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):
# 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_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_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
