repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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UQ360 | UQ360-main/uq360/algorithms/actively_learned_model/actively_learned_model.py | import numpy as np
import pandas as pd
from uq360.algorithms.builtinuq import BuiltinUQ
class ActivelyLearnedModel(BuiltinUQ):
"""ActivelyLearnedModel assumes an existing BuiltinUQ model, and implements an active learning training of this model. This code is supporting Pestourie et al. "Active learning of deep ... | 5,515 | 43.483871 | 293 | py |
UQ360 | UQ360-main/uq360/algorithms/layer_scoring/knn.py | import numpy as np
from uq360.algorithms.layer_scoring.latent_scorer import LatentScorer
from uq360.utils.transformers.nearest_neighbors import BaseNearestNeighbors
class KNNScorer(LatentScorer):
"""KNN-based latent space anomaly detector. Return some measure of distance to the training data."""
def _proces... | 3,683 | 37.778947 | 138 | py |
UQ360 | UQ360-main/uq360/algorithms/layer_scoring/latent_scorer.py | import abc
from abc import ABC
import torch
from uq360.algorithms.posthocuq import PostHocUQ
from uq360.utils.latent_features import LatentFeatures
class LatentScorer(PostHocUQ, ABC):
"""PostHoc Uncertainty Quantification base class for analyzing latent representations of data from a model"""
def __init__(s... | 1,715 | 31.377358 | 113 | py |
UQ360 | UQ360-main/uq360/algorithms/layer_scoring/aklpe.py | import numpy as np
from sklearn.model_selection import ShuffleSplit
from uq360.algorithms.layer_scoring.latent_scorer import LatentScorer
from uq360.utils.transformers.nearest_neighbors import BaseNearestNeighbors
class AKLPEScorer(LatentScorer):
"""Implementation of Averaged K nearest neighbors Localized P-valu... | 6,126 | 32.298913 | 118 | py |
UQ360 | UQ360-main/uq360/algorithms/layer_scoring/mahalanobis.py | import numpy as np
import torch
from sklearn.covariance import EmpiricalCovariance
from uq360.algorithms.layer_scoring.latent_scorer import LatentScorer
from uq360.utils.transformers.group_scaler import GroupScaler
class MahalanobisScorer(LatentScorer):
"""Implementation of the Mahalanobis Adversarial/Out-of-dis... | 2,899 | 30.182796 | 104 | py |
UQ360 | UQ360-main/uq360/algorithms/homoscedastic_gaussian_process_regression/homoscedastic_gaussian_process_regression.py | from collections import namedtuple
import botorch
import gpytorch
import numpy as np
import torch
from botorch.models import SingleTaskGP
from botorch.utils.transforms import normalize
from gpytorch.constraints import GreaterThan
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from uq360... | 7,026 | 42.645963 | 143 | py |
UQ360 | UQ360-main/uq360/utils/generate_1D_regression_data.py | import matplotlib.pyplot as plt
import numpy as np
import numpy.random as npr
import torch as torch
def make_data_gap(seed, data_count=100):
import GPy
npr.seed(0)
x = np.hstack([np.linspace(-5, -2, int(data_count/2)), np.linspace(2, 5, int(data_count/2))])
x = x[:, np.newaxis]
k = GPy.kern.RBF(in... | 1,959 | 30.612903 | 121 | py |
UQ360 | UQ360-main/uq360/utils/latent_features.py | from typing import Callable, List, Union
from torch import no_grad
from torch.nn import Module
class LatentFeatures:
def __init__(
self,
model: Callable,
layer: Union[Module, List[Module]],
post_processing_fn=None,
out_device: str = "cpu",
):
self.model = mode... | 1,124 | 24 | 79 | py |
CVNet | CVNet-main/core/checkpoint.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Functions that handle saving and loading of checkpoints."""
import os
import torch
from core.config import cfg
d... | 1,228 | 30.512821 | 112 | py |
CVNet | CVNet-main/core/CVNet_tester.py | r""" Test code of Correlation Verification Network """
# written by Seongwon Lee (won4113@yonsei.ac.kr)
import torch
import core.checkpoint as checkpoint
from core.config import cfg
from model.CVNet_Rerank_model import CVNet_Rerank
from test.test_model import test_model
def setup_model():
"""Sets up a model for t... | 1,004 | 31.419355 | 108 | py |
CVNet | CVNet-main/test/dataset.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""ImageNet dataset."""
import os
import re
import cv2
import numpy as np
import core.transforms as transforms
impor... | 3,570 | 35.438776 | 123 | py |
CVNet | CVNet-main/test/test_loader.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Data loader."""
import os
import torch
from test.dataset import DataSet
# Default data directory (/path/pycls/p... | 1,204 | 25.195652 | 102 | py |
CVNet | CVNet-main/test/test_model.py | # written by Seongwon Lee (won4113@yonsei.ac.kr)
import os
import torch
import numpy as np
from tqdm import tqdm
from test.config_gnd import config_gnd
from test.test_utils import extract_feature, rerank_ranks_revisitop, test_revisitop
from test.dataset import DataSet
@torch.no_grad()
def test_model(model, data_dir... | 3,823 | 41.021978 | 175 | py |
CVNet | CVNet-main/test/test_utils.py | # written by Seongwon Lee (won4113@yonsei.ac.kr)
import torch
import torch.nn.functional as F
from tqdm import tqdm
import os
import numpy as np
import test.test_loader as loader
from test.evaluate import compute_map
@torch.no_grad()
def extract_feature(model, data_dir, dataset, gnd_fn, split, scale_list):
with... | 6,006 | 35.852761 | 135 | py |
CVNet | CVNet-main/model/CVlearner.py | r""" Learner of Correlation Verification Network """
# written by Seongwon Lee (won4113@yonsei.ac.kr)
# Original code: HSNet (https://github.com/juhongm999/hsnet)
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base.conv4d import CenterPivotConv4d as Conv4d
class CVLearner(nn.Module):
de... | 3,307 | 46.942029 | 134 | py |
CVNet | CVNet-main/model/resnet.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""ResNe(X)t models."""
import torch
import torch.nn as nn
# Stage depths for ImageNet models
_IN_STAGE_DS = {50: (... | 8,266 | 35.100437 | 106 | py |
CVNet | CVNet-main/model/CVNet_Rerank_model.py | r""" Correlation Verification Network """
# written by Seongwon Lee (won4113@yonsei.ac.kr)
# Original code: HSNet (https://github.com/juhongm999/hsnet)
from functools import reduce
from operator import add
import torch
import torch.nn as nn
from model.resnet import ResNet
from .base.feature import extract_feat_res_... | 2,875 | 37.346667 | 136 | py |
CVNet | CVNet-main/model/base/correlation.py | r""" Provides functions that builds/manipulates correlation tensors """
# Original code: HSNet (https://github.com/juhongm999/hsnet)
import torch
import numpy as np
import torch.nn.functional as F
import math
from torch.nn.functional import interpolate as resize
from .geometry import Geometry
class Correlation:
@... | 2,852 | 37.554054 | 119 | py |
CVNet | CVNet-main/model/base/conv4d.py | r""" Implementation of center-pivot 4D convolution """
# Original code: HSNet (https://github.com/juhongm999/hsnet)
import torch
import torch.nn as nn
class CenterPivotConv4d(nn.Module):
r""" CenterPivot 4D conv"""
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=True):
... | 3,234 | 42.133333 | 158 | py |
CVNet | CVNet-main/model/base/geometry.py | r""" Provides functions that manipulate boxes and points """
# Original code: HSNet (https://github.com/juhongm999/hsnet)
import math
import torch.nn.functional as F
import torch
class Geometry(object):
@classmethod
def initialize(cls, img_size):
cls.img_size = img_size
cls.spatial_side = i... | 5,003 | 36.066667 | 110 | py |
OptimizerAmalgamation | OptimizerAmalgamation-main/baseline.py | """Evaluate Baseline.
Baseline results are saved in the ```./baseline``` folder.
Examples
--------
python baseline.py --problem=conv_train --optimizer=adam
Arguments
---------
--vgpu : int >= 1
(debug) Number of virtual GPUs to create for testing. If 1, no virtual GPUs
are created, and a mirrored strategy is... | 3,884 | 28.431818 | 79 | py |
STCE | STCE-main/ace_helpers.py | """ collection of various helper functions for running ACE"""
from multiprocessing import dummy as multiprocessing
import sys
import os
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec
import tcavvideo.model as model
import numpy as np
from PIL import Image
from skimage.segmentation import m... | 18,901 | 36.728543 | 116 | py |
STCE | STCE-main/tcavvideo/model.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, software
dis... | 19,119 | 37.470825 | 124 | py |
STCE | STCE-main/tcavvideo/preparing.py | """
load pre-trained keras imagenet network and keras labels
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow
from tensorflow.keras.applications import vgg16, resnet50
from tensorflow.keras.optimizers import SGD
import json
vgg16_model... | 1,439 | 30.304348 | 102 | py |
STCE | STCE-main/tcavvideo/tcav_examples/discrete/kdd99_model.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, software
dis... | 6,416 | 34.453039 | 117 | py |
DeepE | DeepE-main/buildtrain.py | import numpy as np
import math
import torch
import random
import copy
from scipy.stats import rankdata
import random
def evaluate(model,x_test,batch_size,target_dict):
#target_dict:用于filter
len_test = len(x_test)
batch_num = math.ceil(len(x_test) / batch_size)
tail_scores_all = []
tail_label = ... | 4,219 | 33.876033 | 160 | py |
DeepE | DeepE-main/model.py | import torch
from torch.nn import functional as F, Parameter
from torch.autograd import Variable
from torch.autograd import Variable
from torch.nn.init import xavier_normal_, xavier_uniform_
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class DeepEBlock(torch.nn.Module):
def __init__(s... | 6,007 | 35.192771 | 168 | py |
DeepE | DeepE-main/DeepE.py | from sklearn.metrics import roc_auc_score
import torch
import numpy as np
import os
import time
import datetime
from builddata_softplus import *
from collections import Counter
import random
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
import copy
from common import *
from buildtrain import *
from... | 4,268 | 53.037975 | 296 | py |
DeepE | DeepE-main/common.py | import torch
import numpy as np
import random
import math
from scipy.stats import rankdata
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
def get_doubles(tra... | 1,700 | 27.35 | 56 | py |
bleurt | bleurt-master/bleurt/wmt/downloaders.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 30,408 | 38.338939 | 84 | py |
mdeq | mdeq-master/tools/cls_valid.py | # Modified based on the HRNet repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import shutil
import pprint
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.... | 4,221 | 30.984848 | 78 | py |
mdeq | mdeq-master/tools/cls_train.py | # Modified based on the HRNet repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import pprint
import shutil
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import tor... | 9,874 | 37.574219 | 130 | py |
mdeq | mdeq-master/tools/seg_test.py | # Modified based on the HRNet repo.
import argparse
import os
import pprint
import shutil
import sys
import logging
import time
import timeit
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import _init_paths
import models
import datasets
from con... | 5,288 | 32.264151 | 100 | py |
mdeq | mdeq-master/tools/seg_train.py | # Modified based on the HRNet repo.
import argparse
import os
import pprint
import shutil
import sys
import logging
import time
import timeit
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
from torch.utils.data.distributed impor... | 13,283 | 38.301775 | 104 | py |
mdeq | mdeq-master/lib/modules/broyden.py | # Modified based on the DEQ repo.
import torch
from torch import nn
import torch.nn.functional as functional
from torch.autograd import Function
import numpy as np
import pickle
import sys
import os
from scipy.optimize import root
import time
from termcolor import colored
def _safe_norm(v):
if not torch.isfinit... | 8,424 | 35.004274 | 126 | py |
mdeq | mdeq-master/lib/modules/deq2d.py | # Modified based on the DEQ repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from torch import nn
import torch.nn.functional as functional
from torch.autograd import Function
import torch.autograd as autograd
import numpy as np
import pick... | 6,772 | 35.809783 | 120 | py |
mdeq | mdeq-master/lib/modules/optimizations.py | # Modified based on the DEQ repo.
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
class VariationalHidDropout2d(nn.Module):
def __init__(self, dropout=0.0, spatial=True):
"""
Hidden-to-hidden (VD-based) dropout that applies the same mask... | 5,027 | 34.408451 | 124 | py |
mdeq | mdeq-master/lib/core/seg_criterion.py | # Modified based on the HRNet repo.
import torch
import torch.nn as nn
from torch.nn import functional as F
class CrossEntropy(nn.Module):
def __init__(self, ignore_label=-1, weight=None):
super(CrossEntropy, self).__init__()
self.ignore_label = ignore_label
self.criterion = nn.CrossEntrop... | 2,168 | 39.924528 | 90 | py |
mdeq | mdeq-master/lib/core/cls_evaluate.py | # Modified based on the HRNet repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
... | 763 | 27.296296 | 73 | py |
mdeq | mdeq-master/lib/core/seg_function.py | # Modified based on the HRNet repo.
import logging
import os
import time
import numpy as np
import numpy.ma as ma
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.distributed as dist
from torch.nn import functional as F
from utils.utils import AverageMeter
from utils.utils import get_confusion_... | 7,369 | 33.92891 | 101 | py |
mdeq | mdeq-master/lib/core/cls_function.py | # Modified based on the HRNet repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import logging
import torch
from core.cls_evaluate import accuracy
logger = logging.getLogger(__name__)
def train(config, train_loader, model, criterion, o... | 5,324 | 32.917197 | 100 | py |
mdeq | mdeq-master/lib/models/mdeq_core.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import logging
import functools
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
sys.path.app... | 18,659 | 39.129032 | 125 | py |
mdeq | mdeq-master/lib/models/mdeq_forward_backward.py | # Modified based on the DEQ repo.
import torch
from torch import nn
import torch.nn.functional as functional
from torch.autograd import Function
import numpy as np
import sys
sys.path.append("../../")
from modules.deq2d import *
__author__ = "shaojieb"
class MDEQWrapper(DEQModule2d):
def __init__(self, func, f... | 1,135 | 30.555556 | 98 | py |
mdeq | mdeq-master/lib/models/mdeq.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import logging
import functools
from termcolor import colored
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.n... | 10,807 | 39.328358 | 125 | py |
mdeq | mdeq-master/lib/datasets/base_dataset.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Ke Sun (sunk@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
import os
import cv2
import numpy as np
import rando... | 9,255 | 39.243478 | 80 | py |
mdeq | mdeq-master/lib/datasets/cityscapes.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Ke Sun (sunk@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
import os
import cv2
import numpy as np
from PIL imp... | 8,297 | 39.478049 | 80 | py |
mdeq | mdeq-master/lib/datasets/pascal_ctx.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Ke Sun (sunk@mail.ustc.edu.cn)
# Referring to the implementation in
# https://github.com/zhanghang1989/PyTorch-Encoding
# -------------------------------------------... | 4,981 | 35.632353 | 80 | py |
mdeq | mdeq-master/lib/datasets/lip.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Ke Sun (sunk@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
import os
import cv2
import numpy as np
import torc... | 4,817 | 35.5 | 80 | py |
mdeq | mdeq-master/lib/utils/utils.py | # Modified based on the HRNet repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import time
from pathlib import Path
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
class FullModel(nn.Module... | 5,736 | 29.354497 | 79 | py |
mdeq | mdeq-master/lib/utils/modelsummary.py | # Modified based on the HRNet repo.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
from collections import namedtuple
import torch
import torch.nn as nn
def get_model_summary(model, *input_tensors, item_length=26, verbose=False):... | 4,657 | 35.108527 | 114 | py |
torchac | torchac-master/setup.py | import setuptools
import json
def _get_long_description():
with open('README.md', 'r') as f:
long_description_lines = []
skip = False
for line in f:
if '<div' in line:
skip = True
if '</div>' in line:
skip = False
if skip:
continue
long_description_lines.a... | 980 | 23.525 | 51 | py |
torchac | torchac-master/torchac/torchac.py | import os
import torch
import numpy as np
from torch.utils.cpp_extension import load
PRECISION = 16
# Load on-the-fly with ninja.
torchac_dir = os.path.dirname(os.path.realpath(__file__))
backend_dir = os.path.join(torchac_dir, 'backend')
torchac_backend = load(
name="torchac_backend",
sources=[os.path.join(bac... | 5,620 | 35.738562 | 81 | py |
torchac | torchac-master/torchac/__init__.py |
from torchac.torchac import encode_float_cdf
from torchac.torchac import decode_float_cdf
from torchac.torchac import encode_int16_normalized_cdf
from torchac.torchac import decode_int16_normalized_cdf
| 203 | 33 | 55 | py |
torchac | torchac-master/examples/mnist_autoencoder/mnist_autoencoder_example.py | import argparse
import dataclasses
import itertools
import warnings
import os
import time
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import torchvision
import matplotlib.pyplot as plt
import matplotlib
try:
import torchac
except ImportError:
raise ImportError('torchac i... | 15,333 | 32.77533 | 88 | py |
torchac | torchac-master/tests/test.py | import pytest
import torch
from torchac import torchac
def test_out_of_range_symbol():
cdf_float = torch.tensor([0., 1/3, 2/3, 1.], dtype=torch.float32).reshape(1, -1)
assert list(_encode_decode(cdf_float, [10],
needs_normalization=False,
check_input_bound... | 3,231 | 30.686275 | 82 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/tools/test.py | import argparse
import os
import tempfile
import os.path as osp
import shutil
import numpy as np
import resource
import mmcv
import torch
import torch.distributed as dist
from mmcv.runner import load_checkpoint, get_dist_info
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
import sys
sys.path.append... | 10,814 | 36.814685 | 111 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/tools/save_feat.py | import argparse
import os
import shutil
import tempfile
import torch
import resource
import torch.distributed as dist
import mmcv
from mmcv.runner import load_checkpoint, get_dist_info
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
import sys
sys.path.append(os.getcwd())
from mllt.apis import init... | 18,972 | 40.245652 | 138 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/tools/train.py | from __future__ import division
import os
import sys
sys.path.append(os.getcwd())
import argparse
from mmcv import Config, mkdir_or_exist
import mmcv
import os.path as osp
from mllt.datasets import build_dataset
from mllt.apis import (train_classifier, init_dist, get_root_logger,
set_random_see... | 3,514 | 31.247706 | 117 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/apis/train.py | from __future__ import division
import re
from collections import OrderedDict
import torch
from mmcv.runner import EpochBasedRunner, DistSamplerSeedHook, obj_from_dict
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner.checkpoint import load_checkpoint
from mllt import datasets
from... | 11,415 | 36.185668 | 131 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/apis/env.py | import logging
import os
import random
import subprocess
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from mmcv.runner import get_dist_info
def init_dist(launcher, backend='nccl', **kwargs):
if mp.get_start_method(allow_none=True) is None:
mp.set_sta... | 2,086 | 27.986111 | 70 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/core/evaluation/eval_hooks.py | import torch.distributed as dist
from mmcv.runner import Hook, obj_from_dict
from mmcv.parallel import scatter, collate
from torch.utils.data import Dataset
from .mean_ap import eval_map
from mllt import datasets
from .eval_tools import *
class DistEvalHook(Hook):
def __init__(self, dataset, interval=1, split=Fal... | 3,687 | 35.514851 | 80 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/core/evaluation/eval_tools.py | import numpy as np
from mllt.datasets.dataset_wrappers import ConcatDataset, RepeatDataset
import pickle
from mllt.datasets import build_dataset
import os
import os.path as osp
from mmcv import Config, mkdir_or_exist
from .display import *
import shutil
import torch
from sklearn.metrics import average_precision_score
f... | 6,139 | 37.616352 | 158 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/core/evaluation/mean_ap.py | import mmcv
import numpy as np
from terminaltables import AsciiTable
from .class_names import get_classes
from sklearn.metrics import average_precision_score, accuracy_score
import torch
def eval_map(results,
gt_labels,
dataset=None,
print_summary=True):
"""Evaluate mAP of a... | 7,224 | 31.111111 | 96 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/core/utils/dist_utils.py | from collections import OrderedDict
import torch.distributed as dist
from torch._utils import (_flatten_dense_tensors, _unflatten_dense_tensors,
_take_tensors)
from mmcv.runner import OptimizerHook
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
... | 1,941 | 32.482759 | 75 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/builder.py | import mmcv
from torch import nn
from mllt.utils import build_from_cfg
from .registry import (BACKBONES, NECKS, HEADS, LOSSES, CLASSIFIERS)
def build(cfg, registry, default_args=None):
if isinstance(cfg, list):
modules = [build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg]
return nn.Seq... | 765 | 21.529412 | 80 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/plugins/generalized_attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from mmcv.cnn import kaiming_init
class GeneralizedAttention(nn.Module):
"""GeneralizedAttention module.
See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
(https://arxiv.org/abs/1711... | 15,139 | 38.324675 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/necks/pfc.py | from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torch
import torchvision
from ..registry import NECKS
@NECKS.register_module
class PFC(nn.Module):
def __init__(self, in_channels, out_channels, dropout, norm=False,relu=False,layers=1):
super(PFC, self).__init_... | 1,585 | 28.37037 | 91 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/necks/mlp.py | from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torchvision
from ..registry import NECKS
@NECKS.register_module
class MLP(nn.Module):
def __init__(self, in_channels, bottle_neck, out_channels, dropout1, dropout2, norm=False):
super(MLP, self).__init__()
... | 1,606 | 29.903846 | 95 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/necks/fpn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from ..registry import NECKS
from ..utils import ConvModule
@NECKS.register_module
class FPN(nn.Module):
def __init__(self,
in_channels,
out_channels,
start_leve... | 4,186 | 31.457364 | 101 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/classifiers/base.py | import logging
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
import torch.nn as nn
from mmcv.parallel import DataContainer as DC
class BaseClassifier(nn.Module):
"""Base class for classifiers"""
__metaclass__ = ABCMeta
def __init__(self):
super(BaseClassifier, self).__i... | 2,151 | 28.479452 | 77 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/classifiers/simple.py | import torch.nn as nn
from .base import BaseClassifier
from .. import builder
from ..registry import CLASSIFIERS
from mmcv.parallel import DataContainer as DC
import torch
import numpy as np
@CLASSIFIERS.register_module
class SimpleClassifier(BaseClassifier):
def __init__(self,
backbone,
... | 4,796 | 31.632653 | 125 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/utils/weight_init.py | import numpy as np
import torch.nn as nn
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
i... | 1,455 | 29.978723 | 71 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/utils/norm.py | import torch.nn as nn
norm_cfg = {
# format: layer_type: (abbreviation, module)
'BN': ('bn', nn.BatchNorm2d),
'SyncBN': ('bn', nn.SyncBatchNorm),
'GN': ('gn', nn.GroupNorm),
# and potentially 'SN'
}
def build_norm_layer(cfg, num_features, postfix=''):
""" Build normalization layer
Args:
... | 1,684 | 29.089286 | 74 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/utils/scale.py | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
| 266 | 19.538462 | 73 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/utils/conv_ws.py | import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1... | 1,335 | 27.425532 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/utils/conv_module.py | import warnings
import torch.nn as nn
from mmcv.cnn import kaiming_init, constant_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
# TODO: octave conv
}
def build_conv_layer(cfg, *args, **kwargs):
""" Build convolution layer
... | 5,543 | 32.804878 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/losses/resample_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import mmcv
from .utils import weight_reduce_loss
from ..registry import LOSSES
from .cross_entropy_loss import cross_entropy, _expand_binary_labels, binary_cross_entropy, partial_cross_entropy
import numpy as np
import time
import matplotlib.pyplot as ... | 8,066 | 38.935644 | 156 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/losses/utils.py | import functools
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = ... | 2,982 | 29.438776 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/losses/accuracy.py | import torch.nn as nn
import torch
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
res = []
mask = target >= 0
for k in topk:
_, idx = pred.topk... | 1,259 | 25.808511 | 99 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/losses/focal_loss.py | import torch
import torch.nn as nn
from ..registry import LOSSES
from .cross_entropy_loss import cross_entropy, binary_cross_entropy, partial_cross_entropy, kpos_cross_entropy
@LOSSES.register_module
class FocalLoss(nn.Module):
def __init__(self,
use_sigmoid=False,
use_kpos=Fal... | 1,968 | 32.372881 | 110 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/losses/cross_entropy_loss.py | from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import weight_reduce_loss
from ..registry import LOSSES
import numpy as np
def _squeeze_binary_labels(label):
if label.size(1) == 1:
squeeze_label = label.view(len(label), -1)
else:
... | 5,557 | 30.224719 | 84 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/backbones/resnet.py | import logging
import torch.nn as nn
import torch.utils.checkpoint as cp
from torch.nn.modules.batchnorm import _BatchNorm
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mllt.models.plugins import GeneralizedAttention
from ..registry import BACKBONES
from ..utils impor... | 14,037 | 30.904545 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/backbones/inception3.py | """Inception based base model and classifier cloned from the set-operations experiments.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.inception import InceptionA, InceptionB, InceptionC, InceptionD
from torchvision.models.inception import InceptionE, InceptionAux, Basic... | 4,579 | 36.85124 | 100 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/backbones/backbone_collection.py | # This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate
import torch
from torch.autograd import Variable
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.nn.utils.weight_norm import WeightNorm
# Basic ResNet model
def init_layer(L):
... | 14,981 | 35.541463 | 206 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/backbones/resnext.py | import math
import torch.nn as nn
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
class Bottleneck(_Bottleneck):
def __init__(self, inplanes, planes, groups=1, base_width=4, **kwargs):
"""Bo... | 6,084 | 32.805556 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/heads/weldon_head.py | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
from torch.autograd import Function
from mmcv.cnn import constant_init, kaiming_init
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
class WeldonPool2dFunction(Function):
... | 5,010 | 30.31875 | 150 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/heads/cls_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
from torch.nn import Parameter
import numpy as np
import mmcv
@HEADS.register_module
class ClsHead(nn.Module):
"""Simplest classification head, with only o... | 2,706 | 32.419753 | 108 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/heads/gcn_head.py | import torchvision.models as models
from torch.nn import Parameter
from torch.autograd import Variable
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
import pickle
class G... | 5,013 | 30.142857 | 116 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/models/heads/cos_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
import math
from torch.nn.parameter import Parameter
@HEADS.register_module
class CosHead(nn.Module):
"""Simplest classification head, with only one fc lay... | 2,856 | 31.465909 | 100 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/custom.py | import os.path as osp
import mmcv
import numpy as np
from mmcv.parallel import DataContainer as DC
from torch.utils.data import Dataset
from .registry import DATASETS
from .transforms import ImageTransform, Numpy2Tensor
from .utils import to_tensor, random_scale
from .extra_aug import ExtraAugmentation
import cv2
@D... | 11,975 | 33.915452 | 98 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/miniImagenet.py | # This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate
import torch
from PIL import Image
import json
import numpy as np
import torchvision.transforms as transforms
import os
from .custom import CustomDataset
from .registry import DATASETS
import mmcv
import random
import pickle
... | 2,834 | 31.965116 | 92 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/utils.py | import copy
from collections import Sequence
import mmcv
from mmcv.runner import obj_from_dict
import torch
import numpy as np
from .. import datasets
__all__ = [
'to_tensor', 'random_scale', 'get_dataset'
]
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Suppo... | 3,270 | 27.946903 | 72 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/dataset_wrappers.py | import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .registry import DATASETS
@DATASETS.register_module
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for... | 1,991 | 31.129032 | 78 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/transforms.py | import mmcv
import numpy as np
import torch
__all__ = [
'ImageTransform', 'Numpy2Tensor'
]
class ImageTransform(object):
"""Preprocess an image.
1. rescale the image to expected size
2. normalize the image
3. flip the image (if needed)
4. pad the image (if needed)
5. transpose to (c, h, ... | 1,748 | 27.672131 | 79 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/loader/sampler.py | from __future__ import division
import math
import torch
import numpy as np
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
from torch.utils.data import DistributedSampler as _DistributedSampler
import random
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, nu... | 11,738 | 34.572727 | 140 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/mllt/datasets/loader/build_loader.py | from functools import partial
from mmcv.runner import get_dist_info
from mmcv.parallel import collate
from torch.utils.data import DataLoader
from .sampler import GroupSampler, DistributedGroupSampler, DistributedSampler, FastRandomIdentitySampler, ClassAwareSampler
# https://github.com/pytorch/pytorch/issues/973
imp... | 2,583 | 37 | 124 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/configs/voc/LT_resnet50_pfc_DB.py | # model settings
model = dict(
type='SimpleClassifier',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='PFC',
in_channe... | 3,201 | 27.846847 | 90 | py |
DistributionBalancedLoss | DistributionBalancedLoss-master/configs/coco/LT_resnet50_pfc_DB.py | # model settings
model = dict(
type='SimpleClassifier',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='PFC',
in_channe... | 3,532 | 29.196581 | 85 | py |
pyserini | pyserini-master/scripts/generate_trec_covid_round1_OSHU_RUN2.py | #Title: TREC_COVID_Round1_OHSU.py
#Author: Jimmy Chen, School of Medicine, OHSU
#Description: Generate 1000 documents per topic in Round 1 TREC_COVID and get trec_eval metrics
# To replicate OSHU_RUN2
# Results: https://ir.nist.gov/covidSubmit/archive/round1/OHSU_RUN2.pdf
#
# In root pyserini directory:
#
# 1. wget ht... | 8,278 | 37.328704 | 127 | py |
pyserini | pyserini-master/scripts/kilt/encode_kilt_topics.py | #
# Pyserini: Reproducible IR research with sparse and dense representations
#
# 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... | 2,179 | 39.37037 | 112 | py |
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