def get_train_symbol(cls, backbone, neck, rpn_head, roi_extractor, bbox_head, num_branch, scaleaware):
gt_bbox = X.var("gt_bbox")
im_info = X.var("im_info")
if scaleaware:
valid_ranges = X.var("valid_ranges")
rpn_cls_label = X.var("rpn_cls_label")
rpn_reg_target = X.var("rpn_reg_target")
rpn_reg_weight = X.var("rpn_reg_weight")
im_info = TridentResNetV2Builder.stack_branch_symbols([im_info] * num_branch)
gt_bbox = TridentResNetV2Builder.stack_branch_symbols([gt_bbox] * num_branch)
if scaleaware:
valid_ranges = X.reshape(valid_ranges, (-3, -2))
rpn_cls_label = X.reshape(rpn_cls_label, (-3, -2))
rpn_reg_target = X.reshape(rpn_reg_target, (-3, -2))
rpn_reg_weight = X.reshape(rpn_reg_weight, (-3, -2))
rpn_feat = backbone.get_rpn_feature()
rcnn_feat = backbone.get_rcnn_feature()
rpn_feat = neck.get_rpn_feature(rpn_feat)
rcnn_feat = neck.get_rcnn_feature(rcnn_feat)
rpn_loss = rpn_head.get_loss(rpn_feat, rpn_cls_label, rpn_reg_target, rpn_reg_weight)
if scaleaware:
proposal, bbox_cls, bbox_target, bbox_weight = rpn_head.get_sampled_proposal_with_filter(rpn_feat, gt_bbox, im_info, valid_ranges)
else:
proposal, bbox_cls, bbox_target, bbox_weight = rpn_head.get_sampled_proposal(rpn_feat, gt_bbox, im_info)
roi_feat = roi_extractor.get_roi_feature(rcnn_feat, proposal)
bbox_loss = bbox_head.get_loss(roi_feat, bbox_cls, bbox_target, bbox_weight)
return X.group(rpn_loss + bbox_loss)
def get_test_symbol(backbone, neck, rpn_head, roi_extractor,
mask_roi_extractor, bbox_head, mask_head,
bbox_post_processor, num_branch):
rec_id, im_id, im_info, proposal, proposal_score = \
TridentFasterRcnn.get_rpn_test_symbol(backbone, neck, rpn_head, num_branch)
im_info_branches = TridentResNetV2Builder.stack_branch_symbols(
[im_info] * num_branch)
rcnn_feat = backbone.get_rcnn_feature()
rcnn_feat = neck.get_rcnn_feature(rcnn_feat)
roi_feat = roi_extractor.get_roi_feature(rcnn_feat, proposal)
cls_score, bbox_xyxy = bbox_head.get_prediction(
roi_feat, im_info_branches, proposal)
post_cls_score, post_bbox_xyxy, post_cls = bbox_post_processor.get_post_processing(
cls_score, bbox_xyxy)
mask_roi_feat = mask_roi_extractor.get_roi_feature(
rcnn_feat, post_bbox_xyxy)
mask = mask_head.get_prediction(mask_roi_feat)
# fold batch size into roi size for trident only
post_cls_score = X.reshape(post_cls_score, (-3, -2),
name="post_cls_score_fold")
post_bbox_xyxy = X.reshape(post_bbox_xyxy, (-3, -2),
name="post_bbox_xyxy_fold")
post_cls = X.reshape(post_cls, (-3, -2), name="post_cls_fold")
return X.group([
rec_id, im_id, im_info, post_cls_score, post_bbox_xyxy, post_cls,
mask
])
def __init__(self, pBackbone):
super().__init__(pBackbone)
p = self.p
b = TridentResNetV2Builder()
self.c4, self.c5 = b.get_backbone("mxnet", p.depth, "c4c5", p.normalizer, p.fp16,
p.num_branch, p.branch_dilates, p.branch_ids,
p.branch_bn_shared, p.branch_conv_shared, p.branch_deform)
def __init__(self, pBackbone):
super(TridentMXNetResNetV2, self).__init__(pBackbone)
p = pBackbone
b = TridentResNetV2Builder()
self.symbol = b.get_backbone("mxnet", p.depth, "c4", p.normalizer,
p.fp16, p.num_branch, p.branch_dilates,
p.branch_ids, p.branch_bn_shared,
p.branch_conv_shared, p.branch_deform)
def get_test_symbol(cls, backbone, neck, rpn_head, roi_extractor, bbox_head, num_branch):
rec_id, im_id, im_info, proposal, proposal_score = \
TridentFasterRcnn.get_rpn_test_symbol(backbone, neck, rpn_head, num_branch)
im_info_branches = TridentResNetV2Builder.stack_branch_symbols([im_info] * num_branch)
rcnn_feat = backbone.get_rcnn_feature()
rcnn_feat = neck.get_rcnn_feature(rcnn_feat)
roi_feat = roi_extractor.get_roi_feature(rcnn_feat, proposal)
cls_score, bbox_xyxy = bbox_head.get_prediction(roi_feat, im_info_branches, proposal)
cls_score = X.reshape(cls_score, (-3, -2))
bbox_xyxy = X.reshape(bbox_xyxy, (-3, -2))
return X.group([rec_id, im_id, im_info, cls_score, bbox_xyxy])
def get_rpn_test_symbol(cls, backbone, neck, rpn_head, num_branch):
if cls._rpn_output is not None:
return cls._rpn_output
im_info = X.var("im_info")
im_id = X.var("im_id")
rec_id = X.var("rec_id")
rpn_feat = backbone.get_rpn_feature()
rpn_feat = neck.get_rpn_feature(rpn_feat)
im_info_branches = TridentResNetV2Builder.stack_branch_symbols([im_info] * num_branch)
(proposal, proposal_score) = rpn_head.get_all_proposal(rpn_feat, im_info_branches)
cls._rpn_output = X.group([rec_id, im_id, im_info, proposal, proposal_score])
return cls._rpn_output
