def fpn(up_from, up_to):
ch = up_to.shape[1]
b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))
conv1 = fluid.layers.conv2d(
up_from, ch, 1, act='relu', bias_attr=b_attr)
if self.use_transposed_conv2d:
w_attr = ParamAttr(
learning_rate=0.,
regularizer=L2Decay(0.),
initializer=Bilinear())
upsampling = fluid.layers.conv2d_transpose(
conv1,
ch,
output_size=None,
filter_size=4,
padding=1,
stride=2,
groups=ch,
param_attr=w_attr,
bias_attr=False,
use_cudnn=True)
else:
upsampling = fluid.layers.resize_bilinear(
conv1, out_shape=up_to.shape[2:])
conv2 = fluid.layers.conv2d(
up_to, ch, 1, act='relu', bias_attr=b_attr)
if self.is_infer:
upsampling = fluid.layers.crop(upsampling, shape=conv2)
# eltwise mul
conv_fuse = upsampling * conv2
return conv_fuse
def fpn(self, up_from, up_to, name, is_sampling=False):
ch = up_to.shape[1]
b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))
conv1 = fluid.layers.conv2d(up_from,
ch,
1,
act='relu',
bias_attr=b_attr,
name=name + "_fpn_conv1")
if is_sampling:
w_attr = ParamAttr(learning_rate=0.,
regularizer=L2Decay(0.),
initializer=Bilinear())
upsampling = fluid.layers.conv2d_transpose(conv1,
ch,
output_size=None,
filter_size=4,
padding=1,
stride=2,
groups=ch,
param_attr=w_attr,
bias_attr=False,
use_cudnn=True,
name=name +
"_fpn_upsampling")
else:
upsampling = conv1
b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))
conv2 = fluid.layers.conv2d(up_to,
ch,
1,
act='relu',
bias_attr=b_attr,
name=name + "_fpn_conv2")
# eltwise mul
conv_fuse = upsampling * conv2
return fluid.layers.relu(x=conv_fuse)