code stringlengths 17 6.64M |
|---|
@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... |
class ASPP(BaseModule):
'ASPP (Atrous Spatial Pyramid Pooling)\n\n This is an implementation of the ASPP module used in DetectoRS\n (https://arxiv.org/pdf/2006.02334.pdf)\n\n Args:\n in_channels (int): Number of input channels.\n out_channels (int): Number of channels produced by this modul... |
@NECKS.register_module()
class RFP(FPN):
'RFP (Recursive Feature Pyramid)\n\n This is an implementation of RFP in `DetectoRS\n <https://arxiv.org/pdf/2006.02334.pdf>`_. Different from standard FPN, the\n input of RFP should be multi level features along with origin input image\n of backbone.\n\n Ar... |
class DetectionBlock(BaseModule):
"Detection block in YOLO neck.\n\n Let out_channels = n, the DetectionBlock contains:\n Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer.\n The first 6 ConvLayers are formed the following way:\n 1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n.\n The Conv2D layer is 1... |
@NECKS.register_module()
class YOLOV3Neck(BaseModule):
"The neck of YOLOV3.\n\n It can be treated as a simplified version of FPN. It\n will take the result from Darknet backbone and do some upsampling and\n concatenation. It will finally output the detection result.\n\n Note:\n The input feats ... |
@NECKS.register_module()
class YOLOXPAFPN(BaseModule):
"Path Aggregation Network used in YOLOX.\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out_channels (int): Number of output channels (used at each scale)\n num_csp_blocks (int): Number of bottlenecks in CSP... |
@PLUGIN_LAYERS.register_module()
class DropBlock(nn.Module):
'Randomly drop some regions of feature maps.\n\n Please refer to the method proposed in `DropBlock\n <https://arxiv.org/abs/1810.12890>`_ for details.\n\n Args:\n drop_prob (float): The probability of dropping each block.\n bloc... |
class BaseRoIHead(BaseModule, metaclass=ABCMeta):
'Base class for RoIHeads.'
def __init__(self, bbox_roi_extractor=None, bbox_head=None, mask_roi_extractor=None, mask_head=None, shared_head=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None):
super(BaseRoIHead, self).__init__(init_cf... |
@HEADS.register_module()
class ConvFCBBoxHead(BBoxHead):
'More general bbox head, with shared conv and fc layers and two optional\n separated branches.\n\n .. code-block:: none\n\n /-> cls convs -> cls fcs -> cls\n shared convs -> shared fcs\n ... |
@HEADS.register_module()
class Shared2FCBBoxHead(ConvFCBBoxHead):
def __init__(self, fc_out_channels=1024, *args, **kwargs):
super(Shared2FCBBoxHead, self).__init__(*args, num_shared_convs=0, num_shared_fcs=2, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, fc_out_channels=fc_out_channels... |
@HEADS.register_module()
class Shared4Conv1FCBBoxHead(ConvFCBBoxHead):
def __init__(self, fc_out_channels=1024, *args, **kwargs):
super(Shared4Conv1FCBBoxHead, self).__init__(*args, num_shared_convs=4, num_shared_fcs=1, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, fc_out_channels=fc_ou... |
class BasicResBlock(BaseModule):
'Basic residual block.\n\n This block is a little different from the block in the ResNet backbone.\n The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.\n\n Args:\n in_channels (int): Channels of the input feature map.\n out_channels (i... |
@HEADS.register_module()
class DoubleConvFCBBoxHead(BBoxHead):
'Bbox head used in Double-Head R-CNN\n\n .. code-block:: none\n\n /-> cls\n /-> shared convs ->\n \\-> reg\n roi features\n ... |
@HEADS.register_module()
class SCNetBBoxHead(ConvFCBBoxHead):
'BBox head for `SCNet <https://arxiv.org/abs/2012.10150>`_.\n\n This inherits ``ConvFCBBoxHead`` with modified forward() function, allow us\n to get intermediate shared feature.\n '
def _forward_shared(self, x):
'Forward function ... |
@HEADS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
'RoI head for Double Head RCNN.\n\n https://arxiv.org/abs/1904.06493\n '
def __init__(self, reg_roi_scale_factor, **kwargs):
super(DoubleHeadRoIHead, self).__init__(**kwargs)
self.reg_roi_scale_factor = reg_roi_scale_fac... |
@HEADS.register_module()
class CoarseMaskHead(FCNMaskHead):
'Coarse mask head used in PointRend.\n\n Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample\n the input feature map instead of upsample it.\n\n Args:\n num_convs (int): Number of conv layers in the head. Default: 0... |
@HEADS.register_module()
class DynamicMaskHead(FCNMaskHead):
'Dynamic Mask Head for\n `Instances as Queries <http://arxiv.org/abs/2105.01928>`_\n\n Args:\n num_convs (int): Number of convolution layer.\n Defaults to 4.\n roi_feat_size (int): The output size of RoI extractor,\n ... |
@HEADS.register_module()
class FeatureRelayHead(BaseModule):
'Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.\n\n Args:\n in_channels (int, optional): number of input channels. Default: 256.\n conv_out_channels (int, optional): number of output channels before\n ... |
@HEADS.register_module()
class FusedSemanticHead(BaseModule):
'Multi-level fused semantic segmentation head.\n\n .. code-block:: none\n\n in_1 -> 1x1 conv ---\n |\n in_2 -> 1x1 conv -- |\n ||\n in_3 -> 1x1 conv - ||\n ... |
@HEADS.register_module()
class GlobalContextHead(BaseModule):
'Global context head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.\n\n Args:\n num_convs (int, optional): number of convolutional layer in GlbCtxHead.\n Default: 4.\n in_channels (int, optional): number of input chann... |
@HEADS.register_module()
class HTCMaskHead(FCNMaskHead):
def __init__(self, with_conv_res=True, *args, **kwargs):
super(HTCMaskHead, self).__init__(*args, **kwargs)
self.with_conv_res = with_conv_res
if self.with_conv_res:
self.conv_res = ConvModule(self.conv_out_channels, sel... |
@HEADS.register_module()
class SCNetMaskHead(FCNMaskHead):
'Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.\n\n Args:\n conv_to_res (bool, optional): if True, change the conv layers to\n ``SimplifiedBasicBlock``.\n '
def __init__(self, conv_to_res=True, **kwargs):
s... |
@HEADS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
'Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.\n\n Args:\n conv_to_res (bool, optional): if True, change the conv layers to\n ``SimplifiedBasicBlock``.\n '
def __init__(self, conv_to_res=True, **kwargs):... |
@HEADS.register_module()
class PISARoIHead(StandardRoIHead):
'The RoI head for `Prime Sample Attention in Object Detection\n <https://arxiv.org/abs/1904.04821>`_.'
def forward_train(self, x, img_metas, proposal_list, gt_bboxes, gt_labels, gt_bboxes_ignore=None, gt_masks=None):
"Forward function fo... |
@SHARED_HEADS.register_module()
class ResLayer(BaseModule):
def __init__(self, depth, stage=3, stride=2, dilation=1, style='pytorch', norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, with_cp=False, dcn=None, pretrained=None, init_cfg=None):
super(ResLayer, self).__init__(init_cfg)
se... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.