repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
fairness-indicators | fairness-indicators-master/tensorboard_plugin/tensorboard_plugin_fairness_indicators/version.py | # Copyright 2018 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 721 | 37 | 76 | py |
fairness-indicators | fairness-indicators-master/tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 7,434 | 38.131579 | 94 | py |
fairness-indicators | fairness-indicators-master/tensorboard_plugin/tensorboard_plugin_fairness_indicators/demo.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 1,508 | 30.4375 | 80 | py |
fairness-indicators | fairness-indicators-master/tensorboard_plugin/tensorboard_plugin_fairness_indicators/__init__.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 689 | 45 | 80 | py |
fairness-indicators | fairness-indicators-master/tensorboard_plugin/tensorboard_plugin_fairness_indicators/summary_v2.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 2,135 | 38.555556 | 80 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/example_model.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 5,062 | 33.678082 | 126 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/version.py | # Copyright 2019 Google LLC. All Rights Reserved.
#
# 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 a... | 722 | 39.166667 | 74 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/__init__.py | # Copyright 2019 Google LLC. All Rights Reserved.
#
# 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 a... | 717 | 38.888889 | 74 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/example_model_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 4,656 | 37.808333 | 80 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/tutorial_utils/util.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 7,181 | 33.695652 | 81 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/tutorial_utils/__init__.py | # Copyright 2019 Google LLC. All Rights Reserved.
#
# 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 a... | 795 | 45.823529 | 74 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/tutorial_utils/util_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | 10,697 | 31.516717 | 98 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/remediation/weight_utils_test.py | """Tests for fairness_indicators.remediation.weight_utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
# Standard imports
from fairness_indicators.remediation import weight_utils
import tensorflow.compat.v1 as tf
EvalResult = co... | 7,739 | 33.70852 | 80 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/remediation/__init__.py | # Copyright 2019 Google LLC. All Rights Reserved.
#
# 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 a... | 596 | 41.642857 | 74 | py |
fairness-indicators | fairness-indicators-master/fairness_indicators/remediation/weight_utils.py | """Utilities to suggest weights based on model analysis results."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from typing import Any, Dict, Mapping, Text
import tensorflow_model_analysis as tfma
def create_percentage_difference_dictionary(
eval... | 3,613 | 36.645833 | 80 | py |
XGBOD | XGBOD-master/xgbod_demo.py | '''
Demo codes for XGBOD.
Author: Yue Zhao
notes: the demo code simulates the use of XGBOD with some changes to expedite
the execution. Use the full code for the production.
'''
import os
import random
import scipy.io as scio
import numpy as np
from sklearn.preprocessing import StandardScaler, normalize
from sklearn... | 8,726 | 39.21659 | 79 | py |
XGBOD | XGBOD-master/plots.py | import os
import matplotlib.pyplot as plt
import numpy as np
# initialize the results of the experiements
# arrhythmia
prc_gr_arr = [0.5606, 0.5976, 0.5986, 0.6053, 0.6109, 0.6219, 0.6076, 0.6115]
prc_ac_arr = [0.5606, 0.5976, 0.5719, 0.5961, 0.6041, 0.5792, 0.6019, 0.6115]
prc_rd_arr = [0.5606, 0.5993, 0.5788, 0.6356... | 4,491 | 37.393162 | 77 | py |
XGBOD | XGBOD-master/xgbod_full.py | import os
import pandas as pd
import numpy as np
import scipy.io as scio
from sklearn.preprocessing import StandardScaler, Normalizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.neighbors import LocalOutlierFactor
from sklearn.linear_model import Logistic... | 12,621 | 35.479769 | 118 | py |
XGBOD | XGBOD-master/models/knn.py | import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import KDTree
from sklearn.exceptions import NotFittedError
from scipy.stats import scoreatpercentile
class Knn(object):
'''
Knn class for outlier detection
support original knn, average knn, and median knn
'''
... | 2,777 | 29.195652 | 78 | py |
XGBOD | XGBOD-master/models/hbos.py | import numpy as np
import math
from sklearn.preprocessing import MinMaxScaler
from scipy.stats import scoreatpercentile
class Hbos(object):
def __init__(self, bins=10, alpha=0.3, beta=0.5, contamination=0.05):
self.bins = bins
self.alpha = alpha
self.beta = beta
self.contamination... | 4,636 | 38.632479 | 79 | py |
XGBOD | XGBOD-master/models/utility.py | import numpy as np
from scipy.stats import scoreatpercentile
from sklearn.metrics import precision_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score
def get_precn(y, y_pred):
'''
Utlity function to calculate precision@n
:param y: ground truth
:param y_pre... | 2,271 | 26.373494 | 71 | py |
XGBOD | XGBOD-master/models/generate_TOS.py | import numpy as np
import pandas as pd
from models.utility import get_precn
from sklearn.metrics import roc_auc_score
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import LocalOutlierFactor
from sklearn.svm import OneClassSVM
from sklearn.ensemble import IsolationForest
from PyNomaly import loop... | 6,799 | 34.416667 | 81 | py |
XGBOD | XGBOD-master/models/glosh.py | import hdbscan
import numpy as np
from sklearn.preprocessing import StandardScaler
from models.utility import get_precn
class Glosh(object):
def __init__(self, min_cluster_size=5):
self.min_cluster_size = min_cluster_size
def fit(self, X_train):
self.X_train = X_train
def sample_scores(s... | 1,118 | 27.692308 | 64 | py |
XGBOD | XGBOD-master/models/__init__.py | 0 | 0 | 0 | py | |
XGBOD | XGBOD-master/models/select_TOS.py | import random
import numpy as np
from scipy.stats import pearsonr
from models.utility import get_top_n
def random_select(X, X_train_new_orig, roc_list, p):
s_feature_rand = random.sample(range(0, len(roc_list)), p)
X_train_new_rand = X_train_new_orig[:, s_feature_rand]
X_train_all_rand = np.concatenate((... | 1,777 | 29.655172 | 75 | py |
La-MAML | La-MAML-main/main.py | import importlib
import datetime
import argparse
import time
import os
import ipdb
from tqdm import tqdm
import torch
from torch.autograd import Variable
import parser as file_parser
from metrics.metrics import confusion_matrix
from utils import misc_utils
from main_multi_task import life_experience_iid, eval_iid_tas... | 6,437 | 32.185567 | 154 | py |
La-MAML | La-MAML-main/parser.py | # coding=utf-8
import os
import argparse
def get_parser():
parser = argparse.ArgumentParser(description='Continual learning')
parser.add_argument('--expt_name', type=str, default='test_lamaml',
help='name of the experiment')
# model details
parser.add_argument('--model', type=s... | 7,611 | 53.76259 | 127 | py |
La-MAML | La-MAML-main/main_multi_task.py | import time
import os
from tqdm import tqdm
import torch
from torch.autograd import Variable
def eval_iid_tasks(model, tasks, args):
model.eval()
result = []
for t, task_loader in enumerate(tasks):
rt = 0
for (i, (x, y, super_y)) in enumerate(task_loader):
if args.cuda:
... | 2,818 | 30.674157 | 154 | py |
La-MAML | La-MAML-main/download.py | ########################################################################
#
# Functions for downloading and extracting data-files from the internet.
#
# Implemented in Python 3.5
#
########################################################################
#
# This file is part of the TensorFlow Tutorials available at:
#
#... | 4,353 | 32.236641 | 86 | py |
La-MAML | La-MAML-main/val_data_format.py | import io
import glob
import os
from shutil import move
from os.path import join
from os import listdir, rmdir
target_folder = './tiny-imagenet-200/val/'
val_dict = {}
with open('./tiny-imagenet-200/val/val_annotations.txt', 'r') as f:
for line in f.readlines():
split_line = line.split('\t')
val_d... | 882 | 26.59375 | 67 | py |
La-MAML | La-MAML-main/get_data.py | # Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import download
import argparse
def get_mnist_data(url, data_dir):
print("Downloading {} into {}".format(url, data_dir))... | 1,423 | 34.6 | 152 | py |
La-MAML | La-MAML-main/metrics/metrics.py | ### We directly copied the metrics.py model file from the GEM project https://github.com/facebookresearch/GradientEpisodicMemory
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from ... | 2,348 | 28 | 128 | py |
La-MAML | La-MAML-main/dataloaders/idataset.py |
import numpy as np
from PIL import Image
import torch
from torchvision import datasets, transforms
import os
from dataloaders import cifar_info
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, x, y, trsf, pretrsf = None, imgnet_like = False, super_y = None):
self.x, self.y = x, y
... | 4,465 | 29.8 | 105 | py |
La-MAML | La-MAML-main/dataloaders/cifar_info.py | from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
from torchvision.datasets.vision import VisionDataset
from torchvision.datasets.utils import check_integrity, download_an... | 8,912 | 36.607595 | 100 | py |
La-MAML | La-MAML-main/dataloaders/task_sampler.py | # coding=utf-8
import numpy as np
import torch
import warnings
import ipdb
class MultiTaskSampler(object):
'''
MultiTaskSampler: yield a batch of indexes at each iteration.
Indexes are calculated by keeping in account 'classes_per_it' and 'num_samples',
In fact at every iteration the batch indexes will... | 3,363 | 39.047619 | 103 | py |
La-MAML | La-MAML-main/dataloaders/class_incremental_loader.py | import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
from dataloaders.idataset import _get_datasets, DummyDataset
import random
import ipdb
# --------
# Datase... | 14,676 | 38.138667 | 119 | py |
La-MAML | La-MAML-main/dataloaders/multi_task_loader.py | import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
from dataloaders.idataset import _get_datasets, DummyDataset
from dataloaders.task_sampler import MultiTaskS... | 21,088 | 41.863821 | 164 | py |
La-MAML | La-MAML-main/dataloaders/task_incremental_loader.py | import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import datasets
from dataloaders.idataset import DummyArrayDataset
import os
class IncrementalLoader:
def __init__(
self,
opt,
shuffle=True,
seed=1,
):
self.... | 3,964 | 30.468254 | 120 | py |
La-MAML | La-MAML-main/utils/misc_utils.py | import datetime
import glob
import json
import os
import random
import ipdb
import numpy as np
import torch
from tqdm import tqdm
def to_onehot(targets, n_classes):
onehot = torch.zeros(targets.shape[0], n_classes).to(targets.device)
onehot.scatter_(dim=1, index=targets.long().view(-1, 1), value=1.)
retu... | 4,173 | 26.642384 | 103 | py |
La-MAML | La-MAML-main/model/lamaml.py | import random
import numpy as np
import ipdb
import math
import torch
import torch.nn as nn
from model.lamaml_base import *
class Net(BaseNet):
def __init__(self,
n_inputs,
n_outputs,
n_tasks,
args):
super(Net, self).__init__... | 3,695 | 31.421053 | 126 | py |
La-MAML | La-MAML-main/model/meta-bgd.py | import random
from random import shuffle
import numpy as np
import ipdb
import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import model.meta.learner as Learner
import model.meta.modelfactory as mf
from model.optimizers_lib import optimizers_lib
from ast import literal_eval
"""
This ba... | 11,558 | 34.897516 | 121 | py |
La-MAML | La-MAML-main/model/gem.py | ### This is a copy of GEM from https://github.com/facebookresearch/GradientEpisodicMemory.
### In order to ensure complete reproducability, we do not change the file and treat it as a baseline.
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in th... | 9,366 | 36.468 | 112 | py |
La-MAML | La-MAML-main/model/lamaml_base.py | import random
from random import shuffle
import numpy as np
import ipdb
import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import model.meta.learner as Learner
import model.meta.modelfactory as mf
from scipy.stats import pearsonr
import datetime
class BaseNet(torch.nn.Module):
def ... | 4,545 | 29.10596 | 112 | py |
La-MAML | La-MAML-main/model/lamaml_cifar.py | import random
import numpy as np
import ipdb
import math
import torch
import torch.nn as nn
from model.lamaml_base import *
class Net(BaseNet):
def __init__(self,
n_inputs,
n_outputs,
n_tasks,
args):
super(Net, self).__init__(n... | 5,732 | 35.987097 | 119 | py |
La-MAML | La-MAML-main/model/agem.py | ### This is a pytorch implementation of AGEM based on https://github.com/facebookresearch/agem.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
i... | 9,569 | 34.576208 | 112 | py |
La-MAML | La-MAML-main/model/meralg1.py | # An implementation of MER Algorithm 1 from https://openreview.net/pdf?id=B1gTShAct7
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.o... | 6,478 | 31.888325 | 159 | py |
La-MAML | La-MAML-main/model/iid2.py | import torch
import numpy as np
import random
import model.meta.learner as Learner
import model.meta.modelfactory as mf
import ipdb
import sys
if not sys.warnoptions:
import warnings
warnings.simplefilter("once")
"""
Multi task
big batch size, set increment 100 so that it is treated as 1 task with all c... | 2,878 | 30.637363 | 107 | py |
La-MAML | La-MAML-main/model/eralg4.py | # An implementation of Experience Replay (ER) with reservoir sampling and without using tasks from Algorithm 4 of https://openreview.net/pdf?id=B1gTShAct7
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory o... | 9,335 | 32.342857 | 154 | py |
La-MAML | La-MAML-main/model/icarl.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
import random
import model.meta.learner as Learner
import model.meta.modelfactory as mf
import sys
... | 10,217 | 40.536585 | 116 | py |
La-MAML | La-MAML-main/model/meta/modelfactory.py | import ipdb
class ModelFactory():
def __init__(self):
pass
@staticmethod
def get_model(model_type, sizes, dataset='mnist', args=None):
net_list = []
if "mnist" in dataset:
if model_type=="linear":
for i in range(0, len(sizes) - 1):
n... | 2,530 | 30.246914 | 116 | py |
La-MAML | La-MAML-main/model/meta/learner.py | import math
import os
import sys
import traceback
import numpy as np
import ipdb
import torch
from torch import nn
from torch.nn import functional as F
class Learner(nn.Module):
def __init__(self, config, args = None):
"""
:param config: network config file, type:list of (string, list)
:... | 10,679 | 34.364238 | 143 | py |
La-MAML | La-MAML-main/model/optimizers_lib/bgd_optimizer.py | import torch
from torch.optim.optimizer import Optimizer
class BGD(Optimizer):
"""Implements BGD.
A simple usage of BGD would be:
for samples, labels in batches:
for mc_iter in range(mc_iters):
optimizer.randomize_weights()
output = model.forward(samples)
loss = ... | 5,328 | 46.580357 | 119 | py |
La-MAML | La-MAML-main/model/optimizers_lib/optimizers_lib.py | import torch.optim as optim
from .bgd_optimizer import BGD
def bgd(model, **kwargs):
# logger = kwargs.get("logger", None)
# assert(logger is not None)
bgd_params = {
"mean_eta": kwargs.get("mean_eta", 1),
"std_init": kwargs.get("std_init", 0.02),
"mc_iters": kwargs.get("mc_iters",... | 2,099 | 37.181818 | 151 | py |
La-MAML | La-MAML-main/model/optimizers_lib/__init__.py | from .optimizers_lib import * | 29 | 29 | 29 | py |
fiery | fiery-master/evaluate.py | from argparse import ArgumentParser
import torch
from tqdm import tqdm
from fiery.data import prepare_dataloaders
from fiery.trainer import TrainingModule
from fiery.metrics import IntersectionOverUnion, PanopticMetric
from fiery.utils.network import preprocess_batch
from fiery.utils.instance import predict_instance_... | 3,908 | 36.951456 | 106 | py |
fiery | fiery-master/visualise.py | import os
from argparse import ArgumentParser
from glob import glob
import cv2
import numpy as np
import torch
import torchvision
import matplotlib as mpl
import matplotlib.pyplot as plt
from PIL import Image
from fiery.trainer import TrainingModule
from fiery.utils.network import NormalizeInverse
from fiery.utils.in... | 5,095 | 36.470588 | 118 | py |
fiery | fiery-master/train.py | import os
import time
import socket
import torch
import pytorch_lightning as pl
from pytorch_lightning.plugins import DDPPlugin
from fiery.config import get_parser, get_cfg
from fiery.data import prepare_dataloaders
from fiery.trainer import TrainingModule
def main():
args = get_parser().parse_args()
cfg = g... | 1,540 | 29.215686 | 101 | py |
fiery | fiery-master/fiery/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialRegressionLoss(nn.Module):
def __init__(self, norm, ignore_index=255, future_discount=1.0):
super(SpatialRegressionLoss, self).__init__()
self.norm = norm
self.ignore_index = ignore_index
self.future_di... | 3,378 | 33.835052 | 111 | py |
fiery | fiery-master/fiery/data.py | import os
from PIL import Image
import numpy as np
import cv2
import torch
import torchvision
from pyquaternion import Quaternion
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.splits import create_splits_scenes
from nuscenes.utils.data_classes import Box
from lyft_dataset_sdk.lyftdataset import LyftDatas... | 19,735 | 41.62635 | 136 | py |
fiery | fiery-master/fiery/config.py | import argparse
from fvcore.common.config import CfgNode as _CfgNode
def convert_to_dict(cfg_node, key_list=[]):
"""Convert a config node to dictionary."""
_VALID_TYPES = {tuple, list, str, int, float, bool}
if not isinstance(cfg_node, _CfgNode):
if type(cfg_node) not in _VALID_TYPES:
... | 4,568 | 29.46 | 114 | py |
fiery | fiery-master/fiery/metrics.py | from typing import Optional
import torch
from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.metrics.functional.classification import stat_scores_multiple_classes
from pytorch_lightning.metrics.functional.reduction import reduce
class IntersectionOverUnion(Metric):
"""Computes intersection... | 11,415 | 43.59375 | 117 | py |
fiery | fiery-master/fiery/trainer.py | import torch
import torch.nn as nn
import pytorch_lightning as pl
from fiery.config import get_cfg
from fiery.models.fiery import Fiery
from fiery.losses import ProbabilisticLoss, SpatialRegressionLoss, SegmentationLoss
from fiery.metrics import IntersectionOverUnion, PanopticMetric
from fiery.utils.geometry import cu... | 11,419 | 42.754789 | 112 | py |
fiery | fiery-master/fiery/models/distributions.py | import torch
import torch.nn as nn
from fiery.layers.convolutions import Bottleneck
class DistributionModule(nn.Module):
"""
A convolutional net that parametrises a diagonal Gaussian distribution.
"""
def __init__(
self, in_channels, latent_dim, min_log_sigma, max_log_sigma):
super()... | 1,871 | 31.842105 | 114 | py |
fiery | fiery-master/fiery/models/future_prediction.py | import torch
from fiery.layers.convolutions import Bottleneck
from fiery.layers.temporal import SpatialGRU
class FuturePrediction(torch.nn.Module):
def __init__(self, in_channels, latent_dim, n_gru_blocks=3, n_res_layers=3):
super().__init__()
self.n_gru_blocks = n_gru_blocks
# Convoluti... | 1,488 | 39.243243 | 97 | py |
fiery | fiery-master/fiery/models/fiery.py | import torch
import torch.nn as nn
from fiery.models.encoder import Encoder
from fiery.models.temporal_model import TemporalModelIdentity, TemporalModel
from fiery.models.distributions import DistributionModule
from fiery.models.future_prediction import FuturePrediction
from fiery.models.decoder import Decoder
from fi... | 15,090 | 43.385294 | 118 | py |
fiery | fiery-master/fiery/models/temporal_model.py | import torch.nn as nn
from fiery.layers.temporal import Bottleneck3D, TemporalBlock
class TemporalModel(nn.Module):
def __init__(
self, in_channels, receptive_field, input_shape, start_out_channels=64, extra_in_channels=0,
n_spatial_layers_between_temporal_layers=0, use_pyramid_pooling=Tr... | 2,120 | 32.666667 | 104 | py |
fiery | fiery-master/fiery/models/encoder.py | import torch.nn as nn
from efficientnet_pytorch import EfficientNet
from fiery.layers.convolutions import UpsamplingConcat
class Encoder(nn.Module):
def __init__(self, cfg, D):
super().__init__()
self.D = D
self.C = cfg.OUT_CHANNELS
self.use_depth_distribution = cfg.USE_DEPTH_DIST... | 3,910 | 36.247619 | 119 | py |
fiery | fiery-master/fiery/models/decoder.py | import torch.nn as nn
from torchvision.models.resnet import resnet18
from fiery.layers.convolutions import UpsamplingAdd
class Decoder(nn.Module):
def __init__(self, in_channels, n_classes, predict_future_flow):
super().__init__()
backbone = resnet18(pretrained=False, zero_init_residual=True)
... | 3,676 | 38.967391 | 106 | py |
fiery | fiery-master/fiery/layers/convolutions.py | from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
"""2D convolution followed by
- an optional normalisation (batch norm or instance norm)
- an optional activation (ReLU, LeakyReLU, or ... | 7,593 | 34.32093 | 114 | py |
fiery | fiery-master/fiery/layers/temporal.py | from collections import OrderedDict
import torch
import torch.nn as nn
from fiery.layers.convolutions import ConvBlock
from fiery.utils.geometry import warp_features
class SpatialGRU(nn.Module):
"""A GRU cell that takes an input tensor [BxTxCxHxW] and an optional previous state and passes a
convolutional ga... | 11,152 | 38.549645 | 120 | py |
fiery | fiery-master/fiery/utils/visualisation.py | import numpy as np
import torch
import matplotlib.pylab
from fiery.utils.instance import predict_instance_segmentation_and_trajectories
DEFAULT_COLORMAP = matplotlib.pylab.cm.jet
def flow_to_image(flow: np.ndarray, autoscale: bool = False) -> np.ndarray:
"""
Applies colour map to flow which should be a 2 ch... | 12,488 | 32.572581 | 121 | py |
fiery | fiery-master/fiery/utils/network.py | import torch
import torch.nn as nn
import torchvision
def pack_sequence_dim(x):
b, s = x.shape[:2]
return x.view(b * s, *x.shape[2:])
def unpack_sequence_dim(x, b, s):
return x.view(b, s, *x.shape[1:])
def preprocess_batch(batch, device, unsqueeze=False):
for key, value in batch.items():
if... | 1,236 | 27.113636 | 89 | py |
fiery | fiery-master/fiery/utils/geometry.py | import PIL
import numpy as np
import torch
from pyquaternion import Quaternion
def resize_and_crop_image(img, resize_dims, crop):
# Bilinear resizing followed by cropping
img = img.resize(resize_dims, resample=PIL.Image.BILINEAR)
img = img.crop(crop)
return img
def update_intrinsics(intrinsics, top... | 10,875 | 33.526984 | 117 | py |
fiery | fiery-master/fiery/utils/instance.py | from typing import Tuple
import torch
import torch.nn.functional as F
import numpy as np
from scipy.optimize import linear_sum_assignment
from fiery.utils.geometry import mat2pose_vec, pose_vec2mat, warp_features
# set ignore index to 0 for vis
def convert_instance_mask_to_center_and_offset_label(instance_img, futu... | 13,871 | 40.657658 | 119 | py |
fiery | fiery-master/fiery/utils/lyft_splits.py | TRAIN_LYFT_INDICES = [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16,
17, 18, 19, 20, 21, 23, 24, 27, 28, 29, 30, 31, 32,
33, 35, 36, 37, 39, 41, 43, 44, 45, 46, 47, 48, 49,
50, 51, 52, 53, 55, 56, 59, 60, 62, 63, 65, 68, 69,
70, 71, 7... | 1,113 | 64.529412 | 86 | py |
public-apis | public-apis-master/scripts/validate/format.py | # -*- coding: utf-8 -*-
import re
import sys
from string import punctuation
from typing import List, Tuple, Dict
# Temporary replacement
# The descriptions that contain () at the end must adapt to the new policy later
punctuation = punctuation.replace('()', '')
anchor = '###'
auth_keys = ['apiKey', 'OAuth', 'X-Masha... | 8,464 | 29.44964 | 168 | py |
public-apis | public-apis-master/scripts/validate/__init__.py | # -*- coding: utf-8 -*-
from validate import format
from validate import links
| 80 | 15.2 | 27 | py |
public-apis | public-apis-master/scripts/validate/links.py | # -*- coding: utf-8 -*-
import re
import sys
import random
from typing import List, Tuple
import requests
from requests.models import Response
def find_links_in_text(text: str) -> List[str]:
"""Find links in a text and return a list of URLs."""
link_pattern = re.compile(r'((?:https?://|www\d{0,3}[.]|[a-z0-... | 8,022 | 28.281022 | 211 | py |
public-apis | public-apis-master/scripts/tests/test_validate_links.py | # -*- coding: utf-8 -*-
import unittest
from validate.links import find_links_in_text
from validate.links import check_duplicate_links
from validate.links import fake_user_agent
from validate.links import get_host_from_link
from validate.links import has_cloudflare_protection
class FakeResponse():
def __init__(... | 5,725 | 32.098266 | 100 | py |
public-apis | public-apis-master/scripts/tests/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
public-apis | public-apis-master/scripts/tests/test_validate_format.py | # -*- coding: utf-8 -*-
import unittest
from validate.format import error_message
from validate.format import get_categories_content
from validate.format import check_alphabetical_order
from validate.format import check_title
from validate.format import check_description, max_description_length
from validate.format i... | 18,154 | 37.875803 | 155 | py |
LiDAR2INS | LiDAR2INS-master/eigen3/debug/gdb/printers.py | # -*- coding: utf-8 -*-
# This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2009 Benjamin Schindler <[email protected]>
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
... | 9,617 | 29.533333 | 160 | py |
LiDAR2INS | LiDAR2INS-master/eigen3/debug/gdb/__init__.py | # Intentionally empty
| 22 | 10.5 | 21 | py |
LiDAR2INS | LiDAR2INS-master/eigen3/scripts/relicense.py | # This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2012 Keir Mierle <[email protected]>
#
# This Source Code Form is subject to the terms of the Mozilla
# Public License v. 2.0. If a copy of the MPL was not distributed
# with this file, You can obtain one at http://m... | 2,368 | 32.842857 | 77 | py |
LiDAR2INS | LiDAR2INS-master/ceres/scripts/make_docs.py | #!/usr/bin/python
# encoding: utf-8
#
# Ceres Solver - A fast non-linear least squares minimizer
# Copyright 2015 Google Inc. All rights reserved.
# http://ceres-solver.org/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are me... | 4,409 | 34.28 | 87 | py |
LiDAR2INS | LiDAR2INS-master/ceres/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Ceres Solver documentation build configuration file, created by
# sphinx-quickstart on Sun Jan 20 20:34:07 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
... | 7,957 | 31.748971 | 94 | py |
LiDAR2INS | LiDAR2INS-master/ceres/internal/ceres/schur_eliminator_template.py | # Ceres Solver - A fast non-linear least squares minimizer
# Copyright 2017 Google Inc. All rights reserved.
# http://ceres-solver.org/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source cod... | 5,928 | 37.00641 | 86 | py |
LiDAR2INS | LiDAR2INS-master/ceres/internal/ceres/generate_bundle_adjustment_tests.py | # Ceres Solver - A fast non-linear least squares minimizer
# Copyright 2018 Google Inc. All rights reserved.
# http://ceres-solver.org/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source cod... | 11,723 | 42.910112 | 82 | py |
LiDAR2INS | LiDAR2INS-master/ceres/internal/ceres/partitioned_matrix_view_template.py | # Ceres Solver - A fast non-linear least squares minimizer
# Copyright 2015 Google Inc. All rights reserved.
# http://ceres-solver.org/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source cod... | 5,983 | 38.111111 | 101 | py |
LiDAR2INS | LiDAR2INS-master/ceres/internal/ceres/generate_template_specializations.py | # Ceres Solver - A fast non-linear least squares minimizer
# Copyright 2015 Google Inc. All rights reserved.
# http://ceres-solver.org/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source cod... | 9,677 | 38.182186 | 80 | py |
xgboost | xgboost-master/tests/ci_build/test_r_package.py | """Utilities for packaging R code and running tests."""
import argparse
import os
import shutil
import subprocess
from pathlib import Path
from platform import system
from test_utils import R_PACKAGE, ROOT, DirectoryExcursion, cd, print_time, record_time
def get_mingw_bin() -> str:
return os.path.join("c:/rtools... | 10,217 | 31.438095 | 88 | py |
xgboost | xgboost-master/tests/ci_build/tidy.py | #!/usr/bin/env python
import argparse
import json
import os
import re
import shutil
import subprocess
import sys
from multiprocessing import Pool, cpu_count
from time import time
import yaml
def call(args):
'''Subprocess run wrapper.'''
completed = subprocess.run(args,
stdout=s... | 10,858 | 34.486928 | 82 | py |
xgboost | xgboost-master/tests/ci_build/change_version.py | """
1. Modify ``CMakeLists.txt`` in source tree and ``python-package/xgboost/VERSION`` if
needed, run CMake .
If this is a RC release, the Python version has the form <major>.<minor>.<patch>rc1
2. Modify ``DESCRIPTION`` and ``configure.ac`` in R-package. Run ``autoreconf``.
3. Run ``mvn`` in ``jvm-packages``
If... | 5,198 | 31.49375 | 88 | py |
xgboost | xgboost-master/tests/ci_build/lint_python.py | import argparse
import os
import pathlib
import subprocess
import sys
from collections import Counter
from multiprocessing import Pool, cpu_count
from typing import Dict, List, Tuple
from test_utils import PY_PACKAGE, ROOT, cd, print_time, record_time
class LintersPaths:
"""The paths each linter run on."""
... | 8,177 | 29.401487 | 96 | py |
xgboost | xgboost-master/tests/ci_build/rename_whl.py | import os
import sys
from contextlib import contextmanager
@contextmanager
def cd(path):
path = os.path.normpath(path)
cwd = os.getcwd()
os.chdir(path)
print("cd " + path)
try:
yield path
finally:
os.chdir(cwd)
if len(sys.argv) != 4:
print('Usage: {} [wheel to rename] [co... | 1,240 | 25.978261 | 96 | py |
xgboost | xgboost-master/tests/ci_build/test_utils.py | """Utilities for the CI."""
import os
from datetime import datetime, timedelta
from functools import wraps
from typing import Any, Callable, Dict, TypedDict, TypeVar, Union
class DirectoryExcursion:
def __init__(self, path: Union[os.PathLike, str]) -> None:
self.path = path
self.curdir = os.path.n... | 2,141 | 24.807229 | 82 | py |
xgboost | xgboost-master/tests/python/test_data_iterator.py | from typing import Callable, Dict, List
import numpy as np
import pytest
from hypothesis import given, settings, strategies
from scipy.sparse import csr_matrix
import xgboost as xgb
from xgboost import testing as tm
from xgboost.data import SingleBatchInternalIter as SingleBatch
from xgboost.testing import IteratorFo... | 5,556 | 29.201087 | 89 | py |
xgboost | xgboost-master/tests/python/test_dmatrix.py | import os
import tempfile
import numpy as np
import pytest
import scipy.sparse
from hypothesis import given, settings, strategies
from scipy.sparse import csr_matrix, rand
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import np_dtypes
rng = np.random.RandomState(1)
dpath = 'demo/... | 16,765 | 34.748401 | 95 | py |
xgboost | xgboost-master/tests/python/test_survival.py | import json
import os
from typing import List, Optional, Tuple, cast
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = tm.data_dir(__file__)
@pytest.fixture(scope="module")
def toy_data() -> Tuple[xgb.DMatrix, np.ndarray, np.ndarray]:
X = np.array([1, 2, 3, 4, 5])... | 5,895 | 33.887574 | 87 | py |
xgboost | xgboost-master/tests/python/test_interaction_constraints.py | import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestInteractionConstraints:
def run_interaction_constraints(
self, tree_method, feature_names=None, interaction_constraints='[[0, 1]]'
):
x1 = np.ran... | 4,864 | 39.882353 | 91 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.