From 32df98e970c8848d3d9fd72492aba8bc030377d8 Mon Sep 17 00:00:00 2001
From: Cao Yuhang <yhcao6@gmail.com>
Date: Thu, 20 Jun 2019 22:25:42 +0800
Subject: [PATCH] Add reduction_override flag (#839)
* add reduction_override flag
* change default value of reduction_override as None
* add assertion, fix format
* delete redudant statement in util
* delete redudant comment
---
mmdet/core/bbox/samplers/ohem_sampler.py | 2 +-
mmdet/models/bbox_heads/bbox_head.py | 11 ++++++++---
mmdet/models/losses/balanced_l1_loss.py | 13 +++++++++++--
mmdet/models/losses/cross_entropy_loss.py | 12 ++++++++++--
mmdet/models/losses/focal_loss.py | 12 ++++++++++--
mmdet/models/losses/ghm_loss.py | 3 +++
mmdet/models/losses/iou_loss.py | 13 +++++++++++--
mmdet/models/losses/smooth_l1_loss.py | 13 +++++++++++--
mmdet/models/losses/utils.py | 11 ++++++-----
9 files changed, 71 insertions(+), 19 deletions(-)
diff --git a/mmdet/core/bbox/samplers/ohem_sampler.py b/mmdet/core/bbox/samplers/ohem_sampler.py
index 800a1c2..0711d97 100644
--- a/mmdet/core/bbox/samplers/ohem_sampler.py
+++ b/mmdet/core/bbox/samplers/ohem_sampler.py
@@ -36,7 +36,7 @@ class OHEMSampler(BaseSampler):
label_weights=cls_score.new_ones(cls_score.size(0)),
bbox_targets=None,
bbox_weights=None,
- reduce=False)['loss_cls']
+ reduction_override='none')['loss_cls']
_, topk_loss_inds = loss.topk(num_expected)
return inds[topk_loss_inds]
diff --git a/mmdet/models/bbox_heads/bbox_head.py b/mmdet/models/bbox_heads/bbox_head.py
index c67ea8a..436592c 100644
--- a/mmdet/models/bbox_heads/bbox_head.py
+++ b/mmdet/models/bbox_heads/bbox_head.py
@@ -97,12 +97,16 @@ class BBoxHead(nn.Module):
label_weights,
bbox_targets,
bbox_weights,
- reduce=True):
+ reduction_override=None):
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, avg_factor=avg_factor)
+ cls_score,
+ labels,
+ label_weights,
+ avg_factor=avg_factor,
+ reduction_override=reduction_override)
losses['acc'] = accuracy(cls_score, labels)
if bbox_pred is not None:
pos_inds = labels > 0
@@ -115,7 +119,8 @@ class BBoxHead(nn.Module):
pos_bbox_pred,
bbox_targets[pos_inds],
bbox_weights[pos_inds],
- avg_factor=bbox_targets.size(0))
+ avg_factor=bbox_targets.size(0),
+ reduction_override=reduction_override)
return losses
def get_det_bboxes(self,
diff --git a/mmdet/models/losses/balanced_l1_loss.py b/mmdet/models/losses/balanced_l1_loss.py
index 2dee674..8593396 100644
--- a/mmdet/models/losses/balanced_l1_loss.py
+++ b/mmdet/models/losses/balanced_l1_loss.py
@@ -46,7 +46,16 @@ class BalancedL1Loss(nn.Module):
self.reduction = reduction
self.loss_weight = loss_weight
- def forward(self, pred, target, weight=None, avg_factor=None, **kwargs):
+ def forward(self,
+ pred,
+ target,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
+ **kwargs):
+ assert reduction_override in (None, 'none', 'mean', 'sum')
+ reduction = (
+ reduction_override if reduction_override else self.reduction)
loss_bbox = self.loss_weight * balanced_l1_loss(
pred,
target,
@@ -54,7 +63,7 @@ class BalancedL1Loss(nn.Module):
alpha=self.alpha,
gamma=self.gamma,
beta=self.beta,
- reduction=self.reduction,
+ reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_bbox
diff --git a/mmdet/models/losses/cross_entropy_loss.py b/mmdet/models/losses/cross_entropy_loss.py
index 1921978..2f2ce69 100644
--- a/mmdet/models/losses/cross_entropy_loss.py
+++ b/mmdet/models/losses/cross_entropy_loss.py
@@ -73,13 +73,21 @@ class CrossEntropyLoss(nn.Module):
else:
self.cls_criterion = cross_entropy
- def forward(self, cls_score, label, weight=None, avg_factor=None,
+ def forward(self,
+ cls_score,
+ label,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
**kwargs):
+ assert reduction_override in (None, 'none', 'mean', 'sum')
+ reduction = (
+ reduction_override if reduction_override else self.reduction)
loss_cls = self.loss_weight * self.cls_criterion(
cls_score,
label,
weight,
- reduction=self.reduction,
+ reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_cls
diff --git a/mmdet/models/losses/focal_loss.py b/mmdet/models/losses/focal_loss.py
index b8ccfa0..7a46356 100644
--- a/mmdet/models/losses/focal_loss.py
+++ b/mmdet/models/losses/focal_loss.py
@@ -59,7 +59,15 @@ class FocalLoss(nn.Module):
self.reduction = reduction
self.loss_weight = loss_weight
- def forward(self, pred, target, weight=None, avg_factor=None):
+ def forward(self,
+ pred,
+ target,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None):
+ assert reduction_override in (None, 'none', 'mean', 'sum')
+ reduction = (
+ reduction_override if reduction_override else self.reduction)
if self.use_sigmoid:
loss_cls = self.loss_weight * sigmoid_focal_loss(
pred,
@@ -67,7 +75,7 @@ class FocalLoss(nn.Module):
weight,
gamma=self.gamma,
alpha=self.alpha,
- reduction=self.reduction,
+ reduction=reduction,
avg_factor=avg_factor)
else:
raise NotImplementedError
diff --git a/mmdet/models/losses/ghm_loss.py b/mmdet/models/losses/ghm_loss.py
index 7beeb47..95656a2 100644
--- a/mmdet/models/losses/ghm_loss.py
+++ b/mmdet/models/losses/ghm_loss.py
@@ -15,6 +15,7 @@ def _expand_binary_labels(labels, label_weights, label_channels):
return bin_labels, bin_label_weights
+# TODO: code refactoring to make it consistent with other losses
@LOSSES.register_module
class GHMC(nn.Module):
"""GHM Classification Loss.
@@ -90,6 +91,7 @@ class GHMC(nn.Module):
return loss * self.loss_weight
+# TODO: code refactoring to make it consistent with other losses
@LOSSES.register_module
class GHMR(nn.Module):
"""GHM Regression Loss.
@@ -116,6 +118,7 @@ class GHMR(nn.Module):
self.acc_sum = torch.zeros(bins).cuda()
self.loss_weight = loss_weight
+ # TODO: support reduction parameter
def forward(self, pred, target, label_weight, avg_factor=None):
"""Calculate the GHM-R loss.
diff --git a/mmdet/models/losses/iou_loss.py b/mmdet/models/losses/iou_loss.py
index 7c235cd..967e576 100644
--- a/mmdet/models/losses/iou_loss.py
+++ b/mmdet/models/losses/iou_loss.py
@@ -78,15 +78,24 @@ class IoULoss(nn.Module):
self.reduction = reduction
self.loss_weight = loss_weight
- def forward(self, pred, target, weight=None, avg_factor=None, **kwargs):
+ def forward(self,
+ pred,
+ target,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
+ **kwargs):
if weight is not None and not torch.any(weight > 0):
return (pred * weight).sum() # 0
+ assert reduction_override in (None, 'none', 'mean', 'sum')
+ reduction = (
+ reduction_override if reduction_override else self.reduction)
loss = self.loss_weight * iou_loss(
pred,
target,
weight,
eps=self.eps,
- reduction=self.reduction,
+ reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss
diff --git a/mmdet/models/losses/smooth_l1_loss.py b/mmdet/models/losses/smooth_l1_loss.py
index 6a098fc..75d71e8 100644
--- a/mmdet/models/losses/smooth_l1_loss.py
+++ b/mmdet/models/losses/smooth_l1_loss.py
@@ -24,13 +24,22 @@ class SmoothL1Loss(nn.Module):
self.reduction = reduction
self.loss_weight = loss_weight
- def forward(self, pred, target, weight=None, avg_factor=None, **kwargs):
+ def forward(self,
+ pred,
+ target,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
+ **kwargs):
+ assert reduction_override in (None, 'none', 'mean', 'sum')
+ reduction = (
+ reduction_override if reduction_override else self.reduction)
loss_bbox = self.loss_weight * smooth_l1_loss(
pred,
target,
weight,
beta=self.beta,
- reduction=self.reduction,
+ reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_bbox
diff --git a/mmdet/models/losses/utils.py b/mmdet/models/losses/utils.py
index b902c64..5c16e06 100644
--- a/mmdet/models/losses/utils.py
+++ b/mmdet/models/losses/utils.py
@@ -42,12 +42,13 @@ def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
loss = reduce_loss(loss, reduction)
- # otherwise average the loss by avg_factor
else:
- if reduction != 'mean':
- raise ValueError(
- 'avg_factor can only be used with reduction="mean"')
- loss = loss.sum() / avg_factor
+ # if reduction is mean, then average the loss by avg_factor
+ if reduction == 'mean':
+ loss = loss.sum() / avg_factor
+ # if reduction is 'none', then do nothing, otherwise raise an error
+ elif reduction != 'none':
+ raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
--
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