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add fepan lite

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LDOUBLEV 2022-04-27 16:21:40 +08:00
parent 2b3f89f0e2
commit 8c173feb3f
2 changed files with 106 additions and 19 deletions
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6
ppocr/modeling/necks/__init__.py
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@ -16,7 +16,7 @@ __all__ = ['build_neck']
def build_neck(config): def build_neck(config):
from .db_fpn import DBFPN, CAFPN, FEPAN from .db_fpn import DBFPN, CAFPN, FEPAN, FEPANLite
from .east_fpn import EASTFPN from .east_fpn import EASTFPN
from .sast_fpn import SASTFPN from .sast_fpn import SASTFPN
from .rnn import SequenceEncoder from .rnn import SequenceEncoder
@ -26,8 +26,8 @@ def build_neck(config):
from .fce_fpn import FCEFPN from .fce_fpn import FCEFPN
from .pren_fpn import PRENFPN from .pren_fpn import PRENFPN
support_dict = [ support_dict = [
'FPN', 'FCEFPN', 'FEPAN', 'DBFPN', 'CAFPN', 'EASTFPN', 'SASTFPN', 'FPN', 'FCEFPN', 'FEPAN', 'FEPANLite', 'DBFPN', 'CAFPN', 'EASTFPN',
'SequenceEncoder', 'PGFPN', 'TableFPN', 'PRENFPN' 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN', 'PRENFPN'
] ]
module_name = config.pop('name') module_name = config.pop('name')

119
ppocr/modeling/necks/db_fpn.py
View File

@ -30,7 +30,7 @@ sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../../..')))
from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule
class ConvBNLayer(nn.Layer): class DSConv(nn.Layer):
def __init__(self, def __init__(self,
in_channels, in_channels,
out_channels, out_channels,
@ -40,7 +40,7 @@ class ConvBNLayer(nn.Layer):
groups=None, groups=None,
if_act=True, if_act=True,
act="relu"): act="relu"):
super(ConvBNLayer, self).__init__() super(DSConv, self).__init__()
if groups == None: if groups == None:
groups = in_channels groups = in_channels
self.if_act = if_act self.if_act = if_act
@ -268,23 +268,109 @@ class FEPAN(nn.Layer):
self.out_channels = out_channels self.out_channels = out_channels
weight_attr = paddle.nn.initializer.KaimingUniform() weight_attr = paddle.nn.initializer.KaimingUniform()
self.ins_conv = [] self.ins_conv = nn.LayerList()
self.inp_conv = [] self.inp_conv = nn.LayerList()
# pan head # pan head
self.pan_head_conv = [] self.pan_head_conv = nn.LayerList()
self.pan_lat_conv = [] self.pan_lat_conv = nn.LayerList()
for i in range(len(in_channels)): for i in range(len(in_channels)):
self.ins_conv.append( self.ins_conv.append(
nn.Conv2D( nn.Conv2D(
in_channels=in_channels[0], in_channels=in_channels[i],
out_channels=self.out_channels, out_channels=self.out_channels,
kernel_size=1, kernel_size=1,
weight_attr=ParamAttr(initializer=weight_attr), weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False)) bias_attr=False))
self.inp_conv.append( self.inp_conv.append(
ConvBNLayer( nn.Conv2D(
in_channels=self.out_channels,
out_channels=self.out_channels // 4,
kernel_size=9,
padding=4,
weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False))
if i > 0:
self.pan_head_conv.append(
nn.Conv2D(
in_channels=self.out_channels // 4,
out_channels=self.out_channels // 4,
kernel_size=3,
padding=1,
stride=2,
weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False))
self.pan_lat_conv.append(
nn.Conv2D(
in_channels=self.out_channels // 4,
out_channels=self.out_channels // 4,
kernel_size=9,
padding=4,
weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False))
def forward(self, x):
c2, c3, c4, c5 = x
in5 = self.ins_conv[3](c5)
in4 = self.ins_conv[2](c4)
in3 = self.ins_conv[1](c3)
in2 = self.ins_conv[0](c2)
out4 = in4 + F.upsample(
in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16
out3 = in3 + F.upsample(
out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8
out2 = in2 + F.upsample(
out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4
f5 = self.inp_conv[3](in5)
f4 = self.inp_conv[2](out4)
f3 = self.inp_conv[1](out3)
f2 = self.inp_conv[0](out2)
pan3 = f3 + self.pan_head_conv[0](f2)
pan4 = f4 + self.pan_head_conv[1](pan3)
pan5 = f5 + self.pan_head_conv[2](pan4)
p2 = self.pan_lat_conv[0](f2)
p3 = self.pan_lat_conv[1](pan3)
p4 = self.pan_lat_conv[2](pan4)
p5 = self.pan_lat_conv[3](pan5)
p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
return fuse
class FEPANLite(nn.Layer):
def __init__(self, in_channels, out_channels, **kwargs):
super(FEPANLite, self).__init__()
self.out_channels = out_channels
weight_attr = paddle.nn.initializer.KaimingUniform()
self.ins_conv = nn.LayerList()
self.inp_conv = nn.LayerList()
# pan head
self.pan_head_conv = nn.LayerList()
self.pan_lat_conv = nn.LayerList()
for i in range(len(in_channels)):
self.ins_conv.append(
nn.Conv2D(
in_channels=in_channels[i],
out_channels=self.out_channels,
kernel_size=1,
weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False))
self.inp_conv.append(
DSConv(
in_channels=self.out_channels, in_channels=self.out_channels,
out_channels=self.out_channels // 4, out_channels=self.out_channels // 4,
kernel_size=9, kernel_size=9,
@ -300,8 +386,9 @@ class FEPAN(nn.Layer):
stride=2, stride=2,
weight_attr=ParamAttr(initializer=weight_attr), weight_attr=ParamAttr(initializer=weight_attr),
bias_attr=False)) bias_attr=False))
self.pan_lat_conv.append( self.pan_lat_conv.append(
ConvBNLayer( DSConv(
in_channels=self.out_channels // 4, in_channels=self.out_channels // 4,
out_channels=self.out_channels // 4, out_channels=self.out_channels // 4,
kernel_size=9, kernel_size=9,
@ -327,14 +414,14 @@ class FEPAN(nn.Layer):
f3 = self.inp_conv[1](out3) f3 = self.inp_conv[1](out3)
f2 = self.inp_conv[0](out2) f2 = self.inp_conv[0](out2)
pan3 = f3 + self.pan_head[0](f2) pan3 = f3 + self.pan_head_conv[0](f2)
pan4 = f4 + self.pan_head[1](pan3) pan4 = f4 + self.pan_head_conv[1](pan3)
pan5 = f5 + self.pan_head[2](pan4) pan5 = f5 + self.pan_head_conv[2](pan4)
p2 = self.pan_lat[0](f2) p2 = self.pan_lat_conv[0](f2)
p3 = self.pan_lat[1](pan3) p3 = self.pan_lat_conv[1](pan3)
p4 = self.pan_lat[2](pan4) p4 = self.pan_lat_conv[2](pan4)
p5 = self.pan_lat[3](pan5) p5 = self.pan_lat_conv[3](pan5)
p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1) p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1) p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)