def get_roi_feature(self, conv_fpn_feat, rois, trans, image_rois,
batch_image):
p = self.p
rcnn_stride = p.stride
roi_canonical_scale = p.roi_canonical_scale
roi_canonical_level = p.roi_canonical_level
_trans = mx.sym.reshape(trans, [-1, image_rois, 2])
roi_group, offset_group = fpn_roi_assign_offset(
mx.symbol, rois, _trans, rcnn_stride, roi_canonical_scale,
roi_canonical_level)
rois_fpn = dict()
offset_fpn = dict()
for i, stride in enumerate(rcnn_stride):
rois_fpn["stride%s" % stride] = mx.sym.reshape(
roi_group[i], [-1, 4])
offset_fpn["stride%s" % stride] = mx.sym.tile(
mx.sym.reshape(offset_group[i], [-1, 2, 1, 1]),
(1, 1, p.out_size, p.out_size))
if p.fp16:
for stride in rcnn_stride:
conv_fpn_feat["stride%s" % stride] = X.to_fp32(
conv_fpn_feat["stride%s" % stride],
name="fpn_stride%s_to_fp32")
batch_pad = mx.sym.reshape(
mx.sym.repeat(mx.sym.arange(batch_image),
repeats=image_rois,
axis=0), [-1, 1])
fpn_roi_feats = list()
for stride in rcnn_stride:
feat_lvl = conv_fpn_feat["stride%s" % stride]
rois_lvl = rois_fpn["stride%s" % stride]
offset_lvl = offset_fpn["stride%s" % stride]
rois_lvl = mx.sym.concat(batch_pad, rois_lvl, dim=1)
roi_feat = mx.sym.contrib.DeformablePSROIPooling(
data=feat_lvl,
rois=rois_lvl,
trans=offset_lvl,
spatial_scale=1. / stride,
output_dim=256,
group_size=1,
pooled_size=p.out_size,
part_size=0,
sample_per_part=4,
trans_std=0.1,
no_trans=False,
name='delta_r_pooled_feat_stride%s' % stride)
fpn_roi_feats.append(roi_feat)
roi_feat = X.add_n(*fpn_roi_feats)
if p.fp16:
roi_feat = X.to_fp16(roi_feat, name="delta_r_roi_feat_to_fp16")
return roi_feat
def get_roi_feature(self, conv_fpn_feat, proposal):
p = self.p
rcnn_stride = p.stride
roi_canonical_scale = p.roi_canonical_scale
roi_canonical_level = p.roi_canonical_level
group = mx.symbol.Custom(
op_type="assign_layer_fpn",
rois=proposal,
rcnn_stride=rcnn_stride,
roi_canonical_scale=roi_canonical_scale,
roi_canonical_level=roi_canonical_level,
name="assign_layer_fpn"
)
proposal_fpn = dict()
for i, stride in enumerate(rcnn_stride):
proposal_fpn["stride%s" % stride] = group[i]
if p.fp16:
for stride in rcnn_stride:
conv_fpn_feat["stride%s" % stride] = X.to_fp32(
conv_fpn_feat["stride%s" % stride],
name="fpn_stride%s_to_fp32"
)
fpn_roi_feats = list()
for stride in rcnn_stride:
feat_lvl = conv_fpn_feat["stride%s" % stride]
proposal_lvl = proposal_fpn["stride%s" % stride]
roi_feat = X.roi_align(
feat_lvl,
rois=proposal_lvl,
out_size=p.out_size,
stride=stride,
name="roi_align"
)
roi_feat = X.reshape(
data=roi_feat,
shape=(-3, -2),
name='roi_feat_reshape'
)
fpn_roi_feats.append(roi_feat)
roi_feat = X.add_n(*fpn_roi_feats)
if p.fp16:
roi_feat = X.to_fp16(roi_feat, name="roi_feat_to_fp16")
return roi_feat
def get_roi_feature(self, conv_fpn_feat, proposal):
p = self.p
rcnn_stride = p.stride
group = mx.symbol.Custom(rois=proposal, op_type='assign_layer_fpn')
proposal_fpn = dict()
proposal_fpn["stride4"] = group[1]
proposal_fpn["stride8"] = group[2]
proposal_fpn["stride16"] = group[3]
proposal_fpn["stride32"] = group[4]
if p.fp16:
for stride in rcnn_stride:
conv_fpn_feat["stride%s" % stride] = X.to_fp32(
conv_fpn_feat["stride%s" % stride],
name="fpn_stride%s_to_fp32")
fpn_roi_feats = list()
for stride in rcnn_stride:
feat_lvl = conv_fpn_feat["stride%s" % stride]
proposal_lvl = proposal_fpn["stride%s" % stride]
roi_feat = X.roi_align(feat_lvl,
rois=proposal_lvl,
out_size=p.out_size,
stride=stride,
name="roi_align")
roi_feat = X.reshape(data=roi_feat,
shape=(-3, -2),
name='roi_feat_reshape')
fpn_roi_feats.append(roi_feat)
roi_feat = X.add_n(*fpn_roi_feats)
if p.fp16:
roi_feat = X.to_fp16(roi_feat, name="roi_feat_to_fp16")
return roi_feat