The Alan Turing Institute
License: See the LICENSE file.
"""
import argparse
import datetime
import json
import matplotlib.pyplot as plt
import pandas as pd
def parse... | 3,606 | 27.401575 | 76 | py |
TCPD | TCPD-master/utils/validate_dataset.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Validate the dataset schema of a given file.
Note that this script requires the ``jsonschema`` package.
Author: G.J.J. van den Burg
License: This file is part of TCPD. See the LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import json
... | 4,659 | 30.486486 | 83 | py |
TCPD | TCPD-master/utils/check_checksums.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Validate the datasets by checksum
Author: G.J.J. van den Burg
License: This file is part of TCPD, see the top-level LICENSE file.
Copyright: 2019, The Alan Turing Institute
"""
import argparse
import hashlib
import os
import json
def parse_args():
parser = arg... | 2,382 | 24.902174 | 78 | py |
UDAStrongBaseline | UDAStrongBaseline-master/sbs_traindbscan_unc.py | from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import random
import numpy as np
import sys
from sklearn.cluster import DBSCAN
# from sklearn.preprocessing import normalize
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import Dat... | 23,722 | 40.692443 | 151 | py |
UDAStrongBaseline | UDAStrongBaseline-master/sbs_traindbscan.py | from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import random
import numpy as np
import sys
from sklearn.cluster import DBSCAN
# from sklearn.preprocessing import normalize
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import Dat... | 22,980 | 40.0375 | 151 | py |
UDAStrongBaseline | UDAStrongBaseline-master/source_pretrain.py | from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import random
import numpy as np
import sys
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader
from UDAsbs import datasets
from UDAsbs import models
from UDAsbs.trainers... | 9,253 | 39.946903 | 154 | py |
UDAStrongBaseline | UDAStrongBaseline-master/sbs_trainkmeans.py | from __future__ import print_function, absolute_import
import argparse
import os
import os.path as osp
import random
import numpy as np
import sys
from sklearn.cluster import DBSCAN,KMeans
# from sklearn.preprocessing import normalize
import torch
from torch import nn
from torch.backends import cudnn
from torch.util... | 23,432 | 42.718284 | 162 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/evaluators.py | from __future__ import print_function, absolute_import
import time
from collections import OrderedDict
import numpy as np
import torch
from .evaluation_metrics import cmc, mean_ap
from .feature_extraction import extract_cnn_feature
from .utils.meters import AverageMeter
from .utils.rerank import re_ranking
def extra... | 6,592 | 37.109827 | 126 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/trainers.py | from __future__ import print_function, absolute_import
import time
import torch
import torch.nn as nn
from torch.nn import functional as F
from .evaluation_metrics import accuracy
from .loss import SoftTripletLoss_vallia, CrossEntropyLabelSmooth, SoftTripletLoss, SoftEntropy
from .memorybank.NCECriterion import Multi... | 24,831 | 41.01692 | 163 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/__init__.py | from __future__ import absolute_import
from . import datasets
from . import evaluation_metrics
from . import feature_extraction
from . import loss
from . import metric_learning
from . import models
from . import utils
from . import dist_metric
from . import evaluators
from . import trainers
__version__ = '1.0.0'
| 316 | 20.133333 | 38 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/dist_metric.py | from __future__ import absolute_import
import torch
from .evaluators import extract_features
from .metric_learning import get_metric
class DistanceMetric(object):
def __init__(self, algorithm='euclidean', *args, **kwargs):
super(DistanceMetric, self).__init__()
self.algorithm = algorithm
... | 926 | 28.903226 | 63 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/multigpu.py | import time
import torch
# from util import MovingAverage
def aggreg_multi_gpu(model, dataloader, hc, dim, TYPE=torch.float64, model_gpus=1):
""""Accumulate activations and save them on multiple GPUs
* this function assumes the model is on the first `model_gpus` GPUs
so that it can write the acti... | 5,048 | 40.04878 | 123 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/sinkhornknopp.py | import torch
import torch.nn as nn
import time
import numpy as np
from UDAsbs.multigpu import gpu_mul_Ax, gpu_mul_xA, aggreg_multi_gpu, gpu_mul_AB
from scipy.special import logsumexp
def py_softmax(x, axis=None):
"""stable softmax"""
return np.exp(x - logsumexp(x, axis=axis, keepdims=True))
def cpu_sk(self)... | 8,028 | 36.872642 | 117 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/memorybank/alias_multinomial.py | import torch
class AliasMethod(object):
"""
From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
"""
def __init__(self, probs):
if probs.sum() > 1:
probs.div_(probs.sum())
K = len(probs)
self.prob = to... | 1,968 | 28.833333 | 120 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/memorybank/NCEAverage.py | import torch
from torch import nn
from torch.nn import functional as F
import math
from numpy.testing import assert_almost_equal
def normalize(x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
... | 22,548 | 44.370221 | 151 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/memorybank/NCECriterion.py | import torch
from torch import nn
import torch.nn.functional as F
eps = 1e-7
class NCECriterion(nn.Module):
"""
Eq. (12): L_{memorybank}
"""
def __init__(self, n_data):
super(NCECriterion, self).__init__()
self.n_data = n_data
def forward(self, x):
bsz = x.shape[0]
... | 3,840 | 29.975806 | 108 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/models/resnet_multi.py | from __future__ import absolute_import
from torch import nn
from torch.nn import functional as F
from torch.nn import Parameter
from torch.nn import init
import torchvision
import torch
from ..layers import (
IBN,
Non_local,
get_norm,
)
from .gem_pooling import GeneralizedMeanPoolingP
__all__ = ['ResNet'... | 9,954 | 36.566038 | 129 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/models/memory_bank.py | import torch
from torch import nn
from torch.nn import functional as F
import math
from numpy.testing import assert_almost_equal
def normalize(x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
... | 7,998 | 38.019512 | 154 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/models/resnet.py | from __future__ import absolute_import
from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torchvision
import torch
from ..layers import (
IBN,
Non_local,
get_norm,
)
from .gem_pooling import GeneralizedMeanPoolingP
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resn... | 8,933 | 34.879518 | 129 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/models/gem_pooling.py | # encoding: utf-8
"""
@author: l1aoxingyu
@contact: sherlockliao01@gmail.com
"""
import torch
import torch.nn.functional as F
from torch import nn
class GeneralizedMeanPooling(nn.Module):
r"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function comp... | 1,764 | 35.020408 | 106 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/models/dsbn.py | import torch
import torch.nn as nn
# Domain-specific BatchNorm
class DSBN2d(nn.Module):
def __init__(self, planes):
super(DSBN2d, self).__init__()
self.num_features = planes
self.BN_S = nn.BatchNorm2d(planes)
self.BN_T = nn.BatchNorm2d(planes)
def forward(self, x):
if ... | 2,669 | 32.797468 | 68 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/models/__init__.py | from __future__ import absolute_import
from .resnet import *
# from .resnet_sbs import resnet50_sbs
from .resnet_multi import resnet50_multi,resnet50_multi_sbs
__factory = {
'resnet18': resnet18,
'resnet34': resnet34,
'resnet50': resnet50,
'resnet101': resnet101,
'resnet152': resnet152,
'resnet... | 2,014 | 34.982143 | 80 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/datasets/dukemtmc.py | from __future__ import print_function, absolute_import
import os.path as osp
import glob
import re
import urllib
import zipfile
from ..utils.data import BaseImageDataset
from ..utils.osutils import mkdir_if_missing
from ..utils.serialization import write_json
class DukeMTMC(BaseImageDataset):
"""
DukeMTMC-re... | 4,391 | 38.927273 | 121 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/datasets/msmt17.py | from __future__ import print_function, absolute_import
import os.path as osp
import tarfile
import glob
import re
import urllib
import zipfile
from ..utils.osutils import mkdir_if_missing
from ..utils.serialization import write_json
style='MSMT17_V1'
def _pluck_msmt(list_file, subdir, ncl, pattern=re.compile(r'([-\d... | 3,315 | 32.494949 | 105 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/datasets/personx.py | from __future__ import print_function, absolute_import
import os.path as osp
import glob
import re
import urllib
import zipfile
from ..utils.data import BaseImageDataset
from ..utils.osutils import mkdir_if_missing
from ..utils.serialization import write_json
class personX(BaseImageDataset):
dataset_dir = '.'
... | 3,185 | 37.853659 | 115 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/datasets/personxval.py | from __future__ import print_function, absolute_import
import os.path as osp
import glob
import re
import urllib
import zipfile
from ..utils.data import BaseImageDataset
from ..utils.osutils import mkdir_if_missing
from ..utils.serialization import write_json
class personXval(BaseImageDataset):
dataset_dir = '.... | 4,015 | 38.372549 | 120 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/datasets/market1501.py | from __future__ import print_function, absolute_import
import os.path as osp
import glob
import re
import urllib
import zipfile
from ..utils.data import BaseImageDataset
from ..utils.osutils import mkdir_if_missing
from ..utils.serialization import write_json
class Market1501(BaseImageDataset):
"""
Market1501... | 3,578 | 37.902174 | 115 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/datasets/__init__.py | from __future__ import absolute_import
import warnings
from .dukemtmc import DukeMTMC
from .market1501 import Market1501
from .msmt17 import MSMT17
from .personx import personX
from .personxval import personXval
__factory = {
'market1501': Market1501,
'dukemtmc': DukeMTMC,
'msmt17': MSMT17,
'personx':... | 1,372 | 26.46 | 79 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/batch_norm.py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import logging
import torch
import torch.nn.functional as F
from torch import nn
__all__ = [
"BatchNorm",
"IBN",
"GhostBatchNorm",
"FrozenBatchNorm",
"SyncBatchNorm",
"get_norm",
]
class BatchNorm(nn.BatchNorm... | 8,165 | 39.029412 | 118 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/non_local.py | # encoding: utf-8
import torch
from torch import nn
from .batch_norm import get_norm
class Non_local(nn.Module):
def __init__(self, in_channels, bn_norm, num_splits, reduc_ratio=2):
super(Non_local, self).__init__()
self.in_channels = in_channels
self.inter_channels = reduc_ratio // red... | 1,901 | 33.581818 | 94 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/__init__.py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
from torch import nn
# from .batch_drop import BatchDrop
# from .attention import *
from .batch_norm import *
# from .context_block import ContextBlock
from .non_local import Non_local
# from .se_layer import SELayer
# from .frn import F... | 622 | 23.92 | 69 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/sync_bn/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/sync_bn/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 746 | 23.9 | 59 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/sync_bn/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import contextlib
import... | 15,978 | 39.35101 | 116 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/sync_bn/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,385 | 30.813333 | 95 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/sync_bn/comm.py | # -*- coding: utf-8 -*-
# File : comm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import queue
import collections
import threading... | 4,449 | 31.246377 | 117 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/layers/sync_bn/__init__.py | # -*- coding: utf-8 -*-
# File : __init__.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
from .batchnorm import SynchronizedBatchNorm1... | 507 | 35.285714 | 96 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/feature_extraction/cnn.py | from __future__ import absolute_import
from collections import OrderedDict
from ..utils import to_torch
def extract_cnn_feature(model, inputs, modules=None):
model.eval()
# with torch.no_grad():
inputs = to_torch(inputs).cuda()
if modules is None:
outputs = model(inputs)
outputs = ou... | 705 | 25.148148 | 56 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/feature_extraction/database.py | from __future__ import absolute_import
import h5py
import numpy as np
from torch.utils.data import Dataset
class FeatureDatabase(Dataset):
def __init__(self, *args, **kwargs):
super(FeatureDatabase, self).__init__()
self.fid = h5py.File(*args, **kwargs)
def __enter__(self):
return se... | 1,311 | 24.230769 | 59 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/feature_extraction/__init__.py | from __future__ import absolute_import
from .cnn import extract_cnn_feature
from .database import FeatureDatabase
__all__ = [
'extract_cnn_feature',
'FeatureDatabase',
]
| 180 | 17.1 | 38 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/loss/invariance.py | import torch
import torch.nn.functional as F
from torch import nn, autograd
from torch.autograd import Variable, Function
import numpy as np
import math
import warnings
warnings.filterwarnings("ignore")
class ExemplarMemory(Function):
def __init__(self, em, alpha=0.01):
super(ExemplarMemory, self).__init_... | 2,793 | 31.870588 | 94 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/loss/triplet.py | from __future__ import absolute_import
import torch
from torch import nn
import torch.nn.functional as F
def euclidean_dist(x, y):
m, n = x.size(0), y.size(0)
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
dist = xx + yy
dist.addm... | 7,326 | 39.038251 | 160 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/loss/crossentropy.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class CrossEntropyLabelSmooth(nn.Module):
def __init__(self, num_classes, epsilon=0.1, reduce=True):
super(CrossEntropyLabelSmooth, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon
self.logsoftmax = nn.LogSoftmax(dim=1... | 1,162 | 28.075 | 82 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/loss/multisoftmax.py | import torch
from torch import nn
import torch.nn.functional as F
eps = 1e-7
class NCECriterion(nn.Module):
"""
Eq. (12): L_{memorybank}
"""
def __init__(self, n_data):
super(NCECriterion, self).__init__()
self.n_data = n_data
def forward(self, x):
bsz = x.shape[0]
... | 3,800 | 29.166667 | 108 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/loss/__init__.py | from __future__ import absolute_import
from .triplet import SoftTripletLoss_vallia, SoftTripletLoss
from .crossentropy import CrossEntropyLabelSmooth, SoftEntropy
from .multisoftmax import MultiSoftmaxLoss
from .invariance import InvNet
__all__ = [
'SoftTripletLoss_vallia',
'CrossEntropyLabelSmooth',
'Soft... | 393 | 25.266667 | 62 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/metric_learning/kissme.py | from __future__ import absolute_import
import numpy as np
from metric_learn.base_metric import BaseMetricLearner
def validate_cov_matrix(M):
M = (M + M.T) * 0.5
k = 0
I = np.eye(M.shape[0])
while True:
try:
_ = np.linalg.cholesky(M)
break
except np.linalg.LinAl... | 1,654 | 28.553571 | 82 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/metric_learning/euclidean.py | from __future__ import absolute_import
import numpy as np
from metric_learn.base_metric import BaseMetricLearner
class Euclidean(BaseMetricLearner):
def __init__(self):
self.M_ = None
def metric(self):
return self.M_
def fit(self, X):
self.M_ = np.eye(X.shape[1])
self.X_... | 425 | 18.363636 | 54 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/metric_learning/__init__.py | from __future__ import absolute_import
from metric_learn import (ITML_Supervised, LMNN, LSML_Supervised,
SDML_Supervised, NCA, LFDA, RCA_Supervised)
from .euclidean import Euclidean
from .kissme import KISSME
__factory = {
'euclidean': Euclidean,
'kissme': KISSME,
'itml': ITML_S... | 653 | 24.153846 | 69 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/metric_learning/distance.py | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
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:
... | 2,454 | 31.733333 | 86 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/lr_scheduler.py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
from bisect import bisect_right
import torch
from torch.optim.lr_scheduler import *
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRSched... | 1,807 | 30.172414 | 80 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/loss_and_miner_utils.py | import torch
import numpy as np
import math
from . import common_functions as c_f
def logsumexp(x, keep_mask=None, add_one=True, dim=1):
max_vals, _ = torch.max(x, dim=dim, keepdim=True)
inside_exp = x - max_vals
exp = torch.exp(inside_exp)
if keep_mask is not None:
exp = exp*keep_mask
ins... | 7,816 | 34.694064 | 114 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/common_functions.py | import collections
import torch
from torch.autograd import Variable
import numpy as np
import os
import logging
import glob
import scipy.stats
import re
NUMPY_RANDOM = np.random
class Identity(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x
def try_n... | 9,084 | 28.306452 | 113 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/logging.py | from __future__ import absolute_import
import os
import sys
from .osutils import mkdir_if_missing
class Logger(object):
def __init__(self, fpath=None):
self.console = sys.stdout
self.file = None
if fpath is not None:
mkdir_if_missing(os.path.dirname(fpath))
self.fi... | 876 | 20.925 | 52 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/faiss_rerank.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVPR_2017_paper.pdf
Matlab version: https://githu... | 4,838 | 38.663934 | 126 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/faiss_utils.py | import os
import numpy as np
import faiss
import torch
def swig_ptr_from_FloatTensor(x):
assert x.is_contiguous()
assert x.dtype == torch.float32
return faiss.cast_integer_to_float_ptr(
x.storage().data_ptr() + x.storage_offset() * 4)
def swig_ptr_from_LongTensor(x):
assert x.is_contiguous()
... | 3,182 | 28.201835 | 92 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/__init__.py | from __future__ import absolute_import
import torch
def to_numpy(tensor):
if torch.is_tensor(tensor):
return tensor.cpu().numpy()
elif type(tensor).__module__ != 'numpy':
raise ValueError("Cannot convert {} to numpy array"
.format(type(tensor)))
return tensor
de... | 594 | 26.045455 | 60 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/rerank.py | #!/usr/bin/env python2/python3
# -*- coding: utf-8 -*-
"""
Source: https://github.com/zhunzhong07/person-re-ranking
Created on Mon Jun 26 14:46:56 2017
@author: luohao
Modified by Yixiao Ge, 2020-3-14.
CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017... | 8,856 | 41.37799 | 119 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/meters.py | from __future__ import absolute_import
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def reset(self):
self.val = 0
self.avg = 0
self.sum... | 496 | 20.608696 | 59 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/serialization.py | from __future__ import print_function, absolute_import
import json
import os.path as osp
import shutil
import torch
from torch.nn import Parameter
from .osutils import mkdir_if_missing
def read_json(fpath):
with open(fpath, 'r') as f:
obj = json.load(f)
return obj
def write_json(obj, fpath):
m... | 1,758 | 27.370968 | 78 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/osutils.py | from __future__ import absolute_import
import os
import errno
def mkdir_if_missing(dir_path):
try:
os.makedirs(dir_path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
| 214 | 16.916667 | 38 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/data/sampler.py | from __future__ import absolute_import
from collections import defaultdict
import math
import numpy as np
import copy
import random
import torch
from torch.utils.data.sampler import (
Sampler, SequentialSampler, RandomSampler, SubsetRandomSampler,
WeightedRandomSampler)
def No_index(a, b):
assert isinsta... | 3,547 | 32.471698 | 108 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/data/base_dataset.py | # encoding: utf-8
"""
@author: sherlock
@contact: sherlockliao01@gmail.com
"""
import numpy as np
class BaseDataset(object):
"""
Base class of reid dataset
"""
def get_imagedata_info(self, data):
pids, cams = [], []
for item in data:
pids += [item[1]]
cams +=... | 2,237 | 31.911765 | 112 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/data/transformer.py | from __future__ import absolute_import
from torchvision.transforms import *
from PIL import Image
import random
import math
import numpy as np
class RectScale(object):
def __init__(self, height, width, interpolation=Image.BILINEAR):
self.height = height
self.width = width
self.interpolatio... | 3,358 | 33.989583 | 96 | py |
UDAStrongBaseline | UDAStrongBaseline-master/UDAsbs/utils/data/__init__.py | from __future__ import absolute_import
from .base_dataset import BaseImageDataset
from .preprocessor import Preprocessor
class IterLoader:
def __init__(self, loader, length=None):
self.loader = loader
self.length = length
self.iter = None
def __len__(self):
if (self.length is ... | 619 | 23.8 | 44 | py |
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