def get_loss(self, conv_feat, cls_label, bbox_target, bbox_weight):
p = self.p
batch_roi = p.image_roi * p.batch_image
batch_image = p.batch_image
cls_logit, bbox_delta = self.get_output(conv_feat)
scale_loss_shift = 128.0 if p.fp16 else 1.0
# classification loss
cls_loss = X.softmax_output(data=cls_logit,
label=cls_label,
normalization='batch',
grad_scale=1.0 * scale_loss_shift,
name='bbox_cls_loss')
# bounding box regression
reg_loss = X.smooth_l1(bbox_delta - bbox_target,
scalar=1.0,
name='bbox_reg_l1')
reg_loss = bbox_weight * reg_loss
reg_loss = X.loss(
reg_loss,
grad_scale=1.0 / batch_roi * scale_loss_shift,
name='bbox_reg_loss',
)
# append label
cls_label = X.reshape(cls_label,
shape=(batch_image, -1),
name='bbox_label_reshape')
cls_label = X.block_grad(cls_label, name='bbox_label_blockgrad')
# output
return cls_loss, reg_loss, cls_label
def get_loss(self, conv_feat, cls_label, bbox_target, bbox_weight):
p = self.p
batch_image = p.batch_image
image_anchor = p.anchor_generate.image_anchor
cls_logit, bbox_delta = self.get_output(conv_feat)
scale_loss_shift = 128.0 if p.fp16 else 1.0
# classification loss
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_loss = X.softmax_output(data=cls_logit_reshape,
label=cls_label,
multi_output=True,
normalization='valid',
use_ignore=True,
ignore_label=-1,
grad_scale=1.0 * scale_loss_shift,
name="rpn_cls_loss")
# regression loss
reg_loss = X.smooth_l1((bbox_delta - bbox_target),
scalar=3.0,
name='rpn_reg_l1')
reg_loss = bbox_weight * reg_loss
reg_loss = X.loss(reg_loss,
grad_scale=1.0 / (batch_image * image_anchor) *
scale_loss_shift,
name='rpn_reg_loss')
return cls_loss, reg_loss
def get_loss(self, conv_fpn_feat, cls_label, bbox_target, bbox_weight):
p = self.p
batch_image = p.batch_image
image_anchor = p.anchor_generate.image_anchor
rpn_stride = p.anchor_generate.stride
cls_logit_dict, bbox_delta_dict = self.get_output(conv_fpn_feat)
scale_loss_shift = 128.0 if p.fp16 else 1.0
rpn_cls_logit_list = []
rpn_bbox_delta_list = []
for stride in rpn_stride:
rpn_cls_logit = cls_logit_dict[stride]
rpn_bbox_delta = bbox_delta_dict[stride]
rpn_cls_logit_reshape = X.reshape(
data=rpn_cls_logit,
shape=(0, 2, -1),
name="rpn_cls_score_reshape_stride%s" % stride
)
rpn_bbox_delta_reshape = X.reshape(
data=rpn_bbox_delta,
shape=(0, 0, -1),
name="rpn_bbox_pred_reshape_stride%s" % stride
)
rpn_bbox_delta_list.append(rpn_bbox_delta_reshape)
rpn_cls_logit_list.append(rpn_cls_logit_reshape)
# concat output of each level
rpn_bbox_delta_concat = X.concat(rpn_bbox_delta_list, axis=2, name="rpn_bbox_pred_concat")
rpn_cls_logit_concat = X.concat(rpn_cls_logit_list, axis=2, name="rpn_cls_score_concat")
cls_loss = X.softmax_output(
data=rpn_cls_logit_concat,
label=cls_label,
multi_output=True,
normalization='valid',
use_ignore=True,
ignore_label=-1,
grad_scale=1.0 * scale_loss_shift,
name="rpn_cls_loss"
)
# regression loss
reg_loss = X.smooth_l1(
(rpn_bbox_delta_concat - bbox_target),
scalar=3.0,
name='rpn_reg_l1'
)
reg_loss = bbox_weight * reg_loss
reg_loss = X.loss(
reg_loss,
grad_scale=1.0 / (batch_image * image_anchor) * scale_loss_shift,
name='rpn_reg_loss'
)
return cls_loss, reg_loss
def get_loss(self, conv_feat, gt_bboxes, im_infos, rpn_groups):
p = self.p
num_class = p.num_class
batch_image = p.batch_image
image_anchor = p.anchor_generate.image_anchor
cls_logit, bbox_delta = self.get_output(conv_feat)
scale_loss_shift = 128.0 if p.fp16 else 1.0
cls_label = X.var("rpn_cls_label")
bbox_target = X.var("rpn_reg_target")
bbox_weight = X.var("rpn_reg_weight")
# classification loss
cls_logit_reshape = X.reshape(
cls_logit,
shape=(0, -4, num_class, -1, 0,
0), # (N,C,H,W) -> (N,num_class,C/num_class,H,W)
name="rpn_cls_logit_reshape")
cls_loss = None
if p.use_groupsoftmax:
cls_loss = mx.sym.contrib.GroupSoftmaxOutput(
data=cls_logit_reshape,
label=cls_label,
group=rpn_groups,
multi_output=True,
normalization='valid',
use_ignore=True,
ignore_label=-1,
grad_scale=1.0 * scale_loss_shift,
name="rpn_cls_loss")
else:
cls_loss = X.softmax_output(data=cls_logit_reshape,
label=cls_label,
multi_output=True,
normalization='valid',
use_ignore=True,
ignore_label=-1,
grad_scale=1.0 * scale_loss_shift,
name="rpn_cls_loss")
# regression loss
reg_loss = X.smooth_l1((bbox_delta - bbox_target),
scalar=3.0,
name='rpn_reg_l1')
reg_loss = bbox_weight * reg_loss
reg_loss = X.loss(reg_loss,
grad_scale=1.0 / (batch_image * image_anchor) *
scale_loss_shift,
name='rpn_reg_loss')
return cls_loss, reg_loss
def get_loss(self, conv_fpn_feat, gt_bbox, im_info):
p = self.p
batch_image = p.batch_image
image_anchor = p.anchor_assign.image_anchor
rpn_stride = p.anchor_generate.stride
anchor_scale = p.anchor_generate.scale
anchor_ratio = p.anchor_generate.ratio
num_anchor = len(p.anchor_generate.ratio) * len(
p.anchor_generate.scale)
cls_logit_dict, bbox_delta_dict = self.get_output(conv_fpn_feat)
scale_loss_shift = 128.0 if p.fp16 else 1.0
rpn_cls_logit_list = []
rpn_bbox_delta_list = []
feat_list = []
for stride in rpn_stride:
rpn_cls_logit = cls_logit_dict[stride]
rpn_bbox_delta = bbox_delta_dict[stride]
rpn_cls_logit_reshape = X.reshape(
data=rpn_cls_logit,
shape=(0, 2, num_anchor, -1),
name="rpn_cls_score_reshape_stride%s" % stride)
rpn_bbox_delta_reshape = X.reshape(
data=rpn_bbox_delta,
shape=(0, 0, -1),
name="rpn_bbox_pred_reshape_stride%s" % stride)
rpn_bbox_delta_list.append(rpn_bbox_delta_reshape)
rpn_cls_logit_list.append(rpn_cls_logit_reshape)
feat_list.append(rpn_cls_logit)
if p.nnvm_rpn_target:
from mxnext.tvm.rpn_target import _fpn_rpn_target_batch
anchor_list = [
self.anchor_dict["stride%s" % s] for s in rpn_stride
]
gt_bbox = mx.sym.slice_axis(gt_bbox, axis=-1, begin=0, end=4)
max_side = p.anchor_generate.max_side
allowed_border = p.anchor_assign.allowed_border
fg_fraction = p.anchor_assign.pos_fraction
fg_thr = p.anchor_assign.pos_thr
bg_thr = p.anchor_assign.neg_thr
cls_label, bbox_target, bbox_weight = _fpn_rpn_target_batch(
mx.sym, feat_list, anchor_list, gt_bbox, im_info, batch_image,
num_anchor, max_side, rpn_stride, allowed_border, image_anchor,
fg_fraction, fg_thr, bg_thr)
else:
cls_label = X.var("rpn_cls_label")
bbox_target = X.var("rpn_reg_target")
bbox_weight = X.var("rpn_reg_weight")
# concat output of each level
rpn_bbox_delta_concat = X.concat(rpn_bbox_delta_list,
axis=2,
name="rpn_bbox_pred_concat")
rpn_cls_logit_concat = X.concat(rpn_cls_logit_list,
axis=-1,
name="rpn_cls_score_concat")
cls_loss = X.softmax_output(data=rpn_cls_logit_concat,
label=cls_label,
multi_output=True,
normalization='valid',
use_ignore=True,
ignore_label=-1,
grad_scale=1.0 * scale_loss_shift,
name="rpn_cls_loss")
# regression loss
reg_loss = X.smooth_l1((rpn_bbox_delta_concat - bbox_target),
scalar=3.0,
name='rpn_reg_l1')
reg_loss = bbox_weight * reg_loss
reg_loss = X.loss(reg_loss,
grad_scale=1.0 / (batch_image * image_anchor) *
scale_loss_shift,
name='rpn_reg_loss')
return cls_loss, reg_loss, X.stop_grad(cls_label,
"rpn_cls_label_blockgrad")
def get_loss(self, conv_feat, cls_label, bbox_target, bbox_weight):
import mxnet as mx
p = self.p
stride = p.anchor_generate.stride
if not isinstance(stride, tuple):
stride = (stride)
num_class = p.num_class
num_base_anchor = len(p.anchor_generate.ratio) * len(
p.anchor_generate.scale)
image_per_device = p.batch_image
sync_loss = p.sync_loss or False
cls_logit_dict, bbox_delta_dict = self.get_output(conv_feat)
cls_logit_reshape_list = []
bbox_delta_reshape_list = []
scale_loss_shift = 128.0 if p.fp16 else 1.0
if sync_loss:
fg_count = X.var("rpn_fg_count") * image_per_device
fg_count = mx.sym.slice_axis(fg_count, axis=0, begin=0, end=1)
# reshape logit and delta
for s in stride:
# (N, A * C, H, W) -> (N, A, C, H * W)
cls_logit = X.reshape(data=cls_logit_dict["stride%s" % s],
shape=(0, num_base_anchor, num_class - 1,
-1),
name="cls_stride%s_reshape" % s)
# (N, A, C, H * W) -> (N, A, H * W, C)
cls_logit = X.transpose(data=cls_logit,
axes=(0, 1, 3, 2),
name="cls_stride%s_transpose" % s)
# (N, A, H * W, C) -> (N, A * H * W, C)
cls_logit = X.reshape(data=cls_logit,
shape=(0, -3, 0),
name="cls_stride%s_transpose_reshape" % s)
# (N, A * 4, H, W) -> (N, A * 4, H * W)
bbox_delta = X.reshape(data=bbox_delta_dict["stride%s" % s],
shape=(0, 0, -1),
name="bbox_stride%s_reshape" % s)
cls_logit_reshape_list.append(cls_logit)
bbox_delta_reshape_list.append(bbox_delta)
cls_logit_concat = X.concat(cls_logit_reshape_list,
axis=1,
name="bbox_logit_concat")
bbox_delta_concat = X.concat(bbox_delta_reshape_list,
axis=2,
name="bbox_delta_concat")
# classification loss
if sync_loss:
cls_loss = X.focal_loss(data=cls_logit_concat,
label=cls_label,
alpha=p.focal_loss.alpha,
gamma=p.focal_loss.gamma,
workspace=1500,
out_grad=True)
cls_loss = mx.sym.broadcast_div(cls_loss, fg_count)
cls_loss = X.make_loss(cls_loss,
grad_scale=scale_loss_shift,
name="cls_loss")
else:
cls_loss = X.focal_loss(data=cls_logit_concat,
label=cls_label,
normalization='valid',
alpha=p.focal_loss.alpha,
gamma=p.focal_loss.gamma,
grad_scale=1.0 * scale_loss_shift,
workspace=1024,
name="cls_loss")
scalar = 0.11
# regression loss
bbox_loss = bbox_weight * X.smooth_l1(
data=bbox_delta_concat - bbox_target,
scalar=math.sqrt(1 / scalar),
name="bbox_loss")
if sync_loss:
bbox_loss = mx.sym.broadcast_div(bbox_loss, fg_count)
else:
bbox_loss = X.bbox_norm(data=bbox_loss,
label=cls_label,
name="bbox_norm")
reg_loss = X.make_loss(data=bbox_loss,
grad_scale=1.0 * scale_loss_shift,
name="reg_loss")
return cls_loss, reg_loss
def get_loss(self, rois, roi_feat, fpn_conv_feats, cls_label, bbox_target,
bbox_weight, gt_bbox):
'''
Args:
rois: [batch_image, image_roi, 4]
roi_feat: [batch_image * image_roi, 256, roi_size, roi_size]
fpn_conv_feats: dict of FPN features, each [batch_image, in_channels, fh, fw]
cls_label: [batch_image * image_roi]
bbox_target: [batch_image * image_roi, num_class * 4]
bbox_weight: [batch_image * image_roi, num_class * 4]
gt_bbox: [batch_image, max_gt_num, 4]
Returns:
cls_loss: [batch_image * image_roi, num_class]
reg_loss: [batch_image * image_roi, num_class * 4]
tsd_cls_loss: [batch_image * image_roi, num_class]
tsd_reg_loss: [batch_image * image_roi, num_class * 4]
tsd_cls_pc_loss: [batch_image * image_roi]
tsd_reg_pc_loss: [batch_image * image_roi]
cls_label: [batch_image, image_roi]
'''
p = self.p
assert not p.regress_target.class_agnostic
batch_image = p.batch_image
image_roi = p.image_roi
batch_roi = batch_image * image_roi
smooth_l1_scalar = p.regress_target.smooth_l1_scalar or 1.0
cls_logit, bbox_delta, tsd_cls_logit, tsd_bbox_delta, delta_c, delta_r = self.get_output(
fpn_conv_feats, roi_feat, rois, is_train=True)
rois_r = self._get_delta_r_box(delta_r, rois)
tsd_reg_target = self.get_reg_target(
rois_r, gt_bbox) # [batch_roi, num_class*4]
scale_loss_shift = 128 if self.p.fp16 else 1.0
# origin loss
cls_loss = X.softmax_output(data=cls_logit,
label=cls_label,
normalization='batch',
grad_scale=1.0 * scale_loss_shift,
name='bbox_cls_loss')
reg_loss = X.smooth_l1(bbox_delta - bbox_target,
scalar=smooth_l1_scalar,
name='bbox_reg_l1')
reg_loss = bbox_weight * reg_loss
reg_loss = X.loss(
reg_loss,
grad_scale=1.0 / batch_roi * scale_loss_shift,
name='bbox_reg_loss',
)
# tsd loss
tsd_cls_loss = X.softmax_output(data=tsd_cls_logit,
label=cls_label,
normalization='batch',
grad_scale=1.0 * scale_loss_shift,
name='tsd_bbox_cls_loss')
tsd_reg_loss = X.smooth_l1(tsd_bbox_delta - tsd_reg_target,
scalar=smooth_l1_scalar,
name='tsd_bbox_reg_l1')
tsd_reg_loss = bbox_weight * tsd_reg_loss
tsd_reg_loss = X.loss(
tsd_reg_loss,
grad_scale=1.0 / batch_roi * scale_loss_shift,
name='tsd_bbox_reg_loss',
)
losses = [
cls_loss, reg_loss, tsd_cls_loss, tsd_reg_loss, tsd_cls_pc_loss
]
if p.TSD.pc_cls:
losses.append(
self.cls_pc_loss(cls_logit, tsd_cls_logit, cls_label,
scale_loss_shift))
if p.TSD.pc_reg:
losses.append(
self.reg_pc_loss(bbox_delta, tsd_bbox_delta, rois, rois_r,
gt_bbox, cls_label, scale_loss_shift))
# append label
cls_label = X.reshape(cls_label,
shape=(batch_image, -1),
name='bbox_label_reshape')
cls_label = X.block_grad(cls_label, name='bbox_label_blockgrad')
losses.append(cls_label)
return tuple(losses)
def get_loss(self, conv_feat, gt_bbox):
from models.RepPoints.point_ops import (_gen_points, _offset_to_pts,
_point_target,
_offset_to_boxes, _points2bbox)
p = self.p
batch_image = p.batch_image
num_points = p.point_generate.num_points
scale = p.point_generate.scale
stride = p.point_generate.stride
transform = p.point_generate.transform
target_scale = p.point_target.target_scale
num_pos = p.point_target.num_pos
pos_iou_thr = p.bbox_target.pos_iou_thr
neg_iou_thr = p.bbox_target.neg_iou_thr
min_pos_iou = p.bbox_target.min_pos_iou
pts_out_inits, pts_out_refines, cls_outs = self.get_output(conv_feat)
points = dict()
bboxes = dict()
pts_coordinate_preds_inits = dict()
pts_coordinate_preds_refines = dict()
for s in stride:
# generate points on base coordinate according to stride and size of feature map
points["stride%s" % s] = _gen_points(mx.symbol,
pts_out_inits["stride%s" % s],
s)
# generate bbox after init stage
bboxes["stride%s" % s] = _offset_to_boxes(
mx.symbol,
points["stride%s" % s],
X.block_grad(pts_out_inits["stride%s" % s]),
s,
transform,
moment_transfer=self.moment_transfer)
# generate final offsets in init stage
pts_coordinate_preds_inits["stride%s" % s] = _offset_to_pts(
mx.symbol, points["stride%s" % s],
pts_out_inits["stride%s" % s], s, num_points)
# generate final offsets in refine stage
pts_coordinate_preds_refines["stride%s" % s] = _offset_to_pts(
mx.symbol, points["stride%s" % s],
pts_out_refines["stride%s" % s], s, num_points)
# for init stage, use points assignment
point_proposals = mx.symbol.tile(X.concat(
[points["stride%s" % s] for s in stride],
axis=1,
name="point_concat"),
reps=(batch_image, 1, 1))
points_labels_init, points_gts_init, points_weight_init = _point_target(
mx.symbol,
point_proposals,
gt_bbox,
batch_image,
"point",
scale=target_scale,
num_pos=num_pos)
# for refine stage, use max iou assignment
box_proposals = X.concat([bboxes["stride%s" % s] for s in stride],
axis=1,
name="box_concat")
points_labels_refine, points_gts_refine, points_weight_refine = _point_target(
mx.symbol,
box_proposals,
gt_bbox,
batch_image,
"box",
pos_iou_thr=pos_iou_thr,
neg_iou_thr=neg_iou_thr,
min_pos_iou=min_pos_iou)
bboxes_out_strides = dict()
for s in stride:
cls_outs["stride%s" % s] = X.reshape(
X.transpose(data=cls_outs["stride%s" % s], axes=(0, 2, 3, 1)),
(0, -3, -2))
bboxes_out_strides["stride%s" % s] = mx.symbol.repeat(
mx.symbol.ones_like(
mx.symbol.slice_axis(
cls_outs["stride%s" % s], begin=0, end=1, axis=-1)),
repeats=4,
axis=-1) * s
# cls branch
cls_outs_concat = X.concat([cls_outs["stride%s" % s] for s in stride],
axis=1,
name="cls_concat")
cls_loss = X.focal_loss(data=cls_outs_concat,
label=points_labels_refine,
normalization='valid',
alpha=p.focal_loss.alpha,
gamma=p.focal_loss.gamma,
grad_scale=1.0,
workspace=1500,
name="cls_loss")
# init box branch
pts_inits_concat_ = X.concat(
[pts_coordinate_preds_inits["stride%s" % s] for s in stride],
axis=1,
name="pts_init_concat_")
pts_inits_concat = X.reshape(pts_inits_concat_, (-3, -2),
name="pts_inits_concat")
bboxes_inits_concat_ = _points2bbox(
mx.symbol,
pts_inits_concat,
transform,
y_first=False,
moment_transfer=self.moment_transfer)
bboxes_inits_concat = X.reshape(bboxes_inits_concat_,
(-4, batch_image, -1, -2))
normalize_term = X.concat(
[bboxes_out_strides["stride%s" % s] for s in stride],
axis=1,
name="normalize_term") * scale
pts_init_loss = X.smooth_l1(
data=(bboxes_inits_concat - points_gts_init) / normalize_term,
scalar=3.0,
name="pts_init_l1_loss")
pts_init_loss = pts_init_loss * points_weight_init
pts_init_loss = X.bbox_norm(data=pts_init_loss,
label=points_labels_init,
name="pts_init_norm_loss")
pts_init_loss = X.make_loss(data=pts_init_loss,
grad_scale=0.5,
name="pts_init_loss")
points_init_labels = X.block_grad(points_labels_refine,
name="points_init_labels")
# refine box branch
pts_refines_concat_ = X.concat(
[pts_coordinate_preds_refines["stride%s" % s] for s in stride],
axis=1,
name="pts_refines_concat_")
pts_refines_concat = X.reshape(pts_refines_concat_, (-3, -2),
name="pts_refines_concat")
bboxes_refines_concat_ = _points2bbox(
mx.symbol,
pts_refines_concat,
transform,
y_first=False,
moment_transfer=self.moment_transfer)
bboxes_refines_concat = X.reshape(bboxes_refines_concat_,
(-4, batch_image, -1, -2))
pts_refine_loss = X.smooth_l1(
data=(bboxes_refines_concat - points_gts_refine) / normalize_term,
scalar=3.0,
name="pts_refine_l1_loss")
pts_refine_loss = pts_refine_loss * points_weight_refine
pts_refine_loss = X.bbox_norm(data=pts_refine_loss,
label=points_labels_refine,
name="pts_refine_norm_loss")
pts_refine_loss = X.make_loss(data=pts_refine_loss,
grad_scale=1.0,
name="pts_refine_loss")
points_refine_labels = X.block_grad(points_labels_refine,
name="point_refine_labels")
return cls_loss, pts_init_loss, pts_refine_loss, points_init_labels, points_refine_labels
def emd_loss(self,
cls_logit,
cls_label,
cls_sec_logit,
cls_sec_label,
bbox_delta,
bbox_target,
bbox_sec_delta,
bbox_sec_target,
bbox_weight,
bbox_sec_weight,
prefix=""):
p = self.p
smooth_l1_scalar = p.regress_target.smooth_l1_scalar or 1.0
scale_loss_shift = 128.0 if p.fp16 else 1.0
cls_loss11 = self.softmax_entropy(cls_logit,
cls_label,
prefix=prefix + 'cls_loss11')
cls_loss12 = self.softmax_entropy(cls_sec_logit,
cls_sec_label,
prefix=prefix + 'cls_loss12')
cls_loss1 = cls_loss11 + cls_loss12
cls_loss21 = self.softmax_entropy(cls_logit,
cls_sec_label,
prefix=prefix + 'cls_loss21')
cls_loss22 = self.softmax_entropy(cls_sec_logit,
cls_label,
prefix=prefix + 'cls_loss22')
cls_loss2 = cls_loss21 + cls_loss22
# bounding box regression
reg_loss11 = X.smooth_l1(bbox_delta - bbox_target,
scalar=smooth_l1_scalar,
name=prefix + 'bbox_reg_l1_11')
reg_loss11 = bbox_weight * reg_loss11
reg_loss12 = X.smooth_l1(bbox_sec_delta - bbox_sec_target,
scalar=smooth_l1_scalar,
name=prefix + 'bbox_reg_l1_12')
reg_loss12 = bbox_sec_weight * reg_loss12
reg_loss1 = reg_loss11 + reg_loss12
reg_loss21 = X.smooth_l1(bbox_delta - bbox_sec_target,
scalar=smooth_l1_scalar,
name=prefix + 'bbox_reg_l1_21')
reg_loss21 = bbox_sec_weight * reg_loss21
reg_loss22 = X.smooth_l1(bbox_sec_delta - bbox_target,
scalar=smooth_l1_scalar,
name=prefix + 'bbox_reg_l1_22')
reg_loss22 = bbox_weight * reg_loss22
reg_loss2 = reg_loss21 + reg_loss22
cls_reg_loss1 = mx.sym.sum(cls_loss1, axis=-1) + mx.sym.sum(reg_loss1,
axis=-1)
cls_reg_loss2 = mx.sym.sum(cls_loss2, axis=-1) + mx.sym.sum(reg_loss2,
axis=-1)
cls_reg_loss = mx.sym.minimum(cls_reg_loss1, cls_reg_loss2)
cls_reg_loss = mx.sym.mean(cls_reg_loss)
cls_reg_loss = X.loss(cls_reg_loss,
grad_scale=1.0 * scale_loss_shift,
name=prefix + 'cls_reg_loss')
return cls_reg_loss
def get_loss(self, conv_feat, cls_label, bbox_target, bbox_weight):
p = self.p
stride = p.anchor_generate.stride
if not isinstance(stride, tuple):
stride = (stride)
num_class = p.num_class
num_base_anchor = len(p.anchor_generate.ratio) * len(
p.anchor_generate.scale)
cls_logit_list, bbox_delta_list = self.get_output(conv_feat)
# reshape logit and delta
for i, s in enumerate(stride):
# (N, A * C, H, W) -> (N, A, C, H * W)
cls_logit = X.reshape(data=cls_logit_list[i],
shape=(0, num_base_anchor, num_class - 1,
-1),
name="cls_stride%s_reshape" % s)
# (N, A, C, H * W) -> (N, A, H * W, C)
cls_logit = X.transpose(data=cls_logit,
axes=(0, 1, 3, 2),
name="cls_stride%s_transpose" % s)
# (N, A, H * W, C) -> (N, A * H * W, C)
cls_logit = X.reshape(data=cls_logit,
shape=(0, -3, 0),
name="cls_stride%s_transpose_reshape" % s)
# (N, A * 4, H, W) -> (N, A * 4, H * W)
bbox_delta = X.reshape(data=bbox_delta_list[i],
shape=(0, 0, -1),
name="bbox_stride%s_reshape" % s)
cls_logit_list[i] = cls_logit
bbox_delta_list[i] = bbox_delta
cls_logit_concat = X.concat(cls_logit_list,
axis=1,
name="bbox_logit_concat")
bbox_delta_concat = X.concat(bbox_delta_list,
axis=2,
name="bbox_delta_concat")
# classification loss
cls_loss = X.focal_loss(data=cls_logit_concat,
label=cls_label,
normalization='valid',
alpha=p.focal_loss.alpha,
gamma=p.focal_loss.gamma,
grad_scale=1.0,
workspace=1024,
name="cls_loss")
scalar = 0.11
# regression loss
bbox_norm = X.bbox_norm(data=bbox_delta_concat - bbox_target,
label=cls_label,
name="bbox_norm")
bbox_loss = bbox_weight * X.smooth_l1(
data=bbox_norm, scalar=math.sqrt(1 / scalar), name="bbox_loss")
reg_loss = X.make_loss(data=bbox_loss, grad_scale=1.0, name="reg_loss")
return cls_loss, reg_loss
