Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
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lightly | lightly-master/tests/loss/test_VICRegLLoss.py | import unittest
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from lightly.loss import VICRegLLoss
class TestVICRegLLoss(unittest.TestCase):
def test_forward(self) -> None:
torch.manual_seed(0)
criterion = VICRegLLoss()
g... | 6,906 | 36.134409 | 116 | py |
lightly | lightly-master/tests/loss/test_VICRegLoss.py | import unittest
import pytest
import torch
import torch.nn.functional as F
from pytest_mock import MockerFixture
from torch import Tensor
from torch import distributed as dist
from lightly.loss import VICRegLoss
class TestVICRegLoss:
def test__gather_distributed(self, mocker: MockerFixture) -> None:
moc... | 5,386 | 32.459627 | 116 | py |
lightly | lightly-master/tests/loss/test_barlow_twins_loss.py | import pytest
from pytest_mock import MockerFixture
from torch import distributed as dist
from lightly.loss.barlow_twins_loss import BarlowTwinsLoss
class TestBarlowTwinsLoss:
def test__gather_distributed(self, mocker: MockerFixture) -> None:
mock_is_available = mocker.patch.object(dist, "is_available", ... | 792 | 33.478261 | 88 | py |
lightly | lightly-master/tests/models/test_ModelUtils.py | import copy
import unittest
import torch
import torch.nn as nn
from lightly.models import utils
from lightly.models.utils import (
_no_grad_trunc_normal,
activate_requires_grad,
batch_shuffle,
batch_unshuffle,
deactivate_requires_grad,
nearest_neighbors,
normalize_weight,
update_moment... | 13,742 | 35.357143 | 87 | py |
lightly | lightly-master/tests/models/test_ModelsBYOL.py | import unittest
import torch
import torch.nn as nn
import torchvision
import lightly
from lightly.models import BYOL, ResNetGenerator
def get_backbone(resnet, num_ftrs=64):
last_conv_channels = list(resnet.children())[-1].in_features
backbone = nn.Sequential(
lightly.models.batchnorm.get_norm_layer(... | 4,235 | 35.205128 | 87 | py |
lightly | lightly-master/tests/models/test_ModelsMoCo.py | import unittest
import torch
import torch.nn as nn
import torchvision
import lightly
from lightly.models import MoCo, ResNetGenerator
def get_backbone(resnet, num_ftrs=64):
last_conv_channels = list(resnet.children())[-1].in_features
backbone = nn.Sequential(
lightly.models.batchnorm.get_norm_layer(... | 4,132 | 36.572727 | 82 | py |
lightly | lightly-master/tests/models/test_ModelsNNCLR.py | import unittest
import torch
import torch.nn as nn
import torchvision
from lightly.models import NNCLR
from lightly.models.modules import NNMemoryBankModule
def resnet_generator(name: str):
if name == "resnet18":
return torchvision.models.resnet18()
elif name == "resnet50":
return torchvisio... | 5,345 | 41.094488 | 83 | py |
lightly | lightly-master/tests/models/test_ModelsSimCLR.py | import unittest
import torch
import torch.nn as nn
import torchvision
import lightly
from lightly.models import ResNetGenerator, SimCLR
def get_backbone(resnet, num_ftrs=64):
last_conv_channels = list(resnet.children())[-1].in_features
backbone = nn.Sequential(
lightly.models.batchnorm.get_norm_laye... | 4,146 | 36.7 | 82 | py |
lightly | lightly-master/tests/models/test_ModelsSimSiam.py | import unittest
import torch
import torch.nn as nn
import torchvision
from lightly.models import SimSiam
def resnet_generator(name: str):
if name == "resnet18":
return torchvision.models.resnet18()
elif name == "resnet50":
return torchvision.models.resnet50()
raise NotImplementedError
... | 4,483 | 40.137615 | 83 | py |
lightly | lightly-master/tests/models/test_ProjectionHeads.py | import unittest
import torch
import lightly
from lightly.models.modules.heads import (
BarlowTwinsProjectionHead,
BYOLPredictionHead,
BYOLProjectionHead,
DINOProjectionHead,
MoCoProjectionHead,
MSNProjectionHead,
NNCLRPredictionHead,
NNCLRProjectionHead,
SimCLRProjectionHead,
S... | 12,093 | 42.503597 | 87 | py |
lightly | lightly-master/tests/models/modules/test_masked_autoencoder.py | import unittest
import torch
import torchvision
from lightly import _torchvision_vit_available
from lightly.models import utils
if _torchvision_vit_available:
from lightly.models.modules import MAEBackbone, MAEDecoder, MAEEncoder
@unittest.skipUnless(_torchvision_vit_available, "Torchvision ViT not available")... | 4,602 | 34.407692 | 85 | py |
lightly | lightly-master/tests/transforms/test_GaussianBlur.py | import unittest
from PIL import Image
from lightly.transforms import GaussianBlur
class TestGaussianBlur(unittest.TestCase):
def test_on_pil_image(self):
for w in range(1, 100):
for h in range(1, 100):
gaussian_blur = GaussianBlur()
sample = Image.new("RGB", (... | 651 | 27.347826 | 51 | py |
lightly | lightly-master/tests/transforms/test_Jigsaw.py | import unittest
from PIL import Image
from lightly.transforms import Jigsaw
class TestJigsaw(unittest.TestCase):
def test_on_pil_image(self):
crop = Jigsaw()
sample = Image.new("RGB", (255, 255))
crop(sample)
| 241 | 17.615385 | 45 | py |
lightly | lightly-master/tests/transforms/test_Solarize.py | import unittest
from PIL import Image
from lightly.transforms.solarize import RandomSolarization
class TestRandomSolarization(unittest.TestCase):
def test_on_pil_image(self):
for w in [32, 64, 128]:
for h in [32, 64, 128]:
solarization = RandomSolarization(0.5)
... | 393 | 25.266667 | 58 | py |
lightly | lightly-master/tests/transforms/test_dino_transform.py | from PIL import Image
from lightly.transforms.dino_transform import DINOTransform, DINOViewTransform
def test_view_on_pil_image():
single_view_transform = DINOViewTransform(crop_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 32, 32)
... | 710 | 31.318182 | 80 | py |
lightly | lightly-master/tests/transforms/test_fastsiam_transform.py | from PIL import Image
from lightly.transforms.fast_siam_transform import FastSiamTransform
def test_multi_view_on_pil_image():
multi_view_transform = FastSiamTransform(num_views=3, input_size=32)
sample = Image.new("RGB", (100, 100))
output = multi_view_transform(sample)
assert len(output) == 3
a... | 441 | 30.571429 | 72 | py |
lightly | lightly-master/tests/transforms/test_location_to_NxN_grid.py | import torch
import lightly.transforms.random_crop_and_flip_with_grid as test_module
def test_location_to_NxN_grid():
# create a test instance of the Location class
test_location = test_module.Location(
left=10,
top=20,
width=100,
height=200,
image_height=244,
... | 1,105 | 30.6 | 77 | py |
lightly | lightly-master/tests/transforms/test_mae_transform.py | from PIL import Image
from lightly.transforms.mae_transform import MAETransform
def test_multi_view_on_pil_image():
multi_view_transform = MAETransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = multi_view_transform(sample)
assert len(output) == 1
assert output[0].shape == (3, ... | 328 | 26.416667 | 57 | py |
lightly | lightly-master/tests/transforms/test_moco_transform.py | from PIL import Image
from lightly.transforms.moco_transform import MoCoV1Transform, MoCoV2Transform
def test_moco_v1_multi_view_on_pil_image():
multi_view_transform = MoCoV1Transform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = multi_view_transform(sample)
assert len(output) == 2
... | 702 | 30.954545 | 78 | py |
lightly | lightly-master/tests/transforms/test_msn_transform.py | from PIL import Image
from lightly.transforms.msn_transform import MSNTransform, MSNViewTransform
def test_view_on_pil_image():
single_view_transform = MSNViewTransform(crop_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 32, 32)
def ... | 696 | 30.681818 | 75 | py |
lightly | lightly-master/tests/transforms/test_multi_crop_transform.py | from lightly.transforms.multi_crop_transform import MultiCropTranform
| 70 | 34.5 | 69 | py |
lightly | lightly-master/tests/transforms/test_multi_view_transform.py | import unittest
import torchvision.transforms as T
from PIL import Image
from lightly.transforms.multi_view_transform import MultiViewTransform
def test_multi_view_on_pil_image():
multi_view_transform = MultiViewTransform(
[
T.RandomHorizontalFlip(p=0.1),
T.RandomVerticalFlip(p=0... | 489 | 23.5 | 70 | py |
lightly | lightly-master/tests/transforms/test_pirl_transform.py | from PIL import Image
from lightly.transforms.pirl_transform import PIRLTransform
def test_multi_view_on_pil_image():
multi_view_transform = PIRLTransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = multi_view_transform(sample)
assert len(output) == 2
assert output[0].shape == (... | 376 | 28 | 59 | py |
lightly | lightly-master/tests/transforms/test_rotation.py | from PIL import Image
from lightly.transforms.rotation import (
RandomRotate,
RandomRotateDegrees,
random_rotation_transform,
)
def test_RandomRotate_on_pil_image():
random_rotate = RandomRotate()
sample = Image.new("RGB", (100, 100))
random_rotate(sample)
def test_RandomRotateDegrees_on_pi... | 941 | 30.4 | 75 | py |
lightly | lightly-master/tests/transforms/test_simclr_transform.py | from PIL import Image
from lightly.transforms.simclr_transform import SimCLRTransform, SimCLRViewTransform
def test_view_on_pil_image():
single_view_transform = SimCLRViewTransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 3... | 619 | 30 | 84 | py |
lightly | lightly-master/tests/transforms/test_simsiam_transform.py | from PIL import Image
from lightly.transforms.simsiam_transform import SimSiamTransform, SimSiamViewTransform
def test_view_on_pil_image():
single_view_transform = SimSiamViewTransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (... | 624 | 30.25 | 87 | py |
lightly | lightly-master/tests/transforms/test_smog_transform.py | from PIL import Image
from lightly.transforms.smog_transform import SMoGTransform, SmoGViewTransform
def test_view_on_pil_image():
single_view_transform = SmoGViewTransform(crop_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 32, 32)
... | 653 | 31.7 | 78 | py |
lightly | lightly-master/tests/transforms/test_swav_transform.py | from PIL import Image
from lightly.transforms.swav_transform import SwaVTransform, SwaVViewTransform
def test_view_on_pil_image():
single_view_transform = SwaVViewTransform()
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 100, 100)
def test_m... | 643 | 31.2 | 78 | py |
lightly | lightly-master/tests/transforms/test_vicreg_transform.py | from PIL import Image
from lightly.transforms.vicreg_transform import VICRegTransform, VICRegViewTransform
def test_view_on_pil_image():
single_view_transform = VICRegViewTransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 3... | 619 | 30 | 84 | py |
lightly | lightly-master/tests/transforms/test_vicregl_transform.py | from PIL import Image
from lightly.transforms.vicregl_transform import VICRegLTransform, VICRegLViewTransform
def test_view_on_pil_image():
single_view_transform = VICRegLViewTransform()
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.shape == (3, 100, 100)
... | 1,086 | 32.96875 | 87 | py |
lightly | lightly-master/tests/utils/__init__.py | 0 | 0 | 0 | py | |
lightly | lightly-master/tests/utils/test_debug.py | import math
import unittest
import numpy as np
import torch
from PIL import Image
from lightly.data import collate
from lightly.utils import debug
try:
import matplotlib.pyplot as plt
MATPLOTLIB_AVAILABLE = True
except ImportError:
MATPLOTLIB_AVAILABLE = False
BATCH_SIZE = 10
DIMENSION = 10
class Tes... | 3,075 | 35.619048 | 85 | py |
lightly | lightly-master/tests/utils/test_dist.py | import unittest
from unittest import mock
import torch
from lightly.utils import dist
class TestDist(unittest.TestCase):
def test_eye_rank_undist(self):
self.assertTrue(torch.all(dist.eye_rank(3) == torch.eye(3)))
def test_eye_rank_dist(self):
n = 3
zeros = torch.zeros((n, n)).bool(... | 1,158 | 33.088235 | 76 | py |
lightly | lightly-master/tests/utils/test_io.py | import csv
import json
import sys
import tempfile
import unittest
import numpy as np
from lightly.utils.io import (
check_embeddings,
check_filenames,
save_custom_metadata,
save_embeddings,
save_schema,
save_tasks,
)
from tests.api_workflow.mocked_api_workflow_client import (
MockedApiWork... | 6,311 | 36.129412 | 88 | py |
lightly | lightly-master/tests/utils/test_scheduler.py | import unittest
import torch
from torch import nn
from lightly.utils.scheduler import CosineWarmupScheduler, cosine_schedule
class TestScheduler(unittest.TestCase):
def test_cosine_schedule(self):
self.assertAlmostEqual(cosine_schedule(1, 10, 0.99, 1.0), 0.99030154, 6)
self.assertAlmostEqual(cos... | 2,304 | 34.461538 | 81 | py |
lightly | lightly-master/tests/utils/test_version_compare.py | import unittest
from lightly.utils import version_compare
class TestVersionCompare(unittest.TestCase):
def test_valid_versions(self):
# general test of smaller than version numbers
self.assertEqual(version_compare.version_compare("0.1.4", "1.2.0"), -1)
self.assertEqual(version_compare.ver... | 1,259 | 36.058824 | 88 | py |
lightly | lightly-master/tests/utils/benchmarking/__init__.py | 0 | 0 | 0 | py | |
lightly | lightly-master/tests/utils/benchmarking/test_benchmark_module.py | import unittest
import torch
from pytorch_lightning import Trainer
from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
from torch.optim import SGD
from torch.utils.data import DataLoader
from torchvision.datasets import FakeData
from torchvision.transforms import ToTensor
from lightly.data import Light... | 2,840 | 32.034884 | 84 | py |
lightly | lightly-master/tests/utils/benchmarking/test_knn.py | import torch
import torch.nn.functional as F
from lightly.utils.benchmarking import knn
def test_knn() -> None:
feature_bank = torch.tensor(
[
[1.0, 1.0, 1.0],
[-1.0, 1.0, 1.0],
[-1.0, -1.0, 1.0],
[-1.0, -1.0, -1.0],
]
).t()
feature_labels =... | 1,832 | 25.185714 | 91 | py |
lightly | lightly-master/tests/utils/benchmarking/test_knn_classifier.py | from typing import Tuple
import pytest
import torch
from pytorch_lightning import Trainer
from torch import Tensor, nn
from torch.utils.data import DataLoader, Dataset
from lightly.utils.benchmarking import KNNClassifier
class TestKNNClassifier:
def test(self) -> None:
# Define 4 training points from 4 ... | 5,630 | 41.659091 | 88 | py |
lightly | lightly-master/tests/utils/benchmarking/test_linear_classifier.py | import torch
from pytorch_lightning import Trainer
from torch import nn
from torch.utils.data import DataLoader
from torchvision.datasets import FakeData
from torchvision.transforms import ToTensor
from lightly.utils.benchmarking import LinearClassifier
class TestLinearClassifier:
def test__finetune(self) -> Non... | 4,403 | 41.346154 | 88 | py |
lightly | lightly-master/tests/utils/benchmarking/test_metric_callback.py | import torch
from pytorch_lightning import LightningModule, Trainer
from torch.utils.data import DataLoader
from torchvision.datasets import FakeData
from torchvision.transforms import ToTensor
from lightly.utils.benchmarking import MetricCallback
class TestMetricCallback:
def test(self) -> None:
callbac... | 1,673 | 36.2 | 81 | py |
lightly | lightly-master/tests/utils/benchmarking/test_online_linear_classifier.py | import pytest
import torch
from pytorch_lightning import LightningModule, Trainer
from torch import Tensor, nn
from torch.optim import SGD
from torch.utils.data import DataLoader
from torchvision.datasets import FakeData
from torchvision.transforms import ToTensor
from lightly.utils.benchmarking import OnlineLinearCla... | 2,948 | 36.807692 | 85 | py |
lightly | lightly-master/tests/utils/benchmarking/test_topk.py | import torch
from lightly.utils.benchmarking import topk
def test_mean_topk_accuracy() -> None:
predicted_classes = torch.tensor(
[
[1, 2, 3, 4],
[4, 1, 10, 0],
[3, 1, 5, 8],
]
)
targets = torch.tensor([1, 10, 8])
assert topk.mean_topk_accuracy(pred... | 460 | 19.954545 | 86 | py |
BioNEV | BioNEV-master/README.md | # BioNEV (Biomedical Network Embedding Evaluation)
## 1. Introduction
This repository contains source code and datasets for paper ["Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations"](https://arxiv.org/pdf/1906.05017.pdf) (accepted by **Bioinformatics**). This work aims to systematically e... | 10,080 | 57.953216 | 513 | md |
BioNEV | BioNEV-master/setup.py | # -*- coding: utf-8 -*-
"""Setup module."""
import setuptools
if __name__ == '__main__':
setuptools.setup()
| 115 | 11.888889 | 26 | py |
BioNEV | BioNEV-master/src/bionev/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
BioNEV | BioNEV-master/src/bionev/__main__.py | # -*- coding: utf-8 -*-
"""Entrypoint module, in case you use ``python -m bionev``.
Why does this file exist, and why ``__main__``? For more info, read:
- https://www.python.org/dev/peps/pep-0338/
- https://docs.python.org/3/using/cmdline.html#cmdoption-m
"""
from .main import more_main
if __name__ == '__main__':... | 337 | 23.142857 | 68 | py |
BioNEV | BioNEV-master/src/bionev/embed_train.py | # -*- coding: utf-8 -*-
import ast
import logging
import os
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
from bionev.GAE.train_model import gae_model
from bionev.OpenNE import gf, grarep, hope, lap, line, node2vec, sdne
from bionev.SVD.model import SVD_embedding
from bionev.stru... | 4,371 | 36.367521 | 115 | py |
BioNEV | BioNEV-master/src/bionev/evaluation.py | # -*- coding: utf-8 -*-
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, average_precision_score, f1_score, roc_auc_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from bionev.utils import *
def LinkPredict... | 4,002 | 37.12381 | 103 | py |
BioNEV | BioNEV-master/src/bionev/main.py | # -*- coding: utf-8 -*-
import datetime
import getpass
import json
import os
import random
import time
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
import numpy as np
from bionev.embed_train import embedding_training, load_embedding, read_node_labels, split_train_test_graph
from bionev.evaluati... | 9,368 | 46.318182 | 135 | py |
BioNEV | BioNEV-master/src/bionev/utils.py | # -*- coding: utf-8 -*-
import copy
import itertools
import random
import networkx as nx
import numpy as np
import bionev.OpenNE.graph as og
import bionev.struc2vec.graph as sg
def read_for_OpenNE(filename, weighted=False):
G = og.Graph()
print("Loading training graph for learning embedding...")
G.read... | 6,094 | 33.050279 | 105 | py |
BioNEV | BioNEV-master/src/bionev/GAE/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
BioNEV | BioNEV-master/src/bionev/GAE/initialization.py | # -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
def weight_variable_glorot(input_dim, output_dim, name=""):
"""Create a weight variable with Glorot & Bengio (AISTATS 2010)
initialization.
"""
init_range = np.sqrt(6.0 / (input_dim + output_dim))
initial = tf.random_uniform([inpu... | 472 | 30.533333 | 76 | py |
BioNEV | BioNEV-master/src/bionev/GAE/layers.py | # -*- coding: utf-8 -*-
import tensorflow as tf
from bionev.GAE.initialization import *
# global unique layer ID dictionary for layer name assignment
_LAYER_UIDS = {}
def get_layer_uid(layer_name=''):
"""Helper function, assigns unique layer IDs
"""
if layer_name not in _LAYER_UIDS:
_LAYER_UIDS... | 4,048 | 31.392 | 107 | py |
BioNEV | BioNEV-master/src/bionev/GAE/model.py | # -*- coding: utf-8 -*-
import tensorflow as tf
from bionev.GAE.layers import GraphConvolution, GraphConvolutionSparse, InnerProductDecoder
flags = tf.app.flags
FLAGS = flags.FLAGS
class Model(object):
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging'}
for kwarg in kwargs.keys()... | 5,047 | 39.709677 | 109 | py |
BioNEV | BioNEV-master/src/bionev/GAE/optimizer.py | # -*- coding: utf-8 -*-
import tensorflow as tf
class OptimizerAE(object):
def __init__(self, preds, labels, pos_weight, norm, learning_rate):
preds_sub = preds
labels_sub = labels
self.cost = norm * tf.reduce_mean(
tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, t... | 1,989 | 44.227273 | 118 | py |
BioNEV | BioNEV-master/src/bionev/GAE/preprocessing.py | # -*- coding: utf-8 -*-
import numpy as np
import scipy.sparse as sp
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
sparse_mx = sparse_mx.tocoo()
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
values = sparse_mx.data
shape = sparse_mx.shape
return coo... | 4,060 | 34.938053 | 104 | py |
BioNEV | BioNEV-master/src/bionev/GAE/train_model.py | # -*- coding: utf-8 -*-
import time
import numpy as np
import scipy.sparse as sp
import tensorflow as tf
from bionev.GAE.model import GCNModelAE, GCNModelVAE
from bionev.GAE.optimizer import OptimizerAE, OptimizerVAE
from bionev.GAE.preprocessing import construct_feed_dict, preprocess_graph, sparse_to_tuple
# # Tr... | 5,069 | 41.605042 | 114 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/classify.py | # -*- coding: utf-8 -*-
import numpy
from sklearn.metrics import f1_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
class TopKRanker(OneVsRestClassifier):
def predict(self, X, top_k_list):
probs = numpy.asarray(super(TopKRanker, self).predict... | 3,142 | 30.747475 | 90 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/gf.py | # -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
__author__ = "Wang Binlu"
__email__ = "wblmail@whu.edu.cn"
class GraphFactorization(object):
def __init__(self, graph, rep_size=128, epoch=120, learning_rate=0.003, weight_decay=1.):
self.g = graph
self.node_size = graph.G.numbe... | 2,561 | 33.621622 | 117 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/graph.py | # -*- coding: utf-8 -*-
"""Graph utilities."""
import networkx as nx
import numpy as np
__author__ = "Zhang Zhengyan"
__email__ = "zhangzhengyan14@mails.tsinghua.edu.cn"
class Graph(object):
def __init__(self):
self.G = None
self.look_up_dict = {}
self.look_back_list = []
self.... | 3,533 | 28.45 | 79 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/grarep.py | # -*- coding: utf-8 -*-
import numpy as np
from scipy.sparse.linalg import svds
from sklearn.preprocessing import normalize
class GraRep(object):
def __init__(self, graph, Kstep, dim):
self.g = graph
self.Kstep = Kstep
assert dim % Kstep == 0
self.dim = int(dim / Kstep)
s... | 3,001 | 35.168675 | 79 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/hope.py | # -*- coding: utf-8 -*-
import networkx as nx
import numpy as np
import scipy.sparse.linalg as lg
__author__ = "Alan WANG"
__email__ = "alan1995wang@outlook.com"
class HOPE(object):
def __init__(self, graph, d):
'''
d: representation vector dimension
'''
self._d = d
sel... | 1,605 | 25.766667 | 73 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/lap.py | # -*- coding: utf-8 -*-
import networkx as nx
import numpy as np
from scipy.sparse.linalg import eigsh
__author__ = "Wang Binlu"
__email__ = "wblmail@whu.edu.cn"
class LaplacianEigenmaps(object):
def __init__(self, graph, rep_size=128):
self.g = graph
self.node_size = self.g.G.number_of_nodes()
... | 2,399 | 33.285714 | 90 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/line.py | # -*- coding: utf-8 -*-
import math
import random
import numpy as np
import tensorflow as tf
from sklearn.linear_model import LogisticRegression
from bionev.OpenNE.classify import Classifier, read_node_label
class _LINE(object):
def __init__(self, graph, rep_size=128, batch_size=1000, negative_ratio=5, order=... | 11,252 | 39.478417 | 114 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/node2vec.py | # -*- coding: utf-8 -*-
from gensim.models import Word2Vec
from bionev.OpenNE import walker
class Node2vec(object):
def __init__(self, graph, path_length, num_paths, dim, p=1.0, q=1.0, dw=False, **kwargs):
kwargs["workers"] = kwargs.get("workers", 1)
if dw:
kwargs["hs"] = 1
... | 1,593 | 31.530612 | 93 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/sdne.py | # -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
__author__ = "Wang Binlu"
__email__ = "wblmail@whu.edu.cn"
def fc_op(input_op, name, n_out, layer_collector, act_func=tf.nn.leaky_relu):
n_in = input_op.get_shape()[-1].value
with tf.name_scope(name) as scope:
kernel = tf.Variable(tf... | 11,631 | 36.766234 | 119 | py |
BioNEV | BioNEV-master/src/bionev/OpenNE/walker.py | # -*- coding: utf-8 -*-
import random
import numpy as np
def deepwalk_walk_wrapper(class_instance, walk_length, start_node):
class_instance.deepwalk_walk(walk_length, start_node)
class BasicWalker:
def __init__(self, G, workers):
self.G = G.G
self.node_size = G.node_size
self.look_... | 6,186 | 28.461905 | 123 | py |
BioNEV | BioNEV-master/src/bionev/SVD/__init__.py | 0 | 0 | 0 | py | |
BioNEV | BioNEV-master/src/bionev/SVD/model.py | import networkx as nx
import numpy as np
from scipy.sparse.linalg import svds
def SVD_embedding(G, output_filename, size=100):
node_list = list(G.nodes())
adjacency_matrix = nx.adjacency_matrix(G, node_list)
adjacency_matrix = adjacency_matrix.astype(float)
# adjacency_matrix = sparse.csc_matrix(adjac... | 949 | 30.666667 | 69 | py |
BioNEV | BioNEV-master/src/bionev/struc2vec/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
BioNEV | BioNEV-master/src/bionev/struc2vec/algorithms.py | # -*- coding: utf-8 -*-
import math
import random
from collections import deque
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
from bionev.struc2vec.utils import *
def generate_parameters_random_walk(workers):
logging.info('Loading distances_nets from disk...')
sum_wei... | 6,624 | 30.103286 | 115 | py |
BioNEV | BioNEV-master/src/bionev/struc2vec/algorithms_distances.py | # -*- coding: utf-8 -*-
import math
import os
from collections import deque
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
from fastdtw import fastdtw
from bionev.struc2vec.utils import *
limiteDist = 20
def getDegreeListsVertices(g, vertices, calcUntilLayer):
degreeList =... | 20,177 | 28.074928 | 128 | py |
BioNEV | BioNEV-master/src/bionev/struc2vec/graph.py | # -*- coding: utf-8 -*-
"""Graph utilities."""
import logging
from collections import Iterable, defaultdict
from concurrent.futures import ProcessPoolExecutor
from io import open
from itertools import permutations
from multiprocessing import cpu_count
from time import time
from six import iterkeys
from six.moves imp... | 6,465 | 22.512727 | 102 | py |
BioNEV | BioNEV-master/src/bionev/struc2vec/struc2vec.py | # -*- coding: utf-8 -*-
from bionev.struc2vec.algorithms import *
from bionev.struc2vec.algorithms_distances import *
class Graph:
def __init__(self, g, workers, is_directed=False, untilLayer=None):
logging.info(" - Converting graph to dict...")
self.G = g.gToDict()
logging.info("Graph c... | 6,956 | 31.059908 | 119 | py |
BioNEV | BioNEV-master/src/bionev/struc2vec/utils.py | # -*- coding: utf-8 -*-
import inspect
import logging
import os.path
import pickle as pickle
from itertools import islice
from time import time
dir_f = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
folder_pickles = dir_f + "/pickles/"
def returnPathStruc2vec():
return dir_f
def isP... | 1,341 | 23.851852 | 92 | py |
null | LERG-main/README.md | # LERG
LERG (Local Explanation of Response Generation) is a unified approach to explain why a conditional text generation model will predict a text.
For more details, please refer to the paper [Local Explanation of Dialogue Response Generation, Neurips 2021](https://arxiv.org/pdf/2106.06528.pdf).
## Install
LERG c... | 3,445 | 41.02439 | 164 | md |
null | LERG-main/eval.py | from target_models import GPT
from lerg.metrics import ppl_c, ppl_c_add
from lerg.visualize import plot_interactions
import tqdm
import sys
import json
import torch
import numpy as np
import os
from datetime import datetime
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--explain_m... | 3,915 | 44.534884 | 164 | py |
null | LERG-main/explain.py | from lerg.perturbation_models import RandomPM, LIMERandomPM
from lerg.RG_explainers import LERG_LIME, LERG_R, LERG_SHAP, LERG_SHAP_log
from target_models import GPT
import torch
import tqdm
import sys
import json
import os
from datetime import datetime
from argparse import ArgumentParser
parser = ArgumentParser()
par... | 2,528 | 36.746269 | 136 | py |
null | LERG-main/target_models.py | import torch
import torch.nn.functional as F
from transformers import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel
from transformers import GPT2Tokenizer, GPT2LMHeadModel
def get_sum_multi_head_attentions(multi_head_attentions):
return sum(torch.sum(x,1) for x in multi_head_attentions)
class GPT:
def __init__(sel... | 4,007 | 55.450704 | 135 | py |
null | LERG-main/lerg/RG_explainers.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.linear_model import Ridge
import numpy as np
import random
class Explainer():
"""
The base class for various explainers
arguments:
model_f:... | 8,905 | 39.666667 | 166 | py |
null | LERG-main/lerg/__init__.py | 0 | 0 | 0 | py | |
null | LERG-main/lerg/metrics.py | import torch
import numpy as np
import random
import pdb
from scipy.stats import skew
from collections import Counter
def get_expl(x, expl, ratio=0.2, remain_masks=False):
if expl is None:
x_entities = [tok for tok in x if random.random() < ratio] if not remain_masks else [tok if random.random() < ratio e... | 2,391 | 33.666667 | 171 | py |
null | LERG-main/lerg/perturbation_models.py | import torch
import warnings
import math
import random
import numpy as np
import pdb
import scipy as sp
import sklearn
from transformers import BartTokenizer, BartForConditionalGeneration
import torch
def binomial_coef_dist(n):
dist = [math.comb(n, i+1) for i in range(n//2)]
total = sum(dist)
dist = [dens... | 4,757 | 33.230216 | 130 | py |
null | LERG-main/lerg/visualize.py | import numpy as np
import matplotlib.pyplot as plt
def plot_interactions(phi_map,x,y):
values = np.around([[phi_map[(i,j)].item() for i in range(len(x))] for j in range(len(y))], decimals=2)
fig = plt.figure()
ax = plt.axes()
im = ax.imshow(values, cmap=plt.get_cmap('Reds'))
ax.set_xticks(np.arang... | 733 | 33.952381 | 107 | py |
null | vnncomp2021_results-main/README.md | # vnncomp2021_results
results for vnncomp 2021. The csv files for all tools are in results_csv. The scores are computed using process_results.py, with stdout redirected to the output_*.txt files.
Summary scores are near the end of the file. You can check a specific benchmark by looking in the file. For example, to se... | 3,058 | 57.826923 | 283 | md |
null | vnncomp2021_results-main/process_results.py | """
Process vnncomp results
Stanley Bak
"""
from typing import Dict, List
import glob
import csv
from pathlib import Path
from collections import defaultdict
import numpy as np
class ToolResult:
"""Tool's result"""
# columns
CATEGORY = 0
NETWORK = 1
PROP = 2
PREPARE_TIME = 3
RESULT = 4
... | 18,053 | 32.557621 | 125 | py |
null | vnncomp2021_results-main/compare_cifar2020/README.md | comparison for cifar between 2020 and 2021.
The numbered files are created using tail to get the last 138 benchmarks from last years results files. For example:
tail -n 138 ggn-all-verinet.txt > 6.txt
sum.py is then executed to print out the summary statistics in the table
| 278 | 33.875 | 116 | md |
null | vnncomp2021_results-main/compare_cifar2020/sum.py | 'stanley bak'
def main():
'main entry point'
vio2021 = 0
holds2021 = 0
with open('2021.csv') as f:
for line in f:
if '(v)' in line:
vio2021 += 1
elif '(h)' in line:
holds2021 += 1
unknown2021 = 138 - vio2021 - holds2021
print(f"... | 1,367 | 26.36 | 110 | py |
null | emil-main/README.md | <img src="images/emil.png" width="800" />
# EMIL
Implementation of the EMIL architecture. Illustrated with MNIST and a simplified ResNet backbone.
## Usage
```python
import torch
from emil import EMIL
net = EMIL(
output_type = 'multiclass',
num_inp_channels = 1,
num_fmap_channels = 128,
att_dim = 128,
num... | 1,491 | 23.866667 | 119 | md |
null | emil-main/emil.py | import torch
from torch import nn
from resnet import resnet18
class EMIL(nn.Module):
def __init__(self, output_type, num_inp_channels, num_fmap_channels, att_dim, num_classes, patch_size, patch_stride, k_min):
super().__init__()
self.num_classes = num_classes
self.k_min = k_min
sel... | 1,789 | 34.098039 | 128 | py |
null | emil-main/main.py | import os
import numpy as np
import torch
from torch import nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from emil import EMIL
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')... | 2,595 | 25.222222 | 101 | py |
null | emil-main/resnet.py | from typing import Type, Any, Callable, Union, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from torch.hub import load_state_dict_from_url
# from https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py, based on commit d367a01
# changes forward method to output feature ... | 15,042 | 36.327543 | 118 | py |
null | emil-main/vis.py | import os
import sys
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from emil import EMIL
from vis_utils import *
os.environ["CUDA_VISIBLE_DEVICES"]="0"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
img_s... | 2,153 | 27.72 | 129 | py |
null | emil-main/vis_utils.py | import numpy as np
from matplotlib import pyplot as plt
import torch
from torch import nn
def get_heatmap(patch_scores, fmap_dims, patch_size, patch_stride, img_size):
"""
Returns a heatmap of *img_size*
*patch_scores* is either patch pred probabilities or attention weights
"""
device = ... | 2,330 | 36 | 126 | py |
evo | evo-master/README.md | # evo
***Python package for the evaluation of odometry and SLAM***
| Linux / macOS / Windows / ROS / ROS2 |
| :---: |
| [](https://dev.azure.com/michl2222/michl2222/_build/latest?definitionId=1&branchName=... | 11,345 | 47.076271 | 580 | md |
evo | evo-master/_config.yml | theme: jekyll-theme-cayman | 26 | 26 | 26 | yml |
evo | evo-master/azure-pipelines.yml | # https://docs.microsoft.com/azure/devops/pipelines/languages/python
# https://docs.microsoft.com/en-us/azure/devops/pipelines/languages/docker
trigger:
- master
pr:
autoCancel: true
# PRs into ...
branches:
include:
- master
schedules:
- cron: "0 0 * * *"
displayName: 'daily build'
branches:
i... | 2,315 | 21.057143 | 96 | yml |