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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linbp-attack | linbp-attack-master/attack/imagenet/models/pnasnet.py | from __future__ import print_function, division, absolute_import
from collections import OrderedDict
import torch
import torch.nn as nn
pretrained_settings = {
'pnasnet5large': {
'imagenet': {
'url': '-',
'input_space': 'RGB',
'input_size': [3, 331, 331],
'... | 17,685 | 43.774684 | 88 | py |
linbp-attack | linbp-attack-master/attack/imagenet/models/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resnet18': 'https://download.pytorch.org/models... | 9,898 | 36.496212 | 97 | py |
linbp-attack | linbp-attack-master/attack/imagenet/models/senet.py | from __future__ import print_function, division, absolute_import
from collections import OrderedDict
import math
import torch
import torch.nn as nn
__all__ = ['SENet', 'senet154']
pretrained_settings = {
'senet154': {
'imagenet': {
'url': '-',
'input_space': 'RGB',
'inp... | 13,630 | 34.590078 | 83 | py |
linbp-attack | linbp-attack-master/attack/imagenet/models/inceptionv3.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Inception3(nn.Module):
def __init__(self, num_classes=1000, aux_logits=True, transform_input=False):
super(Inception3, self).__init__()
self.aux_logits = aux_logits
self.transform_input = transform_input
se... | 11,536 | 36.33657 | 88 | py |
linbp-attack | linbp-attack-master/attack/cifar10/test.py | import os, sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.utils.data as data
import torchvision.transforms as transforms
import models
# import numpy as np
import torchvision.datasets as DATASETS
import argparse
import numpy as np
parser = argparse.Ar... | 4,881 | 35.706767 | 153 | py |
linbp-attack | linbp-attack-master/attack/cifar10/utils.py | import os
import torch
import torchvision.transforms as T
from torch.utils.data import Dataset
import torch.nn as nn
import argparse
import models
import torch.nn.functional as F
from torch.backends import cudnn
import pickle
import numpy as np
import csv
import PIL.Image as Image
# Selected cifar-10. The .csv file fo... | 3,961 | 32.016667 | 99 | py |
linbp-attack | linbp-attack-master/attack/cifar10/attack_vgg19.py | import os
import torch
import torchvision.transforms as T
import torch.nn as nn
import argparse
import models
from torch.backends import cudnn
import numpy as np
from utils import Normalize, input_diversity, vgg19_forw, vgg19_ila_forw, ILAProjLoss, SelectedCifar10
parser = argparse.ArgumentParser()
parser.add_argument... | 5,763 | 39.307692 | 150 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/vgg.py |
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
import torch
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
class VGG(nn.Module):
def __init__(self, features, num_classes=1000):
super(VGG, self).__init__()
... | 3,731 | 27.707692 | 113 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/pyramidnet.py | import torch
import torch.nn as nn
import math
__all__ = ['pyramidnet272']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def calc_prob(curr_layer, total_layers, p... | 5,819 | 34.487805 | 115 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/densenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = ['densenet']
from torch.autograd import Variable
class Bottleneck(nn.Module):
def __init__(self, inplanes, expansion=4, growthRate=12, dropRate=0):
super(Bottleneck, self).__init__()
planes = expansion * gr... | 4,724 | 30.711409 | 99 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/resnext.py | from __future__ import division
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
__all__ = ['resnext']
class ResNeXtBottleneck(nn.Module):
def __init__(self, in_channels, out_channels, stride, cardinality, widen_factor):
""" Constructor
Args:
in_channel... | 5,072 | 43.113043 | 144 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['wrn']
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(inplac... | 3,896 | 40.457447 | 116 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/__init__.py | import os
import os.path as osp
import torch
from models.gdas.lib.scheduler import load_config
from models.gdas.lib.scheduler import load_config
from models.gdas.lib.nas import model_types
from models.gdas.lib.nas import NetworkCIFAR as Network
__all__ = ['gdas']
def gdas(checkpoint_fname):
checkpoint = torch.l... | 697 | 29.347826 | 93 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/scheduler/scheduler.py |
import torch
from bisect import bisect_right
class MultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, milestones, gammas, last_epoch=-1):
if not list(milestones) == sorted(milestones):
raise ValueError('Milestones should be a list of'
' increasing in... | 1,124 | 35.290323 | 90 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/ImageNet.py | import torch
import torch.nn as nn
from .construct_utils import Cell, Transition
class AuxiliaryHeadImageNet(nn.Module):
def __init__(self, C, num_classes):
"""assuming input size 14x14"""
super(AuxiliaryHeadImageNet, self).__init__()
self.features = nn.Sequential(
nn.ReLU(inplace=True),
nn.... | 3,272 | 30.171429 | 85 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/CifarNet.py | import torch
import torch.nn as nn
from .construct_utils import Cell, Transition
class AuxiliaryHeadCIFAR(nn.Module):
def __init__(self, C, num_classes):
"""assuming input size 8x8"""
super(AuxiliaryHeadCIFAR, self).__init__()
self.features = nn.Sequential(
nn.ReLU(inplace=True),
nn.AvgPool2... | 2,755 | 29.622222 | 89 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/model_search.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from .head_utils import CifarHEAD, ImageNetHEAD
from .operations import OPS, FactorizedReduce, ReLUConvBN
from .genotypes import PRIMITIVES, Genotype
class MixedOp(nn.Module):
def __init__(self, C, stride):
... | 5,177 | 30.005988 | 128 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/head_utils.py | import torch
import torch.nn as nn
class ImageNetHEAD(nn.Sequential):
def __init__(self, C, stride=2):
super(ImageNetHEAD, self).__init__()
self.add_module('conv1', nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False))
self.add_module('bn1' , nn.BatchNorm2d(C // 2))
self.add_module(... | 729 | 35.5 | 103 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/construct_utils.py | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from .operations import OPS, FactorizedReduce, ReLUConvBN, Identity
def random_select(length, ratio):
clist = []
index = random.randint(0, length-1)
for i in range(length):
if i == index or random.random() < ratio:
clist.... | 5,003 | 31.705882 | 99 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/SE_Module.py | import torch
import torch.nn as nn
# Squeeze and Excitation module
class SqEx(nn.Module):
def __init__(self, n_features, reduction=16):
super(SqEx, self).__init__()
if n_features % reduction != 0:
raise ValueError('n_features must be divisible by reduction (default = 16)')
self.linear1 = nn.Line... | 762 | 26.25 | 82 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas/operations.py | import torch
import torch.nn as nn
OPS = {
'none' : lambda C, stride, affine: Zero(stride),
'avg_pool_3x3' : lambda C, stride, affine: nn.Sequential(
nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
... | 4,318 | 34.113821 | 129 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/datasets/test_dataset.py | import os, sys, torch
import torchvision.transforms as transforms
from .TieredImageNet import TieredImageNet
from .MetaBatchSampler import MetaBatchSampler
root_dir = os.environ['TORCH_HOME'] + '/tiered-imagenet'
print ('root : {:}'.format(root_dir))
means, stds = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
lists =... | 1,324 | 37.970588 | 149 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/datasets/test_NLP.py | import os, sys, torch
from .LanguageDataset import SentCorpus, BatchSentLoader
if __name__ == '__main__':
path = '../../data/data/penn'
corpus = SentCorpus( path )
loader = BatchSentLoader(corpus.test, 10)
for i, d in enumerate(loader):
print('{:} :: {:}'.format(i, d.size()))
| 291 | 25.545455 | 56 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/datasets/get_dataset_with_transform.py |
import os, sys, torch
import os.path as osp
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from ..utils import Cutout
from .TieredImageNet import TieredImageNet
Dataset2Class = {'cifar10' : 10,
'cifar100': 100,
'... | 3,189 | 40.973684 | 141 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/datasets/TieredImageNet.py | from __future__ import print_function
import numpy as np
from PIL import Image
import pickle as pkl
import os, cv2, csv, glob
import torch
import torch.utils.data as data
class TieredImageNet(data.Dataset):
def __init__(self, root_dir, split, transform=None):
self.split = split
self.root_dir = root_dir
... | 3,090 | 35.364706 | 193 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/datasets/LanguageDataset.py | import os
import torch
from collections import Counter
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
self.counter = Counter()
self.total = 0
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word... | 3,362 | 26.341463 | 78 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/datasets/MetaBatchSampler.py | # coding=utf-8
import numpy as np
import torch
class MetaBatchSampler(object):
def __init__(self, labels, classes_per_it, num_samples, iterations):
'''
Initialize MetaBatchSampler
Args:
- labels: an iterable containing all the labels for the current dataset
samples indexes will be infered from ... | 2,497 | 36.848485 | 102 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas_rnn/utils.py | import torch
import torch.nn as nn
import os, shutil
import numpy as np
def repackage_hidden(h):
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def batchify(data, bsz, use_cuda):
nbatch = data.size(0) // bsz
data = data.narrow(0, 0, nbatch * bsz)... | 1,812 | 26.059701 | 133 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas_rnn/basemodel.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .genotypes import STEPS
from .utils import mask2d, LockedDropout, embedded_dropout
INITRANGE = 0.04
def none_func(x):
return x * 0
class DARTSCell(nn.Module):
def __init__(self, ninp, nhid, dropouth, dropoutx, genotype):
s... | 5,547 | 29.483516 | 102 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/nas_rnn/model_search.py | import copy, torch
import torch.nn as nn
import torch.nn.functional as F
from collections import namedtuple
from .genotypes import PRIMITIVES, STEPS, CONCAT, Genotype
from .basemodel import DARTSCell, RNNModel
class DARTSCellSearch(DARTSCell):
def __init__(self, ninp, nhid, dropouth, dropoutx):
super(DARTSCell... | 3,544 | 32.761905 | 124 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/utils/save_meta.py | import torch
import os, sys
import os.path as osp
import numpy as np
def tensor2np(x):
if isinstance(x, np.ndarray): return x
if x.is_cuda: x = x.cpu()
return x.numpy()
class Save_Meta():
def __init__(self):
self.reset()
def __repr__(self):
return ('{name}'.format(name=self.__class__.__name__)+'(n... | 1,649 | 31.352941 | 168 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/utils/model_utils.py | import torch
import torch.nn as nn
import numpy as np
def count_parameters_in_MB(model):
if isinstance(model, nn.Module):
return np.sum(np.prod(v.size()) for v in model.parameters())/1e6
else:
return np.sum(np.prod(v.size()) for v in model)/1e6
class Cutout(object):
def __init__(self, length):
sel... | 923 | 24.666667 | 92 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/utils/flop_benchmark.py | import copy, torch
def print_FLOPs(model, shape, logs):
print_log, log = logs
model = copy.deepcopy( model )
model = add_flops_counting_methods(model)
model = model.cuda()
model.eval()
cache_inputs = torch.zeros(*shape).cuda()
#print_log('In the calculating function : cache input size : {:}'.format(cac... | 4,077 | 35.088496 | 103 | py |
linbp-attack | linbp-attack-master/attack/cifar10/models/gdas/lib/utils/evaluation_utils.py | import torch
def obtain_accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in top... | 453 | 25.705882 | 65 | py |
s2anet | s2anet-master/setup.py | import os
import platform
import subprocess
import time
from setuptools import Extension, dist, find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
dist.Distribution().fetch_build_eggs(['Cython', 'numpy>=1.11.1'])
import numpy as np # noqa: E402, isort:skip
from Cython.Build impo... | 8,115 | 32.958159 | 111 | py |
s2anet | s2anet-master/tools/test.py | import argparse
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from mmdet.apis import init_dist
from mmdet.core import coc... | 8,637 | 35.447257 | 79 | py |
s2anet | s2anet-master/tools/convert_model.py | import argparse
import subprocess
from collections import OrderedDict
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output c... | 1,357 | 29.863636 | 77 | py |
s2anet | s2anet-master/tools/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,028 | 27.583333 | 77 | py |
s2anet | s2anet-master/tools/upgrade_model_version.py | import argparse
import re
from collections import OrderedDict
import torch
def convert(in_file, out_file):
"""Convert keys in checkpoints.
There can be some breaking changes during the development of mmdetection,
and this tool is used for upgrading checkpoints trained with old versions
to the latest... | 1,322 | 29.767442 | 77 | py |
s2anet | s2anet-master/tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from pycocotools.coco import ... | 17,478 | 35.953488 | 79 | py |
s2anet | s2anet-master/tools/train.py | from __future__ import division
import argparse
import os
import os.path as osp
import warnings
import torch
from mmcv import Config
from mmdet import __version__
from mmdet.apis import (get_root_logger, init_dist, set_random_seed,
train_detector)
from mmdet.datasets import build_dataset
from... | 3,887 | 31.672269 | 83 | py |
s2anet | s2anet-master/tools/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,830 | 42.044944 | 78 | py |
s2anet | s2anet-master/demo/webcam_demo.py | import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector, show_result
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='chec... | 1,243 | 26.644444 | 79 | py |
s2anet | s2anet-master/configs/faster_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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='FPN',
in_channels=[25... | 5,328 | 29.451429 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_ind... | 7,572 | 30.036885 | 78 | py |
s2anet | s2anet-master/configs/retinanet_r101_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[2... | 3,802 | 28.253846 | 77 | py |
s2anet | s2anet-master/configs/fast_mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
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='FPN',
in_channels=[256,... | 4,901 | 30.831169 | 77 | py |
s2anet | s2anet-master/configs/faster_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck... | 5,385 | 29.429379 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 7,434 | 30.371308 | 78 | py |
s2anet | s2anet-master/configs/faster_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck... | 5,385 | 29.429379 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,747 | 29.412698 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
# pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
... | 5,767 | 28.88601 | 78 | py |
s2anet | s2anet-master/configs/faster_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
... | 5,400 | 29.005556 | 78 | py |
s2anet | s2anet-master/configs/retinanet_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=... | 3,856 | 28.219697 | 77 | py |
s2anet | s2anet-master/configs/fast_mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 4,904 | 30.850649 | 77 | py |
s2anet | s2anet-master/configs/rpn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
... | 3,924 | 28.734848 | 78 | py |
s2anet | s2anet-master/configs/faster_rcnn_ohem_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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='FPN',
in_channels=[25... | 5,326 | 29.44 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
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='FPN',
in_channels=[256,... | 5,774 | 29.394737 | 78 | py |
s2anet | s2anet-master/configs/ssd512_coco.py | # model settings
input_size = 512
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,959 | 28.333333 | 79 | py |
s2anet | s2anet-master/configs/faster_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[... | 5,331 | 29.468571 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=d... | 5,801 | 29.376963 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
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='FPN',
... | 7,377 | 30.395745 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 8,033 | 30.382813 | 78 | py |
s2anet | s2anet-master/configs/rpn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
... | 3,924 | 28.734848 | 78 | py |
s2anet | s2anet-master/configs/fast_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 4,344 | 31.185185 | 78 | py |
s2anet | s2anet-master/configs/rpn_r50_fpn_1x.py | # model settings
model = dict(
type='RPN',
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='FPN',
in_channels=[256, 512,... | 3,867 | 28.753846 | 78 | py |
s2anet | s2anet-master/configs/retinanet_r50_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
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='FPN',
in_channels=[256... | 3,799 | 28.230769 | 77 | py |
s2anet | s2anet-master/configs/retinanet_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=... | 3,856 | 28.219697 | 77 | py |
s2anet | s2anet-master/configs/fast_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
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='FPN',
in_channels=[256,... | 4,341 | 31.162963 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
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='FPN',
... | 7,976 | 30.405512 | 78 | py |
s2anet | s2anet-master/configs/rpn_r50_caffe_c4_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requ... | 3,893 | 28.953846 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 7,434 | 30.371308 | 78 | py |
s2anet | s2anet-master/configs/fast_mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
f... | 4,653 | 29.418301 | 75 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 8,033 | 30.382813 | 78 | py |
s2anet | s2anet-master/configs/ssd300_coco.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,942 | 28.207407 | 79 | py |
s2anet | s2anet-master/configs/mask_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=d... | 5,801 | 29.376963 | 78 | py |
s2anet | s2anet-master/configs/fast_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
f... | 4,525 | 30.65035 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_ind... | 7,929 | 29.976563 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,979 | 30.417323 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,380 | 30.408511 | 78 | py |
s2anet | s2anet-master/configs/rpn_r101_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,870 | 28.776923 | 78 | py |
s2anet | s2anet-master/configs/ghm/retinanet_ghm_r50_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
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='FPN',
in_channels=[256... | 3,805 | 28.053435 | 77 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=True, deformable_gr... | 5,492 | 29.859551 | 78 | py |
s2anet | s2anet-master/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=False, deformable_gro... | 5,901 | 29.739583 | 78 | py |
s2anet | s2anet-master/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=... | 7,534 | 30.659664 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=True, deformable_gr... | 5,485 | 29.820225 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
d... | 5,589 | 29.546448 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_dpool_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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='FPN',
in_channels=[25... | 5,468 | 29.21547 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_mdpool_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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='FPN',
in_channels=[25... | 5,478 | 29.270718 | 78 | py |
s2anet | s2anet-master/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=... | 8,133 | 30.649805 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=False, deformable_g... | 5,485 | 29.820225 | 78 | py |
s2anet | s2anet-master/configs/htc/htc_r101_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet101',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
st... | 8,666 | 30.402174 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_x101_32x4d_fpn_20e_16gpu.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(... | 8,720 | 30.370504 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_r50_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,663 | 30.391304 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_x101_64x4d_fpn_20e_16gpu.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(... | 8,720 | 30.370504 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(... | 9,015 | 30.305556 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_r50_fpn_1x.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,661 | 30.384058 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_without_semantic_r50_fpn_1x.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,049 | 30.445313 | 78 | py |
s2anet | s2anet-master/configs/reppoints/bbox_r50_grid_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,342 | 28.344595 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_x101_dcn_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0,... | 4,457 | 28.72 | 79 | py |
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