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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RVMDE | RVMDE-main/model/rvmde.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from model.radar_retinanet import ResNet102, ResNet_radar
"""
Parts of the code is borrowed from https://github.com/lochenchou/DORN_radar
If you use this code for your research please cite him as well.
For further details please visit https://github.c... | 2,549 | 33 | 112 | py |
RVMDE | RVMDE-main/dataloader/nusc_loader1.py | import os
import random
import numpy as np
import torch
import PIL
from PIL import Image
from scipy import interpolate
from torchvision import transforms as T
from torch.utils.data import Dataset
from dataloader import nusc_utils
# import nusc_utils
from nuscenes.nuscenes import NuScenes
from nuscenes.utils import spli... | 7,548 | 30.987288 | 145 | py |
pmdarima | pmdarima-master/doc/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# pmdarima documentation build configuration file, created by
# sphinx-quickstart on Sun Sep 3 15:16:29 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# a... | 7,518 | 30.329167 | 79 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/main.py | import click
import torch
import logging
import random
import numpy as np
from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from DeepSAD import DeepSAD
from datasets.main import load_dataset
##############################################################################... | 13,188 | 53.954167 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baseline_kde.py | import click
import torch
import logging
import random
import numpy as np
from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from baselines.kde import KDE
from datasets.main import load_dataset
############################################################################... | 9,298 | 50.375691 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baseline_isoforest.py | import click
import torch
import logging
import random
import numpy as np
from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from baselines.isoforest import IsoForest
from datasets.main import load_dataset
################################################################... | 9,754 | 52.016304 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baseline_SemiDGM.py | import click
import torch
import logging
import random
import numpy as np
from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from baselines.SemiDGM import SemiDeepGenerativeModel
from datasets.main import load_dataset
####################################################... | 13,325 | 54.294606 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baseline_ssad.py | import click
import torch
import logging
import random
import numpy as np
import cvxopt as co
from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from baselines.ssad import SSAD
from datasets.main import load_dataset
######################################################... | 8,981 | 49.745763 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baseline_ocsvm.py | import click
import torch
import logging
import random
import numpy as np
from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from baselines.ocsvm import OCSVM
from datasets.main import load_dataset
########################################################################... | 8,888 | 49.794286 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/DeepSAD.py | import json
import torch
from base.base_dataset import BaseADDataset
from networks.main import build_network, build_autoencoder
from optim.DeepSAD_trainer import DeepSADTrainer
from optim.ae_trainer import AETrainer
class DeepSAD(object):
"""A class for the Deep SAD method.
Attributes:
eta: Deep SAD... | 6,508 | 39.179012 | 116 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/fmnist_LeNet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from base.base_net import BaseNet
class FashionMNIST_LeNet(BaseNet):
def __init__(self, rep_dim=64):
super().__init__()
self.rep_dim = rep_dim
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(1, 16, 5, bias... | 2,508 | 31.584416 | 75 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/mlp.py | import torch.nn as nn
import torch.nn.functional as F
from base.base_net import BaseNet
class MLP(BaseNet):
def __init__(self, x_dim, h_dims=[128, 64], rep_dim=32, bias=False):
super().__init__()
self.rep_dim = rep_dim
neurons = [x_dim, *h_dims]
layers = [Linear_BN_leakyReLU(ne... | 2,231 | 27.987013 | 109 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/vae.py | import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .layers.stochastic import GaussianSample
from .inference.distributions import log_standard_gaussian, log_gaussian
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
class Encoder(nn.Module):
"""
Encoder, ... | 4,673 | 31.013699 | 117 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/dgm.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .vae import VariationalAutoencoder, Encoder, Decoder
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
class Classifier(nn.Module):
"""
Classifier network, i.e. q(y|x), for two classes (0: n... | 4,282 | 33.540323 | 116 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/mnist_LeNet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from base.base_net import BaseNet
class MNIST_LeNet(BaseNet):
def __init__(self, rep_dim=32):
super().__init__()
self.rep_dim = rep_dim
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(1, 8, 5, bias=False, ... | 2,151 | 28.888889 | 73 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/cifar10_LeNet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from base.base_net import BaseNet
class CIFAR10_LeNet(BaseNet):
def __init__(self, rep_dim=128):
super().__init__()
self.rep_dim = rep_dim
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(3, 32, 5, bias=Fal... | 3,003 | 35.192771 | 101 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/layers/stochastic.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
class Stochastic(nn.Module):
"""
Base stochastic layer that uses the reparametrization trick (Kingma and Welling, 2013) to draw a sampl... | 1,458 | 26.018519 | 114 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/layers/standard.py | import torch
from torch.nn import Module
from torch.nn import init
from torch.nn.parameter import Parameter
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
class Standardize(Module):
"""
Applies (element-wise) standardization with trainable translation parameter μ and scale parameter σ... | 1,646 | 30.075472 | 118 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/networks/inference/distributions.py | import math
import torch
import torch.nn.functional as F
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
def log_standard_gaussian(x):
"""
Evaluates the log pdf of a standard normal distribution at x.
:param x: point to evaluate
:return: log N(x|0,I)
"""
return torch.su... | 1,213 | 27.904762 | 120 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baselines/kde.py | import json
import logging
import time
import torch
import numpy as np
from torch.utils.data import DataLoader
from sklearn.neighbors import KernelDensity
from sklearn.metrics import roc_auc_score
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.model_selection import GridSearchCV
from base.base_da... | 6,538 | 38.630303 | 118 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baselines/isoforest.py | import json
import logging
import time
import torch
import numpy as np
from torch.utils.data import DataLoader
from sklearn.ensemble import IsolationForest
from sklearn.metrics import roc_auc_score
from base.base_dataset import BaseADDataset
from networks.main import build_autoencoder
class IsoForest(object):
""... | 5,732 | 37.736486 | 118 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baselines/SemiDGM.py | import json
import torch
from base.base_dataset import BaseADDataset
from networks.main import build_network, build_autoencoder
from optim import SemiDeepGenerativeTrainer, VAETrainer
class SemiDeepGenerativeModel(object):
"""A class for the Semi-Supervised Deep Generative model (M1+M2 model).
Paper: Kingma... | 5,482 | 41.503876 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baselines/ocsvm.py | import json
import logging
import time
import torch
import numpy as np
from torch.utils.data import DataLoader
from sklearn.svm import OneClassSVM
from sklearn.metrics import roc_auc_score
from base.base_dataset import BaseADDataset
from networks.main import build_autoencoder
class OCSVM(object):
"""A class for ... | 8,812 | 38.698198 | 118 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/baselines/ssad.py | import json
import logging
import time
import torch
import numpy as np
from torch.utils.data import DataLoader
from .shallow_ssad.ssad_convex import ConvexSSAD
from sklearn.metrics import roc_auc_score
from sklearn.metrics.pairwise import pairwise_kernels
from base.base_dataset import BaseADDataset
from networks.main ... | 9,957 | 39.644898 | 118 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/optim/DeepSAD_trainer.py | from base.base_trainer import BaseTrainer
from base.base_dataset import BaseADDataset
from base.base_net import BaseNet
from torch.utils.data.dataloader import DataLoader
from sklearn.metrics import roc_auc_score
import logging
import time
import torch
import torch.optim as optim
import numpy as np
class DeepSADTrai... | 6,498 | 36.350575 | 117 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/optim/SemiDGM_trainer.py | from base.base_trainer import BaseTrainer
from base.base_dataset import BaseADDataset
from base.base_net import BaseNet
from optim.variational import SVI, ImportanceWeightedSampler
from utils.misc import binary_cross_entropy
from sklearn.metrics import roc_auc_score
import logging
import time
import torch
import torch... | 7,261 | 37.42328 | 116 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/optim/variational.py | import torch
import torch.nn.functional as F
from torch import nn
from itertools import repeat
from utils import enumerate_discrete, log_sum_exp
from networks import log_standard_categorical
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
class ImportanceWeightedSampler(object):
"""
Im... | 2,681 | 27.531915 | 103 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/optim/vae_trainer.py | from base.base_trainer import BaseTrainer
from base.base_dataset import BaseADDataset
from base.base_net import BaseNet
from utils.misc import binary_cross_entropy
from sklearn.metrics import roc_auc_score
import logging
import time
import torch
import torch.optim as optim
import numpy as np
class VAETrainer(BaseTra... | 5,024 | 34.892857 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/optim/ae_trainer.py | from base.base_trainer import BaseTrainer
from base.base_dataset import BaseADDataset
from base.base_net import BaseNet
from sklearn.metrics import roc_auc_score
import logging
import time
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
class AETrainer(BaseTrainer):
def __init_... | 4,960 | 35.211679 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/datasets/preprocessing.py | import torch
import numpy as np
def create_semisupervised_setting(labels, normal_classes, outlier_classes, known_outlier_classes,
ratio_known_normal, ratio_known_outlier, ratio_pollution):
"""
Create a semi-supervised data setting.
:param labels: np.array with labels of ... | 3,563 | 52.19403 | 113 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/datasets/odds.py | from torch.utils.data import DataLoader, Subset
from base.base_dataset import BaseADDataset
from base.odds_dataset import ODDSDataset
from .preprocessing import create_semisupervised_setting
import torch
class ODDSADDataset(BaseADDataset):
def __init__(self, root: str, dataset_name: str, n_known_outlier_classes... | 2,278 | 46.479167 | 119 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/datasets/fmnist.py | from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import FashionMNIST
from base.torchvision_dataset import TorchvisionDataset
from .preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
class FashionMNIST_Dataset(To... | 3,575 | 40.581395 | 120 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/datasets/cifar10.py | from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import CIFAR10
from base.torchvision_dataset import TorchvisionDataset
from .preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
import numpy as np
class CIFAR10_D... | 3,520 | 39.471264 | 120 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/datasets/mnist.py | from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import MNIST
from base.torchvision_dataset import TorchvisionDataset
from .preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
class MNIST_Dataset(TorchvisionDatas... | 3,489 | 39.581395 | 120 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/base/base_net.py | import logging
import torch.nn as nn
import numpy as np
class BaseNet(nn.Module):
"""Base class for all neural networks."""
def __init__(self):
super().__init__()
self.logger = logging.getLogger(self.__class__.__name__)
self.rep_dim = None # representation dimensionality, i.e. dim of... | 797 | 28.555556 | 102 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/base/odds_dataset.py | from pathlib import Path
from torch.utils.data import Dataset
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from torchvision.datasets.utils import download_url
import os
import torch
import numpy as np
class ODDSDatase... | 4,370 | 38.378378 | 112 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/base/torchvision_dataset.py | from .base_dataset import BaseADDataset
from torch.utils.data import DataLoader
class TorchvisionDataset(BaseADDataset):
"""TorchvisionDataset class for datasets already implemented in torchvision.datasets."""
def __init__(self, root: str):
super().__init__(root)
def loaders(self, batch_size: in... | 823 | 44.777778 | 105 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/base/base_dataset.py | from abc import ABC, abstractmethod
from torch.utils.data import DataLoader
class BaseADDataset(ABC):
"""Anomaly detection dataset base class."""
def __init__(self, root: str):
super().__init__()
self.root = root # root path to data
self.n_classes = 2 # 0: normal, 1: outlier
... | 1,006 | 36.296296 | 105 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/base/__init__.py | from .base_dataset import *
from .torchvision_dataset import *
from .odds_dataset import *
from .base_net import *
from .base_trainer import *
| 143 | 23 | 34 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/utils/misc.py | import torch
from torch.autograd import Variable
# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
def enumerate_discrete(x, y_dim):
"""
Generates a 'torch.Tensor' of size batch_size x n_labels of the given label.
:param x: tensor with batch size to mimic
:param y_dim: number of... | 1,422 | 29.276596 | 89 | py |
Deep-SAD-PyTorch | Deep-SAD-PyTorch-master/src/utils/visualization/plot_images_grid.py | import torch
import matplotlib
matplotlib.use('Agg') # or 'PS', 'PDF', 'SVG'
import matplotlib.pyplot as plt
import numpy as np
from torchvision.utils import make_grid
def plot_images_grid(x: torch.tensor, export_img, title: str = '', nrow=8, padding=2, normalize=False, pad_value=0):
"""Plot 4D Tensor of images... | 777 | 27.814815 | 116 | py |
hotr | hotr-main/main.py | # ------------------------------------------------------------------------
# HOTR official code : main.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
#... | 10,091 | 43.45815 | 120 | py |
hotr | hotr-main/hotr/models/detr.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/models/detr.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookrese... | 7,519 | 45.708075 | 117 | py |
hotr | hotr-main/hotr/models/post_process.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/models/post_process.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import time
import copy
import torch
import t... | 7,586 | 46.716981 | 182 | py |
hotr | hotr-main/hotr/models/hotr_matcher.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/models/hotr_matcher.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import torch
from scipy.optimize import linea... | 10,931 | 52.588235 | 162 | py |
hotr | hotr-main/hotr/models/detr_matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
from scipy.optimize import linear_sum_assignment
from torch import nn
from hotr.util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
class Hungaria... | 4,249 | 51.469136 | 119 | py |
hotr | hotr-main/hotr/models/feed_forward.py | import torch.nn.functional as F
from torch import nn
class MLP(nn.Module):
""" Very simple multi-layer perceptron (also called FFN)"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
... | 589 | 35.875 | 103 | py |
hotr | hotr-main/hotr/models/position_encoding.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
from hotr.util.misc import NestedTensor
class PositionEmbeddingSine(nn.Module):
"""
This is a more standard version of the position em... | 3,340 | 36.539326 | 103 | py |
hotr | hotr-main/hotr/models/backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Backbone modules.
"""
from collections import OrderedDict
import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision.models._utils import IntermediateLayerGetter
from typing import Dict, List
from hot... | 4,448 | 36.70339 | 113 | py |
hotr | hotr-main/hotr/models/transformer.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/models/transformer.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/faceb... | 12,967 | 40.299363 | 106 | py |
hotr | hotr-main/hotr/models/criterion.py | # ------------------------------------------------------------------------
# HOTR official code : main.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
#... | 15,866 | 52.424242 | 159 | py |
hotr | hotr-main/hotr/models/hotr.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/models/hotr.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.fu... | 7,397 | 47.671053 | 153 | py |
hotr | hotr-main/hotr/util/misc.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/util/misc.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresear... | 10,809 | 30.424419 | 95 | py |
hotr | hotr-main/hotr/util/logger.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/util/logger.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookrese... | 5,559 | 37.082192 | 114 | py |
hotr | hotr-main/hotr/util/box_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_... | 3,338 | 29.354545 | 110 | py |
hotr | hotr-main/hotr/metrics/utils.py | import torch
import numpy as np
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def compute_overlap(a, b):
if type(a) == torch.Tensor:
if len(a.shape) == 2:
area = (b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1)
iw = torch.m... | 3,396 | 36.744444 | 112 | py |
hotr | hotr-main/hotr/metrics/vcoco/ap_role.py | import numpy as np
import torch
from hotr.metrics.utils import _compute_ap, compute_overlap
class APRole(object):
def __init__(self, act_name, scenario_flag=True, iou_threshold=0.5):
self.act_name = act_name
self.iou_threshold = iou_threshold
self.scenario_flag = scenario_flag
# sc... | 8,424 | 42.65285 | 137 | py |
hotr | hotr-main/hotr/engine/evaluator_vcoco.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/engine/evaluator_vcoco.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/f... | 3,322 | 37.195402 | 111 | py |
hotr | hotr-main/hotr/engine/evaluator_hico.py | import math
import os
import sys
from typing import Iterable
import numpy as np
import copy
import itertools
import torch
import hotr.util.misc as utils
import hotr.util.logger as loggers
from hotr.data.evaluators.hico_eval import HICOEvaluator
@torch.no_grad()
def hico_evaluate(model, postprocessors, data_loader, d... | 1,994 | 35.272727 | 111 | py |
hotr | hotr-main/hotr/engine/evaluator_coco.py | import os
import torch
import hotr.util.misc as utils
import hotr.util.logger as loggers
from hotr.data.evaluators.coco_eval import CocoEvaluator
@torch.no_grad()
def coco_evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir):
model.eval()
criterion.eval()
metric_logger = lo... | 2,777 | 43.806452 | 97 | py |
hotr | hotr-main/hotr/engine/trainer.py | # ------------------------------------------------------------------------
# HOTR official code : engine/trainer.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresear... | 3,230 | 47.954545 | 138 | py |
hotr | hotr-main/hotr/data/datasets/hico.py | # ------------------------------------------------------------------------
# HOTR official code : hotr/data/datasets/hico.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from QPIC (https://github.com/hitac... | 10,116 | 40.633745 | 125 | py |
hotr | hotr-main/hotr/data/datasets/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch.utils.data
import torchvision
from hotr.data.datasets.coco import build as build_coco
from hotr.data.datasets.vcoco import build as build_vcoco
from hotr.data.datasets.hico import build as build_hico
def get_coco_api_from_dataset(data... | 908 | 36.875 | 70 | py |
hotr | hotr-main/hotr/data/datasets/vcoco.py | # Copyright (c) Kakaobrain, Inc. and its affiliates. All Rights Reserved
"""
V-COCO dataset which returns image_id for evaluation.
"""
from pathlib import Path
from PIL import Image
import os
import numpy as np
import json
import torch
import torch.utils.data
import torchvision
from torch.utils.data import Dataset
fr... | 18,328 | 38.16453 | 132 | py |
hotr | hotr-main/hotr/data/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
from pathlib import Path
import torch
import torch.utils.data
import torchvision
fr... | 5,299 | 32.974359 | 118 | py |
hotr | hotr-main/hotr/data/evaluators/vcoco_eval.py | # Copyright (c) KakaoBrain, Inc. and its affiliates. All Rights Reserved
"""
V-COCO evaluator that works in distributed mode.
"""
import os
import numpy as np
import torch
from hotr.util.misc import all_gather
from hotr.metrics.vcoco.ap_role import APRole
from functools import partial
def init_vcoco_evaluators(human_... | 2,305 | 39.45614 | 109 | py |
hotr | hotr-main/hotr/data/evaluators/coco_eval.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO evaluator that works in distributed mode.
Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
The difference is that there is less copy-pasting from pycocotools
in the end of the file, as ... | 8,739 | 33.007782 | 103 | py |
hotr | hotr-main/hotr/data/transforms/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Transforms and data augmentation for both image + bbox.
"""
import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from hotr.util.box_ops import box_xyxy_to_cxcywh
from hotr.util.... | 13,378 | 33.481959 | 118 | py |
covid19-severity-prediction | covid19-severity-prediction-master/functions/update_predictions_plot.py | from os.path import join as oj
import os
import sys
import inspect
from datetime import timedelta
import datetime
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
sys.path.append(parentdir + '/modeling')
import loa... | 6,694 | 39.08982 | 196 | py |
covid19-severity-prediction | covid19-severity-prediction-master/viz/viz_interactive.py | from bokeh.sampledata import us_states, us_counties
from bokeh.plotting import figure, show, output_notebook, output_file, save
from bokeh import palettes
from bokeh.models import ColorBar,HoverTool,LinearColorMapper,ColumnDataSource,FixedTicker, LogColorMapper
output_notebook()
import re
import numpy as np
from modeli... | 15,168 | 42.34 | 154 | py |
eXdpn | eXdpn-main/exdpn/guards/xgboost_guard.py | """
.. include:: ./../../docs/_templates/md/guards/guard.md
"""
import io
from xgboost import XGBClassifier
from matplotlib import pyplot as plt
from matplotlib.figure import Figure
from exdpn.data_preprocessing.data_preprocessing import apply_ohe
from exdpn.guards import Guard
from exdpn.data_preprocessing import fi... | 15,976 | 45.580175 | 201 | py |
eXdpn | eXdpn-main/exdpn/guards/__init__.py | """
.. include:: ./../../docs/_templates/md/guards/guards.md
"""
from exdpn.guards.guard import Guard
from exdpn.guards.decision_tree_guard import Decision_Tree_Guard
from exdpn.guards.neural_network_guard import Neural_Network_Guard
from exdpn.guards.logistic_regression_guard import Logistic_Regression_Guard
from ex... | 635 | 36.411765 | 76 | py |
Gradient-Embedding-Perturbation | Gradient-Embedding-Perturbation-master/main.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import os
import argparse
import csv
import random
import time
import numpy as np
from models import resnet20, GEP
from utils import get_data_loader, get_sigma, restor... | 12,315 | 38.729032 | 180 | py |
Gradient-Embedding-Perturbation | Gradient-Embedding-Perturbation-master/utils.py |
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import os
import numpy as np
from rdp_accountant import compute_rdp, get_privacy_spent
def get_data_loader(dataset, batchsize):
if(dataset == 'svhn'):
transform=transforms.Compose([
transforms... | 4,818 | 36.944882 | 167 | py |
Gradient-Embedding-Perturbation | Gradient-Embedding-Perturbation-master/models/resnet_cifar.py | import torch
import torch.nn as nn
import numpy as np
import math
#The ResNet models for CIFAR in https://arxiv.org/abs/1512.03385.
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padd... | 4,076 | 24.322981 | 115 | py |
Gradient-Embedding-Perturbation | Gradient-Embedding-Perturbation-master/models/basis_matching.py | import torch
import torch.nn as nn
import numpy as np
import math
#package for computing individual gradients
from backpack import backpack, extend
from backpack.extensions import BatchGrad
def flatten_tensor(tensor_list):
for i in range(len(tensor_list)):
tensor_list[i] = tensor_list[i].reshape([tensor_... | 8,057 | 38.116505 | 179 | py |
DetNAS | DetNAS-master/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 2,084 | 28.785714 | 100 | py |
DetNAS | DetNAS-master/Supernet-ImageNet/detnasnet.py | import torch.nn as nn
from .shuffle_blocks import ConvBNReLU, ShuffleNetV2BlockSearched, blocks_key, FC
class ShuffleNetV2DetNAS(nn.Module):
def __init__(self, model_size='DETNAS-300M'):
super(ShuffleNetV2DetNAS, self).__init__()
print('Model size is {}.'.format(model_size))
n_class = 100... | 3,712 | 39.358696 | 136 | py |
DetNAS | DetNAS-master/Supernet-ImageNet/flops.py | import torch
import torch.nn as nn
import pickle
class Shufflenet(nn.Module):
def __init__(self, inp, oup, mid_channels, *, ksize, stride):
super(Shufflenet, self).__init__()
self.stride = stride
assert stride in [1, 2]
assert ksize in [3, 5, 7]
self.base_mid_channel = mi... | 11,926 | 33.671512 | 97 | py |
DetNAS | DetNAS-master/Supernet-ImageNet/utils.py | import os
import re
import torch
import torch.nn as nn
class CrossEntropyLabelSmooth(nn.Module):
def __init__(self, num_classes, epsilon):
super(CrossEntropyLabelSmooth, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon
self.logsoftmax = nn.LogSoftmax(dim=1)
... | 2,720 | 27.34375 | 65 | py |
DetNAS | DetNAS-master/Supernet-ImageNet/shuffle_blocks.py | import torch
import torch.nn as nn
batch_norm = nn.BatchNorm2d
blocks_key = [
'shufflenet_3x3',
'shufflenet_5x5',
'shufflenet_7x7',
'xception_3x3',
]
Blocks = {
'shufflenet_3x3': lambda prefix, in_channels, output_channels, base_mid_channels, stride, bn_training: conv1x1_dwconv_conv1x1(prefix, in_... | 10,293 | 46.220183 | 209 | py |
DetNAS | DetNAS-master/Supernet-ImageNet/train.py | import os
import sys
import torch
import argparse
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import cv2
import numpy as np
import PIL
from PIL import Image
import time
import logging
import argparse
from detnasnet import ShuffleNetV2DetNAS
from utils import... | 10,827 | 36.209622 | 129 | py |
DetNAS | DetNAS-master/tools/test_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch... | 4,136 | 35.289474 | 114 | py |
DetNAS | DetNAS-master/tools/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
r"""
Basic training script for PyTorch
"""
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:s... | 7,245 | 32.086758 | 111 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/pytorch_distributed_syncbn/syncbn.py | import math
from queue import Queue
from IPython import embed
import torch
import torch.distributed as dist
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.nn.modules.batchnorm import _BatchNorm
import torch.nn.functional as F
import syncbn_gpu
from maskrcnn_benchmark... | 4,911 | 37.984127 | 124 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/pytorch_distributed_syncbn/test.py | import torch
import apex
import os
from IPython import embed
from torch import nn
import torch.nn.functional as F
import argparse
import numpy as np
import torch.distributed as dist
from syncbn import DistributedSyncBN
from test_case import TestCase
def main():
parser = argparse.ArgumentParser(description="PyTorc... | 2,745 | 27.309278 | 89 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/pytorch_distributed_syncbn/lib/gpu/setup.py | from setuptools import setup
from torch.utils.cpp_extension import CUDAExtension, BuildExtension
setup(name='syncbn_gpu',
ext_modules=[CUDAExtension('syncbn_gpu', ['syncbn_cuda.cpp', 'syncbn_cuda_kernel.cu'])],
cmdclass={'build_ext': BuildExtension}) | 263 | 43 | 94 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/pytorch_distributed_syncbn/lib/cpu/setup.py | from setuptools import setup
from torch.utils.cpp_extension import CppExtension, BuildExtension
setup(name='syncbn_cpu',
ext_modules=[CppExtension('syncbn_cpu', ['syncbn_cpu.cpp'])],
cmdclass={'build_ext': BuildExtension}) | 235 | 38.333333 | 67 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/solver/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_s... | 1,817 | 33.301887 | 80 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/solver/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .lr_scheduler import WarmupMultiStepLR
def make_optimizer(cfg, model):
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
lr = cfg.SOLVER.BASE_LR
... | 976 | 29.53125 | 79 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/batch_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters
are fixed
"""
def __init__(self, n):
super(FrozenBatchNorm2d, self).__init__()... | 1,094 | 33.21875 | 71 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/roi_pool.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
from apex import amp
class _ROIPool(Function... | 1,900 | 27.80303 | 74 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
from apex import amp
class _ROIAlign(Functio... | 2,154 | 29.785714 | 85 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# TODO maybe push this to nn?
def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n = torch.abs(input - tar... | 481 | 27.352941 | 71 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/sigmoid_focal_loss.py | import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
# TODO: Use JIT to replace CUDA implementation in the future.
class _SigmoidFocalLoss(Function):
@staticmethod
def forward(ctx, logits, targets, gamma... | 2,300 | 29.68 | 118 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import glob
import os.path
import torch
try:
from torch.utils.cpp_extension import load as load_ext
from torch.utils.cpp_extension import CUDA_HOME
except ImportError:
raise ImportError("The cpp layer extensions requires PyTorch 0.4 o... | 1,165 | 28.15 | 80 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
helper class that supports empty tensors on some nn functions.
Ideally, add support directly in PyTorch to empty tensors in
those functions.
This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
import m... | 6,661 | 31.656863 | 88 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .batch_norm import FrozenBatchNorm2d
from .misc import Conv2d
from .misc import DFConv2d
from .misc import ConvTranspose2d
from .misc import BatchNorm2d
from .misc import interpolate
from .nms import nms
from .roi_align import RO... | 1,327 | 26.666667 | 105 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/dcn/deform_conv_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
class DeformConvFunction(Function):
@staticmethod
def forward(
ctx,
input,
offset,
weight,
... | 8,309 | 30.596958 | 83 | py |
DetNAS | DetNAS-master/maskrcnn_benchmark/layers/dcn/deform_pool_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
... | 2,595 | 26.041667 | 99 | py |
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