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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partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/mutation_options.py | import argparse
import numpy as np
import random
import copy
import tequila as tq
from typing import Union
from collections import Counter
from time import time
from vqe_utils import convert_PQH_to_tq_QH, convert_tq_QH_to_PQH,\
fold_unitary_into_hamiltonian
from energy_optimization import minimi... | 26,780 | 33.962141 | 191 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/hacked_openfermion_qubit_operator.py | import tequila as tq
import sympy
import copy
#from param_hamiltonian import get_geometry, generate_ucc_ansatz
from hacked_openfermion_symbolic_operator import SymbolicOperator
# Define products of all Pauli operators for symbolic multiplication.
_PAULI_OPERATOR_PRODUCTS = {
('I', 'I'): (1., 'I'),
('I', 'X')... | 9,918 | 29.614198 | 85 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/HEA.py | import tequila as tq
import numpy as np
from tequila import gates as tq_g
from tequila.objective.objective import Variable
def generate_HEA(num_qubits, circuit_id=11, num_layers=1):
"""
This function generates different types of hardware efficient
circuits as in this paper
https://onlinelibrary.wiley.... | 18,425 | 45.530303 | 172 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/vqe_utils.py | import tequila as tq
import numpy as np
from hacked_openfermion_qubit_operator import ParamQubitHamiltonian
from openfermion import QubitOperator
from HEA import *
def get_ansatz_circuit(ansatz_type, geometry, basis_set=None, trotter_steps = 1, name=None, circuit_id=None,num_layers=1):
"""
This function gene... | 27,934 | 51.807183 | 162 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/n2/n2_serial_bl_2.25/hacked_openfermion_symbolic_operator.py | import abc
import copy
import itertools
import re
import warnings
import sympy
import tequila as tq
from tequila.objective.objective import Objective, Variable
from openfermion.config import EQ_TOLERANCE
COEFFICIENT_TYPES = (int, float, complex, sympy.Expr, Variable, Objective)
class SymbolicOperator(metaclass=ab... | 25,797 | 35.697013 | 97 | py |
pyIFD | pyIFD-main/test_pyifd.py | from tests.validate_algo import validate_algo
def test_adq1():
assert validate_algo('tests/data/168_image.jpg', 'tests/data/168_ADQ1.mat', 'ADQ1', 0.9) is True
def test_adq2():
assert validate_algo('tests/data/168_image.jpg', 'tests/data/168_ADQ2.mat', 'ADQ2', 0.9) is True
def test_adq3():
assert vali... | 1,836 | 28.629032 | 101 | py |
pyIFD | pyIFD-main/setup.py | from setuptools import find_packages, setup
setup(
name='pyIFD',
version='0.0.2',
extras_require=dict(tests=['pytest']),
packages=find_packages(where="src"),
package_dir={"": "src"},
include_package_data=True,
setup_requires=[
'cython','numpy'],
install_requires=[
'cytho... | 489 | 20.304348 | 43 | py |
pyIFD | pyIFD-main/src/__init__.py | 0 | 0 | 0 | py | |
pyIFD | pyIFD-main/src/pyIFD/BLK.py | """
This module provides the BLK algorithm
JPEG-block-artifact-based detector, solution 1.
Algorithm attribution:
Li, Weihai, Yuan Yuan, and Nenghai Yu. "Passive detection of doctored JPEG
image via block artifact grid extraction." Signal Processing 89, no. 9 (2009):
1821-1829.
Based on code from:
Zampoglou, M., Pap... | 5,382 | 31.823171 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/CAGI.py | """
This module provides the CAGI algorithm
JPEG-grid-alignment-abnormality-based detector.
Algorithm attribution:
Iakovidou, Chryssanthi, Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. "Content-aware detection of JPEG grid inconsistencies for intuitive image forensics." Journal of Visual Communicat... | 28,116 | 28.411088 | 251 | py |
pyIFD | pyIFD-main/src/pyIFD/GHOST.py | """
This module provides the GHOST algorithm
JPEG-block-artifact-based detector, solution 3 (leveraging JPEG ghosts).
Algorithm attribution:
Farid, Hany. "Exposing digital forgeries from JPEG ghosts." Information Forensics and Security, IEEE Transactions on 4, no. 1 (2009): 154-160.
Based on code from:
Zampoglou, M.... | 3,318 | 37.593023 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/NOI5.py | """
This module provides the NOI5 algorithm
Noise-variance-inconsistency detector, solution 5 (leveraging Principal Component Analysis).
Algorithm attribution:
H. Zeng, Y. Zhan, X. Kang, X. Lin, Image splicing localization using PCA-based
noise level estimation, Multimedia Tools & Applications, 2017.76(4):4783
http:/... | 13,151 | 33.25 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/NADQ.py | """
This module provides the NADQ algorithm
Aligned- and Non-aligned-double-JPEG-compression-based detector.
Algorithm attribution:
T.Bianchi, A.Piva, "Image Forgery Localization via Block-Grained
Analysis of JPEG Artifacts", IEEE Transactions on Information Forensics &
Security, vol. 7, no. 3, June 2012, pp. 100... | 13,218 | 36.028011 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/DCT.py | """
This module provides the DCT algorithm
JPEG-block-artifact-based detector, solution 2 (leveraging Discrete Cosine Transforms).
Algorithm attribution:
Ye, Shuiming, Qibin Sun, and Ee-Chien Chang. "Detecting digital image forgeries
by measuring inconsistencies of blocking artifact." In Multimedia and Expo, 2007
IEE... | 4,867 | 31.891892 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/NOI2.py | """
This module provides the NOI2 algorithm
Noise-variance-inconsistency detector, solution 2.
Algorithm attribution:
Lyu, Siwei, Xunyu Pan, and Xing Zhang. "Exposing region splicing forgeries
with blind local noise estimation." International Journal of Computer Vision
110, no. 2 (2014): 202-221.
Based on code from:... | 8,753 | 27.891089 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/NOI1.py | """
This module provides the NOI1 algorithm
Noise-variance-inconsistency detector, solution 1.
Algorithm attribution:
Mahdian, Babak, and Stanislav Saic. "Using noise inconsistencies for blind
image forensics." Image and Vision Computing 27, no. 10 (2009): 1497-1503.
Based on code from:
Zampoglou, M., Papadopoulos, ... | 1,717 | 33.36 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/ADQ1.py | """
This module provides the ADQ1 module.
Aligned-double-JPEG-compression-based detector, solution 1.
Algorithm attribution:
Lin, Zhouchen, Junfeng He, Xiaoou Tang, and Chi-Keung Tang. "Fast, automatic
and fine-grained tampered JPEG image detection via DCT coefficient analysis."
Pattern Recognition 42, no. 11 (2009):... | 14,409 | 39.706215 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/ELA.py | """
This module provides the ELA algorithm
Error-level-analysis-based detector.
Algorithm attribution:
Krawets, Neil. "A Picture's Worth: Digital Image Analysis and Forensics". Online
article on http://www.google.gr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiDg5_c07PLAhVpnXIKHUp8B5QQFgggMAA&u... | 1,647 | 36.454545 | 273 | py |
pyIFD | pyIFD-main/src/pyIFD/util.py | """
This file provides utility functions for pyIFD modules.
"""
import numpy as np
import math
import cv2
from scipy import signal
def minmaxpercent(o, p=0.05):
o = o[np.isfinite(o)]
a = 0
b = 0
p=0.01
if o.size==0:
a=0
b=1
else:
o = np.sort(o)
a = o[int(max(np.... | 8,099 | 29.451128 | 164 | py |
pyIFD | pyIFD-main/src/pyIFD/CFA2.py | """
This module provides the CFA2 algorithm
Color-filter-array-artifact-based detector, solution 2.
Algorithm attribution:
Dirik, Ahmet Emir, and Nasir D. Memon. "Image tamper detection based on
demosaicing artifacts." In ICIP, pp. 1497-1500. 2009.
Based on code from:
Zampoglou, M., Papadopoulos, S., & Kompatsiaris,... | 6,932 | 36.679348 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/ADQ2.py | """
This module provides the ADQ2 Algorithm
Aligned-double-JPEG-compression-based detector, solution 2.
Algorithm attribution:
T. Bianchi, A. De Rosa, and A. Piva, "IMPROVED DCT COEFFICIENT ANALYSIS
FOR FORGERY LOCALIZATION IN JPEG IMAGES", ICASSP 2011, Prague, Czech Republic,
2011, pp. 2444-2447.
Based on code from... | 11,148 | 34.733974 | 191 | py |
pyIFD | pyIFD-main/src/pyIFD/CFA1.py | """
This module provides the CFA1 Algorithm
Color-filter-array-artifact-based detector, solution 1.
Algorithm attribution:
P. Ferrara, T. Bianchi, A. De Rosa and P. Piva,
"Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts",
IEEE Transactions on Information Forensics & Security, vol. 7, no. 5,
Oc... | 7,738 | 28.765385 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/__init__.py | 0 | 0 | 0 | py | |
pyIFD | pyIFD-main/src/pyIFD/ADQ3.py | """
This module provides the ADQ3 algorithm
Aligned-double-JPEG-compression-based detector, solution 3.
Algorithm attribution:
Amerini, Irene, Rudy Becarelli, Roberto Caldelli, and Andrea Del Mastio.
"Splicing forgeries localization through the use of first digit features."
In Information Forensics and Security (WIFS... | 5,636 | 37.609589 | 187 | py |
pyIFD | pyIFD-main/src/pyIFD/NOI4.py | """
This module provides the NOI4 algorithm
Noise-variance-inconsistency detector, solution 4 (leveraging median filters).
Algorith attribution:
https://29a.ch/2015/08/21/noise-analysis-for-image-forensics
Based on code from:
Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2017). Large-scale evaluation of splic... | 1,286 | 27.6 | 187 | py |
pyIFD | pyIFD-main/tests/validate_algo.py | from pyIFD.ADQ1 import detectDQ
from pyIFD.ADQ2 import getJmap
from pyIFD.ADQ3 import BenfordDQ
from pyIFD.BLK import GetBlockGrid
from pyIFD.CAGI import CAGI
from pyIFD.CFA1 import CFA1
from pyIFD.CFA2 import CFA2
from pyIFD.DCT import DCT
from pyIFD.ELA import ELA
from pyIFD.GHOST import GHOST
from pyIFD.NADQ import ... | 12,335 | 29.309582 | 196 | py |
mt3 | mt3-main/setup.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 2,153 | 30.676471 | 74 | py |
mt3 | mt3-main/mt3/inference.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 4,386 | 30.561151 | 76 | py |
mt3 | mt3-main/mt3/vocabularies_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 4,164 | 35.217391 | 80 | py |
mt3 | mt3-main/mt3/run_length_encoding_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 3,362 | 30.138889 | 75 | py |
mt3 | mt3-main/mt3/spectral_ops.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 2,845 | 30.977528 | 78 | py |
mt3 | mt3-main/mt3/network.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 13,894 | 32.890244 | 80 | py |
mt3 | mt3-main/mt3/mixing.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 3,396 | 35.923913 | 80 | py |
mt3 | mt3-main/mt3/metrics_utils.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 7,437 | 36.756345 | 82 | py |
mt3 | mt3-main/mt3/spectrograms.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 2,330 | 27.084337 | 74 | py |
mt3 | mt3-main/mt3/layers.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 32,586 | 38.2142 | 157 | py |
mt3 | mt3-main/mt3/run_length_encoding.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 15,922 | 36.554245 | 93 | py |
mt3 | mt3-main/mt3/metrics_utils_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 8,271 | 30.815385 | 80 | py |
mt3 | mt3-main/mt3/event_codec_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 1,803 | 31.214286 | 74 | py |
mt3 | mt3-main/mt3/version.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 622 | 35.647059 | 74 | py |
mt3 | mt3-main/mt3/note_sequences.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 17,423 | 37.979866 | 90 | py |
mt3 | mt3-main/mt3/datasets.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 14,018 | 42.003067 | 127 | py |
mt3 | mt3-main/mt3/vocabularies.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 9,175 | 31.424028 | 80 | py |
mt3 | mt3-main/mt3/layers_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 21,675 | 38.699634 | 81 | py |
mt3 | mt3-main/mt3/models.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 5,990 | 38.156863 | 80 | py |
mt3 | mt3-main/mt3/metrics.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 15,414 | 38.223919 | 80 | py |
mt3 | mt3-main/mt3/__init__.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 1,052 | 29.970588 | 74 | py |
mt3 | mt3-main/mt3/summaries.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 18,358 | 37.896186 | 80 | py |
mt3 | mt3-main/mt3/tasks.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 14,610 | 35.255583 | 80 | py |
mt3 | mt3-main/mt3/note_sequences_test.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 19,150 | 36.847826 | 103 | py |
mt3 | mt3-main/mt3/preprocessors.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 23,632 | 34.273134 | 80 | py |
mt3 | mt3-main/mt3/event_codec.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 3,898 | 33.504425 | 80 | py |
mt3 | mt3-main/mt3/scripts/extract_monophonic_examples.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 8,754 | 33.742063 | 80 | py |
mt3 | mt3-main/mt3/scripts/dump_task.py | # Copyright 2023 The MT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 2,485 | 29.691358 | 79 | py |
FairAC | FairAC-main/src/utils.py | #%%
import numpy as np
import scipy.sparse as sp
import torch
import os
import pandas as pd
import dgl
def encode_onehot(labels):
classes = set(labels)
classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
enumerate(classes)}
labels_onehot = np.array(list(map(classes_dict.get, l... | 8,676 | 33.84739 | 135 | py |
FairAC | FairAC-main/src/train_fairAC_GNN_report.py | import time
import argparse
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn.functional as F
from utils import accuracy, load_pokec
from models.FairAC import FairAC2, GNN
def parser_args():
# Training settings
parser = argparse.ArgumentParser()
... | 21,680 | 50.376777 | 162 | py |
FairAC | FairAC-main/src/models/HGNN_AC.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class HGNN_AC(nn.Module):
def __init__(self, in_dim, hidden_dim, dropout, activation, num_heads, cuda=False):
super(HGNN_AC, self).__init__()
self.dropout = dropout
self.attentions = [AttentionLayer(in_di... | 2,074 | 38.903846 | 115 | py |
FairAC | FairAC-main/src/models/GCN.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GraphConv
class GCN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(GCN, self).__init__()
self.body = GCN_Body(nfeat,nhid,dropout)
self.fc = nn.Linear(nhid,nclass)
def forward(self, g, ... | 830 | 22.742857 | 53 | py |
FairAC | FairAC-main/src/models/FairGNN.py | import random
import torch.nn as nn
from .GCN import GCN,GCN_Body
from .GAT import GAT,GAT_body
from .SAGE import SAGE_Body
from .HGNN_AC import HGNN_AC
import torch
import torch.nn.functional as F
import numpy as np
def get_model(nfeat, args):
if args.model == "GCN":
model = GCN_Body(nfeat,args.num_hidd... | 10,512 | 39.279693 | 159 | py |
FairAC | FairAC-main/src/models/FairAC.py | import random
import torch.nn as nn
from .GCN import GCN,GCN_Body
from .GAT import GAT,GAT_body
from .SAGE import SAGE_Body
from .HGNN_AC import HGNN_AC
import torch
import torch.nn.functional as F
import numpy as np
def get_model(nfeat, args):
if args.model == "GCN":
model = GCN_Body(nfeat,args.num_hidd... | 6,883 | 40.97561 | 131 | py |
FairAC | FairAC-main/src/models/SAGE.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import SAGEConv
class SAGE(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(SAGE, self).__init__()
self.body = SAGE_Body(nfeat,nhid,dropout)
self.fc = nn.Linear(nhid,nclass)
def forward(self, g... | 848 | 23.257143 | 53 | py |
FairAC | FairAC-main/src/models/__init__.py | from .GCN import *
from .GAT import *
from .HGNN_AC import *
from .FairGNN import *
from .SAGE import * | 103 | 19.8 | 22 | py |
FairAC | FairAC-main/src/models/GAT.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GATConv
class GAT_body(nn.Module):
def __init__(self,
num_layers,
in_dim,
num_hidden,
heads,
feat_drop,
attn_drop,
... | 2,108 | 33.57377 | 115 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/setup.py | # -*- coding: utf-8 -*-
from distutils.core import setup
LONGDOC = """
Engine of Chinese Input Method.
Please go to https://github.com/someus/Pinyin2Hanzi for more info.
具体使用请移步 https://github.com/someus/Pinyin2Hanzi 。
"""
setup(
name='Pinyin2Hanzi',
version='0.1.1',
description='拼音转汉字， Engine of Chinese... | 1,164 | 31.361111 | 66 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/dag.py | # coding: utf-8
from __future__ import (print_function, unicode_literals, absolute_import)
from .interface import AbstractDagParams
from .priorityset import PrioritySet
from .util import xrange
import math
def dag(dag_params, pinyin_list, path_num=6, log=False):
assert( isinstance(dag_params, AbstractDagParams) ... | 1,575 | 27.654545 | 85 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/implement.py | # coding: utf-8
from __future__ import (print_function, unicode_literals, absolute_import)
from .interface import AbstractHmmParams, AbstractDagParams
from .util import as_text
import os
import json
DATA = 'data'
DEFAULT = 'default'
class DefaultHmmParams(AbstractHmmParams):
def __init__(self,):
cur... | 3,400 | 27.341667 | 102 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/priorityset.py | # coding: utf-8
import heapq
class Item(object):
def __init__(self, score, path):
self.__score = score
self.__path = path
@property
def score(self):
return self.__score
@property
def path(self):
return self.__path
def __lt__(self, other):
return sel... | 1,553 | 20.583333 | 74 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/viterbi.py | # coding: utf-8
from __future__ import (print_function, unicode_literals, absolute_import)
from .interface import AbstractHmmParams
from .priorityset import PrioritySet
import math
def viterbi(hmm_params, observations, path_num=6, log=False, min_prob=3.14e-200):
assert( isinstance(hmm_params, AbstractHmmParams) ... | 2,243 | 33.523077 | 85 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/util.py | # coding: utf-8
from __future__ import (print_function, unicode_literals, absolute_import)
import os
import sys
try:
reload(sys)
sys.setdefaultencoding('utf-8')
except:
pass
PY2 = sys.version_info[0] == 2
if not PY2:
# Python 3.x and up
xrange = range
def as_text(v): ## 生成unicode字符串
... | 6,251 | 30.736041 | 114 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/__init__.py | from __future__ import absolute_import
from .interface import AbstractHmmParams, AbstractDagParams
from .implement import DefaultHmmParams, DefaultDagParams
from .priorityset import Item, PrioritySet
from .util import is_chinese, remove_tone, normlize_pinyin, simplify_pinyin, is_pinyin, all_pinyin
from .dag import d... | 351 | 34.2 | 98 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/Pinyin2Hanzi/interface.py | # coding: utf-8
class AbstractHmmParams(object):
def start(self, state):
''' get start prob of state(hanzi) '''
pass
def emission(self, state, observation):
''' state (hanzi) -> observation (pinyin) '''
pass
def transition(self, from_state, to_state):
''' stat... | 547 | 21.833333 | 54 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/dag/gen_phrase.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
import json
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
from ChineseTone import PinyinHelper, PinyinFormat
import jieba
def cut(s):
return jieba.cut(s, cut_all=False)
def writejson2file(obj, filename):
w... | 2,024 | 27.125 | 77 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/dag/gen_finally.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
import json
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
def writejson2file(obj, filename):
with open(filename, 'w') as outfile:
data = json.dumps(obj, indent=4, sort_keys=True)
outfile.write(d... | 2,429 | 26.931034 | 89 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/dag/gen_char.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
import json
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
pinyin2hanzi_file = '../hmm/result/pinyin2hanzi.txt'
base_emission_file = '../hmm/result/base_emission.json'
output_file = './result/dag_char.json'
def w... | 1,904 | 24.065789 | 66 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/hmm/process_article.py | # coding: utf-8
'''
从文章中提取句子，放到sentence.txt中
'''
from __future__ import (print_function, unicode_literals)
import os
import sys
import json
import pypinyin
import argparse
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
try:
reload(sys)
sys.setdefaultencoding('utf-8')
except:
pass
ARTICL... | 1,822 | 20.963855 | 63 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/hmm/process_finally.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import os
import sys
import json
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
try:
reload(sys)
sys.setdefaultencoding('utf-8')
except:
pass
BASE_START_FILE = './result/base_start.json'
BASE_EMISSION_FILE ... | 3,619 | 26.218045 | 93 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/hmm/gen_base.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import os
import sys
import json
from ChineseTone import PinyinHelper
import argparse
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
try:
reload(sys)
sys.setdefaultencoding('utf-8')
except:
pass
SENTENCE_FILE ... | 4,871 | 25.193548 | 123 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/train/hmm/process_hzpy.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import os
import sys
sys.path = ['../..'] + sys.path
from Pinyin2Hanzi import util
try:
reload(sys)
sys.setdefaultencoding('utf-8')
except:
pass
SOURCE_FILE = './hanzipinyin.txt'
ALL_STATES_FILE = './result/all_s... | 1,399 | 22.728814 | 89 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/example/pinyin_list.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
sys.path.append('..')
from Pinyin2Hanzi import all_pinyin
from Pinyin2Hanzi import DefaultDagParams
dagparams = DefaultDagParams()
for py in all_pinyin():
if len(dagparams.get_phrase([py]) ) == 0:
print(py)
print( dag... | 387 | 19.421053 | 57 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/example/viterbi_health_fever.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
sys.path.append('..')
from Pinyin2Hanzi import AbstractHmmParams
from Pinyin2Hanzi import viterbi
class HmmParams(AbstractHmmParams):
def __init__(self,):
self.states = ('Healthy', 'Fever')
self.observations =... | 2,383 | 29.177215 | 110 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/example/viterbi_pinyin2hanzi.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
sys.path.append('..')
from Pinyin2Hanzi import DefaultHmmParams
from Pinyin2Hanzi import viterbi
hmmparams = DefaultHmmParams()
result = viterbi(hmm_params=hmmparams, observations=('ni', 'hao', 'a'), path_num = 5, log = True)
for i... | 2,026 | 27.957143 | 114 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/example/dag_pinyin2hanzi_2.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
sys.path.append('..')
from Pinyin2Hanzi import DefaultDagParams
from Pinyin2Hanzi import dag
dagparams = DefaultDagParams()
print( dag(dagparams, [u'ti', u'chu', u'le', u'jie', u'jve', u'fang', u'an'], path_num=1) )
print( dag(dag... | 541 | 22.565217 | 91 | py |
Pinyin2Hanzi | Pinyin2Hanzi-master/example/dag_pinyin2hanzi.py | # coding: utf-8
from __future__ import (print_function, unicode_literals)
import sys
sys.path.append('..')
from Pinyin2Hanzi import DefaultDagParams
from Pinyin2Hanzi import dag
dagparams = DefaultDagParams()
result = dag(dagparams, ['wo'])
for item in result:
print(item.score, '/'.join(item.path))
print(20*'... | 1,477 | 22.83871 | 91 | py |
Few-shot-WSI | Few-shot-WSI-master/setup.py | #!/usr/bin/env python
import os
import subprocess
import time
from setuptools import find_packages, setup
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
MAJOR = 0
MINOR = 3
PATCH = 0
SUFFIX = ''
if PATCH != '':
SHORT_VERSION = '{}.{}.{}{}'.format(M... | 6,047 | 30.5 | 125 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/test.py | import argparse
import importlib
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup.datasets import build_dataloader, build_dataset
from openselfsup.... | 3,944 | 31.073171 | 83 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/publish_model.py | import argparse
import subprocess
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
args = parser.parse_args()
return args
def process_checkpoint(in_file):
tmp_file = in... | 898 | 25.441176 | 68 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/extract.py | import argparse
import importlib
import numpy as np
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup.utils import dist_forward_collect, nondist_for... | 6,703 | 35.63388 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/upgrade_models.py | import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'--save-path', type=str, required=True, help='destination file name')
args = parser.parse_args()
return args
def main():
ar... | 712 | 24.464286 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/extract_backbone_weights.py | import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser(
description='This script extracts backbone weights from a checkpoint')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'output', type=str, help='destination file name')
args ... | 952 | 28.78125 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/train.py | from __future__ import division
import argparse
import importlib
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from openselfsup import __version__
from openselfsup.apis import set_random_seed, train_model
from openselfsup.datasets import... | 5,150 | 33.112583 | 86 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/count_parameters.py | import argparse
from mmcv import Config
from openselfsup.models import build_model
def parse_args():
parser = argparse.ArgumentParser(description='Train a model')
parser.add_argument('config', help='train config file path')
args = parser.parse_args()
return args
def main():
args = parse_args()
... | 1,201 | 29.820513 | 83 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/prepare_data/convert_subset.py | """
SimCLR provides list files for semi-supervised benchmarks:
https://github.com/google-research/simclr/tree/master/imagenet_subsets/
This script convert the list files into the required format in OpenSelfSup.
"""
import argparse
parser = argparse.ArgumentParser(
description='Convert ImageNet subset lists provide... | 1,162 | 31.305556 | 75 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/prepare_data/create_voc_data_files.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
################################################################################
"""
This script can be used to extract th... | 7,741 | 38.907216 | 80 | py |
Few-shot-WSI | Few-shot-WSI-master/tools/prepare_data/create_voc_low_shot_challenge_samples.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
################################################################################
"""
This script is used to create the low... | 4,741 | 34.924242 | 80 | py |
Few-shot-WSI | Few-shot-WSI-master/wsi_workdir/dict_construction.py | import numpy as np
from openselfsup.third_party import clustering
from scipy.spatial.distance import cdist
import os
import warnings
import time
import pickle as pkl
from sklearn.neighbors import KNeighborsClassifier
from scipy.special import softmax
import argparse
Kmeans = clustering.__dict__['Kmeans']
pth = 'wsi_w... | 3,061 | 39.289474 | 111 | py |
Few-shot-WSI | Few-shot-WSI-master/wsi_workdir/distributed_meta_test.py | import argparse
import datetime
import scipy
import numpy as np
from scipy.stats import t
from sklearn.linear_model import RidgeClassifier, LogisticRegression
from sklearn.neighbors import NearestCentroid
from sklearn.metrics import f1_score
from scipy.spatial.distance import cdist
from tqdm.contrib.concurrent import p... | 6,809 | 43.220779 | 144 | py |
Few-shot-WSI | Few-shot-WSI-master/wsi_workdir/extract.py | import argparse
import importlib
import numpy as np
import os
import os.path as osp
import time
from tqdm import trange,tqdm
import threading
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup... | 3,789 | 29.564516 | 109 | py |
Few-shot-WSI | Few-shot-WSI-master/wsi_workdir/tools/generate_aug_NCT78_task.py | import numpy as np
import argparse
import os
import warnings
import threading
from tqdm import tqdm
warnings.filterwarnings("ignore")
def aug_NCT78_task(out_dir, task_ids, num_shots, options):
task_name = f'9-way-{num_shots}-shot'
out_dir = f'{out_dir}/NCT_78_aug'
for _ in tqdm(range(len(task_ids))):
... | 5,792 | 40.378571 | 144 | py |
Few-shot-WSI | Few-shot-WSI-master/wsi_workdir/tools/generate_task.py | import numpy as np
import argparse
import os
import warnings
import threading
from tqdm import tqdm
warnings.filterwarnings("ignore")
def generate_near_domain_task(out_dir, task_ids, num_shots, options):
out_dir = f'{out_dir}/near'
nv = options['novel_class']
if options['initialization'] or options['over... | 13,175 | 46.225806 | 144 | py |
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