Add loss evaluator (#678)

* Fix license in setup.py

* Add code for loss evaluator

* Configs support loss evaluator

* Fix a little bug

* Fix flake8

* return revised bbox to reg

* return revised bbox to reg

* revision according to comments

* fix flake8

configs/scratch/scratch_mask_rcnn_r50_fpn_gn_6x.py

@@ -27,7 +27,10 @@ model = dict(
         anchor_strides=[4, 8, 16, 32, 64],
         target_means=[.0, .0, .0, .0],
         target_stds=[1.0, 1.0, 1.0, 1.0],
-        use_sigmoid_cls=True),
+        use_sigmoid_cls=True,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
     bbox_roi_extractor=dict(
         type='SingleRoIExtractor',
         roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
@@ -45,7 +48,10 @@ model = dict(
         target_means=[0., 0., 0., 0.],
         target_stds=[0.1, 0.1, 0.2, 0.2],
         reg_class_agnostic=False,
-        norm_cfg=norm_cfg),
+        norm_cfg=norm_cfg,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
     mask_roi_extractor=dict(
         type='SingleRoIExtractor',
         roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -57,7 +63,9 @@ model = dict(
         in_channels=256,
         conv_out_channels=256,
         num_classes=81,
-        norm_cfg=norm_cfg))
+        norm_cfg=norm_cfg,
+        loss_mask=dict(
+            type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
 # model training and testing settings
 train_cfg = dict(
     rpn=dict(
@@ -75,7 +83,6 @@ train_cfg = dict(
             add_gt_as_proposals=False),
         allowed_border=0,
         pos_weight=-1,
-        smoothl1_beta=1 / 9.0,
         debug=False),
     rpn_proposal=dict(
         nms_across_levels=False,

mmdet/models/__init__.py

@@ -5,14 +5,16 @@ from .anchor_heads import *  # noqa: F401,F403
 from .shared_heads import *  # noqa: F401,F403
 from .bbox_heads import *  # noqa: F401,F403
 from .mask_heads import *  # noqa: F401,F403
+from .losses import *  # noqa: F401,F403
 from .detectors import *  # noqa: F401,F403
 from .registry import (BACKBONES, NECKS, ROI_EXTRACTORS, SHARED_HEADS, HEADS,
-                       DETECTORS)
+                       LOSSES, DETECTORS)
 from .builder import (build_backbone, build_neck, build_roi_extractor,
-                      build_shared_head, build_head, build_detector)
+                      build_shared_head, build_head, build_loss,
+                      build_detector)
 
 __all__ = [
-    'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS',
+    'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES',
     'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor',
-    'build_shared_head', 'build_head', 'build_detector'
+    'build_shared_head', 'build_head', 'build_loss', 'build_detector'
 ]

mmdet/models/anchor_heads/anchor_head.py

@@ -6,9 +6,8 @@ import torch.nn as nn
 from mmcv.cnn import normal_init
 
 from mmdet.core import (AnchorGenerator, anchor_target, delta2bbox,
-                        multi_apply, weighted_cross_entropy, weighted_smoothl1,
-                        weighted_binary_cross_entropy,
-                        weighted_sigmoid_focal_loss, multiclass_nms)
+                        multi_apply, multiclass_nms)
+from ..builder import build_loss
 from ..registry import HEADS
 
 
@@ -25,9 +24,8 @@ class AnchorHead(nn.Module):
         anchor_base_sizes (Iterable): Anchor base sizes.
         target_means (Iterable): Mean values of regression targets.
         target_stds (Iterable): Std values of regression targets.
-        use_sigmoid_cls (bool): Whether to use sigmoid loss for
-            classification. (softmax by default)
-        cls_focal_loss (bool): Whether to use focal loss for classification.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
     """  # noqa: W605
 
     def __init__(self,
@@ -40,8 +38,12 @@ class AnchorHead(nn.Module):
                  anchor_base_sizes=None,
                  target_means=(.0, .0, .0, .0),
                  target_stds=(1.0, 1.0, 1.0, 1.0),
-                 use_sigmoid_cls=False,
-                 cls_focal_loss=False):
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)):
         super(AnchorHead, self).__init__()
         self.in_channels = in_channels
         self.num_classes = num_classes
@@ -53,8 +55,15 @@ class AnchorHead(nn.Module):
             anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
         self.target_means = target_means
         self.target_stds = target_stds
-        self.use_sigmoid_cls = use_sigmoid_cls
-        self.cls_focal_loss = cls_focal_loss
+
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        self.sampling = loss_cls['type'] not in ['FocalLoss']
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = num_classes - 1
+        else:
+            self.cls_out_channels = num_classes
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
 
         self.anchor_generators = []
         for anchor_base in self.anchor_base_sizes:
@@ -62,11 +71,6 @@ class AnchorHead(nn.Module):
                 AnchorGenerator(anchor_base, anchor_scales, anchor_ratios))
 
         self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
-        if self.use_sigmoid_cls:
-            self.cls_out_channels = self.num_classes - 1
-        else:
-            self.cls_out_channels = self.num_classes
-
         self._init_layers()
 
     def _init_layers(self):
@@ -130,40 +134,20 @@ class AnchorHead(nn.Module):
         # classification loss
         labels = labels.reshape(-1)
         label_weights = label_weights.reshape(-1)
-        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-            -1, self.cls_out_channels)
-        if self.use_sigmoid_cls:
-            if self.cls_focal_loss:
-                cls_criterion = weighted_sigmoid_focal_loss
-            else:
-                cls_criterion = weighted_binary_cross_entropy
-        else:
-            if self.cls_focal_loss:
-                raise NotImplementedError
-            else:
-                cls_criterion = weighted_cross_entropy
-        if self.cls_focal_loss:
-            loss_cls = cls_criterion(
-                cls_score,
-                labels,
-                label_weights,
-                gamma=cfg.gamma,
-                alpha=cfg.alpha,
-                avg_factor=num_total_samples)
-        else:
-            loss_cls = cls_criterion(
-                cls_score, labels, label_weights, avg_factor=num_total_samples)
+        cls_score = cls_score.permute(0, 2, 3,
+                                      1).reshape(-1, self.cls_out_channels)
+        loss_cls = self.loss_cls(
+            cls_score, labels, label_weights, avg_factor=num_total_samples)
         # regression loss
         bbox_targets = bbox_targets.reshape(-1, 4)
         bbox_weights = bbox_weights.reshape(-1, 4)
         bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
-        loss_reg = weighted_smoothl1(
+        loss_bbox = self.loss_bbox(
             bbox_pred,
             bbox_targets,
             bbox_weights,
-            beta=cfg.smoothl1_beta,
             avg_factor=num_total_samples)
-        return loss_cls, loss_reg
+        return loss_cls, loss_bbox
 
     def loss(self,
              cls_scores,
@@ -178,7 +162,6 @@ class AnchorHead(nn.Module):
 
         anchor_list, valid_flag_list = self.get_anchors(
             featmap_sizes, img_metas)
-        sampling = False if self.cls_focal_loss else True
         label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
         cls_reg_targets = anchor_target(
             anchor_list,
@@ -191,15 +174,14 @@ class AnchorHead(nn.Module):
             gt_bboxes_ignore_list=gt_bboxes_ignore,
             gt_labels_list=gt_labels,
             label_channels=label_channels,
-            sampling=sampling)
+            sampling=self.sampling)
         if cls_reg_targets is None:
             return None
         (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
          num_total_pos, num_total_neg) = cls_reg_targets
         num_total_samples = (
-            num_total_pos
-            if self.cls_focal_loss else num_total_pos + num_total_neg)
-        losses_cls, losses_reg = multi_apply(
+            num_total_pos + num_total_neg if self.sampling else num_total_pos)
+        losses_cls, losses_bbox = multi_apply(
             self.loss_single,
             cls_scores,
             bbox_preds,
@@ -209,7 +191,7 @@ class AnchorHead(nn.Module):
             bbox_weights_list,
             num_total_samples=num_total_samples,
             cfg=cfg)
-        return dict(loss_cls=losses_cls, loss_reg=losses_reg)
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
 
     def get_bboxes(self, cls_scores, bbox_preds, img_metas, cfg,
                    rescale=False):
@@ -251,8 +233,8 @@ class AnchorHead(nn.Module):
         for cls_score, bbox_pred, anchors in zip(cls_scores, bbox_preds,
                                                  mlvl_anchors):
             assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            cls_score = cls_score.permute(1, 2, 0).reshape(
-                -1, self.cls_out_channels)
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
             if self.use_sigmoid_cls:
                 scores = cls_score.sigmoid()
             else:
@@ -279,6 +261,7 @@ class AnchorHead(nn.Module):
         if self.use_sigmoid_cls:
             padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
             mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
-        det_bboxes, det_labels = multiclass_nms(
-            mlvl_bboxes, mlvl_scores, cfg.score_thr, cfg.nms, cfg.max_per_img)
+        det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
+                                                cfg.score_thr, cfg.nms,
+                                                cfg.max_per_img)
         return det_bboxes, det_labels

mmdet/models/anchor_heads/retina_head.py

@@ -28,12 +28,7 @@ class RetinaHead(AnchorHead):
             [2**(i / scales_per_octave) for i in range(scales_per_octave)])
         anchor_scales = octave_scales * octave_base_scale
         super(RetinaHead, self).__init__(
-            num_classes,
-            in_channels,
-            anchor_scales=anchor_scales,
-            use_sigmoid_cls=True,
-            cls_focal_loss=True,
-            **kwargs)
+            num_classes, in_channels, anchor_scales=anchor_scales, **kwargs)
 
     def _init_layers(self):
         self.relu = nn.ReLU(inplace=True)

mmdet/models/anchor_heads/rpn_head.py

@@ -50,7 +50,7 @@ class RPNHead(AnchorHead):
             cfg,
             gt_bboxes_ignore=gt_bboxes_ignore)
         return dict(
-            loss_rpn_cls=losses['loss_cls'], loss_rpn_reg=losses['loss_reg'])
+            loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox'])
 
     def get_bboxes_single(self,
                           cls_scores,

mmdet/models/anchor_heads/ssd_head.py

@@ -10,6 +10,7 @@ from .anchor_head import AnchorHead
 from ..registry import HEADS
 
 
+# TODO: add loss evaluator for SSD
 @HEADS.register_module
 class SSDHead(AnchorHead):
 
@@ -122,13 +123,13 @@ class SSDHead(AnchorHead):
         loss_cls_neg = topk_loss_cls_neg.sum()
         loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
 
-        loss_reg = weighted_smoothl1(
+        loss_bbox = weighted_smoothl1(
             bbox_pred,
             bbox_targets,
             bbox_weights,
             beta=cfg.smoothl1_beta,
             avg_factor=num_total_samples)
-        return loss_cls[None], loss_reg
+        return loss_cls[None], loss_bbox
 
     def loss(self,
              cls_scores,
@@ -167,18 +168,18 @@ class SSDHead(AnchorHead):
                 num_images, -1, self.cls_out_channels) for s in cls_scores
         ], 1)
         all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-        all_label_weights = torch.cat(label_weights_list, -1).view(
-            num_images, -1)
+        all_label_weights = torch.cat(label_weights_list,
+                                      -1).view(num_images, -1)
         all_bbox_preds = torch.cat([
             b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
             for b in bbox_preds
         ], -2)
-        all_bbox_targets = torch.cat(bbox_targets_list, -2).view(
-            num_images, -1, 4)
-        all_bbox_weights = torch.cat(bbox_weights_list, -2).view(
-            num_images, -1, 4)
+        all_bbox_targets = torch.cat(bbox_targets_list,
+                                     -2).view(num_images, -1, 4)
+        all_bbox_weights = torch.cat(bbox_weights_list,
+                                     -2).view(num_images, -1, 4)
 
-        losses_cls, losses_reg = multi_apply(
+        losses_cls, losses_bbox = multi_apply(
             self.loss_single,
             all_cls_scores,
             all_bbox_preds,
@@ -188,4 +189,4 @@ class SSDHead(AnchorHead):
             all_bbox_weights,
             num_total_samples=num_total_pos,
             cfg=cfg)
-        return dict(loss_cls=losses_cls, loss_reg=losses_reg)
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)

mmdet/models/bbox_heads/bbox_head.py

@@ -2,8 +2,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmdet.core import (delta2bbox, multiclass_nms, bbox_target,
-                        weighted_cross_entropy, weighted_smoothl1, accuracy)
+from mmdet.core import delta2bbox, multiclass_nms, bbox_target, accuracy
+from ..builder import build_loss
 from ..registry import HEADS
 
 
@@ -21,7 +21,13 @@ class BBoxHead(nn.Module):
                  num_classes=81,
                  target_means=[0., 0., 0., 0.],
                  target_stds=[0.1, 0.1, 0.2, 0.2],
-                 reg_class_agnostic=False):
+                 reg_class_agnostic=False,
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=False,
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=1.0)):
         super(BBoxHead, self).__init__()
         assert with_cls or with_reg
         self.with_avg_pool = with_avg_pool
@@ -34,6 +40,9 @@ class BBoxHead(nn.Module):
         self.target_stds = target_stds
         self.reg_class_agnostic = reg_class_agnostic
 
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+
         in_channels = self.in_channels
         if self.with_avg_pool:
             self.avg_pool = nn.AvgPool2d(roi_feat_size)
@@ -90,7 +99,7 @@ class BBoxHead(nn.Module):
              reduce=True):
         losses = dict()
         if cls_score is not None:
-            losses['loss_cls'] = weighted_cross_entropy(
+            losses['loss_cls'] = self.loss_cls(
                 cls_score, labels, label_weights, reduce=reduce)
             losses['acc'] = accuracy(cls_score, labels)
         if bbox_pred is not None:
@@ -100,7 +109,7 @@ class BBoxHead(nn.Module):
             else:
                 pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), -1,
                                                4)[pos_inds, labels[pos_inds]]
-            losses['loss_reg'] = weighted_smoothl1(
+            losses['loss_bbox'] = self.loss_bbox(
                 pos_bbox_pred,
                 bbox_targets[pos_inds],
                 bbox_weights[pos_inds],
@@ -132,8 +141,9 @@ class BBoxHead(nn.Module):
         if cfg is None:
             return bboxes, scores
         else:
-            det_bboxes, det_labels = multiclass_nms(
-                bboxes, scores, cfg.score_thr, cfg.nms, cfg.max_per_img)
+            det_bboxes, det_labels = multiclass_nms(bboxes, scores,
+                                                    cfg.score_thr, cfg.nms,
+                                                    cfg.max_per_img)
 
             return det_bboxes, det_labels
 

mmdet/models/bbox_heads/convfc_bbox_head.py

@@ -29,8 +29,8 @@ class ConvFCBBoxHead(BBoxHead):
                  *args,
                  **kwargs):
         super(ConvFCBBoxHead, self).__init__(*args, **kwargs)
-        assert (num_shared_convs + num_shared_fcs + num_cls_convs + num_cls_fcs
-                + num_reg_convs + num_reg_fcs > 0)
+        assert (num_shared_convs + num_shared_fcs + num_cls_convs +
+                num_cls_fcs + num_reg_convs + num_reg_fcs > 0)
         if num_cls_convs > 0 or num_reg_convs > 0:
             assert num_shared_fcs == 0
         if not self.with_cls:
@@ -76,8 +76,8 @@ class ConvFCBBoxHead(BBoxHead):
         if self.with_cls:
             self.fc_cls = nn.Linear(self.cls_last_dim, self.num_classes)
         if self.with_reg:
-            out_dim_reg = (4 if self.reg_class_agnostic else
-                           4 * self.num_classes)
+            out_dim_reg = (4 if self.reg_class_agnostic else 4 *
+                           self.num_classes)
             self.fc_reg = nn.Linear(self.reg_last_dim, out_dim_reg)
 
     def _add_conv_fc_branch(self,

mmdet/models/builder.py

@@ -2,7 +2,7 @@ import mmcv
 from torch import nn
 
 from .registry import (BACKBONES, NECKS, ROI_EXTRACTORS, SHARED_HEADS, HEADS,
-                       DETECTORS)
+                       LOSSES, DETECTORS)
 
 
 def _build_module(cfg, registry, default_args):
@@ -52,5 +52,9 @@ def build_head(cfg):
     return build(cfg, HEADS)
 
 
+def build_loss(cfg):
+    return build(cfg, LOSSES)
+
+
 def build_detector(cfg, train_cfg=None, test_cfg=None):
     return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg))

mmdet/models/losses/__init__.py

+from .cross_entropy_loss import CrossEntropyLoss
+from .focal_loss import FocalLoss
+from .smooth_l1_loss import SmoothL1Loss
+
+__all__ = ['CrossEntropyLoss', 'FocalLoss', 'SmoothL1Loss']

mmdet/models/losses/cross_entropy_loss.py

+import torch.nn as nn
+from mmdet.core import (weighted_cross_entropy, weighted_binary_cross_entropy,
+                        mask_cross_entropy)
+
+from ..registry import LOSSES
+
+
+@LOSSES.register_module
+class CrossEntropyLoss(nn.Module):
+
+    def __init__(self, use_sigmoid=False, use_mask=False, loss_weight=1.0):
+        super(CrossEntropyLoss, self).__init__()
+        assert (use_sigmoid is False) or (use_mask is False)
+        self.use_sigmoid = use_sigmoid
+        self.use_mask = use_mask
+        self.loss_weight = loss_weight
+
+        if self.use_sigmoid:
+            self.cls_criterion = weighted_binary_cross_entropy
+        elif self.use_mask:
+            self.cls_criterion = mask_cross_entropy
+        else:
+            self.cls_criterion = weighted_cross_entropy
+
+    def forward(self, cls_score, label, label_weight, *args, **kwargs):
+        loss_cls = self.loss_weight * self.cls_criterion(
+            cls_score, label, label_weight, *args, **kwargs)
+        return loss_cls

mmdet/models/losses/focal_loss.py

+import torch.nn as nn
+from mmdet.core import weighted_sigmoid_focal_loss
+
+from ..registry import LOSSES
+
+
+@LOSSES.register_module
+class FocalLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=False,
+                 loss_weight=1.0,
+                 gamma=2.0,
+                 alpha=0.25):
+        super(FocalLoss, self).__init__()
+        assert use_sigmoid is True, 'Only sigmoid focaloss supported now.'
+        self.use_sigmoid = use_sigmoid
+        self.loss_weight = loss_weight
+        self.gamma = gamma
+        self.alpha = alpha
+        self.cls_criterion = weighted_sigmoid_focal_loss
+
+    def forward(self, cls_score, label, label_weight, *args, **kwargs):
+        if self.use_sigmoid:
+            loss_cls = self.loss_weight * self.cls_criterion(
+                cls_score,
+                label,
+                label_weight,
+                gamma=self.gamma,
+                alpha=self.alpha,
+                *args,
+                **kwargs)
+        else:
+            raise NotImplementedError
+        return loss_cls

mmdet/models/losses/smooth_l1_loss.py

+import torch.nn as nn
+from mmdet.core import weighted_smoothl1
+
+from ..registry import LOSSES
+
+
+@LOSSES.register_module
+class SmoothL1Loss(nn.Module):
+
+    def __init__(self, beta=1.0, loss_weight=1.0):
+        super(SmoothL1Loss, self).__init__()
+        self.beta = beta
+        self.loss_weight = loss_weight
+
+    def forward(self, pred, target, weight, *args, **kwargs):
+        loss_bbox = self.loss_weight * weighted_smoothl1(
+            pred, target, weight, beta=self.beta, *args, **kwargs)
+        return loss_bbox

mmdet/models/mask_heads/fcn_mask_head.py

@@ -4,9 +4,10 @@ import pycocotools.mask as mask_util
 import torch
 import torch.nn as nn
 
+from ..builder import build_loss
 from ..registry import HEADS
 from ..utils import ConvModule
-from mmdet.core import mask_cross_entropy, mask_target
+from mmdet.core import mask_target
 
 
 @HEADS.register_module
@@ -23,7 +24,9 @@ class FCNMaskHead(nn.Module):
                  num_classes=81,
                  class_agnostic=False,
                  conv_cfg=None,
-                 norm_cfg=None):
+                 norm_cfg=None,
+                 loss_mask=dict(
+                     type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)):
         super(FCNMaskHead, self).__init__()
         if upsample_method not in [None, 'deconv', 'nearest', 'bilinear']:
             raise ValueError(
@@ -40,6 +43,7 @@ class FCNMaskHead(nn.Module):
         self.class_agnostic = class_agnostic
         self.conv_cfg = conv_cfg
         self.norm_cfg = norm_cfg
+        self.loss_mask = build_loss(loss_mask)
 
         self.convs = nn.ModuleList()
         for i in range(self.num_convs):
@@ -106,10 +110,10 @@ class FCNMaskHead(nn.Module):
     def loss(self, mask_pred, mask_targets, labels):
         loss = dict()
         if self.class_agnostic:
-            loss_mask = mask_cross_entropy(mask_pred, mask_targets,
-                                           torch.zeros_like(labels))
+            loss_mask = self.loss_mask(mask_pred, mask_targets,
+                                       torch.zeros_like(labels))
         else:
-            loss_mask = mask_cross_entropy(mask_pred, mask_targets, labels)
+            loss_mask = self.loss_mask(mask_pred, mask_targets, labels)
         loss['loss_mask'] = loss_mask
         return loss
 

mmdet/models/registry.py

@@ -41,4 +41,5 @@ NECKS = Registry('neck')
 ROI_EXTRACTORS = Registry('roi_extractor')
 SHARED_HEADS = Registry('shared_head')
 HEADS = Registry('head')
+LOSSES = Registry('loss')
 DETECTORS = Registry('detector')

setup.py

@@ -102,7 +102,7 @@ if __name__ == '__main__':
             'Programming Language :: Python :: 3.5',
             'Programming Language :: Python :: 3.6',
         ],
-        license='GPLv3',
+        license='Apache License 2.0',
         setup_requires=['pytest-runner'],
         tests_require=['pytest'],
         install_requires=[