文章目录
前言
本篇主要介绍mmdet/core/anchor/anchor_generator.py文件下的AnchorGenerator类。以RetinaNet的配置作为说明。
anchor_generator_cfg = dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128])
1、base_anchors的生成
所谓base_anchors是在初始化AnchorGenerator类借助gen_base_anchors方法产生了基础的9个anchor，这些anchor是原图上的anchor。
@ANCHOR_GENERATORS.register_module()
class AnchorGenerator(object):
def __init__(self,
strides,
ratios,
scales=None,
base_sizes=None,
scale_major=True,
octave_base_scale=None,
scales_per_octave=None,
centers=None,
center_offset=0.):
self.strides = [_pair(stride) for stride in strides]
self.base_sizes = [min(stride) for stride in self.strides
] if base_sizes is None else base_sizes
assert ((octave_base_scale is not None
and scales_per_octave is not None) ^ (scales is not None)), \
'scales and octave_base_scale with scales_per_octave cannot' \
' be set at the same time'
if scales is not None:
self.scales = torch.Tensor(scales)
elif octave_base_scale is not None and scales_per_octave is not None:
octave_scales = np.array(
[2**(i / scales_per_octave) for i in range(scales_per_octave)])
scales = octave_scales * octave_base_scale
self.scales = torch.Tensor(scales)
self.base_anchors = self.gen_base_anchors()
现在具体看下gen_base_anchors方法：
def gen_base_anchors(self):
"""Generate base anchors
Returns:
list(torch.Tensor): Base anchors of a feature grid in multiple
feature levels.
"""
multi_level_base_anchors = []
for i, base_size in enumerate(self.base_sizes):
center = None
if self.centers is not None:
center = self.centers[i]
multi_level_base_anchors.append(
self.gen_single_level_base_anchors(
base_size,
scales=self.scales,
ratios=self.ratios,
center=center))
return multi_level_base_anchors
def gen_single_level_base_anchors(self,
base_size,
scales,
ratios,
center=None):
w = base_size
h = base_size
if center is None:
x_center = self.center_offset * w
y_center = self.center_offset * h
else:
x_center, y_center = center
h_ratios = torch.sqrt(ratios)
w_ratios = 1 / h_ratios
if self.scale_major:
ws = (w * w_ratios[:, None] * scales[None, :]).view(-1)
hs = (h * h_ratios[:, None] * scales[None, :]).view(-1)
else:
ws = (w * scales[:, None] * w_ratios[None, :]).view(-1)
hs = (h * scales[:, None] * h_ratios[None, :]).view(-1)
base_anchors = [
x_center - 0.5 * ws, y_center - 0.5 * hs, x_center + 0.5 * ws,
y_center + 0.5 * hs
]
base_anchors = torch.stack(base_anchors, dim=-1)
return base_anchors
其实上面代码就是下图干的事情：就是stride * scales* ratios = 9
2、grid_anchors的生成
在生成base_anchor基础上，之后需要通过改变每个anchor的中心来广播到整张特征图上面。以grid_anchors方法实现：
def grid_anchors(self, featmap_sizes, device='cuda'):
assert self.num_levels == len(featmap_sizes)
multi_level_anchors = []
for i in range(self.num_levels):
anchors = self.single_level_grid_anchors(
self.base_anchors[i].to(device),
featmap_sizes[i],
self.strides[i],
device=device)
multi_level_anchors.append(anchors)
return multi_level_anchors
贴下single_level_grid_anchors方法
def _meshgrid(self, x, y, row_major=True):
"""Generate mesh grid of x and y
Args:
x (torch.Tensor): Grids of x dimension.
y (torch.Tensor): Grids of y dimension.
row_major (bool, optional): Whether to return y grids first.
Defaults to True.
Returns:
tuple[torch.Tensor]: The mesh grids of x and y.
"""
xx = x.repeat(len(y))
yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
if row_major:
return xx, yy
else:
return yy, xx
def single_level_grid_anchors(self,
base_anchors,
featmap_size,
stride=(16, 16),
device='cuda'):
"""Generate grid anchors of a single level.
Note:
This function is usually called by method 3、valid_flags介绍
简单说下这个方法作用：在模型批次训练过程中，往往会对图像进行pad，pad会出现黑边，后面撒anchor会在pad部分也回撒上anchor，其实这部分anchor应该忽略掉。故该函数就是赋予每个anchor一个标签，若anchor在有效像素位置上，则Ture；否则赋为FALSE。
def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
"""
输入特征图原始尺寸和pad后尺寸
Return:
list(torch.Tensor):返回一个和anchor数量相等的bool型张量
"""
assert self.num_levels == len(featmap_sizes)
multi_level_flags = []
for i in range(self.num_levels):
anchor_stride = self.strides[i]
feat_h, feat_w = featmap_sizes[i]
h, w = pad_shape[:2]
valid_feat_h = min(int(np.ceil(h / anchor_stride[0])), feat_h)
valid_feat_w = min(int(np.ceil(w / anchor_stride[1])), feat_w)
flags = self.single_level_valid_flags((feat_h, feat_w),
(valid_feat_h, valid_feat_w),
self.num_base_anchors[i],
device=device)
multi_level_flags.append(flags)
return multi_level_flags
def single_level_valid_flags(self,
featmap_size,
valid_size,
num_base_anchors,
device='cuda'):
"""Generate the valid flags of anchor in a single feature map
Args:
featmap_size (tuple[int]): 原始特征图
valid_size (tuple[int]): pad后有效尺寸
num_base_anchors (int): 9
device (str, optional): Device where the flags will be put on.
Defaults to 'cuda'.
Returns:
torch.Tensor: The valid flags of each anchor in a single level
feature map.
"""
feat_h, feat_w = featmap_size
valid_h, valid_w = valid_size
assert valid_h feat_h and valid_w feat_w
valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
valid_x[:valid_w] = 1
valid_y[:valid_h] = 1
valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
valid = valid_xx & valid_yy
valid = valid[:, None].expand(valid.size(0),
num_base_anchors).contiguous().view(-1)
return valid
总结
下篇会介绍MaxIOUAssigner，敬请期待。若有问题欢迎+vx：wulele2541612007，拉你进群探讨交流。
Original: https://blog.csdn.net/wulele2/article/details/122409507
Author: 武乐乐~
Title: MMDet逐行解读之AnchorGenerator
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