Refactoring for losses (#761)

* refactoring for losses * update configs for guided anchoring * add all imported losses to __all__ * allow weight=None for binary_cross_entropy * use losses in mmdetection for FCOSHead * bug fix for weight_reduce_loss * add eps to iou_loss and handle weight=None * unify loss api in FCOSHead * fix avg_factor

Refactoring for losses (#761)
* refactoring for losses * update configs for guided anchoring * add all imported losses to __all__ * allow weight=None for binary_cross_entropy * use losses in mmdetection for FCOSHead * bug fix for weight_reduce_loss * add eps to iou_loss and handle weight=None * unify loss api in FCOSHead * fix avg_factor
ba26d8ce · Kai Chen · GitHub · afb7ec86 · ba26d8ce · ba26d8ce
Unverified Commit ba26d8ce authored Jun 11, 2019 by Kai Chen Committed by GitHub Jun 11, 2019
20 changed files
--- a/configs/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py
+++ b/configs/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py
@@ -25,7 +25,16 @@ model = dict(
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
-        strides=[8, 16, 32, 64, 128]))
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
 # training and testing settings
 train_cfg = dict(
    assigner=dict(
@@ -34,9 +43,6 @@ train_cfg = dict(
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
-    smoothl1_beta=0.11,
-    gamma=2.0,
-    alpha=0.25,
    allowed_border=-1,
    pos_weight=-1,
    debug=False)

--- a/configs/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py
+++ b/configs/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py
@@ -26,7 +26,16 @@ model = dict(
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
-        strides=[8, 16, 32, 64, 128]))
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
 # training and testing settings
 train_cfg = dict(
    assigner=dict(
@@ -35,9 +44,6 @@ train_cfg = dict(
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
-    smoothl1_beta=0.11,
-    gamma=2.0,
-    alpha=0.25,
    allowed_border=-1,
    pos_weight=-1,
    debug=False)

--- a/configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py
+++ b/configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py
@@ -25,7 +25,16 @@ model = dict(
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
-        strides=[8, 16, 32, 64, 128]))
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
 # training and testing settings
 train_cfg = dict(
    assigner=dict(
@@ -34,9 +43,6 @@ train_cfg = dict(
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
-    smoothl1_beta=0.11,
-    gamma=2.0,
-    alpha=0.25,
    allowed_border=-1,
    pos_weight=-1,
    debug=False)

--- a/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x.py
+++ b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x.py
@@ -36,8 +36,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),

--- a/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py
+++ b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py
@@ -36,8 +36,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),

--- a/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py
+++ b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py
@@ -40,8 +40,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,

--- a/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py
+++ b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py
@@ -40,8 +40,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,

--- a/configs/guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py
+++ b/configs/guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py
@@ -36,8 +36,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))

--- a/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py
+++ b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py
@@ -36,8 +36,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))

--- a/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py
+++ b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py
@@ -36,8 +36,7 @@ model = dict(
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
-        loss_shape=dict(
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-            type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))

--- a/mmdet/core/__init__.py
+++ b/mmdet/core/__init__.py
 from .anchor import *  # noqa: F401, F403
 from .bbox import *  # noqa: F401, F403
 from .mask import *  # noqa: F401, F403
-from .loss import *  # noqa: F401, F403
 from .evaluation import *  # noqa: F401, F403
 from .post_processing import *  # noqa: F401, F403
 from .utils import *  # noqa: F401, F403
--- a/mmdet/core/loss/__init__.py
+++ b/mmdet/core/loss/__init__.py
-from .losses import (weighted_nll_loss, weighted_cross_entropy,
-                     weighted_binary_cross_entropy, sigmoid_focal_loss,
-                     py_sigmoid_focal_loss, weighted_sigmoid_focal_loss,
-                     mask_cross_entropy, smooth_l1_loss, weighted_smoothl1,
-                     balanced_l1_loss, weighted_balanced_l1_loss, iou_loss,
-                     bounded_iou_loss, weighted_iou_loss, accuracy)
-__all__ = [
-    'weighted_nll_loss', 'weighted_cross_entropy',
-    'weighted_binary_cross_entropy', 'sigmoid_focal_loss',
-    'py_sigmoid_focal_loss', 'weighted_sigmoid_focal_loss',
-    'mask_cross_entropy', 'smooth_l1_loss', 'weighted_smoothl1',
-    'balanced_l1_loss', 'weighted_balanced_l1_loss', 'bounded_iou_loss',
-    'weighted_iou_loss', 'iou_loss', 'accuracy'
-]
--- a/mmdet/core/loss/losses.py
+++ b/mmdet/core/loss/losses.py
-# TODO merge naive and weighted loss.
-import numpy as np
-import torch
-import torch.nn.functional as F
-from ..bbox import bbox_overlaps
-from ...ops import sigmoid_focal_loss
-def weighted_nll_loss(pred, label, weight, avg_factor=None):
-    if avg_factor is None:
-        avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
-    raw = F.nll_loss(pred, label, reduction='none')
-    return torch.sum(raw * weight)[None] / avg_factor
-def weighted_cross_entropy(pred, label, weight, avg_factor=None, reduce=True):
-    if avg_factor is None:
-        avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
-    raw = F.cross_entropy(pred, label, reduction='none')
-    if reduce:
-        return torch.sum(raw * weight)[None] / avg_factor
-    else:
-        return raw * weight / avg_factor
-def weighted_binary_cross_entropy(pred, label, weight, avg_factor=None):
-    if pred.dim() != label.dim():
-        label, weight = _expand_binary_labels(label, weight, pred.size(-1))
-    if avg_factor is None:
-        avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
-    return F.binary_cross_entropy_with_logits(
-        pred, label.float(), weight.float(),
-        reduction='sum')[None] / avg_factor
-def py_sigmoid_focal_loss(pred,
-                          target,
-                          weight,
-                          gamma=2.0,
-                          alpha=0.25,
-                          reduction='mean'):
-    pred_sigmoid = pred.sigmoid()
-    target = target.type_as(pred)
-    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
-    weight = (alpha * target + (1 - alpha) * (1 - target)) * weight
-    weight = weight * pt.pow(gamma)
-    loss = F.binary_cross_entropy_with_logits(
-        pred, target, reduction='none') * weight
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.mean()
-    elif reduction_enum == 2:
-        return loss.sum()
-def weighted_sigmoid_focal_loss(pred,
-                                target,
-                                weight,
-                                gamma=2.0,
-                                alpha=0.25,
-                                avg_factor=None,
-                                num_classes=80):
-    if avg_factor is None:
-        avg_factor = torch.sum(weight > 0).float().item() / num_classes + 1e-6
-    return torch.sum(
-        sigmoid_focal_loss(pred, target, gamma, alpha, 'none') *
-        weight.view(-1, 1))[None] / avg_factor
-def mask_cross_entropy(pred, target, label):
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, reduction='mean')[None]
-def smooth_l1_loss(pred, target, beta=1.0, reduction='mean'):
-    assert beta > 0
-    assert pred.size() == target.size() and target.numel() > 0
-    diff = torch.abs(pred - target)
-    loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
-                       diff - 0.5 * beta)
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.sum() / pred.numel()
-    elif reduction_enum == 2:
-        return loss.sum()
-def weighted_smoothl1(pred, target, weight, beta=1.0, avg_factor=None):
-    if avg_factor is None:
-        avg_factor = torch.sum(weight > 0).float().item() / 4 + 1e-6
-    loss = smooth_l1_loss(pred, target, beta, reduction='none')
-    return torch.sum(loss * weight)[None] / avg_factor
-def balanced_l1_loss(pred,
-                     target,
-                     beta=1.0,
-                     alpha=0.5,
-                     gamma=1.5,
-                     reduction='none'):
-    assert beta > 0
-    assert pred.size() == target.size() and target.numel() > 0
-    diff = torch.abs(pred - target)
-    b = np.e**(gamma / alpha) - 1
-    loss = torch.where(
-        diff < beta, alpha / b *
-        (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff,
-        gamma * diff + gamma / b - alpha * beta)
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, elementwise_mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.sum() / pred.numel()
-    elif reduction_enum == 2:
-        return loss.sum()
-    return loss
-def weighted_balanced_l1_loss(pred,
-                              target,
-                              weight,
-                              beta=1.0,
-                              alpha=0.5,
-                              gamma=1.5,
-                              avg_factor=None):
-    if avg_factor is None:
-        avg_factor = torch.sum(weight > 0).float().item() / 4 + 1e-6
-    loss = balanced_l1_loss(pred, target, beta, alpha, gamma, reduction='none')
-    return torch.sum(loss * weight)[None] / avg_factor
-def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3, reduction='mean'):
-    """Improving Object Localization with Fitness NMS and Bounded IoU Loss,
-    https://arxiv.org/abs/1711.00164.
-    Args:
-        pred (tensor): Predicted bboxes.
-        target (tensor): Target bboxes.
-        beta (float): beta parameter in smoothl1.
-        eps (float): eps to avoid NaN.
-        reduction (str): Reduction type.
-    """
-    pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
-    pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
-    pred_w = pred[:, 2] - pred[:, 0] + 1
-    pred_h = pred[:, 3] - pred[:, 1] + 1
-    with torch.no_grad():
-        target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
-        target_ctry = (target[:, 1] + target[:, 3]) * 0.5
-        target_w = target[:, 2] - target[:, 0] + 1
-        target_h = target[:, 3] - target[:, 1] + 1
-    dx = target_ctrx - pred_ctrx
-    dy = target_ctry - pred_ctry
-    loss_dx = 1 - torch.max(
-        (target_w - 2 * dx.abs()) /
-        (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
-    loss_dy = 1 - torch.max(
-        (target_h - 2 * dy.abs()) /
-        (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
-    loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
-                            (target_w + eps))
-    loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
-                            (target_h + eps))
-    loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
-                            dim=-1).view(loss_dx.size(0), -1)
-    loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
-                       loss_comb - 0.5 * beta)
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.sum() / pred.numel()
-    elif reduction_enum == 2:
-        return loss.sum()
-def weighted_iou_loss(pred,
-                      target,
-                      weight,
-                      style='naive',
-                      beta=0.2,
-                      eps=1e-3,
-                      avg_factor=None):
-    if style not in ['bounded', 'naive']:
-        raise ValueError('Only support bounded iou loss and naive iou loss.')
-    inds = torch.nonzero(weight[:, 0] > 0)
-    if avg_factor is None:
-        avg_factor = inds.numel() + 1e-6
-    if inds.numel() > 0:
-        inds = inds.squeeze(1)
-    else:
-        return (pred * weight).sum()[None] / avg_factor
-    if style == 'bounded':
-        loss = bounded_iou_loss(
-            pred[inds], target[inds], beta=beta, eps=eps, reduction='sum')
-    else:
-        loss = iou_loss(pred[inds], target[inds], eps=eps, reduction='sum')
-    loss = loss[None] / avg_factor
-    return loss
-def accuracy(pred, target, topk=1):
-    if isinstance(topk, int):
-        topk = (topk, )
-        return_single = True
-    else:
-        return_single = False
-    maxk = max(topk)
-    _, pred_label = pred.topk(maxk, 1, True, True)
-    pred_label = pred_label.t()
-    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
-        res.append(correct_k.mul_(100.0 / pred.size(0)))
-    return res[0] if return_single else res
-def _expand_binary_labels(labels, label_weights, label_channels):
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    inds = torch.nonzero(labels >= 1).squeeze()
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds] - 1] = 1
-    bin_label_weights = label_weights.view(-1, 1).expand(
-        label_weights.size(0), label_channels)
-    return bin_labels, bin_label_weights
-def iou_loss(pred_bboxes, target_bboxes, eps=1e-6, reduction='mean'):
-    ious = bbox_overlaps(pred_bboxes, target_bboxes, is_aligned=True) + eps
-    loss = -ious.log()
-    reduction_enum = F._Reduction.get_enum(reduction)
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.mean()
-    elif reduction_enum == 2:
-        return loss.sum()
--- a/mmdet/models/anchor_heads/fcos_head.py
+++ b/mmdet/models/anchor_heads/fcos_head.py
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from mmcv.cnn import normal_init
-from mmdet.core import (sigmoid_focal_loss, iou_loss, multi_apply,
+from mmdet.core import multi_apply, multiclass_nms, distance2bbox
-                        multiclass_nms, distance2bbox)
+from ..builder import build_loss
 from ..registry import HEADS
 from ..utils import bias_init_with_prob, Scale, ConvModule
@@ -22,6 +21,17 @@ class FCOSHead(nn.Module):
                 strides=(4, 8, 16, 32, 64),
                 regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
                                 (512, INF)),
+                 loss_cls=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+                 loss_centerness=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
                 conv_cfg=None,
                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)):
        super(FCOSHead, self).__init__()
@@ -33,6 +43,9 @@ class FCOSHead(nn.Module):
        self.stacked_convs = stacked_convs
        self.strides = strides
        self.regress_ranges = regress_ranges
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.loss_centerness = build_loss(loss_centerness)
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
@@ -139,9 +152,9 @@ class FCOSHead(nn.Module):
        pos_inds = flatten_labels.nonzero().reshape(-1)
        num_pos = len(pos_inds)
-        loss_cls = sigmoid_focal_loss(
+        loss_cls = self.loss_cls(
-            flatten_cls_scores, flatten_labels, cfg.gamma, cfg.alpha,
+            flatten_cls_scores, flatten_labels,
-            'none').sum()[None] / (num_pos + num_imgs)  # avoid num_pos is 0
+            avg_factor=num_pos + num_imgs)  # avoid num_pos is 0
        pos_bbox_preds = flatten_bbox_preds[pos_inds]
        pos_bbox_targets = flatten_bbox_targets[pos_inds]
@@ -154,20 +167,20 @@ class FCOSHead(nn.Module):
            pos_decoded_target_preds = distance2bbox(pos_points,
                                                     pos_bbox_targets)
            # centerness weighted iou loss
-            loss_reg = ((iou_loss(
+            loss_bbox = self.loss_bbox(
                pos_decoded_bbox_preds,
                pos_decoded_target_preds,
-                reduction='none') * pos_centerness_targets).sum() /
+                weight=pos_centerness_targets,
-                        pos_centerness_targets.sum())[None]
+                avg_factor=pos_centerness_targets.sum())
-            loss_centerness = F.binary_cross_entropy_with_logits(
+            loss_centerness = self.loss_centerness(pos_centerness,
-                pos_centerness, pos_centerness_targets, reduction='mean')[None]
+                                                   pos_centerness_targets)
        else:
-            loss_reg = pos_bbox_preds.sum()[None]
+            loss_bbox = pos_bbox_preds.sum()
-            loss_centerness = pos_centerness.sum()[None]
+            loss_centerness = pos_centerness.sum()
        return dict(
            loss_cls=loss_cls,
-            loss_reg=loss_reg,
+            loss_bbox=loss_bbox,
            loss_centerness=loss_centerness)
    def get_bboxes(self,
@@ -196,9 +209,10 @@ class FCOSHead(nn.Module):
            ]
            img_shape = img_metas[img_id]['img_shape']
            scale_factor = img_metas[img_id]['scale_factor']
-            det_bboxes = self.get_bboxes_single(
+            det_bboxes = self.get_bboxes_single(cls_score_list, bbox_pred_list,
-                cls_score_list, bbox_pred_list, centerness_pred_list,
+                                                centerness_pred_list,
-                mlvl_points, img_shape, scale_factor, cfg, rescale)
+                                                mlvl_points, img_shape,
+                                                scale_factor, cfg, rescale)
            result_list.append(det_bboxes)
        return result_list

--- a/mmdet/models/anchor_heads/guided_anchor_head.py
+++ b/mmdet/models/anchor_heads/guided_anchor_head.py
@@ -115,8 +115,7 @@ class GuidedAnchorHead(AnchorHead):
                     alpha=0.25,
                     loss_weight=1.0),
                 loss_shape=dict(
-                     type='IoULoss',
+                     type='BoundedIoULoss',
-                     style='bounded',
                     beta=0.2,
                     loss_weight=1.0),
                 loss_cls=dict(

--- a/mmdet/models/anchor_heads/ssd_head.py
+++ b/mmdet/models/anchor_heads/ssd_head.py
@@ -4,9 +4,9 @@ import torch.nn as nn
 import torch.nn.functional as F
 from mmcv.cnn import xavier_init
-from mmdet.core import (AnchorGenerator, anchor_target, weighted_smoothl1,
+from mmdet.core import AnchorGenerator, anchor_target, multi_apply
-                        multi_apply)
 from .anchor_head import AnchorHead
+from ..losses import smooth_l1_loss
 from ..registry import HEADS
@@ -123,7 +123,7 @@ class SSDHead(AnchorHead):
        loss_cls_neg = topk_loss_cls_neg.sum()
        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
-        loss_bbox = weighted_smoothl1(
+        loss_bbox = smooth_l1_loss(
            bbox_pred,
            bbox_targets,
            bbox_weights,

--- a/mmdet/models/bbox_heads/bbox_head.py
+++ b/mmdet/models/bbox_heads/bbox_head.py
@@ -2,8 +2,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from mmdet.core import delta2bbox, multiclass_nms, bbox_target, accuracy
+from mmdet.core import delta2bbox, multiclass_nms, bbox_target
 from ..builder import build_loss
+from ..losses import accuracy
 from ..registry import HEADS
@@ -99,8 +100,9 @@ class BBoxHead(nn.Module):
             reduce=True):
        losses = dict()
        if cls_score is not None:
+            avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
            losses['loss_cls'] = self.loss_cls(
-                cls_score, labels, label_weights, reduce=reduce)
+                cls_score, labels, label_weights, avg_factor=avg_factor)
            losses['acc'] = accuracy(cls_score, labels)
        if bbox_pred is not None:
            pos_inds = labels > 0

--- a/mmdet/models/losses/__init__.py
+++ b/mmdet/models/losses/__init__.py
-from .cross_entropy_loss import CrossEntropyLoss
+from .accuracy import accuracy, Accuracy
-from .focal_loss import FocalLoss
+from .cross_entropy_loss import (cross_entropy, binary_cross_entropy,
-from .smooth_l1_loss import SmoothL1Loss
+                                 mask_cross_entropy, CrossEntropyLoss)
+from .focal_loss import sigmoid_focal_loss, FocalLoss
+from .smooth_l1_loss import smooth_l1_loss, SmoothL1Loss
 from .ghm_loss import GHMC, GHMR
-from .balanced_l1_loss import BalancedL1Loss
+from .balanced_l1_loss import balanced_l1_loss, BalancedL1Loss
-from .iou_loss import IoULoss
+from .iou_loss import iou_loss, bounded_iou_loss, IoULoss, BoundedIoULoss
+from .utils import reduce_loss, weight_reduce_loss, weighted_loss
 __all__ = [
-    'CrossEntropyLoss', 'FocalLoss', 'SmoothL1Loss', 'BalancedL1Loss',
+    'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
-    'IoULoss', 'GHMC', 'GHMR'
+    'mask_cross_entropy', 'CrossEntropyLoss', 'sigmoid_focal_loss',
+    'FocalLoss', 'smooth_l1_loss', 'SmoothL1Loss', 'balanced_l1_loss',
+    'BalancedL1Loss', 'iou_loss', 'bounded_iou_loss', 'IoULoss',
+    'BoundedIoULoss', 'GHMC', 'GHMR', 'reduce_loss', 'weight_reduce_loss',
+    'weighted_loss'
 ]
--- a/mmdet/models/losses/accuracy.py
+++ b/mmdet/models/losses/accuracy.py
+import torch.nn as nn
+def accuracy(pred, target, topk=1):
+    assert isinstance(topk, (int, tuple))
+    if isinstance(topk, int):
+        topk = (topk, )
+        return_single = True
+    else:
+        return_single = False
+    maxk = max(topk)
+    _, pred_label = pred.topk(maxk, dim=1)
+    pred_label = pred_label.t()
+    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        res.append(correct_k.mul_(100.0 / pred.size(0)))
+    return res[0] if return_single else res
+class Accuracy(nn.Module):
+    def __init__(self, topk=(1, )):
+        super().__init__()
+        self.topk = topk
+    def forward(self, pred, target):
+        return accuracy(pred, target, self.topk)
--- a/mmdet/models/losses/balanced_l1_loss.py
+++ b/mmdet/models/losses/balanced_l1_loss.py
+import numpy as np
+import torch
 import torch.nn as nn
-from mmdet.core import weighted_balanced_l1_loss
+from .utils import reduce_loss, weighted_loss
 from ..registry import LOSSES
+@weighted_loss
+def balanced_l1_loss(pred,
+                     target,
+                     beta=1.0,
+                     alpha=0.5,
+                     gamma=1.5,
+                     reduction='mean'):
+    assert beta > 0
+    assert pred.size() == target.size() and target.numel() > 0
+    diff = torch.abs(pred - target)
+    b = np.e**(gamma / alpha) - 1
+    loss = torch.where(
+        diff < beta, alpha / b *
+        (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff,
+        gamma * diff + gamma / b - alpha * beta)
+    loss = reduce_loss(loss, reduction)
+    return loss
 @LOSSES.register_module
 class BalancedL1Loss(nn.Module):
    """Balanced L1 Loss
@@ -11,21 +35,28 @@ class BalancedL1Loss(nn.Module):
    arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
    """
-    def __init__(self, alpha=0.5, gamma=1.5, beta=1.0, loss_weight=1.0):
+    def __init__(self,
+                 alpha=0.5,
+                 gamma=1.5,
+                 beta=1.0,
+                 reduction='mean',
+                 loss_weight=1.0):
        super(BalancedL1Loss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.beta = beta
+        self.reduction = reduction
        self.loss_weight = loss_weight
-    def forward(self, pred, target, weight, *args, **kwargs):
+    def forward(self, pred, target, weight=None, avg_factor=None, **kwargs):
-        loss_bbox = self.loss_weight * weighted_balanced_l1_loss(
+        loss_bbox = self.loss_weight * balanced_l1_loss(
            pred,
            target,
            weight,
            alpha=self.alpha,
            gamma=self.gamma,
            beta=self.beta,
-            *args,
+            reduction=self.reduction,
+            avg_factor=avg_factor,
            **kwargs)
        return loss_bbox