From f83c8638920fd710d12a1d904a2ee957c58adda3 Mon Sep 17 00:00:00 2001
From: Jon Crall <erotemic@gmail.com>
Date: Tue, 8 Oct 2019 12:21:16 -0400
Subject: [PATCH] Add docs FCOS/Retina, OHEM, Scale, make_optimizer (#1505)
---
mmdet/apis/train.py | 6 ++++++
mmdet/core/bbox/samplers/ohem_sampler.py | 6 ++++++
mmdet/models/anchor_heads/fcos_head.py | 16 ++++++++++++++++
mmdet/models/anchor_heads/retina_head.py | 20 ++++++++++++++++++++
mmdet/models/utils/scale.py | 3 +++
5 files changed, 51 insertions(+)
diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py
index 35ad34c..fee47d6 100644
--- a/mmdet/apis/train.py
+++ b/mmdet/apis/train.py
@@ -82,6 +82,12 @@ def build_optimizer(model, optimizer_cfg):
Returns:
torch.optim.Optimizer: The initialized optimizer.
+
+ Example:
+ >>> model = torch.nn.modules.Conv1d(1, 1, 1)
+ >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9,
+ >>> weight_decay=0.0001)
+ >>> optimizer = build_optimizer(model, optimizer_cfg)
"""
if hasattr(model, 'module'):
model = model.module
diff --git a/mmdet/core/bbox/samplers/ohem_sampler.py b/mmdet/core/bbox/samplers/ohem_sampler.py
index 2500f31..3701d83 100644
--- a/mmdet/core/bbox/samplers/ohem_sampler.py
+++ b/mmdet/core/bbox/samplers/ohem_sampler.py
@@ -5,6 +5,12 @@ from .base_sampler import BaseSampler
class OHEMSampler(BaseSampler):
+ """
+ Online Hard Example Mining Sampler described in [1]_.
+
+ References:
+ .. [1] https://arxiv.org/pdf/1604.03540.pdf
+ """
def __init__(self,
num,
diff --git a/mmdet/models/anchor_heads/fcos_head.py b/mmdet/models/anchor_heads/fcos_head.py
index c01e4ea..a8c2cd4 100644
--- a/mmdet/models/anchor_heads/fcos_head.py
+++ b/mmdet/models/anchor_heads/fcos_head.py
@@ -12,6 +12,22 @@ INF = 1e8
@HEADS.register_module
class FCOSHead(nn.Module):
+ """
+ Fully Convolutional One-Stage Object Detection head from [1]_.
+
+ The FCOS head does not use anchor boxes. Instead bounding boxes are
+ predicted at each pixel and a centerness measure is used to supress
+ low-quality predictions.
+
+ References:
+ .. [1] https://arxiv.org/abs/1904.01355
+
+ Example:
+ >>> self = FCOSHead(11, 7)
+ >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
+ >>> cls_score, bbox_pred, centerness = self.forward(feats)
+ >>> assert len(cls_score) == len(self.scales)
+ """
def __init__(self,
num_classes,
diff --git a/mmdet/models/anchor_heads/retina_head.py b/mmdet/models/anchor_heads/retina_head.py
index 045db70..e3b8143 100644
--- a/mmdet/models/anchor_heads/retina_head.py
+++ b/mmdet/models/anchor_heads/retina_head.py
@@ -9,6 +9,26 @@ from .anchor_head import AnchorHead
@HEADS.register_module
class RetinaHead(AnchorHead):
+ """
+ An anchor-based head used in [1]_.
+
+ The head contains two subnetworks. The first classifies anchor boxes and
+ the second regresses deltas for the anchors.
+
+ References:
+ .. [1] https://arxiv.org/pdf/1708.02002.pdf
+
+ Example:
+ >>> import torch
+ >>> self = RetinaHead(11, 7)
+ >>> x = torch.rand(1, 7, 32, 32)
+ >>> cls_score, bbox_pred = self.forward_single(x)
+ >>> # Each anchor predicts a score for each class except background
+ >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
+ >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
+ >>> assert cls_per_anchor == (self.num_classes - 1)
+ >>> assert box_per_anchor == 4
+ """
def __init__(self,
num_classes,
diff --git a/mmdet/models/utils/scale.py b/mmdet/models/utils/scale.py
index 68c37cd..2461af8 100644
--- a/mmdet/models/utils/scale.py
+++ b/mmdet/models/utils/scale.py
@@ -3,6 +3,9 @@ import torch.nn as nn
class Scale(nn.Module):
+ """
+ A learnable scale parameter
+ """
def __init__(self, scale=1.0):
super(Scale, self).__init__()
--
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