From efd02f86182c5f16531c231ed7e05e0952c78fb3 Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Fri, 23 Nov 2018 20:49:53 +0800
Subject: [PATCH] refactoring for retinanet
---
MODEL_ZOO.md | 8 ++---
configs/retinanet_r50_fpn_1x.py | 8 ++---
mmdet/core/loss/losses.py | 2 +-
.../models/single_stage_heads/retina_head.py | 36 ++++++++++++-------
4 files changed, 32 insertions(+), 22 deletions(-)
diff --git a/MODEL_ZOO.md b/MODEL_ZOO.md
index ed526d5e..e581f17f 100644
--- a/MODEL_ZOO.md
+++ b/MODEL_ZOO.md
@@ -71,13 +71,13 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m
| R-50-FPN | pytorch | Mask | 1x | 5.3 | 0.50 | 10.6 | 36.8 | 34.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010-e030a38f.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_mask_rcnn_r50_fpn_1x_20181010_results.pkl.json) |
| R-50-FPN | pytorch | Mask | 2x | 5.3 | 0.50 | 10.6 | 37.9 | 34.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_mask_rcnn_r50_fpn_2x_20181010_results.pkl.json) |
-### RetinaNet (coming soon)
+### RetinaNet
| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
-| R-50-FPN | caffe | 1x | | | | | |
-| R-50-FPN | pytorch | 1x | | | | | |
-| R-50-FPN | pytorch | 2x | | | | | |
+| R-50-FPN | caffe | 1x | 5.8 | 0.459 | 9.4 | | - |
+| R-50-FPN | pytorch | 1x | 6.2 | 0.487 | 9.1 | | |
+| R-50-FPN | pytorch | 2x | 6.2 | 0.487 | 9.1 | | |
## Comparison with Detectron
diff --git a/configs/retinanet_r50_fpn_1x.py b/configs/retinanet_r50_fpn_1x.py
index 64423bb3..079d6e53 100644
--- a/configs/retinanet_r50_fpn_1x.py
+++ b/configs/retinanet_r50_fpn_1x.py
@@ -22,16 +22,16 @@ model = dict(
in_channels=256,
stacked_convs=4,
feat_channels=256,
+ octave_base_scale=4,
scales_per_octave=3,
- anchor_scale=4,
- anchor_ratios=[1.0, 2.0, 0.5],
+ anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]))
# training and testing settings
train_cfg = dict(
assigner=dict(
- pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0.4, ignore_iof_thr=-1),
+ pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1),
smoothl1_beta=0.11,
gamma=2.0,
alpha=0.25,
@@ -99,7 +99,7 @@ lr_config = dict(
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
- interval=20,
+ interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
diff --git a/mmdet/core/loss/losses.py b/mmdet/core/loss/losses.py
index 14b49f5c..af1ceae7 100644
--- a/mmdet/core/loss/losses.py
+++ b/mmdet/core/loss/losses.py
@@ -31,7 +31,7 @@ def sigmoid_focal_loss(pred,
gamma=2.0,
alpha=0.25,
reduction='elementwise_mean'):
- pred_sigmoid = pred.sigmoid()
+ pred_sigmoid = pred.sigmoid().detach()
pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
weight = (alpha * target + (1 - alpha) * (1 - target)) * weight
weight = weight * pt.pow(gamma)
diff --git a/mmdet/models/single_stage_heads/retina_head.py b/mmdet/models/single_stage_heads/retina_head.py
index 44ddaa6b..cc049239 100644
--- a/mmdet/models/single_stage_heads/retina_head.py
+++ b/mmdet/models/single_stage_heads/retina_head.py
@@ -23,6 +23,14 @@ class RetinaHead(nn.Module):
stacked_convs (int): Number of convolutional layers added for cls and
reg branch.
feat_channels (int): Number of channels for the RPN feature map.
+ scales_per_octave (int): Number of anchor scales per octave.
+ octave_base_scale (int): Base octave scale. Anchor scales are computed
+ as `s*2^(i/n)`, for i in [0, n-1], where s is `octave_base_scale`
+ and n is `scales_per_octave`.
+ anchor_ratios (Iterable): Anchor aspect ratios.
+ anchor_strides (Iterable): Anchor strides.
+ target_means (Iterable): Mean values of regression targets.
+ target_stds (Iterable): Std values of regression targets.
"""
def __init__(self,
@@ -30,30 +38,32 @@ class RetinaHead(nn.Module):
num_classes,
stacked_convs=4,
feat_channels=256,
+ octave_base_scale=4,
scales_per_octave=3,
- anchor_scale=4,
- anchor_ratios=[1.0, 2.0, 0.5],
+ anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]):
+ anchor_base_sizes=None,
+ target_means=(.0, .0, .0, .0),
+ target_stds=(1.0, 1.0, 1.0, 1.0)):
super(RetinaHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
+ self.octave_base_scale = octave_base_scale
self.scales_per_octave = scales_per_octave
- self.anchor_scale = anchor_scale
- self.anchor_strides = anchor_strides
self.anchor_ratios = anchor_ratios
+ self.anchor_strides = anchor_strides
+ self.anchor_base_sizes = list(
+ anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
self.target_means = target_means
self.target_stds = target_stds
+
self.anchor_generators = []
- for anchor_stride in self.anchor_strides:
- octave_scales = np.array([
- 2**(octave / float(scales_per_octave))
- for octave in range(scales_per_octave)
- ])
- octave_scales = octave_scales * anchor_scale
+ for anchor_base in self.anchor_base_sizes:
+ octave_scales = np.array(
+ [2**(i / scales_per_octave) for i in range(scales_per_octave)])
+ anchor_scales = octave_scales * octave_base_scale
self.anchor_generators.append(
- AnchorGenerator(anchor_stride, octave_scales, anchor_ratios))
+ AnchorGenerator(anchor_base, anchor_scales, anchor_ratios))
self.relu = nn.ReLU(inplace=True)
self.num_anchors = int(
len(self.anchor_ratios) * self.scales_per_octave)
--
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