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def build_neck(cfg):
'Build neck.'
return NECKS.build(cfg)
|
def build_roi_extractor(cfg):
'Build roi extractor.'
return ROI_EXTRACTORS.build(cfg)
|
def build_shared_head(cfg):
'Build shared head.'
return SHARED_HEADS.build(cfg)
|
def build_head(cfg):
'Build head.'
return HEADS.build(cfg)
|
def build_loss(cfg):
'Build loss.'
return LOSSES.build(cfg)
|
def build_detector(cfg, train_cfg=None, test_cfg=None):
'Build detector.'
if ((train_cfg is not None) or (test_cfg is not None)):
warnings.warn('train_cfg and test_cfg is deprecated, please specify them in model', UserWarning)
assert ((cfg.get('train_cfg') is None) or (train_cfg is None)), 'train_... |
@HEADS.register_module()
class AnchorFreeHead(BaseDenseHead, BBoxTestMixin):
'Anchor-free head (FCOS, Fovea, RepPoints, etc.).\n\n Args:\n num_classes (int): Number of categories excluding the background\n category.\n in_channels (int): Number of channels in the input feature map.\n ... |
class BaseMaskHead(BaseModule, metaclass=ABCMeta):
'Base class for mask heads used in One-Stage Instance Segmentation.'
def __init__(self, init_cfg):
super(BaseMaskHead, self).__init__(init_cfg)
@abstractmethod
def loss(self, **kwargs):
pass
@abstractmethod
def get_results(s... |
@HEADS.register_module()
class GARetinaHead(GuidedAnchorHead):
'Guided-Anchor-based RetinaNet head.'
def __init__(self, num_classes, in_channels, stacked_convs=4, conv_cfg=None, norm_cfg=None, init_cfg=None, **kwargs):
if (init_cfg is None):
init_cfg = dict(type='Normal', layer='Conv2d', ... |
@HEADS.register_module()
class LADHead(PAAHead):
'Label Assignment Head from the paper: `Improving Object Detection by\n Label Assignment Distillation <https://arxiv.org/pdf/2108.10520.pdf>`_'
@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
def get_label_assignment(self, cls_scores, bb... |
@HEADS.register_module()
class NASFCOSHead(FCOSHead):
'Anchor-free head used in `NASFCOS <https://arxiv.org/abs/1906.04423>`_.\n\n It is quite similar with FCOS head, except for the searched structure of\n classification branch and bbox regression branch, where a structure of\n "dconv3x3, conv3x3, dconv3... |
@HEADS.register_module()
class PISARetinaHead(RetinaHead):
'PISA Retinanet Head.\n\n The head owns the same structure with Retinanet Head, but differs in two\n aspects:\n 1. Importance-based Sample Reweighting Positive (ISR-P) is applied to\n change the positive loss weights.\n ... |
@HEADS.register_module()
class PISASSDHead(SSDHead):
def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore=None):
'Compute losses of the head.\n\n Args:\n cls_scores (list[Tensor]): Box scores for each scale level\n Has shape (N, num_anc... |
@HEADS.register_module()
class RetinaHead(AnchorHead):
'An anchor-based head used in `RetinaNet\n <https://arxiv.org/pdf/1708.02002.pdf>`_.\n\n The head contains two subnetworks. The first classifies anchor boxes and\n the second regresses deltas for the anchors.\n\n Example:\n >>> import torch... |
@HEADS.register_module()
class RetinaSepBNHead(AnchorHead):
'"RetinaHead with separate BN.\n\n In RetinaHead, conv/norm layers are shared across different FPN levels,\n while in RetinaSepBNHead, conv layers are shared across different FPN\n levels, but BN layers are separated.\n '
def __init__(se... |
@HEADS.register_module()
class SSDHead(AnchorHead):
'SSD head used in https://arxiv.org/abs/1512.02325.\n\n Args:\n num_classes (int): Number of categories excluding the background\n category.\n in_channels (int): Number of channels in the input feature map.\n stacked_convs (int... |
@DETECTORS.register_module()
class ATSS(SingleStageDetector):
'Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_.'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg, ... |
@DETECTORS.register_module()
class AutoAssign(SingleStageDetector):
'Implementation of `AutoAssign: Differentiable Label Assignment for Dense\n Object Detection <https://arxiv.org/abs/2007.03496>`_.'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None):
sup... |
class BaseDetector(BaseModule, metaclass=ABCMeta):
'Base class for detectors.'
def __init__(self, init_cfg=None):
super(BaseDetector, self).__init__(init_cfg)
self.fp16_enabled = False
@property
def with_neck(self):
'bool: whether the detector has a neck'
return (hasa... |
@DETECTORS.register_module()
class CascadeRCNN(TwoStageDetector):
'Implementation of `Cascade R-CNN: Delving into High Quality Object\n Detection <https://arxiv.org/abs/1906.09756>`_'
def __init__(self, backbone, neck=None, rpn_head=None, roi_head=None, train_cfg=None, test_cfg=None, pretrained=None, init... |
@DETECTORS.register_module()
class CenterNet(SingleStageDetector):
'Implementation of CenterNet(Objects as Points)\n\n <https://arxiv.org/abs/1904.07850>.\n '
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(CenterNet, self).__i... |
@DETECTORS.register_module()
class DeformableDETR(DETR):
def __init__(self, *args, **kwargs):
super(DETR, self).__init__(*args, **kwargs)
|
@DETECTORS.register_module()
class DETR(SingleStageDetector):
'Implementation of `DETR: End-to-End Object Detection with\n Transformers <https://arxiv.org/pdf/2005.12872>`_'
def __init__(self, backbone, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(DETR, self).__... |
@DETECTORS.register_module()
class FastRCNN(TwoStageDetector):
'Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_'
def __init__(self, backbone, roi_head, train_cfg, test_cfg, neck=None, pretrained=None, init_cfg=None):
super(FastRCNN, self).__init__(backbone=backbone, neck=neck, roi_... |
@DETECTORS.register_module()
class FasterRCNN(TwoStageDetector):
'Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_'
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=None, init_cfg=None):
super(FasterRCNN, self).__init__(backbone=backbone,... |
@DETECTORS.register_module()
class FCOS(SingleStageDetector):
'Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg, t... |
@DETECTORS.register_module()
class FOVEA(SingleStageDetector):
'Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(FOVEA, self).__init__(backbone, neck, bbox_head, train_... |
@DETECTORS.register_module()
class FSAF(SingleStageDetector):
'Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg, t... |
@DETECTORS.register_module()
class GFL(SingleStageDetector):
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, pretrained, init_cfg)
|
@DETECTORS.register_module()
class GridRCNN(TwoStageDetector):
'Grid R-CNN.\n\n This detector is the implementation of:\n - Grid R-CNN (https://arxiv.org/abs/1811.12030)\n - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688)\n '
def __init__(self, backbone, rpn_head, roi_head, ... |
@DETECTORS.register_module()
class HybridTaskCascade(CascadeRCNN):
'Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_'
def __init__(self, **kwargs):
super(HybridTaskCascade, self).__init__(**kwargs)
@property
def with_semantic(self):
'bool: whether the detector has a semant... |
@DETECTORS.register_module()
class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
'Implementation of `Distilling the Knowledge in a Neural Network.\n <https://arxiv.org/abs/1503.02531>`_.\n\n Args:\n teacher_config (str | dict): Config file path\n or the config object of te... |
@DETECTORS.register_module()
class LAD(KnowledgeDistillationSingleStageDetector):
'Implementation of `LAD <https://arxiv.org/pdf/2108.10520.pdf>`_.'
def __init__(self, backbone, neck, bbox_head, teacher_backbone, teacher_neck, teacher_bbox_head, teacher_ckpt, eval_teacher=True, train_cfg=None, test_cfg=None,... |
@DETECTORS.register_module()
class MaskRCNN(TwoStageDetector):
'Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_'
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=None, init_cfg=None):
super(MaskRCNN, self).__init__(backbone=backbone, neck=... |
@DETECTORS.register_module()
class MaskScoringRCNN(TwoStageDetector):
'Mask Scoring RCNN.\n\n https://arxiv.org/abs/1903.00241\n '
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=None, init_cfg=None):
super(MaskScoringRCNN, self).__init__(backbone=bac... |
@DETECTORS.register_module()
class NASFCOS(SingleStageDetector):
'NAS-FCOS: Fast Neural Architecture Search for Object Detection.\n\n https://arxiv.org/abs/1906.0442\n '
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(NASFCOS, ... |
@DETECTORS.register_module()
class PAA(SingleStageDetector):
'Implementation of `PAA <https://arxiv.org/pdf/2007.08103.pdf>`_.'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg,... |
@DETECTORS.register_module()
class PanopticFPN(TwoStagePanopticSegmentor):
'Implementation of `Panoptic feature pyramid\n networks <https://arxiv.org/pdf/1901.02446>`_'
def __init__(self, backbone, neck=None, rpn_head=None, roi_head=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None, sema... |
@DETECTORS.register_module()
class PointRend(TwoStageDetector):
'PointRend: Image Segmentation as Rendering\n\n This detector is the implementation of\n `PointRend <https://arxiv.org/abs/1912.08193>`_.\n\n '
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=... |
@DETECTORS.register_module()
class QueryInst(SparseRCNN):
'Implementation of\n `Instances as Queries <http://arxiv.org/abs/2105.01928>`_'
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=None, init_cfg=None):
super(QueryInst, self).__init__(backbone=backb... |
@DETECTORS.register_module()
class RepPointsDetector(SingleStageDetector):
'RepPoints: Point Set Representation for Object Detection.\n\n This detector is the implementation of:\n - RepPoints detector (https://arxiv.org/pdf/1904.11490)\n '
def __init__(self, backbone, neck, bbox_head, train_... |
@DETECTORS.register_module()
class RetinaNet(SingleStageDetector):
'Implementation of `RetinaNet <https://arxiv.org/abs/1708.02002>`_'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(RetinaNet, self).__init__(backbone, neck, bbox_hea... |
@DETECTORS.register_module()
class SCNet(CascadeRCNN):
'Implementation of `SCNet <https://arxiv.org/abs/2012.10150>`_'
def __init__(self, **kwargs):
super(SCNet, self).__init__(**kwargs)
|
@DETECTORS.register_module()
class SingleStageDetector(BaseDetector):
'Base class for single-stage detectors.\n\n Single-stage detectors directly and densely predict bounding boxes on the\n output features of the backbone+neck.\n '
def __init__(self, backbone, neck=None, bbox_head=None, train_cfg=No... |
@DETECTORS.register_module()
class SOLO(SingleStageInstanceSegmentor):
'`SOLO: Segmenting Objects by Locations\n <https://arxiv.org/abs/1912.04488>`_\n\n '
def __init__(self, backbone, neck=None, bbox_head=None, mask_head=None, train_cfg=None, test_cfg=None, init_cfg=None, pretrained=None):
sup... |
@DETECTORS.register_module()
class SparseRCNN(TwoStageDetector):
'Implementation of `Sparse R-CNN: End-to-End Object Detection with\n Learnable Proposals <https://arxiv.org/abs/2011.12450>`_'
def __init__(self, *args, **kwargs):
super(SparseRCNN, self).__init__(*args, **kwargs)
assert self... |
@DETECTORS.register_module()
class TOOD(SingleStageDetector):
'Implementation of `TOOD: Task-aligned One-stage Object Detection.\n <https://arxiv.org/abs/2108.07755>`_.'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(TOOD, self).... |
@DETECTORS.register_module()
class TridentFasterRCNN(FasterRCNN):
'Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_'
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=None, init_cfg=None):
super(TridentFasterRCNN, self).__init__(backbone=bac... |
@DETECTORS.register_module()
class TwoStageDetector(BaseDetector):
'Base class for two-stage detectors.\n\n Two-stage detectors typically consisting of a region proposal network and a\n task-specific regression head.\n '
def __init__(self, backbone, neck=None, rpn_head=None, roi_head=None, train_cfg... |
@DETECTORS.register_module()
class VFNet(SingleStageDetector):
'Implementation of `VarifocalNet\n (VFNet).<https://arxiv.org/abs/2008.13367>`_'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(VFNet, self).__init__(backbone, neck, ... |
@DETECTORS.register_module()
class YOLACT(SingleStageDetector):
'Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_'
def __init__(self, backbone, neck, bbox_head, segm_head, mask_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(YOLACT, self).__init__(backbone, n... |
@DETECTORS.register_module()
class YOLOV3(SingleStageDetector):
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, pretrained, init_cfg)
def onnx_export(self, img, ... |
@DETECTORS.register_module()
class YOLOF(SingleStageDetector):
'Implementation of `You Only Look One-level Feature\n <https://arxiv.org/abs/2103.09460>`_'
def __init__(self, backbone, neck, bbox_head, train_cfg=None, test_cfg=None, pretrained=None):
super(YOLOF, self).__init__(backbone, neck, bbox... |
@mmcv.jit(coderize=True)
def accuracy(pred, target, topk=1, thresh=None):
'Calculate accuracy according to the prediction and target.\n\n Args:\n pred (torch.Tensor): The model prediction, shape (N, num_class)\n target (torch.Tensor): The target of each prediction, shape (N, )\n topk (int ... |
class Accuracy(nn.Module):
def __init__(self, topk=(1,), thresh=None):
'Module to calculate the accuracy.\n\n Args:\n topk (tuple, optional): The criterion used to calculate the\n accuracy. Defaults to (1,).\n thresh (float, optional): If not None, predictions ... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred, target, beta=1.0, alpha=0.5, gamma=1.5, reduction='mean'):
'Calculate balanced L1 loss.\n\n Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_\n\n Args:\n pred (torch.Tensor): The prediction with shape... |
@LOSSES.register_module()
class BalancedL1Loss(nn.Module):
'Balanced L1 Loss.\n\n arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)\n\n Args:\n alpha (float): The denominator ``alpha`` in the balanced L1 loss.\n Defaults to 0.5.\n gamma (float): The ``gamma`` in the balanced L... |
def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None, class_weight=None, ignore_index=(- 100)):
'Calculate the CrossEntropy loss.\n\n Args:\n pred (torch.Tensor): The prediction with shape (N, C), C is the number\n of classes.\n label (torch.Tensor): The learni... |
def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
'Expand onehot labels to match the size of prediction.'
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
valid_mask = ((labels >= 0) & (labels != ignore_index))
inds = torch.nonzero((valid_mask & (labels <... |
def binary_cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None, class_weight=None, ignore_index=(- 100)):
'Calculate the binary CrossEntropy loss.\n\n Args:\n pred (torch.Tensor): The prediction with shape (N, 1).\n label (torch.Tensor): The learning label of the prediction.... |
def mask_cross_entropy(pred, target, label, reduction='mean', avg_factor=None, class_weight=None, ignore_index=None):
'Calculate the CrossEntropy loss for masks.\n\n Args:\n pred (torch.Tensor): The prediction with shape (N, C, *), C is the\n number of classes. The trailing * indicates arbitr... |
@LOSSES.register_module()
class CrossEntropyLoss(nn.Module):
def __init__(self, use_sigmoid=False, use_mask=False, reduction='mean', class_weight=None, ignore_index=None, loss_weight=1.0):
'CrossEntropyLoss.\n\n Args:\n use_sigmoid (bool, optional): Whether the prediction uses sigmoid\n... |
def dice_loss(pred, target, weight=None, eps=0.001, reduction='mean', naive_dice=False, avg_factor=None):
'Calculate dice loss, there are two forms of dice loss is supported:\n\n - the one proposed in `V-Net: Fully Convolutional Neural\n Networks for Volumetric Medical Image Segmentation\n ... |
@LOSSES.register_module()
class DiceLoss(nn.Module):
def __init__(self, use_sigmoid=True, activate=True, reduction='mean', naive_dice=False, loss_weight=1.0, eps=0.001):
'Compute dice loss.\n\n Args:\n use_sigmoid (bool, optional): Whether to the prediction is\n used for ... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
'`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian\n distribution.\n\n Args:\n pred (torch.Tensor): The prediction.\n gaussian_target (torch.Tenso... |
@LOSSES.register_module()
class GaussianFocalLoss(nn.Module):
'GaussianFocalLoss is a variant of focal loss.\n\n More details can be found in the `paper\n <https://arxiv.org/abs/1808.01244>`_\n Code is modified from `kp_utils.py\n <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0):
'Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning\n Qualified and Distributed Bounding Boxes for Dense Object Detection\n <https://arxiv.org/abs/2006.04388>`_.\n\n Args:\n pred ... |
@weighted_loss
def quality_focal_loss_with_prob(pred, target, beta=2.0):
'Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning\n Qualified and Distributed Bounding Boxes for Dense Object Detection\n <https://arxiv.org/abs/2006.04388>`_.\n Different from `quality_focal_loss`, this function a... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def distribution_focal_loss(pred, label):
'Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning\n Qualified and Distributed Bounding Boxes for Dense Object Detection\n <https://arxiv.org/abs/2006.04388>`_.\n\n Args:\n pred (... |
@LOSSES.register_module()
class QualityFocalLoss(nn.Module):
'Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:\n Learning Qualified and Distributed Bounding Boxes for Dense Object\n Detection <https://arxiv.org/abs/2006.04388>`_.\n\n Args:\n use_sigmoid (bool): Whether sigmoid ope... |
@LOSSES.register_module()
class DistributionFocalLoss(nn.Module):
"Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:\n Learning Qualified and Distributed Bounding Boxes for Dense Object\n Detection <https://arxiv.org/abs/2006.04388>`_.\n\n Args:\n reduction (str): Options are ... |
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(((labels >= 0) & (labels < label_channels)), as_tuple=False).squeeze()
if (inds.numel() > 0):
bin_labels[(inds, labels[inds])] = 1
bin_label_... |
@LOSSES.register_module()
class GHMC(nn.Module):
'GHM Classification Loss.\n\n Details of the theorem can be viewed in the paper\n `Gradient Harmonized Single-stage Detector\n <https://arxiv.org/abs/1811.05181>`_.\n\n Args:\n bins (int): Number of the unit regions for distribution calculation.\... |
@LOSSES.register_module()
class GHMR(nn.Module):
'GHM Regression Loss.\n\n Details of the theorem can be viewed in the paper\n `Gradient Harmonized Single-stage Detector\n <https://arxiv.org/abs/1811.05181>`_.\n\n Args:\n mu (float): The parameter for the Authentic Smooth L1 loss.\n bins... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def knowledge_distillation_kl_div_loss(pred, soft_label, T, detach_target=True):
'Loss function for knowledge distilling using KL divergence.\n\n Args:\n pred (Tensor): Predicted logits with shape (N, n + 1).\n soft_label (Tensor): Target log... |
@LOSSES.register_module()
class KnowledgeDistillationKLDivLoss(nn.Module):
"Loss function for knowledge distilling using KL divergence.\n\n Args:\n reduction (str): Options are `'none'`, `'mean'` and `'sum'`.\n loss_weight (float): Loss weight of current loss.\n T (int): Temperature for di... |
@weighted_loss
def mse_loss(pred, target):
'Warpper of mse loss.'
return F.mse_loss(pred, target, reduction='none')
|
@LOSSES.register_module()
class MSELoss(nn.Module):
'MSELoss.\n\n Args:\n reduction (str, optional): The method that reduces the loss to a\n scalar. Options are "none", "mean" and "sum".\n loss_weight (float, optional): The weight of the loss. Defaults to 1.0\n '
def __init__(s... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
'Smooth L1 loss.\n\n Args:\n pred (torch.Tensor): The prediction.\n target (torch.Tensor): The learning target of the prediction.\n beta (float, optional): The threshold in the piecewise func... |
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def l1_loss(pred, target):
'L1 loss.\n\n Args:\n pred (torch.Tensor): The prediction.\n target (torch.Tensor): The learning target of the prediction.\n\n Returns:\n torch.Tensor: Calculated loss\n '
if (target.numel() == 0):
... |
@LOSSES.register_module()
class SmoothL1Loss(nn.Module):
'Smooth L1 loss.\n\n Args:\n beta (float, optional): The threshold in the piecewise function.\n Defaults to 1.0.\n reduction (str, optional): The method to reduce the loss.\n Options are "none", "mean" and "sum". Defau... |
@LOSSES.register_module()
class L1Loss(nn.Module):
'L1 loss.\n\n Args:\n reduction (str, optional): The method to reduce the loss.\n Options are "none", "mean" and "sum".\n loss_weight (float, optional): The weight of loss.\n '
def __init__(self, reduction='mean', loss_weight=1... |
def reduce_loss(loss, reduction):
'Reduce loss as specified.\n\n Args:\n loss (Tensor): Elementwise loss tensor.\n reduction (str): Options are "none", "mean" and "sum".\n\n Return:\n Tensor: Reduced loss tensor.\n '
reduction_enum = F._Reduction.get_enum(reduction)
if (reduc... |
@mmcv.jit(derivate=True, coderize=True)
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
'Apply element-wise weight and reduce loss.\n\n Args:\n loss (Tensor): Element-wise loss.\n weight (Tensor): Element-wise weights.\n reduction (str): Same as built-in losse... |
def weighted_loss(loss_func):
"Create a weighted version of a given loss function.\n\n To use this decorator, the loss function must have the signature like\n `loss_func(pred, target, **kwargs)`. The function only needs to compute\n element-wise loss without any reduction. This decorator will add weight\... |
@mmcv.jit(derivate=True, coderize=True)
def varifocal_loss(pred, target, weight=None, alpha=0.75, gamma=2.0, iou_weighted=True, reduction='mean', avg_factor=None):
'`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_\n\n Args:\n pred (torch.Tensor): The prediction with shape (N, C), C is the\n ... |
@LOSSES.register_module()
class VarifocalLoss(nn.Module):
def __init__(self, use_sigmoid=True, alpha=0.75, gamma=2.0, iou_weighted=True, reduction='mean', loss_weight=1.0):
'`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_\n\n Args:\n use_sigmoid (bool, optional): Whether the predic... |
@NECKS.register_module()
class BFP(BaseModule):
"BFP (Balanced Feature Pyramids)\n\n BFP takes multi-level features as inputs and gather them into a single one,\n then refine the gathered feature and scatter the refined results to\n multi-level features. This module is used in Libra R-CNN (CVPR 2019), se... |
@NECKS.register_module()
class ChannelMapper(BaseModule):
"Channel Mapper to reduce/increase channels of backbone features.\n\n This is used to reduce/increase channels of backbone features.\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out_channels (int): Number o... |
class Bottleneck(nn.Module):
'Bottleneck block for DilatedEncoder used in `YOLOF.\n\n <https://arxiv.org/abs/2103.09460>`.\n\n The Bottleneck contains three ConvLayers and one residual connection.\n\n Args:\n in_channels (int): The number of input channels.\n mid_channels (int): The number ... |
@NECKS.register_module()
class DilatedEncoder(nn.Module):
'Dilated Encoder for YOLOF <https://arxiv.org/abs/2103.09460>`.\n\n This module contains two types of components:\n - the original FPN lateral convolution layer and fpn convolution layer,\n which are 1x1 conv + 3x3 conv\n - th... |
class Transition(BaseModule):
'Base class for transition.\n\n Args:\n in_channels (int): Number of input channels.\n out_channels (int): Number of output channels.\n '
def __init__(self, in_channels, out_channels, init_cfg=None):
super().__init__(init_cfg)
self.in_channels... |
class UpInterpolationConv(Transition):
'A transition used for up-sampling.\n\n Up-sample the input by interpolation then refines the feature by\n a convolution layer.\n\n Args:\n in_channels (int): Number of input channels.\n out_channels (int): Number of output channels.\n scale_fac... |
class LastConv(Transition):
'A transition used for refining the output of the last stage.\n\n Args:\n in_channels (int): Number of input channels.\n out_channels (int): Number of output channels.\n num_inputs (int): Number of inputs of the FPN features.\n kernel_size (int): Kernel s... |
@NECKS.register_module()
class FPG(BaseModule):
"FPG.\n\n Implementation of `Feature Pyramid Grids (FPG)\n <https://arxiv.org/abs/2004.03580>`_.\n This implementation only gives the basic structure stated in the paper.\n But users can implement different type of transitions to fully explore the\n t... |
@NECKS.register_module()
class FPN(BaseModule):
"Feature Pyramid Network.\n\n This is an implementation of paper `Feature Pyramid Networks for Object\n Detection <https://arxiv.org/abs/1612.03144>`_.\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out_channels (in... |
@NECKS.register_module()
class HRFPN(BaseModule):
'HRFPN (High Resolution Feature Pyramids)\n\n paper: `High-Resolution Representations for Labeling Pixels and Regions\n <https://arxiv.org/abs/1904.04514>`_.\n\n Args:\n in_channels (list): number of channels for each branch.\n out_channels ... |
@NECKS.register_module()
class NASFPN(BaseModule):
'NAS-FPN.\n\n Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture\n for Object Detection <https://arxiv.org/abs/1904.07392>`_\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out_channels (in... |
@NECKS.register_module()
class NASFCOS_FPN(BaseModule):
'FPN structure in NASFPN.\n\n Implementation of paper `NAS-FCOS: Fast Neural Architecture Search for\n Object Detection <https://arxiv.org/abs/1906.04423>`_\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out... |
@NECKS.register_module()
class PAFPN(FPN):
"Path Aggregation Network for Instance Segmentation.\n\n This is an implementation of the `PAFPN in Path Aggregation Network\n <https://arxiv.org/abs/1803.01534>`_.\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out_chan... |
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