From d5d25be2d480b33a12bc7284107ec77c2eb6975b Mon Sep 17 00:00:00 2001
From: Cao Yuhang <yhcao6@gmail.com>
Date: Mon, 15 Apr 2019 11:25:57 +0800
Subject: [PATCH] fix bug in two stage and weighted_binary_cross_entropy_loss
---
mmdet/core/anchor/anchor_target.py | 10 ----------
mmdet/core/loss/losses.py | 12 ++++++++++++
2 files changed, 12 insertions(+), 10 deletions(-)
diff --git a/mmdet/core/anchor/anchor_target.py b/mmdet/core/anchor/anchor_target.py
index 2648977..60c902e 100644
--- a/mmdet/core/anchor/anchor_target.py
+++ b/mmdet/core/anchor/anchor_target.py
@@ -159,16 +159,6 @@ def anchor_target_single(flat_anchors,
neg_inds)
-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 anchor_inside_flags(flat_anchors, valid_flags, img_shape,
allowed_border=0):
img_h, img_w = img_shape[:2]
diff --git a/mmdet/core/loss/losses.py b/mmdet/core/loss/losses.py
index 922e058..1c5bf70 100644
--- a/mmdet/core/loss/losses.py
+++ b/mmdet/core/loss/losses.py
@@ -23,6 +23,8 @@ def weighted_cross_entropy(pred, label, weight, avg_factor=None, reduce=True):
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(
@@ -115,3 +117,13 @@ def accuracy(pred, target, topk=1):
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
--
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