def _get_bbox_head_logit(self, conv_feat):
if self._head_feat is not None:
return self._head_feat
p = self.p
if p.normalizer.__name__ == "fix_bn":
conv_feat = X.convrelu(conv_feat, filter=256, kernel=3, name="bbox_conv1")
conv_feat = X.convrelu(conv_feat, filter=256, kernel=3, name="bbox_conv2")
conv_feat = X.convrelu(conv_feat, filter=256, kernel=3, name="bbox_conv3")
conv_feat = X.convrelu(conv_feat, filter=256, kernel=3, name="bbox_conv4")
elif p.normalizer.__name__ in ["sync_bn", "gn"]:
conv_feat = X.convnormrelu(p.normalizer, conv_feat, filter=256, kernel=3, name="bbox_conv1")
conv_feat = X.convnormrelu(p.normalizer, conv_feat, filter=256, kernel=3, name="bbox_conv2")
conv_feat = X.convnormrelu(p.normalizer, conv_feat, filter=256, kernel=3, name="bbox_conv3")
conv_feat = X.convnormrelu(p.normalizer, conv_feat, filter=256, kernel=3, name="bbox_conv4")
else:
raise NotImplementedError("Unsupported normalizer: {}".format(p.normalizer.__name__))
flatten = X.flatten(conv_feat, name="bbox_feat_flatten")
reshape = X.reshape(flatten, (0, 0, 1, 1), name="bbox_feat_reshape")
if p.normalizer.__name__ == "fix_bn":
fc1 = X.convrelu(reshape, filter=1024, name="bbox_fc1")
elif p.normalizer.__name__ in ["sync_bn", "gn"]:
fc1 = X.convnormrelu(p.normalizer, reshape, filter=1024, name="bbox_fc1")
else:
raise NotImplementedError("Unsupported normalizer: {}".format(p.normalizer.__name__))
self._head_feat = fc1
return self._head_feat
def _get_bbox_head_logit(self, conv_feat):
if self._head_feat is not None:
return self._head_feat
flatten = X.flatten(conv_feat, name="bbox_feat_flatten")
reshape = X.reshape(flatten, (0, 0, 1, 1), name="bbox_feat_reshape")
fc1 = X.convrelu(reshape, filter=1024, name="bbox_fc1")
fc2 = X.convrelu(fc1, filter=1024, name="bbox_fc2")
self._head_feat = fc2
return self._head_feat
def get_output(self, conv_feat):
if self._cls_logit is not None and self._bbox_delta is not None:
return self._cls_logit, self._bbox_delta
p = self.p
num_base_anchor = len(p.anchor_generate.ratio) * len(
p.anchor_generate.scale)
conv_channel = p.head.conv_channel
conv = X.convrelu(conv_feat,
kernel=3,
filter=conv_channel,
name="rpn_conv_3x3",
no_bias=False,
init=X.gauss(0.01))
if p.fp16:
conv = X.to_fp32(conv, name="rpn_conv_3x3_fp32")
cls_logit = X.conv(conv,
filter=2 * num_base_anchor,
name="rpn_cls_logit",
no_bias=False,
init=X.gauss(0.01))
bbox_delta = X.conv(conv,
filter=4 * num_base_anchor,
name="rpn_bbox_delta",
no_bias=False,
init=X.gauss(0.01))
self._cls_logit = cls_logit
self._bbox_delta = bbox_delta
return self._cls_logit, self._bbox_delta
def _get_bbox_head_logit(self, conv_feat):
# comment this for re-infer in test stage
# if self._head_feat is not None:
# return self._head_feat
p = self.p
stage = self.stage
flatten = X.flatten(conv_feat, name="bbox_feat_flatten_" + stage)
reshape = X.reshape(flatten, (0, 0, 1, 1),
name="bbox_feat_reshape_" + stage)
if p.normalizer.__name__ == "fix_bn":
fc1 = X.convrelu(reshape,
filter=1024,
weight=self.fc1_weight,
bias=self.fc1_bias,
no_bias=False,
name="bbox_fc1_" + stage)
fc2 = X.convrelu(fc1,
filter=1024,
weight=self.fc2_weight,
bias=self.fc2_bias,
no_bias=False,
name="bbox_fc2_" + stage)
elif p.normalizer.__name__ in ["sync_bn", "gn"]:
fc1 = X.convnormrelu(p.normalizer,
reshape,
filter=1024,
weight=self.fc1_weight,
bias=self.fc1_bias,
no_bias=False,
name="bbox_fc1_" + stage)
fc2 = X.convnormrelu(p.normalizer,
fc1,
filter=1024,
weight=self.fc2_weight,
bias=self.fc2_bias,
no_bias=False,
name="bbox_fc2_" + stage)
else:
raise NotImplementedError("Unsupported normalizer: {}".format(
p.normalizer.__name__))
self._head_feat = fc2
return self._head_feat
def get_rcnn_feature(self, rcnn_feat):
p = self.pNeck
conv_channel = p.conv_channel
conv_neck = X.convrelu(rcnn_feat,
filter=conv_channel,
no_bias=False,
init=X.gauss(0.01),
name="conv_neck")
return conv_neck
def get_output(self, conv_feat):
if self._cls_logit is not None and self._bbox_delta is not None:
return self._cls_logit, self._bbox_delta
p = self.p
num_base_anchor = len(p.anchor_generate.ratio) * len(p.anchor_generate.scale)
conv_channel = p.head.conv_channel
if p.normalizer.__name__ == "fix_bn":
conv = X.convrelu(
conv_feat,
kernel=3,
filter=conv_channel,
name="rpn_conv_3x3",
no_bias=False,
init=X.gauss(0.01)
)
elif p.normalizer.__name__ in ["sync_bn", "gn"]:
conv = X.convnormrelu(
p.normalizer,
conv_feat,
kernel=3,
filter=conv_channel,
name="rpn_conv_3x3",
no_bias=False,
init=X.gauss(0.01)
)
else:
raise NotImplementedError("Unsupported normalizer: {}".format(p.normalizer.__name__))
if p.fp16:
conv = X.to_fp32(conv, name="rpn_conv_3x3_fp32")
cls_logit = X.conv(
conv,
filter=2 * num_base_anchor,
name="rpn_cls_logit",
no_bias=False,
init=X.gauss(0.01)
)
bbox_delta = X.conv(
conv,
filter=4 * num_base_anchor,
name="rpn_bbox_delta",
no_bias=False,
init=X.gauss(0.01)
)
self._cls_logit = cls_logit
self._bbox_delta = bbox_delta
return self._cls_logit, self._bbox_delta
def _get_mask_head_logit(self, conv_feat):
if self._head_feat is not None:
return self._head_feat
up_stride = int(self.pMask.resolution // self.pMaskRoi.out_size)
dim_reduced = self.pMask.dim_reduced
msra_init = mx.init.Xavier(rnd_type="gaussian", factor_type="out", magnitude=2)
current = conv_feat
for i in range(4):
current = X.convrelu(
current,
name="mask_fcn_conv{}".format(i),
filter=dim_reduced,
kernel=3,
no_bias=False,
init=msra_init
)
mask_up = current
for i in range(up_stride // 2):
weight = X.var(
name="mask_up{}_weight".format(i),
init=msra_init,
lr_mult=1,
wd_mult=1)
mask_up = mx.sym.Deconvolution(
mask_up,
kernel=(2, 2),
stride=(2, 2),
num_filter=dim_reduced,
no_bias=False,
weight=weight,
name="mask_up{}".format(i)
)
mask_up = X.relu(
mask_up,
name="mask_up{}_relu".format(i))
mask_up = X.to_fp32(mask_up, name='mask_up_to_fp32')
self._head_feat = mask_up
return self._head_feat
def get_rcnn_feature(self, rcnn_feat):
p = self.p
if p.normalizer.__name__ == "fix_bn":
rcnn_feat = X.convrelu(rcnn_feat,
filter=p.reduce.channel,
kernel=3,
name="backbone_reduce")
elif p.normalizer.__name__ in ["sync_bn", "gn"]:
rcnn_feat = X.convnormrelu(p.normalizer,
rcnn_feat,
filter=p.reduce.channel,
kernel=3,
name="backbone_reduce")
else:
raise NotImplementedError("Unsupported normalizer: {}".format(
p.normalizer.__name__))
return rcnn_feat