SYMBOL INDEX (544 symbols across 97 files)

FILE: mmdet/apis/env.py
  function init_dist (line 12) | def init_dist(launcher, backend='nccl', **kwargs):
  function _init_dist_pytorch (line 25) | def _init_dist_pytorch(backend, **kwargs):
  function _init_dist_mpi (line 33) | def _init_dist_mpi(backend, **kwargs):
  function _init_dist_slurm (line 37) | def _init_dist_slurm(backend, **kwargs):
  function set_random_seed (line 41) | def set_random_seed(seed):
  function get_root_logger (line 48) | def get_root_logger(log_level=logging.INFO):

FILE: mmdet/apis/inference.py
  function _prepare_data (line 10) | def _prepare_data(img, img_transform, cfg, device):
  function _inference_single (line 26) | def _inference_single(model, img, img_transform, cfg, device):
  function _inference_generator (line 34) | def _inference_generator(model, imgs, img_transform, cfg, device):
  function inference_detector (line 39) | def inference_detector(model, imgs, cfg, device='cuda:0'):
  function show_result (line 51) | def show_result(img, result, dataset='coco', score_thr=0.3):

FILE: mmdet/apis/train.py
  function parse_losses (line 16) | def parse_losses(losses):
  function batch_processor (line 36) | def batch_processor(model, data, train_mode):
  function train_detector (line 46) | def train_detector(model,
  function _dist_train (line 62) | def _dist_train(model, dataset, cfg, validate=False):
  function _non_dist_train (line 99) | def _non_dist_train(model, dataset, cfg, validate=False):

FILE: mmdet/core/anchor/anchor_generator.py
  class AnchorGenerator (line 4) | class AnchorGenerator(object):
    method __init__ (line 6) | def __init__(self, base_size, scales, ratios, scale_major=True, ctr=No...
    method num_base_anchors (line 15) | def num_base_anchors(self):
    method gen_base_anchors (line 18) | def gen_base_anchors(self):
    method _meshgrid (line 45) | def _meshgrid(self, x, y, row_major=True):
    method grid_anchors (line 53) | def grid_anchors(self, featmap_size, stride=16, device='cuda'):
    method valid_flags (line 72) | def valid_flags(self, featmap_size, valid_size, device='cuda'):

FILE: mmdet/core/anchor/anchor_target.py
  function anchor_target (line 7) | def anchor_target(anchor_list,
  function images_to_levels (line 75) | def images_to_levels(target, num_level_anchors):
  function anchor_target_single (line 90) | def anchor_target_single(flat_anchors,
  function expand_binary_labels (line 160) | def expand_binary_labels(labels, label_weights, label_channels):
  function anchor_inside_flags (line 170) | def anchor_inside_flags(flat_anchors, valid_flags, img_shape,
  function unmap (line 184) | def unmap(data, count, inds, fill=0):

FILE: mmdet/core/bbox/assign_sampling.py
  function build_assigner (line 6) | def build_assigner(cfg, **kwargs):
  function build_sampler (line 17) | def build_sampler(cfg, **kwargs):
  function assign_and_sample (line 28) | def assign_and_sample(bboxes, gt_bboxes, gt_bboxes_ignore, gt_labels, cfg):

FILE: mmdet/core/bbox/assigners/assign_result.py
  class AssignResult (line 4) | class AssignResult(object):
    method __init__ (line 6) | def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
    method add_gt_ (line 12) | def add_gt_(self, gt_labels):

FILE: mmdet/core/bbox/assigners/base_assigner.py
  class BaseAssigner (line 4) | class BaseAssigner(metaclass=ABCMeta):
    method assign (line 7) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N...

FILE: mmdet/core/bbox/assigners/max_iou_assigner.py
  class MaxIoUAssigner (line 8) | class MaxIoUAssigner(BaseAssigner):
    method __init__ (line 31) | def __init__(self,
    method assign (line 43) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N...
    method assign_wrt_overlaps (line 87) | def assign_wrt_overlaps(self, overlaps, gt_labels=None):

FILE: mmdet/core/bbox/bbox_target.py
  function bbox_target (line 7) | def bbox_target(pos_bboxes_list,
  function bbox_target_single (line 35) | def bbox_target_single(pos_bboxes,
  function expand_target (line 67) | def expand_target(bbox_targets, bbox_weights, labels, num_classes):

FILE: mmdet/core/bbox/geometry.py
  function bbox_overlaps (line 4) | def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):

FILE: mmdet/core/bbox/samplers/base_sampler.py
  class BaseSampler (line 8) | class BaseSampler(metaclass=ABCMeta):
    method __init__ (line 10) | def __init__(self,
    method _sample_pos (line 24) | def _sample_pos(self, assign_result, num_expected, **kwargs):
    method _sample_neg (line 28) | def _sample_neg(self, assign_result, num_expected, **kwargs):
    method sample (line 31) | def sample(self,

FILE: mmdet/core/bbox/samplers/combined_sampler.py
  class CombinedSampler (line 5) | class CombinedSampler(BaseSampler):
    method __init__ (line 7) | def __init__(self, pos_sampler, neg_sampler, **kwargs):
    method _sample_pos (line 12) | def _sample_pos(self, **kwargs):
    method _sample_neg (line 15) | def _sample_neg(self, **kwargs):

FILE: mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py
  class InstanceBalancedPosSampler (line 7) | class InstanceBalancedPosSampler(RandomSampler):
    method _sample_pos (line 9) | def _sample_pos(self, assign_result, num_expected, **kwargs):

FILE: mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py
  class IoUBalancedNegSampler (line 7) | class IoUBalancedNegSampler(RandomSampler):
    method __init__ (line 9) | def __init__(self,
    method _sample_neg (line 22) | def _sample_neg(self, assign_result, num_expected, **kwargs):

FILE: mmdet/core/bbox/samplers/ohem_sampler.py
  class OHEMSampler (line 7) | class OHEMSampler(BaseSampler):
    method __init__ (line 9) | def __init__(self,
    method hard_mining (line 21) | def hard_mining(self, inds, num_expected, bboxes, labels, feats):
    method _sample_pos (line 38) | def _sample_pos(self,
    method _sample_neg (line 54) | def _sample_neg(self,

FILE: mmdet/core/bbox/samplers/pseudo_sampler.py
  class PseudoSampler (line 7) | class PseudoSampler(BaseSampler):
    method __init__ (line 9) | def __init__(self, **kwargs):
    method _sample_pos (line 12) | def _sample_pos(self, **kwargs):
    method _sample_neg (line 15) | def _sample_neg(self, **kwargs):
    method sample (line 18) | def sample(self, assign_result, bboxes, gt_bboxes, **kwargs):

FILE: mmdet/core/bbox/samplers/random_sampler.py
  class RandomSampler (line 7) | class RandomSampler(BaseSampler):
    method __init__ (line 9) | def __init__(self,
    method random_choice (line 19) | def random_choice(gallery, num):
    method _sample_pos (line 35) | def _sample_pos(self, assign_result, num_expected, **kwargs):
    method _sample_neg (line 45) | def _sample_neg(self, assign_result, num_expected, **kwargs):

FILE: mmdet/core/bbox/samplers/random_sampler_fixnum.py
  class RandomSamplerFixnum (line 8) | class RandomSamplerFixnum(BaseSampler):
    method __init__ (line 10) | def __init__(self,
    method random_choice (line 20) | def random_choice(gallery, num):
    method _sample_pos (line 46) | def _sample_pos(self, assign_result, num_expected, **kwargs):
    method _sample_neg (line 71) | def _sample_neg(self, assign_result, num_expected, **kwargs):
    method sample (line 89) | def sample(self,

FILE: mmdet/core/bbox/samplers/sampling_result.py
  class SamplingResult (line 4) | class SamplingResult(object):
    method __init__ (line 6) | def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
    method bboxes (line 23) | def bboxes(self):

FILE: mmdet/core/bbox/transforms.py
  function bbox2delta (line 6) | def bbox2delta(proposals, gt, means=[0, 0, 0, 0], stds=[1, 1, 1, 1]):
  function delta2bbox (line 34) | def delta2bbox(rois,
  function bbox_flip (line 71) | def bbox_flip(bboxes, img_shape):
  function bbox_mapping (line 91) | def bbox_mapping(bboxes, img_shape, scale_factor, flip):
  function bbox_mapping_back (line 99) | def bbox_mapping_back(bboxes, img_shape, scale_factor, flip):
  function bbox2roi (line 106) | def bbox2roi(bbox_list):
  function roi2bbox (line 128) | def roi2bbox(rois):
  function bbox2result (line 138) | def bbox2result(bboxes, labels, num_classes):

FILE: mmdet/core/evaluation/bbox_overlaps.py
  function bbox_overlaps (line 4) | def bbox_overlaps(bboxes1, bboxes2, mode='iou'):

FILE: mmdet/core/evaluation/class_names.py
  function voc_classes (line 4) | def voc_classes():
  function imagenet_det_classes (line 12) | def imagenet_det_classes():
  function imagenet_vid_classes (line 53) | def imagenet_vid_classes():
  function coco_classes (line 63) | def coco_classes():
  function get_classes (line 89) | def get_classes(dataset):

FILE: mmdet/core/evaluation/coco_utils.py
  function coco_eval (line 9) | def coco_eval(result_file, result_types, coco, max_dets=(100, 300, 1000)):
  function fast_eval_recall (line 41) | def fast_eval_recall(results,
  function xyxy2xywh (line 78) | def xyxy2xywh(bbox):
  function proposal2json (line 88) | def proposal2json(dataset, results):
  function det2json (line 103) | def det2json(dataset, results):
  function segm2json (line 120) | def segm2json(dataset, results):
  function results2json (line 140) | def results2json(dataset, results, out_file):

FILE: mmdet/core/evaluation/eval_hooks.py
  class DistEvalHook (line 19) | class DistEvalHook(Hook):
    method __init__ (line 21) | def __init__(self, dataset, interval=1):
    method _barrier (line 34) | def _barrier(self, rank, world_size):
    method before_run (line 52) | def before_run(self, runner):
    method after_run (line 59) | def after_run(self, runner):
    method after_train_epoch (line 63) | def after_train_epoch(self, runner):
    method evaluate (line 102) | def evaluate(self):
  class DistEvalmAPHook (line 106) | class DistEvalmAPHook(DistEvalHook):
    method evaluate (line 108) | def evaluate(self, runner, results):
  class CocoDistEvalRecallHook (line 144) | class CocoDistEvalRecallHook(DistEvalHook):
    method __init__ (line 146) | def __init__(self,
    method evaluate (line 154) | def evaluate(self, runner, results):
  class CocoDistEvalmAPHook (line 164) | class CocoDistEvalmAPHook(DistEvalHook):
    method evaluate (line 166) | def evaluate(self, runner, results):

FILE: mmdet/core/evaluation/mean_ap.py
  function average_precision (line 9) | def average_precision(recalls, precisions, mode='area'):
  function tpfp_imagenet (line 56) | def tpfp_imagenet(det_bboxes,
  function tpfp_default (line 137) | def tpfp_default(det_bboxes, gt_bboxes, gt_ignore, iou_thr, area_ranges=...
  function get_cls_results (line 202) | def get_cls_results(det_results, gt_bboxes, gt_labels, gt_ignore, class_...
  function eval_map (line 220) | def eval_map(det_results,
  function print_map_summary (line 332) | def print_map_summary(mean_ap, results, dataset=None):

FILE: mmdet/core/evaluation/recall.py
  function _recalls (line 7) | def _recalls(all_ious, proposal_nums, thrs):
  function set_recall_param (line 40) | def set_recall_param(proposal_nums, iou_thrs):
  function eval_recalls (line 62) | def eval_recalls(gts,
  function print_recall_summary (line 105) | def print_recall_summary(recalls,
  function plot_num_recall (line 138) | def plot_num_recall(recalls, proposal_nums):
  function plot_iou_recall (line 163) | def plot_iou_recall(recalls, iou_thrs):

FILE: mmdet/core/loss/losses.py
  function weighted_nll_loss (line 6) | def weighted_nll_loss(pred, label, weight, avg_factor=None):
  function weighted_cross_entropy (line 13) | def weighted_cross_entropy(pred, label, weight, avg_factor=None, reduce=...
  function weighted_binary_cross_entropy (line 23) | def weighted_binary_cross_entropy(pred, label, weight, avg_factor=None):
  function sigmoid_focal_loss (line 31) | def sigmoid_focal_loss(pred,
  function weighted_sigmoid_focal_loss (line 46) | def weighted_sigmoid_focal_loss(pred,
  function mask_cross_entropy (line 60) | def mask_cross_entropy(pred, target, label):
  function smooth_l1_loss (line 68) | def smooth_l1_loss(pred, target, beta=1.0, reduction='elementwise_mean'):
  function weighted_smoothl1 (line 84) | def weighted_smoothl1(pred, target, weight, beta=1.0, avg_factor=None):
  function accuracy (line 91) | def accuracy(pred, target, topk=1):

FILE: mmdet/core/mask/mask_target.py
  function mask_target (line 6) | def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_...
  function mask_target_single (line 15) | def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):

FILE: mmdet/core/mask/utils.py
  function split_combined_polys (line 4) | def split_combined_polys(polys, poly_lens, polys_per_mask):

FILE: mmdet/core/post_processing/bbox_nms.py
  function multiclass_nms (line 6) | def multiclass_nms(multi_bboxes, multi_scores, score_thr, nms_cfg, max_n...

FILE: mmdet/core/post_processing/merge_augs.py
  function merge_aug_proposals (line 9) | def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg):
  function merge_aug_bboxes (line 41) | def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
  function merge_aug_scores (line 68) | def merge_aug_scores(aug_scores):
  function merge_aug_masks (line 76) | def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):

FILE: mmdet/core/utils/dist_utils.py
  function _allreduce_coalesced (line 9) | def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
  function allreduce_grads (line 31) | def allreduce_grads(model, coalesce=True, bucket_size_mb=-1):
  class DistOptimizerHook (line 44) | class DistOptimizerHook(OptimizerHook):
    method __init__ (line 46) | def __init__(self, grad_clip=None, coalesce=True, bucket_size_mb=-1):
    method after_train_iter (line 51) | def after_train_iter(self, runner):

FILE: mmdet/core/utils/misc.py
  function tensor2imgs (line 8) | def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
  function multi_apply (line 21) | def multi_apply(func, *args, **kwargs):
  function unmap (line 27) | def unmap(data, count, inds, fill=0):

FILE: mmdet/datasets/coco.py
  class CocoDataset (line 7) | class CocoDataset(CustomDataset):
    method load_annotations (line 24) | def load_annotations(self, ann_file):
    method get_ann_info (line 39) | def get_ann_info(self, idx):
    method _filter_imgs (line 45) | def _filter_imgs(self, min_size=32):
    method _parse_ann_info (line 56) | def _parse_ann_info(self, ann_info, with_mask=True):

FILE: mmdet/datasets/concat_dataset.py
  class ConcatDataset (line 5) | class ConcatDataset(_ConcatDataset):
    method __init__ (line 15) | def __init__(self, datasets):

FILE: mmdet/datasets/custom.py
  class CustomDataset (line 14) | class CustomDataset(Dataset):
    method __init__ (line 38) | def __init__(self,
    method __len__ (line 114) | def __len__(self):
    method load_annotations (line 117) | def load_annotations(self, ann_file):
    method load_proposals (line 120) | def load_proposals(self, proposal_file):
    method get_ann_info (line 123) | def get_ann_info(self, idx):
    method _filter_imgs (line 126) | def _filter_imgs(self, min_size=32):
    method _set_group_flag (line 134) | def _set_group_flag(self):
    method _rand_another (line 146) | def _rand_another(self, idx):
    method __getitem__ (line 150) | def __getitem__(self, idx):
    method prepare_train_img (line 160) | def prepare_train_img(self, idx):
    method prepare_test_img (line 239) | def prepare_test_img(self, idx):

FILE: mmdet/datasets/extra_aug.py
  class PhotoMetricDistortion (line 8) | class PhotoMetricDistortion(object):
    method __init__ (line 10) | def __init__(self,
    method __call__ (line 20) | def __call__(self, img, boxes, labels):
  class Expand (line 67) | class Expand(object):
    method __init__ (line 69) | def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)):
    method __call__ (line 76) | def __call__(self, img, boxes, labels):
  class RandomCrop (line 92) | class RandomCrop(object):
    method __init__ (line 94) | def __init__(self,
    method __call__ (line 101) | def __call__(self, img, boxes, labels):
  class ExtraAugmentation (line 146) | class ExtraAugmentation(object):
    method __init__ (line 148) | def __init__(self,
    method __call__ (line 161) | def __call__(self, img, boxes, labels):

FILE: mmdet/datasets/loader/build_loader.py
  function build_dataloader (line 15) | def build_dataloader(dataset,

FILE: mmdet/datasets/loader/sampler.py
  class GroupSampler (line 11) | class GroupSampler(Sampler):
    method __init__ (line 13) | def __init__(self, dataset, samples_per_gpu=1):
    method __iter__ (line 24) | def __iter__(self):
    method __len__ (line 47) | def __len__(self):
  class DistributedGroupSampler (line 51) | class DistributedGroupSampler(Sampler):
    method __init__ (line 66) | def __init__(self,
    method __iter__ (line 92) | def __iter__(self):
    method __len__ (line 128) | def __len__(self):
    method set_epoch (line 131) | def set_epoch(self, epoch):

FILE: mmdet/datasets/repeat_dataset.py
  class RepeatDataset (line 4) | class RepeatDataset(object):
    method __init__ (line 6) | def __init__(self, dataset, times):
    method __getitem__ (line 15) | def __getitem__(self, idx):
    method __len__ (line 18) | def __len__(self):

FILE: mmdet/datasets/transforms.py
  class ImageTransform (line 8) | class ImageTransform(object):
    method __init__ (line 18) | def __init__(self,
    method __call__ (line 28) | def __call__(self, img, scale, flip=False, keep_ratio=True):
  function bbox_flip (line 49) | def bbox_flip(bboxes, img_shape):
  class BboxTransform (line 64) | class BboxTransform(object):
    method __init__ (line 72) | def __init__(self, max_num_gts=None):
    method __call__ (line 75) | def __call__(self, bboxes, img_shape, scale_factor, flip=False):
  class MaskTransform (line 90) | class MaskTransform(object):
    method __call__ (line 98) | def __call__(self, masks, pad_shape, scale_factor, flip=False):
  class Numpy2Tensor (line 112) | class Numpy2Tensor(object):
    method __init__ (line 114) | def __init__(self):
    method __call__ (line 117) | def __call__(self, *args):

FILE: mmdet/datasets/utils.py
  function to_tensor (line 15) | def to_tensor(data):
  function random_scale (line 36) | def random_scale(img_scales, mode='range'):
  function show_ann (line 70) | def show_ann(coco, img, ann_info):
  function get_dataset (line 77) | def get_dataset(data_cfg):

FILE: mmdet/datasets/voc.py
  class VOCDataset (line 4) | class VOCDataset(XMLDataset):
    method __init__ (line 11) | def __init__(self, **kwargs):

FILE: mmdet/datasets/xml_style.py
  class XMLDataset (line 10) | class XMLDataset(CustomDataset):
    method __init__ (line 12) | def __init__(self, **kwargs):
    method load_annotations (line 16) | def load_annotations(self, ann_file):
    method get_ann_info (line 32) | def get_ann_info(self, idx):

FILE: mmdet/models/anchor_heads/anchor_head.py
  class AnchorHead (line 16) | class AnchorHead(nn.Module):
    method __init__ (line 33) | def __init__(self,
    method _init_layers (line 72) | def _init_layers(self):
    method init_weights (line 77) | def init_weights(self):
    method forward_single (line 81) | def forward_single(self, x):
    method forward (line 86) | def forward(self, feats):
    method get_anchors (line 89) | def get_anchors(self, featmap_sizes, img_metas):
    method loss_single (line 128) | def loss_single(self, cls_score, bbox_pred, labels, label_weights,
    method loss (line 172) | def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas,
    method get_bboxes (line 210) | def get_bboxes(self, cls_scores, bbox_preds, img_metas, cfg,
    method get_bboxes_single (line 236) | def get_bboxes_single(self,

FILE: mmdet/models/anchor_heads/retina_head.py
  class RetinaHead (line 11) | class RetinaHead(AnchorHead):
    method __init__ (line 13) | def __init__(self,
    method _init_layers (line 34) | def _init_layers(self):
    method init_weights (line 52) | def init_weights(self):
    method forward_single (line 61) | def forward_single(self, x):

FILE: mmdet/models/anchor_heads/rpn_head.py
  class RPNHead (line 13) | class RPNHead(AnchorHead):
    method __init__ (line 15) | def __init__(self, in_channels, **kwargs):
    method _init_layers (line 18) | def _init_layers(self):
    method init_weights (line 25) | def init_weights(self):
    method forward_single (line 30) | def forward_single(self, x):
    method loss (line 37) | def loss(self, cls_scores, bbox_preds, gt_bboxes, img_metas, cfg):
    method get_bboxes_single (line 43) | def get_bboxes_single(self,

FILE: mmdet/models/anchor_heads/ssd_head.py
  class SSDHead (line 14) | class SSDHead(AnchorHead):
    method __init__ (line 16) | def __init__(self,
    method init_weights (line 95) | def init_weights(self):
    method forward (line 100) | def forward(self, feats):
    method loss_single (line 109) | def loss_single(self, cls_score, bbox_pred, labels, label_weights,
    method loss (line 133) | def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas,

FILE: mmdet/models/backbones/resnet.py
  function conv3x3 (line 14) | def conv3x3(in_planes, out_planes, stride=1, dilation=1):
  class BasicBlock (line 26) | class BasicBlock(nn.Module):
    method __init__ (line 29) | def __init__(self,
    method norm1 (line 55) | def norm1(self):
    method norm2 (line 59) | def norm2(self):
    method forward (line 62) | def forward(self, x):
  class Bottleneck (line 81) | class Bottleneck(nn.Module):
    method __init__ (line 84) | def __init__(self,
    method norm1 (line 176) | def norm1(self):
    method norm2 (line 180) | def norm2(self):
    method norm3 (line 184) | def norm3(self):
    method forward (line 187) | def forward(self, x):
  function make_res_layer (line 229) | def make_res_layer(block,
  class ResNet (line 280) | class ResNet(nn.Module):
    method __init__ (line 312) | def __init__(self,
    method norm1 (line 380) | def norm1(self):
    method _make_stem_layer (line 383) | def _make_stem_layer(self):
    method _freeze_stages (line 392) | def _freeze_stages(self):
    method init_weights (line 403) | def init_weights(self, pretrained=None):
    method forward (line 429) | def forward(self, x):
    method train (line 445) | def train(self, mode=True):

FILE: mmdet/models/backbones/resnext.py
  class Bottleneck (line 12) | class Bottleneck(_Bottleneck):
    method __init__ (line 14) | def __init__(self, *args, groups=1, base_width=4, **kwargs):
  function make_res_layer (line 87) | def make_res_layer(block,
  class ResNeXt (line 144) | class ResNeXt(ResNet):
    method __init__ (line 176) | def __init__(self, groups=1, base_width=4, **kwargs):

FILE: mmdet/models/backbones/ssd_vgg.py
  class SSDVGG (line 13) | class SSDVGG(VGG):
    method __init__ (line 19) | def __init__(self,
    method init_weights (line 55) | def init_weights(self, pretrained=None):
    method forward (line 76) | def forward(self, x):
    method _make_extra_layers (line 92) | def _make_extra_layers(self, outplanes):
  class L2Norm (line 119) | class L2Norm(nn.Module):
    method __init__ (line 121) | def __init__(self, n_dims, scale=20., eps=1e-10):
    method forward (line 128) | def forward(self, x):

FILE: mmdet/models/bbox_heads/bbox_head.py
  class BBoxHead (line 11) | class BBoxHead(nn.Module):
    method __init__ (line 15) | def __init__(self,
    method init_weights (line 49) | def init_weights(self):
    method forward (line 57) | def forward(self, x):
    method get_target (line 65) | def get_target(self, sampling_results, gt_bboxes, gt_labels,
    method loss (line 83) | def loss(self,
    method get_det_bboxes (line 104) | def get_det_bboxes(self,
    method refine_bboxes (line 134) | def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
    method regress_by_class (line 174) | def regress_by_class(self, rois, label, bbox_pred, img_meta):

FILE: mmdet/models/bbox_heads/convfc_bbox_head.py
  class ConvFCBBoxHead (line 9) | class ConvFCBBoxHead(BBoxHead):
    method __init__ (line 18) | def __init__(self,
    method _add_conv_fc_branch (line 82) | def _add_conv_fc_branch(self,
    method init_weights (line 123) | def init_weights(self):
    method forward (line 131) | def forward(self, x):
  class SharedFCBBoxHead (line 171) | class SharedFCBBoxHead(ConvFCBBoxHead):
    method __init__ (line 173) | def __init__(self, num_fcs=2, fc_out_channels=1024, *args, **kwargs):

FILE: mmdet/models/bbox_heads/convfc_bbox_head_enhanced.py
  class ConvFCRoIHeadEnhance (line 8) | class ConvFCRoIHeadEnhance(BBoxHead):
    method __init__ (line 17) | def __init__(self,
    method _add_conv_fc_branch (line 85) | def _add_conv_fc_branch(self,
    method init_weights (line 126) | def init_weights(self):
    method forward (line 134) | def forward(self, x, enhanced_feature=None):

FILE: mmdet/models/bbox_heads/graph_bbox_head.py
  class GraphBBoxHead (line 10) | class GraphBBoxHead(BBoxHead):
    method __init__ (line 19) | def __init__(self,
    method _add_conv_fc_branch (line 87) | def _add_conv_fc_branch(self,
    method init_weights (line 131) | def init_weights(self):
    method forward (line 140) | def forward(self, x, geom_f, bs):
    method loss (line 211) | def loss(self, cls_score, bbox_pred, A_pred, A_gt, labels, label_weigh...
    method propagate_em (line 232) | def propagate_em(self, x, A, W):

FILE: mmdet/models/builder.py
  function _build_module (line 7) | def _build_module(cfg, registry, default_args):
  function build (line 26) | def build(cfg, registry, default_args=None):
  function build_backbone (line 34) | def build_backbone(cfg):
  function build_neck (line 38) | def build_neck(cfg):
  function build_roi_extractor (line 42) | def build_roi_extractor(cfg):
  function build_head (line 46) | def build_head(cfg):
  function build_detector (line 50) | def build_detector(cfg, train_cfg=None, test_cfg=None):

FILE: mmdet/models/detectors/base.py
  class BaseDetector (line 12) | class BaseDetector(nn.Module):
    method __init__ (line 17) | def __init__(self):
    method with_neck (line 21) | def with_neck(self):
    method with_bbox (line 25) | def with_bbox(self):
    method with_mask (line 29) | def with_mask(self):
    method extract_feat (line 33) | def extract_feat(self, imgs):
    method extract_feats (line 36) | def extract_feats(self, imgs):
    method forward_train (line 42) | def forward_train(self, imgs, img_metas, **kwargs):
    method simple_test (line 46) | def simple_test(self, img, img_meta, **kwargs):
    method aug_test (line 50) | def aug_test(self, imgs, img_metas, **kwargs):
    method init_weights (line 53) | def init_weights(self, pretrained=None):
    method forward_test (line 58) | def forward_test(self, imgs, img_metas, **kwargs):
    method forward (line 78) | def forward(self, img, img_meta, return_loss=True, **kwargs):
    method show_result (line 84) | def show_result(self,

FILE: mmdet/models/detectors/cascade_rcnn.py
  class CascadeRCNN (line 15) | class CascadeRCNN(BaseDetector, RPNTestMixin):
    method __init__ (line 17) | def __init__(self,
    method with_rpn (line 80) | def with_rpn(self):
    method init_weights (line 83) | def init_weights(self, pretrained=None):
    method extract_feat (line 102) | def extract_feat(self, img):
    method forward_train (line 108) | def forward_train(self,
    method simple_test (line 190) | def simple_test(self, img, img_meta, proposals=None, rescale=False):
    method aug_test (line 307) | def aug_test(self, img, img_meta, proposals=None, rescale=False):
    method show_result (line 310) | def show_result(self, data, result, img_norm_cfg, **kwargs):

FILE: mmdet/models/detectors/fast_rcnn.py
  class FastRCNN (line 6) | class FastRCNN(TwoStageDetector):
    method __init__ (line 8) | def __init__(self,
    method forward_test (line 29) | def forward_test(self, imgs, img_metas, proposals, **kwargs):

FILE: mmdet/models/detectors/faster_rcnn.py
  class FasterRCNN (line 6) | class FasterRCNN(TwoStageDetector):
    method __init__ (line 8) | def __init__(self,

FILE: mmdet/models/detectors/hkrm_rcnn.py
  class HKRMRCNN (line 14) | class HKRMRCNN(BaseDetector, RPNTestMixin, BBoxTestMixin,
    method __init__ (line 17) | def __init__(self,
    method with_rpn (line 70) | def with_rpn(self):
    method init_weights (line 73) | def init_weights(self, pretrained=None):
    method extract_feat (line 90) | def extract_feat(self, img):
    method forward_train (line 96) | def forward_train(self,
    method simple_test (line 217) | def simple_test(self, img, img_meta, proposals=None, rescale=False):
    method aug_test (line 238) | def aug_test(self, imgs, img_metas, rescale=False):
    method simple_test_bboxes_hkrm (line 267) | def simple_test_bboxes_hkrm(self,
    method aug_test_bboxes_hkrm (line 305) | def aug_test_bboxes_hkrm(self, feats, img_metas, proposal_list, rcnn_t...

FILE: mmdet/models/detectors/mask_rcnn.py
  class MaskRCNN (line 6) | class MaskRCNN(TwoStageDetector):
    method __init__ (line 8) | def __init__(self,

FILE: mmdet/models/detectors/reasoning_rcnn.py
  class ReasoningRCNN (line 19) | class ReasoningRCNN(BaseDetector, RPNTestMixin):
    method __init__ (line 21) | def __init__(self,
    method with_rpn (line 131) | def with_rpn(self):
    method init_weights (line 134) | def init_weights(self, pretrained=None):
    method extract_feat (line 154) | def extract_feat(self, img):
    method forward_upper_neck (line 160) | def forward_upper_neck(self, x, stage):
    method forward_train (line 167) | def forward_train(self,
    method simple_test (line 299) | def simple_test(self, img, img_meta, proposals=None, rescale=False):
    method aug_test (line 469) | def aug_test(self, img, img_meta, proposals=None, rescale=False):
    method show_result (line 472) | def show_result(self, data, result, img_norm_cfg, **kwargs):

FILE: mmdet/models/detectors/retinanet.py
  class RetinaNet (line 6) | class RetinaNet(SingleStageDetector):
    method __init__ (line 8) | def __init__(self,

FILE: mmdet/models/detectors/rpn.py
  class RPN (line 11) | class RPN(BaseDetector, RPNTestMixin):
    method __init__ (line 13) | def __init__(self,
    method init_weights (line 28) | def init_weights(self, pretrained=None):
    method extract_feat (line 35) | def extract_feat(self, img):
    method forward_train (line 41) | def forward_train(self, img, img_meta, gt_bboxes=None):
    method simple_test (line 52) | def simple_test(self, img, img_meta, rescale=False):
    method aug_test (line 61) | def aug_test(self, imgs, img_metas, rescale=False):
    method show_result (line 74) | def show_result(self, data, result, img_norm_cfg):

FILE: mmdet/models/detectors/sgrn.py
  class ThreeStageGraphDetector (line 13) | class ThreeStageGraphDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
    method __init__ (line 16) | def __init__(self,
    method with_rpn (line 83) | def with_rpn(self):
    method init_weights (line 86) | def init_weights(self, pretrained=None):
    method extract_feat (line 103) | def extract_feat(self, img):
    method forward_train (line 109) | def forward_train(self,
    method simple_test (line 299) | def simple_test(self, img, img_meta, proposals=None, rescale=False):
    method simple_test_bboxes_ms (line 330) | def simple_test_bboxes_ms(self,
    method aug_test (line 472) | def aug_test(self, imgs, img_metas, rescale=False):
    method _compute_pseudo (line 501) | def _compute_pseudo(self, bb_centre):
    method _create_neighbourhood (line 529) | def _create_neighbourhood(self,
    method _create_neighbourhood_feat (line 572) | def _create_neighbourhood_feat(self, image, top_ind):
    method _create_neighbourhood_pseudo (line 590) | def _create_neighbourhood_pseudo(self, pseudo, top_ind):
  class GraphLearner (line 607) | class GraphLearner(nn.Module):
    method __init__ (line 608) | def __init__(self, in_feature_dim, combined_feature_dim, dropout=0.5):
    method forward (line 632) | def forward(self, graph_nodes):
  class NeighbourhoodGraphConvolution (line 658) | class NeighbourhoodGraphConvolution(Module):
    method __init__ (line 664) | def __init__(self,
    method init_parameters (line 699) | def init_parameters(self):
    method forward (line 706) | def forward(self, neighbourhood_features, neighbourhood_pseudo_coord):
    method get_gaussian_weights (line 733) | def get_gaussian_weights(self, pseudo_coord):
    method convolution (line 760) | def convolution(self, neighbourhood, weights):

FILE: mmdet/models/detectors/single_stage.py
  class SingleStageDetector (line 10) | class SingleStageDetector(BaseDetector):
    method __init__ (line 12) | def __init__(self,
    method init_weights (line 28) | def init_weights(self, pretrained=None):
    method extract_feat (line 39) | def extract_feat(self, img):
    method forward_train (line 45) | def forward_train(self, img, img_metas, gt_bboxes, gt_labels):
    method simple_test (line 52) | def simple_test(self, img, img_meta, rescale=False):
    method aug_test (line 63) | def aug_test(self, imgs, img_metas, rescale=False):

FILE: mmdet/models/detectors/test_mixins.py
  class RPNTestMixin (line 5) | class RPNTestMixin(object):
    method simple_test_rpn (line 7) | def simple_test_rpn(self, x, img_meta, rpn_test_cfg):
    method aug_test_rpn (line 13) | def aug_test_rpn(self, feats, img_metas, rpn_test_cfg):
  class BBoxTestMixin (line 28) | class BBoxTestMixin(object):
    method simple_test_bboxes (line 30) | def simple_test_bboxes(self,
    method aug_test_bboxes (line 53) | def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_c...
  class MaskTestMixin (line 88) | class MaskTestMixin(object):
    method simple_test_mask (line 90) | def simple_test_mask(self,
    method aug_test_mask (line 115) | def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):

FILE: mmdet/models/detectors/two_stage.py
  class TwoStageDetector (line 12) | class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
    method __init__ (line 15) | def __init__(self,
    method with_rpn (line 53) | def with_rpn(self):
    method init_weights (line 56) | def init_weights(self, pretrained=None):
    method extract_feat (line 74) | def extract_feat(self, img):
    method forward_train (line 80) | def forward_train(self,
    method simple_test (line 155) | def simple_test(self, img, img_meta, proposals=None, rescale=False):
    method aug_test (line 176) | def aug_test(self, imgs, img_metas, rescale=False):

FILE: mmdet/models/mask_heads/fcn_mask_head.py
  class FCNMaskHead (line 13) | class FCNMaskHead(nn.Module):
    method __init__ (line 15) | def __init__(self,
    method init_weights (line 73) | def init_weights(self):
    method forward (line 81) | def forward(self, x):
    method get_target (line 91) | def get_target(self, sampling_results, gt_masks, rcnn_train_cfg):
    method loss (line 100) | def loss(self, mask_pred, mask_targets, labels):
    method get_seg_masks (line 110) | def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg,

FILE: mmdet/models/necks/fpn.py
  class FPN (line 10) | class FPN(nn.Module):
    method __init__ (line 12) | def __init__(self,
    method init_weights (line 90) | def init_weights(self):
    method forward (line 95) | def forward(self, inputs):

FILE: mmdet/models/registry.py
  class Registry (line 4) | class Registry(object):
    method __init__ (line 6) | def __init__(self, name):
    method name (line 11) | def name(self):
    method module_dict (line 15) | def module_dict(self):
    method _register_module (line 18) | def _register_module(self, module_class):
    method register_module (line 34) | def register_module(self, cls):

FILE: mmdet/models/roi_extractors/single_level.py
  class SingleRoIExtractor (line 11) | class SingleRoIExtractor(nn.Module):
    method __init__ (line 24) | def __init__(self,
    method num_inputs (line 36) | def num_inputs(self):
    method init_weights (line 40) | def init_weights(self):
    method build_roi_layers (line 43) | def build_roi_layers(self, layer_cfg, featmap_strides):
    method map_roi_levels (line 52) | def map_roi_levels(self, rois, num_levels):
    method forward (line 73) | def forward(self, feats, rois):

FILE: mmdet/models/utils/conv_module.py
  class ConvModule (line 9) | class ConvModule(nn.Module):
    method __init__ (line 11) | def __init__(self,
    method norm (line 68) | def norm(self):
    method init_weights (line 71) | def init_weights(self):
    method forward (line 77) | def forward(self, x, activate=True, norm=True):

FILE: mmdet/models/utils/norm.py
  function build_norm_layer (line 13) | def build_norm_layer(cfg, num_features, postfix=''):

FILE: mmdet/models/utils/weight_init.py
  function xavier_init (line 5) | def xavier_init(module, gain=1, bias=0, distribution='normal'):
  function normal_init (line 15) | def normal_init(module, mean=0, std=1, bias=0):
  function uniform_init (line 21) | def uniform_init(module, a=0, b=1, bias=0):
  function kaiming_init (line 27) | def kaiming_init(module,
  function bias_init_with_prob (line 43) | def bias_init_with_prob(prior_prob):

FILE: mmdet/ops/dcn/functions/deform_conv.py
  class DeformConvFunction (line 8) | class DeformConvFunction(Function):
    method forward (line 11) | def forward(ctx,
    method backward (line 55) | def backward(ctx, grad_output):
    method _output_size (line 92) | def _output_size(input, weight, padding, dilation, stride):
  class ModulatedDeformConvFunction (line 108) | class ModulatedDeformConvFunction(Function):
    method forward (line 111) | def forward(ctx,
    method backward (line 146) | def backward(ctx, grad_output):
    method _infer_shape (line 168) | def _infer_shape(ctx, input, weight):

FILE: mmdet/ops/dcn/functions/deform_pool.py
  class DeformRoIPoolingFunction (line 7) | class DeformRoIPoolingFunction(Function):
    method forward (line 10) | def forward(ctx,
    method backward (line 50) | def backward(ctx, grad_output):

FILE: mmdet/ops/dcn/modules/deform_conv.py
  class DeformConv (line 10) | class DeformConv(nn.Module):
    method __init__ (line 12) | def __init__(self,
    method reset_parameters (line 46) | def reset_parameters(self):
    method forward (line 53) | def forward(self, input, offset):
  class ModulatedDeformConv (line 59) | class ModulatedDeformConv(nn.Module):
    method __init__ (line 61) | def __init__(self,
    method reset_parameters (line 91) | def reset_parameters(self):
    method forward (line 100) | def forward(self, input, offset, mask):
  class ModulatedDeformConvPack (line 106) | class ModulatedDeformConvPack(ModulatedDeformConv):
    method __init__ (line 108) | def __init__(self,
    method init_offset (line 132) | def init_offset(self):
    method forward (line 136) | def forward(self, input):

FILE: mmdet/ops/dcn/modules/deform_pool.py
  class DeformRoIPooling (line 6) | class DeformRoIPooling(nn.Module):
    method __init__ (line 8) | def __init__(self,
    method forward (line 27) | def forward(self, data, rois, offset):
  class DeformRoIPoolingPack (line 36) | class DeformRoIPoolingPack(DeformRoIPooling):
    method __init__ (line 38) | def __init__(self,
    method forward (line 66) | def forward(self, data, rois):
  class ModulatedDeformRoIPoolingPack (line 89) | class ModulatedDeformRoIPoolingPack(DeformRoIPooling):
    method __init__ (line 91) | def __init__(self,
    method forward (line 128) | def forward(self, data, rois):

FILE: mmdet/ops/dcn/src/deform_conv_cuda.cpp
  function shape_check (line 62) | void shape_check(at::Tensor input, at::Tensor offset,
  function deform_conv_forward_cuda (line 153) | int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
  function deform_conv_backward_input_cuda (line 249) | int deform_conv_backward_input_cuda(
  function deform_conv_backward_parameters_cuda (line 347) | int deform_conv_backward_parameters_cuda(
  function modulated_deform_conv_cuda_forward (line 446) | void modulated_deform_conv_cuda_forward(at::Tensor input, at::Tensor wei...
  function modulated_deform_conv_cuda_backward (line 520) | void modulated_deform_conv_cuda_backward(at::Tensor input, at::Tensor we...
  function PYBIND11_MODULE (line 623) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)

FILE: mmdet/ops/dcn/src/deform_pool_cuda.cpp
  function deform_psroi_pooling_cuda_forward (line 53) | void deform_psroi_pooling_cuda_forward(at::Tensor input, at::Tensor bbox,
  function deform_psroi_pooling_cuda_backward (line 92) | void deform_psroi_pooling_cuda_backward(at::Tensor out_grad,
  function PYBIND11_MODULE (line 138) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)

FILE: mmdet/ops/nms/nms_wrapper.py
  function nms (line 9) | def nms(dets, iou_thr, device_id=None):
  function soft_nms (line 37) | def soft_nms(dets, iou_thr, method='linear', sigma=0.5, min_score=1e-3):

FILE: mmdet/ops/nms/setup.py
  function customize_compiler_for_nvcc (line 25) | def customize_compiler_for_nvcc(self):
  class custom_build_ext (line 63) | class custom_build_ext(build_ext):
    method build_extensions (line 65) | def build_extensions(self):

FILE: mmdet/ops/roi_align/functions/roi_align.py
  class RoIAlignFunction (line 6) | class RoIAlignFunction(Function):
    method forward (line 9) | def forward(ctx, features, rois, out_size, spatial_scale, sample_num=0):
    method backward (line 39) | def backward(ctx, grad_output):

FILE: mmdet/ops/roi_align/modules/roi_align.py
  class RoIAlign (line 5) | class RoIAlign(Module):
    method __init__ (line 7) | def __init__(self, out_size, spatial_scale, sample_num=0):
    method forward (line 14) | def forward(self, features, rois):

FILE: mmdet/ops/roi_align/src/roi_align_cuda.cpp
  function roi_align_forward_cuda (line 27) | int roi_align_forward_cuda(at::Tensor features, at::Tensor rois,
  function roi_align_backward_cuda (line 55) | int roi_align_backward_cuda(at::Tensor top_grad, at::Tensor rois,
  function PYBIND11_MODULE (line 82) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: mmdet/ops/roi_pool/functions/roi_pool.py
  class RoIPoolFunction (line 7) | class RoIPoolFunction(Function):
    method forward (line 10) | def forward(ctx, features, rois, out_size, spatial_scale):
    method backward (line 39) | def backward(ctx, grad_output):

FILE: mmdet/ops/roi_pool/modules/roi_pool.py
  class RoIPool (line 5) | class RoIPool(Module):
    method __init__ (line 7) | def __init__(self, out_size, spatial_scale):
    method forward (line 13) | def forward(self, features, rois):

FILE: mmdet/ops/roi_pool/src/roi_pool_cuda.cpp
  function roi_pooling_forward_cuda (line 26) | int roi_pooling_forward_cuda(at::Tensor features, at::Tensor rois,
  function roi_pooling_backward_cuda (line 54) | int roi_pooling_backward_cuda(at::Tensor top_grad, at::Tensor rois,
  function PYBIND11_MODULE (line 83) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: setup.py
  function readme (line 7) | def readme():
  function get_git_hash (line 22) | def get_git_hash():
  function get_hash (line 48) | def get_hash():
  function write_version_py (line 63) | def write_version_py():
  function get_version (line 77) | def get_version():

FILE: tools/coco_eval.py
  function main (line 6) | def main():

FILE: tools/convert_datasets/pascal_voc.py
  function parse_xml (line 13) | def parse_xml(args):
  function cvt_annotations (line 67) | def cvt_annotations(devkit_path, years, split, out_file):
  function parse_args (line 94) | def parse_args():
  function main (line 103) | def main():

FILE: tools/test.py
  function single_test (line 14) | def single_test(model, data_loader, show=False):
  function _data_func (line 35) | def _data_func(data, device_id):
  function parse_args (line 40) | def parse_args():
  function main (line 63) | def main():

FILE: tools/train.py
  function parse_args (line 14) | def parse_args():
  function main (line 42) | def main():

FILE: tools/voc_eval.py
  function voc_eval (line 10) | def voc_eval(result_file, dataset, iou_thr=0.5):
  function main (line 46) | def main():