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 |
|---|---|---|---|---|---|---|
3DG-STFM | 3DG-STFM-master/configs/data/__init__.py | 0 | 0 | 0 | py | |
3DG-STFM | 3DG-STFM-master/configs/data/megadepth_test_1500.py | from configs.data.base import cfg
'''
TEST_BASE_PATH = "inference/megadepth_test_1500_scene_info"
cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth"
cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test"
cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}"
cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/megadepth_test_1500.txt"
cfg.D... | 830 | 42.736842 | 108 | py |
3DG-STFM | 3DG-STFM-master/configs/data/scannet_trainval.py | from configs.data.base import cfg
TRAIN_BASE_PATH = "data/scannet/index"
cfg.DATASET.TRAINVAL_DATA_SOURCE = "ScanNet"
cfg.DATASET.TRAIN_DATA_ROOT = "data/scannet/train"
cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_data/train"
cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/scene_data/train_list/scannet_al... | 897 | 48.888889 | 101 | py |
tencent-ml-images | tencent-ml-images-master/finetune.py | #!/usr/bin/python
"""
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You ... | 11,795 | 42.688889 | 305 | py |
tencent-ml-images | tencent-ml-images-master/flags.py | #!/usr/bin/python
"""
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You m... | 5,301 | 42.105691 | 305 | py |
tencent-ml-images | tencent-ml-images-master/extract_feature.py | #!/usr/bin/python
"""
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You m... | 3,623 | 33.188679 | 305 | py |
tencent-ml-images | tencent-ml-images-master/image_classification.py | #!/usr/bin/python
"""
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You m... | 3,620 | 34.5 | 305 | py |
tencent-ml-images | tencent-ml-images-master/train.py | """
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy o... | 11,270 | 39.253571 | 305 | py |
tencent-ml-images | tencent-ml-images-master/models/resnet.py | """ResNet model
Related papers:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
"""
from __future__ import absolute_im... | 7,099 | 33.803922 | 111 | py |
tencent-ml-images | tencent-ml-images-master/models/__init__.py | 0 | 0 | 0 | py | |
tencent-ml-images | tencent-ml-images-master/data_processing/image_preprocessing.py | """
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy o... | 7,279 | 41.080925 | 305 | py |
tencent-ml-images | tencent-ml-images-master/data_processing/dataset.py | """
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy o... | 2,371 | 39.896552 | 305 | py |
tencent-ml-images | tencent-ml-images-master/data_processing/__init__.py | 0 | 0 | 0 | py | |
tencent-ml-images | tencent-ml-images-master/data/download_urls_multithreading.py | #!/usr/bin/env python
"""
Tencent is pleased to support the open source community by making Tencent ML-Images available.
Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except in compliance with the License. ... | 3,639 | 42.333333 | 207 | py |
tencent-ml-images | tencent-ml-images-master/data/tfrecord.py | #!/usr/bin/python
import sys
import os
import tensorflow as tf
import numpy as np
import imghdr
import threading
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-idx","--indexs", type=str, default="", help="dirs contains train index files")
parser.add_argument("-tfs", "--tfrecords", type=str, de... | 5,799 | 33.117647 | 123 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/main.py | import sys
import argparse
import os
import random
import numpy
import pandas as pd
import torch
import torch.backends.cudnn as cudnn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import agents
import utils
numpy.set_printoptions(edgeitems=5, linewidth=160, formatter={'float': '{:0.6f}'.format})
... | 6,839 | 51.21374 | 134 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/example.py | # Code reused from: https://github.com/kuangliu/pytorch-cifar
import torch
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import net
import losses
import tools
from torchmetrics import AUROC
import random
import numpy
import torchnet as tnt
base_seed = 42
... | 8,369 | 41.923077 | 164 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/calculate_log.py | from __future__ import print_function
import numpy as np
import numpy as np
def get_curve(dir_name, stypes=['Baseline', 'Gaussian_LDA']):
tp, fp = dict(), dict()
tnr_at_tpr95 = dict()
for stype in stypes:
known = np.loadtxt('{}/confidence_{}_In.txt'.format(dir_name, stype), delimiter='\n')
... | 3,803 | 39.042105 | 95 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/detect.py | from __future__ import print_function
import argparse
import torch
import models
import os
import losses
import data_loader
import calculate_log as callog
from torchvision import transforms
parser = argparse.ArgumentParser(description='PyTorch code: OOD detector')
parser.add_argument('--batch_size', type=int, default=... | 7,841 | 44.593023 | 140 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/data_loader.py | import torch
from torchvision import datasets
import os
def getSVHN(batch_size, TF, data_root='data', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'svhn'))
kwargs.pop('input_size', None)
ds = []
if train:
train_loader = torch.utils.data.DataLoader(
... | 3,956 | 46.674699 | 158 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/tools.py | # Code reused from: https://github.com/kuangliu/pytorch-cifar
'''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
_... | 2,429 | 24.578947 | 68 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/net.py | # Code reused from: https://github.com/kuangliu/pytorch-cifar
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import losses
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNor... | 5,441 | 40.861538 | 107 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/analyze.py | import argparse
import os
import torch
import sys
import numpy as np
import pandas as pd
import random
pd.options.display.float_format = '{:,.4f}'.format
pd.set_option('display.width', 160)
parser = argparse.ArgumentParser(description='Analize results in csv files')
parser.add_argument('-p', '--path', default="", ty... | 6,074 | 43.343066 | 135 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/loaders/image.py | import random
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
class ImageLoader:
def __init__(self, args):
self.args = args
self.mnist = False
if args.dataset == "cifar10":
self.normalize = transforms.Normalize((0.491, 0.4... | 4,367 | 52.925926 | 151 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/loaders/__init__.py | from .image import *
| 21 | 10 | 20 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/models/densenet.py | # code reused from: https://github.com/kuangliu/pytorch-cifar
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)... | 7,490 | 39.274194 | 127 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/models/__init__.py | from .proper_resnet import *
from .densenet import *
| 53 | 17 | 28 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/models/proper_resnet.py | # code reused from: https://github.com/akamaster/pytorch_resnet_cifar10
'''
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of the imple... | 6,110 | 35.375 | 130 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/agents/classifier.py | import os
import sys
import torch
import models
import loaders
import losses
import statistics
import math
import torchnet as tnt
import numpy as np
import utils
class ClassifierAgent:
def __init__(self, args):
self.args = args
self.epoch = None
# create dataset
image_loaders = lo... | 25,791 | 58.842227 | 164 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/agents/__init__.py | from .classifier import *
| 26 | 12.5 | 25 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/utils/procedures.py | import os
import pickle
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch
import torch.nn.functional as F
import csv
import numpy as np
from sklearn import metrics
def compute_weights(iterable):
return [sum(iterable) / (iterable[i] * len(iterable)) if iterable[i] != 0 else float("in... | 6,271 | 29.595122 | 126 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/utils/__init__.py | from .procedures import *
| 26 | 12.5 | 25 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/losses/softmax.py | import torch.nn as nn
import torch
import math
class SoftMaxLossFirstPart(nn.Module):
def __init__(self, num_features, num_classes, temperature=1.0):
super(SoftMaxLossFirstPart, self).__init__()
self.num_features = num_features
self.num_classes = num_classes
self.temperature = temp... | 1,694 | 41.375 | 111 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/losses/isomaxplus.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class IsoMaxPlusLossFirstPart(nn.Module):
"""This part replaces the model classifier output layer nn.Linear()"""
def __init__(self, num_features, num_classes, temperature=1.0):
super(IsoMaxPlusLossFirstPart, self).__init__()
se... | 2,771 | 52.307692 | 169 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/losses/__init__.py | from .softmax import *
from .isomax import *
from .isomaxplus import *
| 71 | 17 | 25 | py |
entropic-out-of-distribution-detection | entropic-out-of-distribution-detection-master/losses/isomax.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class IsoMaxLossFirstPart(nn.Module):
"""This part replaces the model classifier output layer nn.Linear()"""
def __init__(self, num_features, num_classes, temperature=1.0):
super(IsoMaxLossFirstPart, self).__init__()
self.num_f... | 2,571 | 50.44 | 117 | py |
stable-continual-learning | stable-continual-learning-master/__init__.py | 0 | 0 | 0 | py | |
stable-continual-learning | stable-continual-learning-master/stable_sgd/main.py | import os
import torch
import numpy as np
import pandas as pd
import torch.nn as nn
from stable_sgd.models import MLP, ResNet18
from stable_sgd.data_utils import get_permuted_mnist_tasks, get_rotated_mnist_tasks, get_split_cifar100_tasks
from stable_sgd.utils import parse_arguments, DEVICE, init_experiment, end_experim... | 4,867 | 29.425 | 128 | py |
stable-continual-learning | stable-continual-learning-master/stable_sgd/utils.py | import uuid
import torch
import argparse
import matplotlib
import numpy as np
import pandas as pd
matplotlib.use('Agg')
import seaborn as sns
from pathlib import Path
import matplotlib.pyplot as plt
from external_libs.hessian_eigenthings import compute_hessian_eigenthings
TRIAL_ID = uuid.uuid4().hex.upper()[0:6]
EXPE... | 5,329 | 35.758621 | 122 | py |
stable-continual-learning | stable-continual-learning-master/stable_sgd/data_utils.py | import numpy as np
import torch
import torchvision
from torch.utils.data import TensorDataset, DataLoader
import torchvision.transforms.functional as TorchVisionFunc
def get_permuted_mnist(task_id, batch_size):
"""
Get the dataset loaders (train and test) for a `single` task of permuted MNIST.
This function will b... | 5,087 | 34.333333 | 200 | py |
stable-continual-learning | stable-continual-learning-master/stable_sgd/models.py | import torch
import torch.nn as nn
from torch.nn.functional import relu, avg_pool2d
class MLP(nn.Module):
"""
Two layer MLP for MNIST benchmarks.
"""
def __init__(self, hiddens, config):
super(MLP, self).__init__()
self.W1 = nn.Linear(784, hiddens)
self.relu = nn.ReLU(inplace=True)
self.dropout_1 = nn.Dro... | 3,240 | 27.182609 | 87 | py |
stable-continual-learning | stable-continual-learning-master/stable_sgd/__init__.py | 0 | 0 | 0 | py | |
stable-continual-learning | stable-continual-learning-master/external_libs/__init__.py | 0 | 0 | 0 | py | |
stable-continual-learning | stable-continual-learning-master/external_libs/hessian_eigenthings/lanczos.py | """ Use scipy/ARPACK implicitly restarted lanczos to find top k eigenthings """
import numpy as np
import torch
from scipy.sparse.linalg import LinearOperator as ScipyLinearOperator
from scipy.sparse.linalg import eigsh
from warnings import warn
def lanczos(
operator,
num_eigenthings=10,
which="LM",
m... | 2,585 | 29.785714 | 87 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/hessian_eigenthings/utils.py | """ small helpers """
import shutil
import sys
import time
TOTAL_BAR_LENGTH = 65.0
term_width = shutil.get_terminal_size().columns
def log(msg):
# TODO make this an actual logger lol
print("[hessian_eigenthings] " + str(msg))
last_time = time.time()
begin_time = last_time
def format_time(seconds):
"... | 2,585 | 24.60396 | 79 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/hessian_eigenthings/hvp_operator.py | """
This module defines a linear operator to compute the hessian-vector product
for a given pytorch model using subsampled data.
"""
import torch
from .power_iter import Operator, deflated_power_iteration
from .lanczos import lanczos
class HVPOperator(Operator):
"""
Use PyTorch autograd for Hessian Vec produc... | 6,060 | 32.486188 | 86 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/hessian_eigenthings/power_iter.py | """
This module contains functions to perform power iteration with deflation
to compute the top eigenvalues and eigenvectors of a linear operator
"""
import numpy as np
import torch
from .utils import log, progress_bar
class Operator:
"""
maps x -> Lx for a linear operator L
"""
def __init__(self, s... | 4,147 | 28.841727 | 87 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/hessian_eigenthings/__init__.py | """ Top-level module for hessian eigenvec computation
This library is cited in our paper.
"""
from .power_iter import power_iteration, deflated_power_iteration
from .lanczos import lanczos
from .hvp_operator import HVPOperator, compute_hessian_eigenthings
__all__ = [
"power_iteration",
"deflated_power_iteratio... | 425 | 24.058824 | 66 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/hessian_eigenthings/spectral_density.py | import numpy as np
import torch
def _lanczos_step(vec, size, current_draw):
pass
def lanczos(
operator,
max_steps=20,
tol=1e-6,
num_lanczos_vectors=None,
init_vec=None,
use_gpu=False,
):
"""
Use the scipy.sparse.linalg.eigsh hook to the ARPACK lanczos algorithm
to find the t... | 1,764 | 27.467742 | 87 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/fc_mnist.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.
"""
Training script for permute MNIST experiment.
"""
from __future__ import print_function
import argparse
import os
import ... | 28,906 | 47.501678 | 201 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/extract_res.py | from six.moves import cPickle as pickle
with open('./ring.pickle', 'rb') as f:
data = pickle.load(f)['mean']
print(data.shape)
print(data[0][-1][-1])
| 153 | 18.25 | 39 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/conv_split_cifar.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.
"""
Training script for split CIFAR 100 experiment.
"""
from __future__ import print_function
import argparse
import os
impor... | 37,561 | 48.816976 | 201 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/er_utils.py | import numpy as np
def update_reservior(images, labels, episodic_images, episodic_labels, M, N):
"""
Update the episodic memory with current example using the reservior sampling
"""
for er_x, er_y in zip(images, labels):
if M > N:
episodic_images[N] = er_x
episodic_label... | 4,885 | 42.625 | 183 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/vgg_utils.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.
import math
import tensorflow as tf
import numpy as np
def vgg_conv_layer(x, kernel_size, out_channels, stride, var_list, pad... | 2,219 | 36.627119 | 128 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/utils.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.
"""
Define some utility functions
"""
import numpy as np
import tensorflow as tf
def clone_variable_list(variable_list):
... | 17,295 | 41.600985 | 166 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/data_utils.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.
"""
Define utility functions for manipulating datasets
"""
import os
import numpy as np
import sys
from copy import deepcopy
... | 21,460 | 31.665145 | 130 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/vis_utils.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.
"""
Define some utility functions
"""
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.colors as ... | 4,627 | 34.875969 | 128 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/__init__.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.
from .data_utils import construct_permute_mnist, construct_split_mnist, construct_split_cifar, construct_rotate_mnist
from .da... | 1,214 | 85.785714 | 181 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/utils/resnet_utils.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.
import math
import tensorflow as tf
import numpy as np
def _conv(x, kernel_size, out_channels, stride, var_list, pad="SAME", ... | 5,216 | 38.225564 | 133 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/model/model.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.
"""
Model defintion
"""
import tensorflow as tf
import numpy as np
import mat... | 66,852 | 48.520741 | 223 | py |
stable-continual-learning | stable-continual-learning-master/external_libs/continual_learning_algorithms/model/__init__.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.
from .model import Model
| 219 | 30.428571 | 70 | py |
snare | snare-master/train.py | import os
from pathlib import Path
import hydra
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
import numpy as np
import random
import torch
import models
from data.dataset import CLIPGraspingDataset
from torch.utils.data import DataLoader
@hydra.main(config_path="cfgs... | 2,139 | 28.315068 | 97 | py |
snare | snare-master/models/single_cls.py | import numpy as np
import json
import os
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_lightning import LightningModule
import wandb
import models.aggregator as agg
class SingleClassifier(LightningModule):
def __init__(self, cfg, train_ds, val_ds):
... | 16,049 | 36.066975 | 226 | py |
snare | snare-master/models/zero_shot_cls.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.single_cls import SingleClassifier
class ZeroShotClassifier(SingleClassifier):
def __init__(self, cfg, train_ds, val_ds):
super().__init__(cfg, train_ds, val_ds)
self.logit_scale = nn.Parameter(torch... | 3,277 | 31.78 | 109 | py |
snare | snare-master/models/aggregator.py | import torch
import torch.nn as nn
class MaxPool(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
def forward(self, x):
x, _ = x.max(dim=-2) # [B 14 512] -> [B 512]
return x
class MeanPool(nn.Module):
def __init__(self, cfg):
super().__init_... | 1,455 | 26.471698 | 86 | py |
snare | snare-master/models/rotator.py | import numpy as np
import collections
import json
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import wandb
from models.single_cls import SingleClassifier
class Rotator(SingleClassifier):
def __init__(self, cfg, train_ds, val_ds):
self.estimate_init_state = False
... | 23,767 | 41.980108 | 335 | py |
snare | snare-master/models/__init__.py | from models.single_cls import SingleClassifier
from models.zero_shot_cls import ZeroShotClassifier
from models.rotator import Rotator
names = {
# classifiers
'single_cls': SingleClassifier,
'zero_shot_cls': ZeroShotClassifier,
# rotators
'rotator': Rotator,
}
| 282 | 20.769231 | 51 | py |
snare | snare-master/scripts/extract_clip_features.py | import os
import torch
from PIL import Image
import numpy as np
from numpy import asarray
import clip
import pickle, gzip, json
from tqdm import tqdm
# Set filepaths
shapenet_images_path = './data/shapenet-images/screenshots'
ann_files = ["train.json", "val.json", "test.json"]
folds = './amt/folds_adversarial'
keys... | 1,724 | 25.953125 | 89 | py |
snare | snare-master/scripts/aggregate_results.py |
import argparse
import json
import os
import numpy as np
import pandas as pd
from scipy.stats import ttest_ind
from tqdm import tqdm
clip_model_types = ['clip-single_cls-maxpool',
'clip-single_cls-meanpool',
'clip-single_cls-random_index',
'clip-single_cls-... | 13,282 | 46.270463 | 187 | py |
snare | snare-master/data/dataset.py | import os
import json
import torch
import torch.utils.data
import numpy as np
import gzip
import json
class CLIPGraspingDataset(torch.utils.data.Dataset):
def __init__(self, cfg, mode='train'):
self.total_views = 14
self.cfg = cfg
self.mode = mode
self.folds = os.path.join(self.cf... | 3,365 | 30.754717 | 130 | py |
snare | snare-master/data/__init__.py | 0 | 0 | 0 | py | |
pySDC | pySDC-master/pySDC/__init__.py | 0 | 0 | 0 | py | |
pySDC | pySDC-master/pySDC/core/Lagrange.py | import numpy as np
from scipy.special import roots_legendre
def computeFejerRule(n):
"""
Compute a Fejer rule of the first kind, using DFT (Waldvogel 2006)
Inspired from quadpy (https://github.com/nschloe/quadpy @Nico_Schlömer)
Parameters
----------
n : int
Number of points for the qu... | 7,567 | 31.34188 | 111 | py |
pySDC | pySDC-master/pySDC/core/Nodes.py | import numpy as np
from scipy.linalg import eigh_tridiagonal
NODE_TYPES = ['EQUID', 'LEGENDRE', 'CHEBY-1', 'CHEBY-2', 'CHEBY-3', 'CHEBY-4']
QUAD_TYPES = ['GAUSS', 'RADAU-LEFT', 'RADAU-RIGHT', 'LOBATTO']
class NodesError(Exception):
"""Exception class to handle error in NodesGenerator class"""
pass
class ... | 7,344 | 33.646226 | 124 | py |
pySDC | pySDC-master/pySDC/core/Sweeper.py | import logging
import numpy as np
import scipy.linalg
import scipy.optimize as opt
from pySDC.core.Errors import ParameterError
from pySDC.core.Level import level
from pySDC.core.Collocation import CollBase
from pySDC.helpers.pysdc_helper import FrozenClass
# short helper class to add params as attributes
class _Pa... | 16,446 | 37.607981 | 118 | py |
pySDC | pySDC-master/pySDC/core/ConvergenceController.py | import logging
from pySDC.helpers.pysdc_helper import FrozenClass
# short helper class to add params as attributes
class Pars(FrozenClass):
def __init__(self, params):
self.control_order = 0 # integer that determines the order in which the convergence controllers are called
self.useMPI = None # ... | 17,029 | 37.355856 | 120 | py |
pySDC | pySDC-master/pySDC/core/Collocation.py | import logging
import numpy as np
from pySDC.core.Nodes import NodesGenerator
from pySDC.core.Errors import CollocationError
from pySDC.core.Lagrange import LagrangeApproximation
class CollBase(object):
"""
Generic collocation class, that contains everything to do integration over
intervals and between n... | 8,334 | 34.021008 | 108 | py |
pySDC | pySDC-master/pySDC/core/Common.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
-----------
Module containing utility classe(s) from which inherit some of the pySDC base
classes.
"""
from pySDC.core.Errors import ReadOnlyError
class _MetaRegisterParams(type):
"""Metaclass for RegisterParams base class"""
def __new__(cls, na... | 2,606 | 33.302632 | 99 | py |
pySDC | pySDC-master/pySDC/core/Level.py | from pySDC.helpers.pysdc_helper import FrozenClass
# short helper class to add params as attributes
class _Pars(FrozenClass):
def __init__(self, params):
self.dt = None
self.dt_initial = None
self.restol = -1.0
self.nsweeps = 1
self.residual_type = 'full_abs'
for k,... | 5,436 | 29.038674 | 116 | py |
pySDC | pySDC-master/pySDC/core/Hooks.py | import logging
from collections import namedtuple
Entry = namedtuple('Entry', ['process', 'time', 'level', 'iter', 'sweep', 'type', 'num_restarts'])
# noinspection PyUnusedLocal,PyShadowingBuiltins,PyShadowingNames
class hooks(object):
"""
Hook class to contain the functions called during the controller run... | 8,349 | 33.504132 | 120 | py |
pySDC | pySDC-master/pySDC/core/BaseTransfer.py | import logging
import scipy.sparse as sp
from pySDC.core.Errors import UnlockError
from pySDC.helpers.pysdc_helper import FrozenClass
from pySDC.core.Lagrange import LagrangeApproximation
# short helper class to add params as attributes
class _Pars(FrozenClass):
def __init__(self, pars):
self.finter = F... | 8,395 | 33.838174 | 119 | py |
pySDC | pySDC-master/pySDC/core/__init__.py | 0 | 0 | 0 | py | |
pySDC | pySDC-master/pySDC/core/Controller.py | import logging
import os
import sys
import numpy as np
from pySDC.core.BaseTransfer import base_transfer
from pySDC.helpers.pysdc_helper import FrozenClass
from pySDC.implementations.convergence_controller_classes.check_convergence import CheckConvergence
from pySDC.implementations.hooks.default_hook import DefaultHoo... | 13,707 | 39.081871 | 124 | py |
pySDC | pySDC-master/pySDC/core/SpaceTransfer.py | import logging
from pySDC.helpers.pysdc_helper import FrozenClass
# short helper class to add params as attributes
class _Pars(FrozenClass):
def __init__(self, pars):
self.periodic = False
self.equidist_nested = True
self.iorder = 2
self.rorder = 2
for k, v in pars.items()... | 2,000 | 29.318182 | 93 | py |
pySDC | pySDC-master/pySDC/core/Step.py | import logging
from pySDC.core import Level as levclass
from pySDC.core.BaseTransfer import base_transfer
from pySDC.core.Errors import ParameterError
from pySDC.helpers.pysdc_helper import FrozenClass
# short helper class to add params as attributes
class _Pars(FrozenClass):
def __init__(self, params):
... | 11,352 | 33.195783 | 118 | py |
pySDC | pySDC-master/pySDC/core/Problem.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
-----------
Module containing the base Problem class for pySDC
"""
import logging
from pySDC.core.Common import RegisterParams
class WorkCounter(object):
"""
Utility class for counting iterations.
Contains one attribute `niter` initialized... | 3,945 | 30.31746 | 135 | py |
pySDC | pySDC-master/pySDC/core/Errors.py | class DataError(Exception):
"""
Error Class handling/indicating problems with data types
"""
pass
class ParameterError(Exception):
"""
Error Class handling/indicating problems with parameters (mostly within dictionaries)
"""
pass
class UnlockError(Exception):
"""
Error clas... | 1,382 | 16.2875 | 89 | py |
pySDC | pySDC-master/pySDC/projects/__init__.py | 0 | 0 | 0 | py | |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/emulate_hard_faults.py | import copy as cp
import random as rd
import numpy as np
# dirty, but easiest: global variables to control the injection and recovery
hard_iter = None
hard_step = None
strategy = None
hard_random = 0.0
hard_stats = []
refdata = None
def hard_fault_injection(S):
"""
Injects a node failure and recovers using ... | 9,040 | 31.174377 | 116 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/postproc_hard_faults_test.py | import matplotlib.pyplot as plt
import numpy as np
from pylab import rcParams
# import os
def create_plots(setup, cwd=''):
"""
Function to create heatmaps for faults at different steps and iterations
Args:
setup (str): name of the setup (heat or advection)
cwd: current working directory... | 2,887 | 28.773196 | 90 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/postproc_hard_faults_detail.py | import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
from pylab import rcParams
# import os
def create_plots(setup, cwd=''):
"""
Function to create detailed heatmaps and the iteration plot for a single fault
Args:
setup (str): name of the setup (heat or ad... | 5,794 | 29.182292 | 115 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/animate_convergence.py | import matplotlib.pyplot as plt
import numpy as np
from matplotlib import animation
from matplotlib import rc
def create_animation(cwd=''):
"""
Function to create an animated convergence plot
Args:
cwd: current working directory
"""
rc('font', family='sans-serif', size=30)
rc('legend... | 4,289 | 31.014925 | 109 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/postproc_boussinesq.py | import matplotlib
matplotlib.use('Agg')
import numpy as np
# import os
import matplotlib.pyplot as plt
from pylab import rcParams
axis_font = {'fontname': 'Arial', 'size': '8', 'family': 'serif'}
fs = 8
ms = 8
lw = 2
def create_plots(cwd=''):
"""
Function to plot the results of the fault-tolerant Boussine... | 5,161 | 29.187135 | 110 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/hard_faults_detail.py | import numpy as np
import pySDC.projects.deprecated.node_failure.emulate_hard_faults as ft
from pySDC.helpers.stats_helper import get_sorted, filter_stats
from pySDC.implementations.problem_classes.AdvectionEquation_ND_FD import advectionNd
from pySDC.implementations.problem_classes.HeatEquation_ND_FD import heatNd_f... | 6,315 | 38.72327 | 111 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/__init__.py | 0 | 0 | 0 | py | |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/postproc_grayscott.py | import os
import numpy as np
import pySDC.helpers.plot_helper as plt_helper
axis_font = {'fontname': 'Arial', 'size': '8', 'family': 'serif'}
fs = 8
ms = 8
lw = 2
def create_plots(cwd=''):
"""
Function to visualize the results of the Gray-Scott show case
Args:
cwd: current working directory
... | 5,592 | 32.291667 | 120 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/grayscott_example.py | import numpy as np
import pySDC.projects.node_failure.emulate_hard_faults as ft
from pySDC.helpers.stats_helper import get_sorted, filter_stats
from pySDC.implementations.problem_classes.GrayScott_1D_FEniCS_implicit import fenics_grayscott
from pySDC.implementations.sweeper_classes.generic_LU import generic_LU
from p... | 5,276 | 34.655405 | 112 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/controller_nonMPI_hard_faults.py | from pySDC.implementations.controller_classes.controller_nonMPI import controller_nonMPI
from pySDC.implementations.convergence_controller_classes.check_convergence import CheckConvergence
from pySDC.projects.node_failure.emulate_hard_faults import hard_fault_injection
class controller_nonMPI_hard_faults(controller_... | 11,076 | 36.805461 | 117 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/boussinesq_example.py | import numpy as np
import pySDC.projects.node_failure.emulate_hard_faults as ft
from pySDC.helpers.stats_helper import get_sorted, filter_stats
from pySDC.implementations.problem_classes.Boussinesq_2D_FD_imex import boussinesq_2d_imex
from pySDC.implementations.sweeper_classes.imex_1st_order import imex_1st_order
fro... | 5,580 | 35.960265 | 112 | py |
pySDC | pySDC-master/pySDC/projects/deprecated/node_failure/hard_faults_test.py | import numpy as np
import pySDC.projects.deprecated.node_failure.emulate_hard_faults as ft
from pySDC.helpers.stats_helper import get_sorted
from pySDC.implementations.problem_classes.AdvectionEquation_ND_FD import advectionNd
from pySDC.implementations.problem_classes.HeatEquation_ND_FD import heatNd_forced
from pyS... | 6,806 | 38.12069 | 112 | py |
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