def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
initialW=None,
proposal_creator_params={},
):
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(**proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
super(RegionProposalNetwork,
self).__init__(conv1=L.Convolution2D(in_channels,
mid_channels,
3,
1,
1,
initialW=initialW),
score=L.Convolution2D(mid_channels,
n_anchor * 2,
1,
1,
0,
initialW=initialW),
loc=L.Convolution2D(mid_channels,
n_anchor * 4,
1,
1,
0,
initialW=initialW))
def __init__(self,
anchor_scales,
feat_strides,
in_channels=256,
mid_channels=256,
ratios=[0.5, 1, 2],
initialW=None,
proposal_creator_params=dict()):
if len(anchor_scales) != len(feat_strides):
raise ValueError(
'length of anchor_scales and feat_strides should be same!')
self.anchor_bases = [
generate_anchor_base(anchor_scales=[s], ratios=ratios)
for s in anchor_scales
]
self.feat_strides = feat_strides
self.proposal_layer = ProposalCreator(**proposal_creator_params)
# note: to share conv layers, number of output channel should be same in all levels.
# debug
a = self.anchor_bases[0]
for ab in self.anchor_bases[1:]:
assert a.shape == ab.shape
n_anchor = self.anchor_bases[0].shape[0]
super(MultilevelRegionProposalNetwork, self).__init__()
with self.init_scope():
# note: according fpn paper, parameters are sharable among levels.
self.conv = L.Convolution2D(in_channels,
mid_channels,
3,
1,
1,
initialW=initialW)
self.score = L.Convolution2D(mid_channels,
n_anchor * 2,
1,
1,
0,
initialW=initialW)
self.loc = L.Convolution2D(mid_channels,
n_anchor * 4,
1,
1,
0,
initialW=initialW)
def __init__(
self, in_channels=512, mid_channels=512, ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32], feat_stride=16,
initialW=None,
proposal_creator_params={},
):
self.anchor_base = generate_anchor_base(
anchor_scales=anchor_scales, ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(**proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
super(RegionProposalNetwork, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(
in_channels, mid_channels, 3, 1, 1, initialW=initialW)
self.score = L.Convolution2D(
mid_channels, n_anchor * 2, 1, 1, 0, initialW=initialW)
self.loc = L.Convolution2D(
mid_channels, n_anchor * 4, 1, 1, 0, initialW=initialW)
