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
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graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/coco/__init__.py | from .coco_eval import do_coco_evaluation
def coco_evaluation(
dataset,
predictions,
output_folder,
box_only,
iou_types,
expected_results,
expected_results_sigma_tol,
):
return do_coco_evaluation(
dataset=dataset,
predictions=predictions,
box_only=box_only,
... | 494 | 21.5 | 62 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/evaluation/coco/coco_eval.py | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
# from lib.scene_parser.rcnn.modeling.roi_heads.mask_head.inference import Masker
from lib.scene_parser.rcnn.structures.bounding_box import BoxList
from lib.scene_parser.rcnn.structures.boxlist_ops import bo... | 14,329 | 34.914787 | 89 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/grouped_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import torch
from torch.utils.data.sampler import BatchSampler
from torch.utils.data.sampler import Sampler
class GroupedBatchSampler(BatchSampler):
"""
Wraps another sampler to yield a mini-batch of indices.
It enfo... | 4,845 | 40.775862 | 88 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/iteration_based_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch.utils.data.sampler import BatchSampler
class IterationBasedBatchSampler(BatchSampler):
"""
Wraps a BatchSampler, resampling from it until
a specified number of iterations have been sampled
"""
def __init__(self, ba... | 1,164 | 35.40625 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/distributed.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed.
# FIXME remove this once c10d fixes the bug it has
import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
class DistributedSampler(S... | 2,569 | 37.358209 | 86 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/samplers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .distributed import DistributedSampler
from .grouped_batch_sampler import GroupedBatchSampler
from .iteration_based_batch_sampler import IterationBasedBatchSampler
__all__ = ["DistributedSampler", "GroupedBatchSampler", "IterationBasedBatchSa... | 328 | 46 | 85 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/transforms/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .transforms import Compose
from .transforms import Resize
from .transforms import RandomHorizontalFlip
from .transforms import ToTensor
from .transforms import Normalize
from .build import build_transforms
| 284 | 30.666667 | 71 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/transforms/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from . import transforms as T
def build_transforms(cfg, is_train=True):
if is_train:
min_size = cfg.INPUT.MIN_SIZE_TRAIN
max_size = cfg.INPUT.MAX_SIZE_TRAIN
flip_horizontal_prob = 0.5 # cfg.INPUT.FLIP_PROB_TRAIN
... | 1,533 | 32.347826 | 121 | py |
graph-rcnn.pytorch | graph-rcnn.pytorch-master/lib/data/transforms/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import torch
import torchvision
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for ... | 3,477 | 27.508197 | 83 | py |
DMCrypt | DMCrypt-main/main.py | import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler, StandardScaler
#import seaborn as sns
import matplotlib.pyplot as plt
import pickle5 as pickle
import sys
import... | 641 | 28.181818 | 97 | py |
DMCrypt | DMCrypt-main/utils/utils.py | import numpy as np
import pickle5 as pickle
def create_sequences(x, window):
newDataframe =[]
for rowIndex in range(x.shape[0]-window):
inputSequence = []
newDataframe.append(x[rowIndex: rowIndex+window])
#newDataframe.append(inputSequence)
return np.array(newDataframe)
def getPr... | 1,086 | 32.96875 | 160 | py |
DMCrypt | DMCrypt-main/model/AdaBoost-LSTM.py | import torch
import torch.nn as nn
import pickle5 as pickle
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error, mean_squared... | 7,495 | 33.703704 | 163 | py |
DMCrypt | DMCrypt-main/model/LSTM.py | import torch
import torch.nn as nn
import pickle
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler, StandardScaler
#import seaborn as sns
import matplotlib.pyplot as plt
import pickle5 as pickle
de... | 1,806 | 38.282609 | 97 | py |
unarXive | unarXive-master/src/extend_matched.py | """
This script takes enhanced chunks of arXiv data and enriches the publications therein with discipline information
and further their included bibliography items with discipline information and arXiv IDs from an OpenAlex data dump,
provided the item matching against the OpenAlex data is successful
"""
from arxiv_tax... | 8,864 | 39.113122 | 120 | py |
unarXive | unarXive-master/src/match_references_openalex.py | """
This script extends parsed arXiv chunks with a set of identifiers by matching the included publications
against OpenAlex data in local DB and against an arXiv metadata table
"""
import psycopg2
import json
import os
import glob
import re
import unidecode
import unicodedata
import sqlite3
import requests
import sys... | 34,022 | 45.039242 | 203 | py |
unarXive | unarXive-master/src/prepare.py | """ Normalize and parse.
"""
import os
import shutil
import sys
import tarfile
import tempfile
import time
from normalize_arxiv_dump import normalize
from parse_latex_tralics import parse
def prepare(in_dir, out_dir, meta_db, tar_fn_patt, write_logs=False):
if not os.path.isdir(in_dir):
print('input dire... | 4,977 | 34.81295 | 79 | py |
unarXive | unarXive-master/src/normalize_arxiv_dump.py | """ Normalize a arXiv dump
- copy PDF files as is
- unzip gzipped single files
- copy if it's a LaTeX file
- extract gzipped tar archives
- try to flatten contents to a single LaTeX file
- ignores non LaTeX contents (HTML, PS, TeX, ...)
"""
import chardet
import gzip
import magic
i... | 9,566 | 36.665354 | 87 | py |
unarXive | unarXive-master/src/parse_latex_tralics.py | """ Convert LaTeX files to S2ORC like JSONL output
"""
import json
import os
import re
import sqlite3
import subprocess
import sys
import tempfile
import uuid
# import IPython
from collections import OrderedDict, defaultdict
from hashlib import sha1
from lxml import etree
from tqdm import tqdm
PDF_EXT_PATT = re.compi... | 23,318 | 35.209627 | 83 | py |
unarXive | unarXive-master/src/utility_scripts/count_licenses.py | import json
import os
import sys
from collections import defaultdict
def license_counts_from_json(fp):
license_counts = defaultdict(int)
with open(fp) as f:
for line in f:
ppr = json.loads(line.strip())
license = ppr.get('metadata', {}).get('license')
license_counts... | 1,091 | 27 | 71 | py |
unarXive | unarXive-master/src/utility_scripts/arxiv_taxonomy.py | """Category and archive definitions.
Copy of https://github.com/arXiv/arxiv-base/blob/
develop/arxiv/taxonomy/definitions.py
retrieved 2023/01/25.
"""
from datetime import date
GROUPS = {
'grp_physics': {
'name': 'Physics',
'start_year': 1991,
'default_archive': 'hep-t... | 90,885 | 40.576395 | 98 | py |
unarXive | unarXive-master/src/utility_scripts/generate_openalex_db.py | """ Reads data from OpenAlex dump files (works type) and it into a local DB
imported are title, authors, citation counts and IDs
"""
import psycopg2
from psycopg2.extras import Json, DictCursor
import json
import os
import glob
import gzip
import re
import unidecode
import unicodedata
def normalize_title(title_... | 9,243 | 39.017316 | 209 | py |
unarXive | unarXive-master/src/utility_scripts/generate_openalex_db_using_locations.py | """ this script reads data from OpenAlex dump files (works type) and imports it into a local DB
extracted are title, authors, citation counts, discipline info, open access URLs and IDs
this version is adapted to fit the new OpenAlex structure including "locations" entities
"""
import psycopg2
from psycopg2.ext... | 9,581 | 39.601695 | 209 | py |
unarXive | unarXive-master/src/utility_scripts/ml_tasks_prep_data.py | """ Generate train/test data for two ML tasks
- content based citation recommendation
- IMRaD classification
based on the full unarXive data set.
Data generation in done for both tasks together because both take
single paragraphs from papers an input. The script nicely prepares
paragraphs (repl... | 14,451 | 36.733681 | 79 | py |
unarXive | unarXive-master/src/utility_scripts/generate_metadata_db.py | """ From an arXiv metadata snapshot as provided by
https://www.kaggle.com/Cornell-University/arxiv
generate an SQLite database with indices for performant access.
"""
import json
import os
import re
import sqlite3
import sys
from tqdm import tqdm
def gen_meta_db(in_fp):
# input prep
in_path, in_f... | 1,808 | 26.409091 | 78 | py |
unarXive | unarXive-master/src/utility_scripts/calc_stats.py | """ Calculate dataset stats across
- time (years / months)
- disciplines (see https://arxiv.org/category_taxonomy)
For
- paragraphs
- paragraph types
- references
- citation markers
- figures
- tables
- mathematical notation
"""
import json
import os
import sys
import numpy as ... | 21,643 | 30.053085 | 79 | py |
unarXive | unarXive-master/src/utility_scripts/filter_permissively_livensed.py | """ Filter every JSONL to only contain permissively licensed papers.
Only use papers licensed
- Public Domain
- CC-Zero
- CC-BY
- CC-BY-SA
such that the final data set can be shared as CC-BY-SA.
"""
import json
import os
import sys
from collections import defaultdict
def is_permissive(licens... | 3,375 | 30.259259 | 77 | py |
unarXive | unarXive-master/src/utility_scripts/ml_tasks_split_data.py | """ Split data pepared by script ml_task_prep_data.py into train,
dev, and test.
Stratified sampling is used wrt.
- target clabel (class)
- (citing) paper discipline
- paper publication year
"""
import json
import math
import os
import random
import sys
import uuid
from collections import defaultd... | 11,615 | 35.759494 | 77 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/test.py | """
Put it all together with a simple MNIST exmaple
"""
from tensorflow.examples.tutorials.mnist import input_data
from keras.optimizers import Adam
from sklearn.metrics import confusion_matrix
from models import mnist_model
from loss import bilinear_loss
from util import *
DATA_DIR = ""
LRATE = 5e-4 ... | 2,044 | 34.877193 | 119 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/loss.py |
import numpy as np
import tensorflow as tf
from keras import backend as K
def loss_function_generator(conf_mat, log=False, alpha=.5):
"""
Generate Bilinear/Log-Bilinear loss functions combined with the rgular cross-entorpy loss
(1 - alpha)*cross_entropy_loss + alpha*bilinar/log-bilinar
:param conf_m... | 1,997 | 38.176471 | 154 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/util.py |
import numpy as np
def confusion_matrix_normalizer(cm, strip_diagonal=True, normalize_rows=True, normalize_matrix=False):
cm = cm.astype(np.float32)
# Get rid of the diagonal. This allows to consider only the error-part of the conf-mat.
if strip_diagonal:
cm -= np.diag(cm) * np.eye(cm.shape[0])
... | 1,284 | 26.934783 | 111 | py |
paper-log-bilinear-loss | paper-log-bilinear-loss-master/models.py |
from keras.layers import Dense, Dropout, Activation, Flatten, Convolution2D, MaxPooling2D
from keras.models import Sequential
def mnist_model():
model = Sequential()
model.add(Convolution2D(20, 5, 5, border_mode='same', activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2))... | 2,914 | 41.246377 | 102 | py |
cb_bakeoff | cb_bakeoff-master/oml_to_vw.py | import argparse
from config import OML_API_KEY
import gzip
import openml
import os
import scipy.sparse as sp
VW_DS_DIR = 'vwdatasets/'
def save_vw_dataset(X, y, did, ds_dir):
n_classes = y.max() + 1
fname = 'ds_{}_{}.vw.gz'.format(did, n_classes)
with gzip.open(os.path.join(ds_dir, fname), 'w') as f:
... | 4,728 | 74.063492 | 2,752 | py |
cb_bakeoff | cb_bakeoff-master/eval_common.py | import gzip
import pickle
import re
import sys
import numpy as np
import pandas as pd
def load_raw(loss_file, adf=True, cb_type=None, min_actions=None, min_size=None, shuffle=False):
if adf:
rgx = re.compile(r'^ds:(.+)\|na:(\d+)\|cb_type:(.*)\|(.*)\|(.*) (.*)$', flags=re.M)
if loss_file.endswith('... | 2,103 | 36.571429 | 96 | py |
cb_bakeoff | cb_bakeoff-master/paper_scatterplots.py | import matplotlib
matplotlib.use('Agg')
import argparse
from eval_loss import load_names
from rank_algos import significance, significance_cs01, preprocess_df_granular, preprocess_df, base_name, set_base_name
import matplotlib.pyplot as plt
import numpy as np
import os
plt.style.use('ggplot')
FIGDIR = '/scratch/clear/... | 13,716 | 44.876254 | 119 | py |
cb_bakeoff | cb_bakeoff-master/full_to_ldf.py | """
Helper script for mslr/yahoo learning-to-rank datasets. To be used as follows (for 10 different shuffles):
### MSLR
cat train.txt vali.txt test.txt | python make_full.py > train_full.txt
for i in {1..10}; do shuf vw_full.txt > vw_full$i.txt; done
for i in {1..10}; do cat vw_full$i.txt | python full... | 1,006 | 37.730769 | 106 | py |
cb_bakeoff | cb_bakeoff-master/make_full.py | """
Helper script for mslr/yahoo learning-to-rank datasets. To be used as follows (for 10 different shuffles):
### MSLR
cat train.txt vali.txt test.txt | python make_full.py > train_full.txt
for i in {1..10}; do shuf vw_full.txt > vw_full$i.txt; done
for i in {1..10}; do cat vw_full$i.txt | python full... | 1,716 | 32.666667 | 106 | py |
cb_bakeoff | cb_bakeoff-master/multilabel_to_vw.py | """
Script for converting multi-label datasets to VW format.
The multi-label datasets in the original libsvm format can be found here:
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html
note: for simulating bandit feedback, use the options `--cbify <num_actions> --cbify_cs` in VW
"""
import a... | 1,210 | 36.84375 | 94 | py |
cb_bakeoff | cb_bakeoff-master/rank_algos.py | import argparse
import numpy as np
import os
import pandas as pd
import pickle
import re
import sys
from collections import defaultdict
from eval_loss import load_names
from scipy.special import erf, erfinv
_base_name = 'disagree'
def base_name():
global _base_name
return _base_name
def set_base_name(name):
... | 10,164 | 36.509225 | 134 | py |
cb_bakeoff | cb_bakeoff-master/paper_tables.py | import argparse
from eval_loss import load_names
from rank_algos import significance, significance_cs01, preprocess_df_granular, preprocess_df, base_name, set_base_name
import numpy as np
MTR_LABEL = 'iwr'
def wins_losses(df, xname, yname, args=None):
rawx = df.loc[df.algo == xname].groupby('ds').rawloss.mean()
... | 26,361 | 44.063248 | 139 | py |
cb_bakeoff | cb_bakeoff-master/eval_loss.py | import eval_common
import argparse
import os
import pickle
import random
import re
import sys
import numpy as np
import pandas as pd
USE_ADF = True
USE_CS = False
DIR_PATTERN_CS = '/scratch/clear/abietti/cb_eval/res_cs/cbresults_{}/'
DIR_PATTERN = '/scratch/clear/abietti/cb_eval/res/cbresults_{}/'
# DIR_PATTERN = '... | 9,825 | 38.943089 | 136 | py |
cb_bakeoff | cb_bakeoff-master/run_vw_job.py | import argparse
import os
import re
import subprocess
import sys
import time
USE_ADF = True
USE_CS = False
RANDOM_TIE = True
VW = '/scratch/clear/abietti/.local/bin/vw'
if USE_CS:
VW_DS_DIR = '/scratch/clear/abietti/cb_eval/vwshuffled_cs/'
DIR_PATTERN = '/scratch/clear/abietti/cb_eval/res_cs/cbresults_{}/'
el... | 6,984 | 34.277778 | 106 | py |
cb_bakeoff | cb_bakeoff-master/best_hyperparams.py | import argparse
import numpy as np
import os
import pandas as pd
import pickle
import re
import sys
from collections import defaultdict
from eval_loss import load_names
from rank_algos import significance, significance_cs01, preprocess_df_granular, preprocess_df, base_name, set_base_name
from scipy.special import erf, ... | 5,328 | 33.380645 | 119 | py |
DCAP | DCAP-main/layer.py | import numpy as np
import torch
import torch.nn.functional as F
from torchfm.utils import get_activation_fn
from torchfm.attention_layer import MultiheadAttentionInnerProduct
class FeaturesLinear(torch.nn.Module):
def __init__(self, field_dims, output_dim=1):
super().__init__()
self.fc = torch.nn.... | 12,567 | 36.404762 | 141 | py |
DCAP | DCAP-main/utils.py | import torch.nn.functional as F
import torch
def get_activation_fn(activation: str):
""" Returns the activation function corresponding to `activation` """
if activation == "relu":
return F.relu
# elif activation == "gelu":
# return gelu
# elif activation == "gelu_fast":
# depre... | 736 | 31.043478 | 81 | py |
DCAP | DCAP-main/attention_layer.py | import numpy as np
import torch
import torch.nn.functional as F
from torchfm.utils import get_activation_fn
class MultiheadAttentionInnerProduct(torch.nn.Module):
def __init__(self, num_fields, embed_dim, num_heads, dropout):
super().__init__()
self.num_fields = num_fields
self.mask = (to... | 14,427 | 40.45977 | 171 | py |
DCAP | DCAP-main/dataset/rapid.py | import math
import shutil
import struct
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class RapidAdvanceDataset(torch.utils.data.Dataset):
"""
MovieLens 1M Dataset
Data preparation... | 1,866 | 27.287879 | 88 | py |
DCAP | DCAP-main/dataset/avazu.py | import shutil
import struct
from collections import defaultdict
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class AvazuDataset(torch.utils.data.Dataset):
"""
Avazu Click-Through Rate Prediction Dataset
Dataset preparation
Remove the infre... | 4,268 | 41.267327 | 119 | py |
DCAP | DCAP-main/dataset/frappe.py | import numpy as np
import pandas as pd
import torch.utils.data
class FrappeDataset(torch.utils.data.Dataset):
"""
Frappe Dataset
Data preparation
treat apps with a rating less than 3 as negative samples
:param dataset_path: frappe dataset path
Reference:
https://?
"""
d... | 1,833 | 33.603774 | 144 | py |
DCAP | DCAP-main/dataset/criteo.py | import math
import shutil
import struct
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class CriteoDataset(torch.utils.data.Dataset):
"""
Criteo Display Advertising Challenge Dataset
... | 5,072 | 41.630252 | 120 | py |
DCAP | DCAP-main/dataset/movielens.py | import numpy as np
import pandas as pd
import torch.utils.data
class MovieLens20MDataset(torch.utils.data.Dataset):
"""
MovieLens 20M Dataset
Data preparation
treat samples with a rating less than 3 as negative samples
:param dataset_path: MovieLens dataset path
Reference:
https... | 2,695 | 32.7 | 103 | py |
DCAP | DCAP-main/model/dcn.py | import torch
from torchfm.layer import FeaturesEmbedding, CrossNetwork, MultiLayerPerceptron
class DeepCrossNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Deep & Cross Network.
Reference:
R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.
"""
def __init_... | 1,159 | 35.25 | 101 | py |
DCAP | DCAP-main/model/fnn.py | import torch
from torchfm.layer import FeaturesEmbedding, MultiLayerPerceptron
class FactorizationSupportedNeuralNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Neural Factorization Machine.
Reference:
W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study ... | 924 | 33.259259 | 121 | py |
DCAP | DCAP-main/model/ffm.py | import torch
from torchfm.layer import FeaturesLinear, FieldAwareFactorizationMachine
class FieldAwareFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Field-aware Factorization Machine.
Reference:
Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 20... | 809 | 30.153846 | 83 | py |
DCAP | DCAP-main/model/wd.py | import torch
from torchfm.layer import FeaturesLinear, MultiLayerPerceptron, FeaturesEmbedding
class WideAndDeepModel(torch.nn.Module):
"""
A pytorch implementation of wide and deep learning.
Reference:
HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.
"""
def __init_... | 931 | 32.285714 | 81 | py |
DCAP | DCAP-main/model/ncf.py | import torch
from torchfm.layer import FeaturesEmbedding, MultiLayerPerceptron
class NeuralCollaborativeFiltering(torch.nn.Module):
"""
A pytorch implementation of Neural Collaborative Filtering.
Reference:
X He, et al. Neural Collaborative Filtering, 2017.
"""
def __init__(self, field_d... | 1,248 | 35.735294 | 101 | py |
DCAP | DCAP-main/model/dcan.py | import torch
from torchfm.layer import (
FeaturesEmbedding,
FeaturesLinear,
MultiLayerPerceptron
)
from torchfm.attention_layer import CrossAttentionNetwork
class DeepCrossAttentionalNetworkModel(torch.nn.Module):
"""
A pytorch implementation of Multihead Attention Factorization Machine Model.
... | 2,471 | 40.2 | 120 | py |
DCAP | DCAP-main/model/afn.py | import math
import torch
import torch.nn.functional as F
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class LNN(torch.nn.Module):
"""
A pytorch implementation of LNN layer
Input shape
- A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Out... | 3,088 | 35.341176 | 107 | py |
DCAP | DCAP-main/model/fnfm.py | import torch
from torchfm.layer import FieldAwareFactorizationMachine, MultiLayerPerceptron, FeaturesLinear
class FieldAwareNeuralFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Field-aware Neural Factorization Machine.
Reference:
L Zhang, et al. Field-aware Neural Fa... | 1,251 | 38.125 | 105 | py |
DCAP | DCAP-main/model/dcap.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, CrossAttentionalProductNetwork, MultiLayerPerceptron
class DeepCrossAttentionalProductNetwork(torch.nn.Module):
"""
A pytorch implementation of inner/outer Product Neural Network.
Reference:
Y Qu, et al. Product-based Neura... | 2,887 | 46.344262 | 113 | py |
DCAP | DCAP-main/model/afi.py | import torch
import torch.nn.functional as F
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class AutomaticFeatureInteractionModel(torch.nn.Module):
"""
A pytorch implementation of AutoInt.
Reference:
W Song, et al. AutoInt: Automatic Feature Interaction Learni... | 2,157 | 43.040816 | 125 | py |
DCAP | DCAP-main/model/nfm.py | import torch
from torchfm.layer import FactorizationMachine, FeaturesEmbedding, MultiLayerPerceptron, FeaturesLinear
class NeuralFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Neural Factorization Machine.
Reference:
X He and TS Chua, Neural Factorization Machines fo... | 1,096 | 33.28125 | 103 | py |
DCAP | DCAP-main/model/hofm.py | import torch
from torchfm.layer import FeaturesLinear, FactorizationMachine, AnovaKernel, FeaturesEmbedding
class HighOrderFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Higher-Order Factorization Machines.
Reference:
M Blondel, et al. Higher-Order Factorization Mach... | 1,473 | 34.095238 | 94 | py |
DCAP | DCAP-main/model/pnn.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, InnerProductNetwork, \
OuterProductNetwork, MultiLayerPerceptron
class ProductNeuralNetworkModel(torch.nn.Module):
"""
A pytorch implementation of inner/outer Product Neural Network.
Reference:
Y Qu, et al. Product-base... | 1,421 | 37.432432 | 118 | py |
DCAP | DCAP-main/model/mhafm.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
from torchfm.attention_layer import CrossAttentionalProductNetwork
class MultiheadAttentionalFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Multihead Attention Factorization Machine Mod... | 2,488 | 45.092593 | 141 | py |
DCAP | DCAP-main/model/dfm.py | import torch
from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class DeepFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of DeepFM.
Reference:
H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR... | 1,049 | 35.206897 | 103 | py |
DCAP | DCAP-main/model/lr.py | import torch
from torchfm.layer import FeaturesLinear
class LogisticRegressionModel(torch.nn.Module):
"""
A pytorch implementation of Logistic Regression.
"""
def __init__(self, field_dims):
super().__init__()
self.linear = FeaturesLinear(field_dims)
def forward(self, x):
... | 461 | 22.1 | 66 | py |
DCAP | DCAP-main/model/xdfm.py | import torch
from torchfm.layer import CompressedInteractionNetwork, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron
class ExtremeDeepFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of xDeepFM.
Reference:
J Lian, et al. xDeepFM: Combining Explicit and Implicit Fe... | 1,157 | 38.931034 | 115 | py |
DCAP | DCAP-main/model/fm.py | import torch
from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear
class FactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Factorization Machine.
Reference:
S Rendle, Factorization Machines, 2010.
"""
def __init__(self, field_dims, e... | 746 | 27.730769 | 81 | py |
DCAP | DCAP-main/model/afm.py | import torch
from torchfm.layer import FeaturesEmbedding, FeaturesLinear, AttentionalFactorizationMachine
class AttentionalFactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Attentional Factorization Machine.
Reference:
J Xiao, et al. Attentional Factorization Machines: ... | 956 | 34.444444 | 132 | py |
NimPlant | NimPlant-main/NimPlant.py | #!/usr/bin/python3
# -----
#
# NimPlant - A light-weight stage 1 implant and C2 written in Nim and Python
# By Cas van Cooten (@chvancooten)
#
# This is a wrapper script to configure and generate NimPlant and its C2 server
#
# -----
import os
import random
import time
import toml
from pathlib import Path
from c... | 9,645 | 33.084806 | 124 | py |
NimPlant | NimPlant-main/client/dist/srdi/ShellcodeRDI.py | import sys
if sys.version_info < (3,0):
print("[!] Sorry, requires Python 3.x")
sys.exit(1)
import struct
from struct import pack
MACHINE_IA64=512
MACHINE_AMD64=34404
def is64BitDLL(bytes):
header_offset = struct.unpack("<L", bytes[60:64])[0]
machine = struct.unpack("<H", bytes[header_offset+4:h... | 29,801 | 135.706422 | 12,132 | py |
NimPlant | NimPlant-main/ui/build-ui.py | #!/usr/bin/python3
# -----
#
# NimPlant - A light-weight stage 1 implant and C2 written in Nim and Python
# By Cas van Cooten (@chvancooten)
#
# This is a helper script to build the Next.JS frontend
# and move it to the right directory for use with Nimplant.
# End-users should not need to use this script, un... | 1,091 | 21.285714 | 78 | py |
NimPlant | NimPlant-main/server/server.py | #!/usr/bin/python3
# -----
#
# NimPlant Server - The "C2-ish"™ handler for the NimPlant payload
# By Cas van Cooten (@chvancooten)
#
# -----
import threading
import time
from .api.server import api_server, server_ip, server_port
from .util.db import initDb, dbInitNewServer, dbPreviousServerSameConfig
from .util.... | 2,093 | 30.253731 | 133 | py |
NimPlant | NimPlant-main/server/__init__.py | 0 | 0 | 0 | py | |
NimPlant | NimPlant-main/server/api/server.py | from ..util.commands import getCommands, handleCommand
from ..util.config import config
from ..util.crypto import randString
from ..util.func import exitServer
from ..util.nimplant import np_server
from flask_cors import CORS
from gevent.pywsgi import WSGIServer
from server.util.db import *
from threading import Threa... | 7,245 | 35.969388 | 88 | py |
NimPlant | NimPlant-main/server/api/__init__.py | 0 | 0 | 0 | py | |
NimPlant | NimPlant-main/server/util/db.py | import sqlite3
from .config import config
from .func import timestamp, nimplantPrint
con = sqlite3.connect(
"server/nimplant.db", check_same_thread=False, detect_types=sqlite3.PARSE_DECLTYPES
)
# Use the Row type to allow easy conversions to dicts
con.row_factory = sqlite3.Row
# Handle bool as 1 (True) and 0 (Fa... | 14,703 | 33.516432 | 124 | py |
NimPlant | NimPlant-main/server/util/notify.py | import os
import requests
import urllib.parse
# This is a placeholder class for easy extensibility, more than anything
# You can easily add your own notification method below, and call it in the 'notify_user' function
# It will then be called when a new implant checks in, passing the NimPlant object (see nimplant.py)
... | 1,576 | 31.854167 | 100 | py |
NimPlant | NimPlant-main/server/util/listener.py | from .config import config
from .crypto import *
from .func import *
from .nimplant import *
from .notify import notify_user
from gevent.pywsgi import WSGIServer
from zlib import decompress, compress
import base64
import flask
import gzip
import hashlib
import io
import json
# Parse configuration from 'config.toml'
tr... | 14,535 | 40.89049 | 154 | py |
NimPlant | NimPlant-main/server/util/commands.py | from .func import log, nimplantPrint
from .nimplant import np_server
from yaml.loader import FullLoader
import shlex
import yaml
def getCommands():
with open("server/util/commands.yaml", "r") as f:
return sorted(yaml.load(f, Loader=FullLoader), key=lambda c: c["command"])
def getCommandList():
retur... | 4,762 | 28.583851 | 116 | py |
NimPlant | NimPlant-main/server/util/config.py | import os, sys, toml
# Parse server configuration
configPath = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), 'config.toml'))
config = toml.load(configPath) | 169 | 33 | 87 | py |
NimPlant | NimPlant-main/server/util/nimplant.py | import datetime, itertools, random, string
from re import T
from secrets import choice
from .config import config
from .func import *
from .db import *
# Parse configuration from 'config.toml'
try:
initialSleepTime = config["nimplant"]["sleepTime"]
initialSleepJitter = config["nimplant"]["sleepTime"]
killD... | 12,863 | 31.484848 | 106 | py |
NimPlant | NimPlant-main/server/util/__init__.py | 0 | 0 | 0 | py | |
NimPlant | NimPlant-main/server/util/func.py | from datetime import datetime
from struct import pack, calcsize
from time import sleep
from zlib import compress
import base64
import os, hashlib, json, sys
# Clear screen
def cls():
if os.name == "nt":
os.system("cls")
else:
os.system("clear")
# Timestamp function
timestampFormat = "%d/%m/%Y... | 16,749 | 28.334501 | 145 | py |
NimPlant | NimPlant-main/server/util/crypto.py | import base64, string, random
from Crypto.Cipher import AES
from Crypto.Util import Counter
# XOR function to transmit key securely. Matches nimplant XOR function in 'client/util/crypto.nim'
def xorString(value, key):
k = key
result = []
for c in value:
character = ord(c)
for f in [0, 8, 16... | 1,825 | 34.115385 | 98 | py |
NimPlant | NimPlant-main/server/util/input.py | import os
# Command history and command / path completion on Linux
if os.name == "posix":
import readline
from .commands import getCommandList
commands = getCommandList()
def list_folder(path):
if path.startswith(os.path.sep):
# absolute path
basedir = os.path.dirname(... | 2,423 | 30.076923 | 130 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/dataRGB.py | # -*- coding:utf-8 -*-
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
import glob
class dataProcess(object):
def __init__(self, out_rows, out_cols, data_path="./data/train/image", label_path="./data/train/label",
test_path="./data/test/image", testlabel_path="./d... | 5,060 | 37.340909 | 122 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/unetRGB.py | # -*- coding:utf-8 -*-
import os
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.c... | 15,916 | 42.135501 | 201 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/mask2str.py | # -*- coding:utf-8 -*-
# E.g. '1 3' implies starting at pixel 1 and running a total of 3 pixels (1,2,3).
# The pixels are numbered from top to bottom, then left to right: 1 is pixel (1,1), 2 is pixel (2,1), etc.
import cv2
import numpy as np
# test = np.array([[0,1,0],[1,0,1]])
# print(np.where(test.flatten(order='F'... | 637 | 28 | 106 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/test2mask2pic.py | # -*- coding:utf-8 -*-
from unetwsess import *
from data import *
myunet = myUnet()
model = myunet.get_unet()
model.load_weights('unet.hdf5')
# test2mask
imgs_train, imgs_mask_train, imgs_test, imgs_testlabels = myunet.load_data()
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
np.save('./results/... | 447 | 21.4 | 76 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/unetRGBD.py | # -*- coding:utf-8 -*-
import os
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.c... | 15,916 | 42.135501 | 202 | py |
CropRowDetection | CropRowDetection-main/unet-rgbd/dataRGBD.py | # -*- coding:utf-8 -*-
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
import glob
class dataProcess(object):
def __init__(self, out_rows, out_cols, data_path="./data/train/image", depth_path="./data/train/depth", label_path="./data/train/label",
test_path="./data... | 5,828 | 39.479167 | 158 | py |
Traffic-Benchmark | Traffic-Benchmark-master/train_benchmark.py | import os
import random
import numpy as np
import torch
# import setproctitle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model',type=str,default='DGCRN',help='model')
parser.add_argument('--data',type=str,default='METR-LA',help='dataset')
args = parser.parse_args()
model = args.model
da... | 6,887 | 46.833333 | 298 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/dcrnn_train_pytorch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
import setproctitle
setproctitle.setproctitle("stmetanet@lifuxian")
def main(args):... | 1,459 | 38.459459 | 129 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/run_demo_pytorch.py | import argparse
import numpy as np
import os
import sys
import yaml
from lib.utils import load_graph_data
from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config[... | 1,264 | 36.205882 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/dcrnn_train.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import yaml
from lib.utils import load_graph_data
from model.tf.dcrnn_supervisor import DCRNNSupervisor
def main(args):
with open(args.config_filename) as f:
... | 1,240 | 32.540541 | 104 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/run_demo.py | import argparse
import numpy as np
import os
import sys
import tensorflow as tf
import yaml
from lib.utils import load_graph_data
from model.tf.dcrnn_supervisor import DCRNNSupervisor
def run_dcrnn(args):
with open(args.config_filename) as f:
config = yaml.load(f)
tf_config = tf.ConfigProto()
if ... | 1,433 | 36.736842 | 108 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/scripts/generate_training_data.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import os
import pandas as pd
def generate_graph_seq2seq_io_data(
df, x_offsets, y_offsets, add_time_in_day=True, add_day_in_... | 3,904 | 30.491935 | 103 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/scripts/gen_adj_mx.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import pandas as pd
import pickle
def get_adjacency_matrix(distance_df, sensor_ids, normalized_k=0.1):
"""
:param distance_df: data frame with three columns: [from,... | 2,790 | 42.609375 | 125 | py |
Traffic-Benchmark | Traffic-Benchmark-master/methods/ST-MetaNet/scripts/eval_baseline_methods.py | import argparse
import numpy as np
import pandas as pd
from statsmodels.tsa.vector_ar.var_model import VAR
from lib import utils
from lib.metrics import masked_rmse_np, masked_mape_np, masked_mae_np
from lib.utils import StandardScaler
def historical_average_predict(df, period=12 * 24 * 7, test_ratio=0.2, null_val=... | 5,893 | 40.507042 | 116 | py |
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