Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
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null | OpenOOD-main/openood/networks/__init__.py | from .ash_net import ASHNet
from .densenet import DenseNet3
# from .mmcls_featext import ImageClassifierWithReturnFeature
from .resnet18_32x32 import ResNet18_32x32
from .resnet18_224x224 import ResNet18_224x224
from .resnet50 import ResNet50
from .utils import get_network
from .wrn import WideResNet
from .swin_t impor... | 360 | 31.818182 | 61 | py |
null | OpenOOD-main/openood/networks/arpl_net.py | ## reference code https://github.com/pytorch/examples/blob/master/dcgan/main.py
import operator
from collections import OrderedDict
from itertools import islice
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.mo... | 22,297 | 33.410494 | 79 | py |
null | OpenOOD-main/openood/networks/ash_net.py | import numpy as np
import torch
import torch.nn as nn
class ASHNet(nn.Module):
def __init__(self, backbone):
super(ASHNet, self).__init__()
self.backbone = backbone
def forward(self, x, return_feature=False, return_feature_list=False):
try:
return self.backbone(x, return_f... | 2,614 | 25.958763 | 79 | py |
null | OpenOOD-main/openood/networks/bit.py | """Bottleneck ResNet v2 with GroupNorm and Weight Standardization."""
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
class Reshape(nn.Module):
def __init__(self, *args):
super(Reshape, self).__init__()
self.shape = args
def forward(sel... | 14,517 | 36.611399 | 80 | py |
null | OpenOOD-main/openood/networks/cider_net.py | import torch.nn as nn
import torch.nn.functional as F
class CIDERNet(nn.Module):
def __init__(self, backbone, head, feat_dim, num_classes):
super(CIDERNet, self).__init__()
self.backbone = backbone
if hasattr(self.backbone, 'fc'):
# remove fc otherwise ddp will
# r... | 1,174 | 31.638889 | 76 | py |
null | OpenOOD-main/openood/networks/conf_branch_net.py | import torch.nn as nn
class ConfBranchNet(nn.Module):
def __init__(self, backbone, num_classes):
super(ConfBranchNet, self).__init__()
self.backbone = backbone
if hasattr(self.backbone, 'fc'):
# remove fc otherwise ddp will
# report unused params
self.b... | 918 | 26.029412 | 58 | py |
null | OpenOOD-main/openood/networks/csi_net.py | import torch.nn as nn
def get_csi_linear_layers(feature_size,
num_classes,
simclr_dim,
shift_trans_type='rotation'):
simclr_layer = nn.Sequential(
nn.Linear(feature_size, feature_size),
nn.ReLU(),
nn.Linear(featu... | 2,816 | 27.744898 | 71 | py |
null | OpenOOD-main/openood/networks/de_resnet18_256x256.py | import torch
import torch.nn as nn
from torch import Tensor
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, upsample=None):
super(BasicBlock, self).__init__()
self.stride = stride
if self.stride == 2:
self.conv1 = nn.ConvTranspose... | 8,342 | 34.502128 | 75 | py |
null | OpenOOD-main/openood/networks/densenet.py | 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)
self.relu = nn.ReLU(inplace=True)
self.conv1... | 6,812 | 36.434066 | 79 | py |
null | OpenOOD-main/openood/networks/draem_net.py | import torch
import torch.nn as nn
class ReconstructiveSubNetwork(nn.Module):
def __init__(self, in_channels=3, out_channels=3, base_width=128):
super(ReconstructiveSubNetwork, self).__init__()
self.encoder = EncoderReconstructive(in_channels, base_width)
self.decoder = DecoderReconstructi... | 14,225 | 41.849398 | 79 | py |
null | OpenOOD-main/openood/networks/dropout_net.py | import torch.nn as nn
import torch.nn.functional as F
class DropoutNet(nn.Module):
def __init__(self, backbone, dropout_p):
super(DropoutNet, self).__init__()
self.backbone = backbone
self.dropout_p = dropout_p
def forward(self, x, use_dropout=True):
if use_dropout:
... | 651 | 27.347826 | 69 | py |
null | OpenOOD-main/openood/networks/dsvdd_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class MNIST_LeNet(nn.Module):
def __init__(self):
super().__init__()
self.rep_dim = 32
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(1, 8, 5, bias=False, padding=2)
self.bn1 = nn.BatchNorm2d(8, eps... | 10,502 | 37.054348 | 76 | py |
null | OpenOOD-main/openood/networks/godin_net.py | import torch
import torch.nn as nn
def norm(x):
norm = torch.norm(x, p=2, dim=1)
x = x / (norm.expand(1, -1).t() + .0001)
return x
class CosineDeconf(nn.Module):
def __init__(self, in_features, num_classes):
super(CosineDeconf, self).__init__()
self.h = nn.Linear(in_features, num_cl... | 3,133 | 26.017241 | 77 | py |
null | OpenOOD-main/openood/networks/lenet.py | import logging
import torch.nn as nn
logger = logging.getLogger(__name__)
class LeNet(nn.Module):
def __init__(self, num_classes, num_channel=3):
super(LeNet, self).__init__()
self.num_classes = num_classes
self.feature_size = 84
self.block1 = nn.Sequential(
nn.Conv2d... | 2,170 | 33.460317 | 79 | py |
null | OpenOOD-main/openood/networks/mcd_net.py | import torch.nn as nn
class MCDNet(nn.Module):
def __init__(self, backbone, num_classes):
super(MCDNet, self).__init__()
self.backbone = backbone
try:
feature_size = backbone.feature_size
except AttributeError:
feature_size = backbone.module.feature_size
... | 732 | 24.275862 | 58 | py |
null | OpenOOD-main/openood/networks/mmcls_featext.py | from mmcls.models import CLASSIFIERS, ImageClassifier
@CLASSIFIERS.register_module()
class ImageClassifierWithReturnFeature(ImageClassifier):
def forward(self, x, *args, **kwargs):
if 'return_feature' in kwargs:
return self.backbone(x)[0][-1]
else:
return super().forward(x,... | 338 | 29.818182 | 56 | py |
null | OpenOOD-main/openood/networks/net_utils_.py | from types import MethodType
import mmcv
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
from mmcls.apis import init_model
import openood.utils.comm as comm
from .bit import KNOWN_MODELS
from .conf_branch_net import ConfBranchNet
from .csi_net import CSINet
from .de_resnet1... | 10,932 | 38.756364 | 79 | py |
null | OpenOOD-main/openood/networks/npos_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class NPOSNet(nn.Module):
def __init__(self, backbone, head, feat_dim, num_classes):
super(NPOSNet, self).__init__()
self.backbone = backbone
if hasattr(self.backbone, 'fc'):
# remove fc otherwise ddp will
... | 1,468 | 33.97619 | 79 | py |
null | OpenOOD-main/openood/networks/opengan.py | from torch import nn
class Generator(nn.Module):
def __init__(self, in_channels=100, feature_size=64, out_channels=512):
super(Generator, self).__init__()
self.nz = in_channels
self.ngf = feature_size
self.nc = out_channels
self.main = nn.Sequential(
# input is... | 2,526 | 37.876923 | 75 | py |
null | OpenOOD-main/openood/networks/openmax_net.py | import torch.nn as nn
import torch.nn.functional as F
class OpenMax(nn.Module):
def __init__(self, backbone, num_classes=50, embed_dim=None):
super(OpenMax, self).__init__()
self.backbone_name = backbone
self.backbone = backbone
self.dim = self.get_backbone_last_layer_out_channel(... | 2,001 | 35.4 | 79 | py |
null | OpenOOD-main/openood/networks/patchcore_net.py | import torch
import torch.nn as nn
class PatchcoreNet(nn.Module):
def __init__(self, backbone):
super(PatchcoreNet, self).__init__()
# def hook_t(module, input, output):
# self.features.append(output)
# path = '/home/pengyunwang/.cache/torch/hub/vision-0.9.0'
# module... | 1,294 | 28.431818 | 68 | py |
null | OpenOOD-main/openood/networks/projection_net.py | import torch.nn as nn
from torchvision.models import resnet18
class ProjectionNet(nn.Module):
def __init__(self,
backbone,
head_layers=[512, 512, 512, 512, 512, 512, 512, 512, 128],
num_classes=2):
super(ProjectionNet, self).__init__()
self.backbo... | 1,343 | 32.6 | 79 | py |
null | OpenOOD-main/openood/networks/react_net.py | import torch.nn as nn
class ReactNet(nn.Module):
def __init__(self, backbone):
super(ReactNet, self).__init__()
self.backbone = backbone
def forward(self, x, return_feature=False, return_feature_list=False):
try:
return self.backbone(x, return_feature, return_feature_list)... | 822 | 31.92 | 79 | py |
null | OpenOOD-main/openood/networks/resnet18_224x224.py | from torchvision.models.resnet import BasicBlock, ResNet
class ResNet18_224x224(ResNet):
def __init__(self,
block=BasicBlock,
layers=[2, 2, 2, 2],
num_classes=1000):
super(ResNet18_224x224, self).__init__(block=block,
... | 2,368 | 30.586667 | 79 | py |
null | OpenOOD-main/openood/networks/resnet18_256x256.py | import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_s... | 8,058 | 34.817778 | 79 | py |
null | OpenOOD-main/openood/networks/resnet18_32x32.py | import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_s... | 5,976 | 34.577381 | 79 | py |
null | OpenOOD-main/openood/networks/resnet18_64x64.py | import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_s... | 5,378 | 36.615385 | 79 | py |
null | OpenOOD-main/openood/networks/resnet50.py | from torchvision.models.resnet import Bottleneck, ResNet
class ResNet50(ResNet):
def __init__(self,
block=Bottleneck,
layers=[3, 4, 6, 3],
num_classes=1000):
super(ResNet50, self).__init__(block=block,
layers=layers,... | 2,337 | 30.173333 | 79 | py |
null | OpenOOD-main/openood/networks/rot_net.py | import torch.nn as nn
class RotNet(nn.Module):
def __init__(self, backbone, num_classes):
super(RotNet, self).__init__()
self.backbone = backbone
if hasattr(self.backbone, 'fc'):
# remove fc otherwise ddp will
# report unused params
self.backbone.fc = n... | 885 | 26.6875 | 58 | py |
null | OpenOOD-main/openood/networks/rts_net.py | import torch
import torch.nn as nn
class RTSNet(nn.Module):
def __init__(self, backbone, feature_size, num_classes,
dof=16):
'''
dof: degree of freedom of variance
'''
super(RTSNet, self).__init__()
self.backbone = backbone
self.feature_size = featu... | 840 | 28 | 67 | py |
null | OpenOOD-main/openood/networks/simclr_net.py | import torch.nn as nn
import torch.nn.functional as F
class SimClrNet(nn.Module):
def __init__(self, backbone, out_dim=128) -> None:
super(SimClrNet, self).__init__()
self.backbone = backbone
feature_dim = backbone.feature_size
self.simclr_head = nn.Sequential(
nn.Line... | 651 | 31.6 | 74 | py |
null | OpenOOD-main/openood/networks/swin_t.py | from torchvision.models.swin_transformer import SwinTransformer
class Swin_T(SwinTransformer):
def __init__(self,
patch_size=[4, 4],
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=[7, 7],
... | 1,667 | 30.471698 | 79 | py |
null | OpenOOD-main/openood/networks/temp.py | """ResNet in PyTorch.
ImageNet-Style ResNet
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
Adapted from: https://github.com/bearpaw/pytorch-classification
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(n... | 6,465 | 32.158974 | 88 | py |
null | OpenOOD-main/openood/networks/udg_net.py | import torch.nn as nn
class UDGNet(nn.Module):
def __init__(self, backbone, num_classes, num_clusters):
super(UDGNet, self).__init__()
self.backbone = backbone
if hasattr(self.backbone, 'fc'):
# remove fc otherwise ddp will
# report unused params
self.ba... | 999 | 32.333333 | 68 | py |
null | OpenOOD-main/openood/networks/utils.py | # import mmcv
from copy import deepcopy
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
# from mmcls.apis import init_model
import openood.utils.comm as comm
from .bit import KNOWN_MODELS
from .conf_branch_net import ConfBranchNet
from .csi_net import get_csi_linear_layers, ... | 15,911 | 38.483871 | 79 | py |
null | OpenOOD-main/openood/networks/vit.py | # model settings
model = dict(type='ImageClassifierWithReturnFeature',
backbone=dict(type='VisionTransformer',
arch='b',
img_size=384,
patch_size=16,
drop_rate=0.1,
init_cf... | 902 | 38.26087 | 58 | py |
null | OpenOOD-main/openood/networks/vit_b_16.py | import torch
from torchvision.models.vision_transformer import VisionTransformer
class ViT_B_16(VisionTransformer):
def __init__(self,
image_size=224,
patch_size=16,
num_layers=12,
num_heads=12,
hidden_dim=768,
m... | 2,131 | 30.820896 | 79 | py |
null | OpenOOD-main/openood/networks/wrn.py | 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, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
s... | 5,513 | 34.121019 | 78 | py |
null | OpenOOD-main/openood/pipelines/__init__.py | from .utils import get_pipeline
| 32 | 15.5 | 31 | py |
null | OpenOOD-main/openood/pipelines/feat_extract_opengan_pipeline.py | from openood.datasets import get_dataloader, get_ood_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.utils import setup_logger
class FeatExtractOpenGANPipeline:
def __init__(self, config) -> None:
self.config = config
def run(self):
... | 1,579 | 33.347826 | 69 | py |
null | OpenOOD-main/openood/pipelines/feat_extract_pipeline.py | from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.utils import setup_logger
class FeatExtractPipeline:
def __init__(self, config) -> None:
self.config = config
def run(self):
# generate output directo... | 1,145 | 30.833333 | 70 | py |
null | OpenOOD-main/openood/pipelines/finetune_pipeline.py | from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from openood.utils import setup_logger
class FinetunePipeline:
def __init__(self, config) -> None:
... | 1,882 | 33.87037 | 78 | py |
null | OpenOOD-main/openood/pipelines/test_acc_pipeline.py | from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.utils import setup_logger
class TestAccPipeline:
def __init__(self, config) -> None:
self.config = config
def run(self):
# generate output directory a... | 924 | 28.83871 | 65 | py |
null | OpenOOD-main/openood/pipelines/test_ad_pipeline.py | from openood.datasets import get_dataloader, get_ood_dataloader
from openood.evaluators.utils import get_evaluator
from openood.networks.utils import get_network
from openood.postprocessors import get_postprocessor
from openood.utils import setup_logger
class TestAdPipeline:
def __init__(self, config) -> None:
... | 1,136 | 32.441176 | 79 | py |
null | OpenOOD-main/openood/pipelines/test_ood_pipeline.py | import time
from openood.datasets import get_dataloader, get_ood_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.postprocessors import get_postprocessor
from openood.utils import setup_logger
class TestOODPipeline:
def __init__(self, config) -> None:
... | 2,281 | 34.65625 | 76 | py |
null | OpenOOD-main/openood/pipelines/test_ood_pipeline_aps.py | from openood.datasets import get_dataloader, get_ood_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.postprocessors import get_postprocessor
from openood.utils import setup_logger
class TestOODPipelineAPS:
def __init__(self, config) -> None:
se... | 1,538 | 33.977273 | 79 | py |
null | OpenOOD-main/openood/pipelines/train_ad_pipeline.py | from openood.datasets import get_dataloader, get_ood_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.postprocessors import get_postprocessor
from openood.preprocessors.utils import get_preprocessor
from openood.recorders import get_recorder
from openood.trai... | 2,301 | 38.016949 | 74 | py |
null | OpenOOD-main/openood/pipelines/train_aux_pipeline.py | from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from openood.utils import setup_logger
class TrainARPLGANPipeline:
def __init__(self, config) -> Non... | 2,091 | 37.036364 | 78 | py |
null | OpenOOD-main/openood/pipelines/train_ddt_pipeline.py | import openood.utils.comm as comm
from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from openood.utils import setup_logger
class TrainPipeline:
def __... | 2,054 | 33.830508 | 78 | py |
null | OpenOOD-main/openood/pipelines/train_oe_pipeline.py | import numpy as np
import torch
import openood.utils.comm as comm
from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from openood.utils import setup_logger
... | 2,300 | 32.838235 | 78 | py |
null | OpenOOD-main/openood/pipelines/train_only_pipeline.py | from openood.datasets import get_feature_dataloader
from openood.networks import get_network
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from openood.utils import setup_logger
class TrainOpenGanPipeline:
def __init__(self, config) -> None:
self.config = config
... | 1,131 | 29.594595 | 72 | py |
null | OpenOOD-main/openood/pipelines/train_opengan_pipeline.py | import numpy as np
import torch
from openood.datasets import get_feature_opengan_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.postprocessors import get_postprocessor
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from... | 2,024 | 33.322034 | 78 | py |
null | OpenOOD-main/openood/pipelines/train_pipeline.py | import numpy as np
import torch
import openood.utils.comm as comm
from openood.datasets import get_dataloader
from openood.evaluators import get_evaluator
from openood.networks import get_network
from openood.recorders import get_recorder
from openood.trainers import get_trainer
from openood.utils import setup_logger
... | 3,143 | 37.341463 | 75 | py |
null | OpenOOD-main/openood/pipelines/utils.py | from openood.utils import Config
from .feat_extract_pipeline import FeatExtractPipeline
from .feat_extract_opengan_pipeline import FeatExtractOpenGANPipeline
from .finetune_pipeline import FinetunePipeline
from .test_acc_pipeline import TestAccPipeline
from .test_ad_pipeline import TestAdPipeline
from .test_ood_pipeli... | 1,309 | 36.428571 | 69 | py |
null | OpenOOD-main/openood/postprocessors/__init__.py | from .ash_postprocessor import ASHPostprocessor
from .base_postprocessor import BasePostprocessor
from .cider_postprocessor import CIDERPostprocessor
from .conf_branch_postprocessor import ConfBranchPostprocessor
from .cutpaste_postprocessor import CutPastePostprocessor
from .dice_postprocessor import DICEPostprocessor... | 2,011 | 50.589744 | 71 | py |
null | OpenOOD-main/openood/postprocessors/ash_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_postprocessor import BasePostprocessor
class ASHPostprocessor(BasePostprocessor):
def __init__(self, config):
super(ASHPostprocessor, self).__init__(config)
self.args = self.co... | 903 | 29.133333 | 70 | py |
null | OpenOOD-main/openood/postprocessors/base_postprocessor.py | from typing import Any
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import openood.utils.comm as comm
class BasePostprocessor:
def __init__(self, config):
self.config = config
def setup(self, net: nn.Module, id_loader_dict, ood_loader_dict):
... | 1,389 | 29.217391 | 78 | py |
null | OpenOOD-main/openood/postprocessors/cider_postprocessor.py | from typing import Any
import faiss
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class CIDERPostprocessor(BasePostprocessor):
def __init__(self, config):
super(CIDERPostprocessor, self).__init__(config)
self.args = ... | 1,964 | 31.75 | 78 | py |
null | OpenOOD-main/openood/postprocessors/conf_branch_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
class ConfBranchPostprocessor(BasePostprocessor):
def __init__(self, config):
super(ConfBranchPostprocessor, self).__init__(config)
self.config = config
@torch.no_grad()
def postp... | 522 | 25.15 | 61 | py |
null | OpenOOD-main/openood/postprocessors/cutpaste_postprocessor.py | from __future__ import division, print_function
from typing import Any
import numpy as np
import torch
import torch.nn as nn
from sklearn.covariance import LedoitWolf as LW
from torch.utils.data import DataLoader
from tqdm import tqdm
class CutPastePostprocessor:
def __init__(self, config):
self.config ... | 4,300 | 35.760684 | 75 | py |
null | OpenOOD-main/openood/postprocessors/dice_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
normalizer = lambda x: x / np.linalg.norm(x, axis=-1, keepdims=True) + 1e-10
class DICEPostprocessor(BasePostprocessor):
def __init__(self, config):
super... | 2,355 | 34.164179 | 76 | py |
null | OpenOOD-main/openood/postprocessors/draem_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
class DRAEMPostprocessor(BasePostprocessor):
def __init__(self, config):
super(DRAEMPostprocessor, self).__init__(config)
@torch.no_grad()
def postprocess(self, net: nn... | 956 | 28.90625 | 63 | py |
null | OpenOOD-main/openood/postprocessors/dropout_postprocessor.py | from typing import Any
import torch
from torch import nn
from .base_postprocessor import BasePostprocessor
class DropoutPostProcessor(BasePostprocessor):
def __init__(self, config):
self.config = config
self.args = config.postprocessor.postprocessor_args
self.dropout_times = self.args.dr... | 810 | 31.44 | 76 | py |
null | OpenOOD-main/openood/postprocessors/dsvdd_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from openood.trainers.dsvdd_trainer import init_center_c
from .base_postprocessor import BasePostprocessor
class DSVDDPostprocessor(BasePostprocessor):
def __init__(self, config):
super(DSVDDPostprocessor, self).__init__(config)
self.hyp... | 1,206 | 31.621622 | 75 | py |
null | OpenOOD-main/openood/postprocessors/ebo_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
class EBOPostprocessor(BasePostprocessor):
def __init__(self, config):
super().__init__(config)
self.args = self.config.postprocessor.postprocessor_args
self.temperature = self.arg... | 919 | 29.666667 | 76 | py |
null | OpenOOD-main/openood/postprocessors/ensemble_postprocessor.py | import os.path as osp
from copy import deepcopy
from typing import Any
import torch
from torch import nn
from .base_postprocessor import BasePostprocessor
class EnsemblePostprocessor(BasePostprocessor):
def __init__(self, config):
super(EnsemblePostprocessor, self).__init__(config)
self.config =... | 1,970 | 36.188679 | 76 | py |
null | OpenOOD-main/openood/postprocessors/gmm_postprocessor.py | from __future__ import print_function
from typing import Any
import numpy as np
import torch
import torch.nn as nn
from sklearn.mixture import GaussianMixture
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .mds_ensemble_postprocessor import (process_feature_type,
... | 8,744 | 42.507463 | 78 | py |
null | OpenOOD-main/openood/postprocessors/godin_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
from openood.preprocessors.transform import normalization_dict
class GodinPostprocessor(BasePostprocessor):
def __init__(self, config):
super(GodinPostprocessor, self).__init__(config)
sel... | 2,023 | 35.142857 | 76 | py |
null | OpenOOD-main/openood/postprocessors/gradnorm_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
class GradNormPostprocessor(BasePostprocessor):
def __init__(self, config):
super().__init__(config)
s... | 1,542 | 29.86 | 72 | py |
null | OpenOOD-main/openood/postprocessors/gram_postprocessor.py | from __future__ import division, print_function
from typing import Any
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
class GRAMPostprocessor(BasePostprocessor):
def... | 6,348 | 36.791667 | 78 | py |
null | OpenOOD-main/openood/postprocessors/info.py | num_classes_dict = {
'cifar10': 10,
'cifar100': 100,
'imagenet200': 200,
'imagenet': 1000
}
| 108 | 14.571429 | 23 | py |
null | OpenOOD-main/openood/postprocessors/kl_matching_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import pairwise_distances_argmin_min
import scipy
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
class KLMatchingPostprocessor... | 2,308 | 32.955882 | 77 | py |
null | OpenOOD-main/openood/postprocessors/knn_postprocessor.py | from typing import Any
import faiss
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
normalizer = lambda x: x / np.linalg.norm(x, axis=-1, keepdims=True) + 1e-10
class KNNPostprocessor(BasePostprocessor):
def __init__(self, config):
... | 2,073 | 31.920635 | 76 | py |
null | OpenOOD-main/openood/postprocessors/maxlogit_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class MaxLogitPostprocessor(BasePostprocessor):
def __init__(self, config):
super().__init__(config)
self.args = self.config.postprocessor.postproc... | 506 | 23.142857 | 64 | py |
null | OpenOOD-main/openood/postprocessors/mcd_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
class MCDPostprocessor(BasePostprocessor):
@torch.no_grad()
def postprocess(self, net: nn.Module, data: Any):
logits1, logits2 = net(data, return_double=True)
score1 = torch.softmax(lo... | 511 | 27.444444 | 60 | py |
null | OpenOOD-main/openood/postprocessors/mds_ensemble_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from scipy import linalg
from sklearn.covariance import (empirical_covariance, ledoit_wolf,
shrunk_covariance)
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantA... | 22,148 | 41.431034 | 79 | py |
null | OpenOOD-main/openood/postprocessors/mds_postprocessor.py | from typing import Any
from copy import deepcopy
import numpy as np
import torch
import torch.nn as nn
import sklearn.covariance
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
class MDSPostprocessor(BasePostprocessor):
def __init__(self, config):
... | 3,062 | 37.2875 | 77 | py |
null | OpenOOD-main/openood/postprocessors/mos_postprocessor.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
def get_group_slices(classes_per_group):
group_slices = []
start = 0
for num_cls in classes_per_group:
end... | 3,841 | 34.574074 | 94 | py |
null | OpenOOD-main/openood/postprocessors/npos_postprocessor.py | from typing import Any
import faiss
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class NPOSPostprocessor(BasePostprocessor):
def __init__(self, config):
super(NPOSPostprocessor, self).__init__(config)
self.args = se... | 1,962 | 31.716667 | 78 | py |
null | OpenOOD-main/openood/postprocessors/odin_postprocessor.py | """Adapted from: https://github.com/facebookresearch/odin."""
from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
from openood.preprocessors.transform import normalization_dict
class ODINPostprocessor(BasePostprocessor):
def __init__(self, config):
... | 2,324 | 32.695652 | 76 | py |
null | OpenOOD-main/openood/postprocessors/opengan_postprocessor.py | from typing import Any
import torch
from .base_postprocessor import BasePostprocessor
class OpenGanPostprocessor(BasePostprocessor):
def __init__(self, config):
super(OpenGanPostprocessor, self).__init__(config)
@torch.no_grad()
def postprocess(self, net, data: Any):
# images input
... | 1,083 | 31.848485 | 65 | py |
null | OpenOOD-main/openood/postprocessors/openmax_postprocessor.py | import libmr
import numpy as np
import scipy.spatial.distance as spd
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
class OpenMax(BasePostprocessor):
def __init__(self, config):
super(OpenMax, self).__init__(co... | 8,078 | 34.747788 | 79 | py |
null | OpenOOD-main/openood/postprocessors/patchcore_postprocessor.py | from __future__ import absolute_import, division, print_function
import abc
import os
import faiss
import numpy as np
import torch
from sklearn.metrics import pairwise_distances
from sklearn.random_projection import SparseRandomProjection
from torch import nn
from torch.nn import functional as F
from tqdm import tqdm... | 12,580 | 35.256484 | 79 | py |
null | OpenOOD-main/openood/postprocessors/rankfeat_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
class RankFeatPostprocessor(BasePostprocessor):
def __init__(self, config):
super(RankFeatPostprocessor, self).__init__(config)
self.config = config
self.args = self.config.postpro... | 2,605 | 34.69863 | 71 | py |
null | OpenOOD-main/openood/postprocessors/rd4ad_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from scipy.ndimage import gaussian_filter
from torch.nn import functional as F
from .base_postprocessor import BasePostprocessor
class Rd4adPostprocessor(BasePostprocessor):
def __init__(self, config):
super(Rd4adPostprocessor,... | 3,562 | 35.731959 | 73 | py |
null | OpenOOD-main/openood/postprocessors/react_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class ReactPostprocessor(BasePostprocessor):
def __init__(self, config):
super(ReactPostprocessor, self).__init__(config)
self.args = self.config.p... | 1,973 | 34.25 | 73 | py |
null | OpenOOD-main/openood/postprocessors/residual_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from numpy.linalg import norm, pinv
from sklearn.covariance import EmpiricalCovariance
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class ResidualPostprocessor(BasePostprocessor):
def __init__(self, config):
... | 2,527 | 37.30303 | 74 | py |
null | OpenOOD-main/openood/postprocessors/rmds_postprocessor.py | from copy import deepcopy
from typing import Any
import numpy as np
import torch
import torch.nn as nn
import sklearn.covariance
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
class RMDSPostprocessor(BasePostprocessor):
def __init__(self, config):
... | 3,718 | 38.989247 | 77 | py |
null | OpenOOD-main/openood/postprocessors/rotpred_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_postprocessor import BasePostprocessor
def kl_div(d1, d2):
"""Compute KL-Divergence between d1 and d2."""
dirty_logs = d1 * torch.log2(d1 / d2)
return torch.sum(torch.where(d1 != 0, dirty_logs, torch.zer... | 2,008 | 33.050847 | 138 | py |
null | OpenOOD-main/openood/postprocessors/rts_postprocessor.py | from typing import Any
import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
class RTSPostprocessor(BasePostprocessor):
def __init__(self, config):
super(RTSPostprocessor, self).__init__(config)
self.args = self.config.postprocessor.postprocessor_args
self.... | 968 | 33.607143 | 68 | py |
null | OpenOOD-main/openood/postprocessors/she_postprocessor.py | from typing import Any
from copy import deepcopy
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from .info import num_classes_dict
def distance(penultimate, target, metric='inner_product'):
if metric == 'inner_product':
return torch.sum(torch.m... | 2,657 | 35.410959 | 77 | py |
null | OpenOOD-main/openood/postprocessors/ssd_postprocessor.py | import torch
import torch.nn as nn
from .base_postprocessor import BasePostprocessor
from .mds_ensemble_postprocessor import get_MDS_stat
class SSDPostprocessor(BasePostprocessor):
def __init__(self, config):
self.config = config
self.postprocessor_args = config.postprocessor.postprocessor_args
... | 955 | 37.24 | 74 | py |
null | OpenOOD-main/openood/postprocessors/temp_scaling_postprocessor.py | from typing import Any
import torch
from torch import nn, optim
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class TemperatureScalingPostprocessor(BasePostprocessor):
"""A decorator which wraps a model with temperature scaling, internalize
'temperature' parameter as part of a net ... | 3,081 | 38.012658 | 80 | py |
null | OpenOOD-main/openood/postprocessors/utils.py | from openood.utils import Config
from .ash_postprocessor import ASHPostprocessor
from .base_postprocessor import BasePostprocessor
from .cider_postprocessor import CIDERPostprocessor
from .conf_branch_postprocessor import ConfBranchPostprocessor
from .cutpaste_postprocessor import CutPastePostprocessor
from .dice_post... | 3,632 | 41.244186 | 71 | py |
null | OpenOOD-main/openood/postprocessors/vim_postprocessor.py | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from numpy.linalg import norm, pinv
from scipy.special import logsumexp
from sklearn.covariance import EmpiricalCovariance
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class VIMPostprocessor(BasePostprocessor):
... | 2,873 | 36.815789 | 75 | py |
null | OpenOOD-main/openood/preprocessors/__init__.py | from .base_preprocessor import BasePreprocessor
from .cutpaste_preprocessor import CutPastePreprocessor
from .draem_preprocessor import DRAEMPreprocessor
from .pixmix_preprocessor import PixMixPreprocessor
from .test_preprocessor import TestStandardPreProcessor
from .utils import get_preprocessor
| 298 | 41.714286 | 55 | py |
null | OpenOOD-main/openood/preprocessors/augmix_preprocessor.py | import torchvision.transforms as tvs_trans
from openood.utils.config import Config
from .transform import Convert, interpolation_modes, normalization_dict
class AugMixPreprocessor():
def __init__(self, config: Config):
self.pre_size = config.dataset.pre_size
self.image_size = config.dataset.imag... | 3,075 | 41.136986 | 79 | py |
null | OpenOOD-main/openood/preprocessors/base_preprocessor.py | import torchvision.transforms as tvs_trans
from openood.utils.config import Config
from .transform import Convert, interpolation_modes, normalization_dict
class BasePreprocessor():
"""For train dataset standard transformation."""
def __init__(self, config: Config):
self.pre_size = config.dataset.pre... | 2,377 | 40.719298 | 79 | py |
null | OpenOOD-main/openood/preprocessors/cider_preprocessor.py | import torchvision.transforms as tvs_trans
from openood.utils.config import Config
from .transform import Convert, interpolation_modes, normalization_dict
class CiderPreprocessor():
def __init__(self, config: Config):
self.pre_size = config.dataset.pre_size
self.image_size = config.dataset.image... | 2,416 | 37.365079 | 78 | py |
null | OpenOOD-main/openood/preprocessors/csi_preprocessor.py | import torchvision.transforms as tvs_trans
from openood.utils.config import Config
from .transform import Convert, interpolation_modes, normalization_dict
class CSIPreprocessor():
def __init__(self, config: Config):
self.pre_size = config.dataset.pre_size
self.image_size = config.dataset.image_s... | 2,331 | 40.642857 | 79 | py |