add fepan lite

ppocr/modeling/necks/__init__.py

@@ -16,7 +16,7 @@ __all__ = ['build_neck']
 
 
 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 .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
@@ -26,8 +26,8 @@ def build_neck(config):
     from .fce_fpn import FCEFPN
     from .pren_fpn import PRENFPN
     support_dict = [
-        'FPN', 'FCEFPN', 'FEPAN', 'DBFPN', 'CAFPN', 'EASTFPN', 'SASTFPN',
-        'SequenceEncoder', 'PGFPN', 'TableFPN', 'PRENFPN'
+        'FPN', 'FCEFPN', 'FEPAN', 'FEPANLite', 'DBFPN', 'CAFPN', 'EASTFPN',
+        'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN', 'PRENFPN'
     ]
 
     module_name = config.pop('name')

ppocr/modeling/necks/db_fpn.py

@@ -30,7 +30,7 @@ sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../../..')))
 from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule
 
 
-class ConvBNLayer(nn.Layer):
+class DSConv(nn.Layer):
     def __init__(self,
                  in_channels,
                  out_channels,
@@ -40,7 +40,7 @@ class ConvBNLayer(nn.Layer):
                  groups=None,
                  if_act=True,
                  act="relu"):
-        super(ConvBNLayer, self).__init__()
+        super(DSConv, self).__init__()
         if groups == None:
             groups = in_channels
         self.if_act = if_act
@@ -268,23 +268,109 @@ class FEPAN(nn.Layer):
         self.out_channels = out_channels
         weight_attr = paddle.nn.initializer.KaimingUniform()
 
-        self.ins_conv = []
-        self.inp_conv = []
+        self.ins_conv = nn.LayerList()
+        self.inp_conv = nn.LayerList()
         # pan head
-        self.pan_head_conv = []
-        self.pan_lat_conv = []
+        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[0],
+                    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(
-                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,
                     out_channels=self.out_channels // 4,
                     kernel_size=9,
@@ -300,8 +386,9 @@ class FEPAN(nn.Layer):
                         stride=2,
                         weight_attr=ParamAttr(initializer=weight_attr),
                         bias_attr=False))
+
             self.pan_lat_conv.append(
-                ConvBNLayer(
+                DSConv(
                     in_channels=self.out_channels // 4,
                     out_channels=self.out_channels // 4,
                     kernel_size=9,
@@ -327,14 +414,14 @@ class FEPAN(nn.Layer):
         f3 = self.inp_conv[1](out3)
         f2 = self.inp_conv[0](out2)
 
-        pan3 = f3 + self.pan_head[0](f2)
-        pan4 = f4 + self.pan_head[1](pan3)
-        pan5 = f5 + self.pan_head[2](pan4)
+        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[0](f2)
-        p3 = self.pan_lat[1](pan3)
-        p4 = self.pan_lat[2](pan4)
-        p5 = self.pan_lat[3](pan5)
+        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)