def get_all_proposal(self, conv_feat, im_info):
if self._proposal is not None:
return self._proposal
p = self.p
rpn_stride = p.anchor_generate.stride
anchor_scale = p.anchor_generate.scale
anchor_ratio = p.anchor_generate.ratio
pre_nms_top_n = p.proposal.pre_nms_top_n
post_nms_top_n = p.proposal.post_nms_top_n
nms_thr = p.proposal.nms_thr
min_bbox_side = p.proposal.min_bbox_side
cls_logit, bbox_delta = self.get_output(conv_feat)
# TODO: remove this reshape hell
cls_logit_reshape = X.reshape(
cls_logit,
shape=(0, -4, 2, -1, 0, 0), # (N,C,H,W) -> (N,2,C/2,H,W)
name="rpn_cls_logit_reshape_"
)
cls_score = X.softmax(
cls_logit_reshape,
axis=1,
name='rpn_cls_score'
)
cls_logit_reshape = X.reshape(
cls_score,
shape=(0, -3, 0, 0),
name='rpn_cls_score_reshape'
)
# TODO: ask all to add is_train filed in RPNParam
proposal = X.proposal(
cls_prob=cls_logit_reshape,
bbox_pred=bbox_delta,
im_info=im_info,
name='proposal',
feature_stride=rpn_stride,
scales=tuple(anchor_scale),
ratios=tuple(anchor_ratio),
rpn_pre_nms_top_n=pre_nms_top_n,
rpn_post_nms_top_n=post_nms_top_n,
threshold=nms_thr,
rpn_min_size=min_bbox_side,
iou_loss=False,
output_score=True
)
self._proposal = proposal
return proposal
def get_all_proposal(self, conv_feat, im_info):
if self._proposal is not None:
return self._proposal
p = self.p
rpn_stride = p.anchor_generate.stride
anchor_scale = p.anchor_generate.scale
anchor_ratio = p.anchor_generate.ratio
pre_nms_top_n = p.proposal.pre_nms_top_n
post_nms_top_n = p.proposal.post_nms_top_n
nms_thr = p.proposal.nms_thr
min_bbox_side = p.proposal.min_bbox_side
cls_logit, bbox_delta = self.get_output(conv_feat)
# TODO: remove this reshape hell
cls_logit_reshape = X.reshape(
cls_logit,
shape=(0, -4, 2, -1, 0, 0), # (N,C,H,W) -> (N,2,C/2,H,W)
name="rpn_cls_logit_reshape_")
cls_score = X.softmax(cls_logit_reshape, axis=1, name='rpn_cls_score')
cls_logit_reshape = X.reshape(cls_score,
shape=(0, -3, 0, 0),
name='rpn_cls_score_reshape')
# TODO: ask all to add is_train filed in RPNParam
proposal = X.proposal(cls_prob=cls_logit_reshape,
bbox_pred=bbox_delta,
im_info=im_info,
name='proposal',
feature_stride=rpn_stride,
scales=tuple(anchor_scale),
ratios=tuple(anchor_ratio),
rpn_pre_nms_top_n=pre_nms_top_n,
rpn_post_nms_top_n=post_nms_top_n,
threshold=nms_thr,
rpn_min_size=min_bbox_side,
iou_loss=False,
output_score=True)
if p.use_symbolic_proposal is not None:
batch_size = p.batch_image
max_side = p.anchor_generate.max_side
assert max_side is not None, "symbolic proposal requires max_side of image"
from mxnext.tvm.proposal import proposal as Proposal
proposal = Proposal(cls_prob=cls_logit_reshape,
bbox_pred=bbox_delta,
im_info=im_info,
name='proposal',
feature_stride=rpn_stride,
scales=tuple(anchor_scale),
ratios=tuple(anchor_ratio),
rpn_pre_nms_top_n=pre_nms_top_n,
rpn_post_nms_top_n=post_nms_top_n,
threshold=nms_thr,
batch_size=batch_size,
max_side=max_side,
output_score=True,
variant="simpledet")
self._proposal = proposal
return proposal