add fpn

ppocr/modeling/necks/__init__.py

@@ -22,7 +22,8 @@ def build_neck(config):
     from .rnn import SequenceEncoder
     from .pg_fpn import PGFPN
     from .table_fpn import TableFPN
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN']
+    from .fpn import FPN
+    support_dict = ['FPN','DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/fpn.py

@@ -0,0 +1,102 @@
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.nn as nn
+import paddle
+import math
+import paddle.nn.functional as F
+
+class Conv_BN_ReLU(nn.Layer):
+    def __init__(self, in_planes, out_planes, kernel_size=1, stride=1, padding=0):
+        super(Conv_BN_ReLU, self).__init__()
+        self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
+                              bias_attr=False)
+        self.bn = nn.BatchNorm2D(out_planes, momentum=0.1)
+        self.relu = nn.ReLU()
+
+        for m in self.sublayers():
+            if isinstance(m, nn.Conv2D):
+                n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Normal(0, math.sqrt(2. / n)))
+            elif isinstance(m, nn.BatchNorm2D):
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(1.0))
+                m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(0.0))
+
+    def forward(self, x):
+        return self.relu(self.bn(self.conv(x)))
+
+class FPN(nn.Layer):
+    def __init__(self, in_channels, out_channels):
+        super(FPN, self).__init__()
+
+        # Top layer
+        self.toplayer_ = Conv_BN_ReLU(in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)
+        # Lateral layers
+        self.latlayer1_ = Conv_BN_ReLU(in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)
+
+        self.latlayer2_ = Conv_BN_ReLU(in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)
+
+        self.latlayer3_ = Conv_BN_ReLU(in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)
+
+        # Smooth layers
+        self.smooth1_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.smooth2_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.smooth3_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+
+        self.out_channels = out_channels * 4
+        for m in self.sublayers():
+            if isinstance(m, nn.Conv2D):
+                n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32',
+                                                   default_initializer=paddle.nn.initializer.Normal(0,
+                                                                                                    math.sqrt(2. / n)))
+            elif isinstance(m, nn.BatchNorm2D):
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32',
+                                                   default_initializer=paddle.nn.initializer.Constant(1.0))
+                m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32',
+                                                 default_initializer=paddle.nn.initializer.Constant(0.0))
+
+    def _upsample(self, x, y, scale=1):
+        _, _, H, W = y.shape
+        return F.upsample(x, size=(H // scale, W // scale), mode='bilinear')
+
+    def _upsample_add(self, x, y):
+        _, _, H, W = y.shape
+        return F.upsample(x, size=(H, W), mode='bilinear') + y
+
+    def forward(self, x):
+        f2, f3, f4, f5 = x
+        p5 = self.toplayer_(f5)
+
+        f4 = self.latlayer1_(f4)
+        p4 = self._upsample_add(p5, f4)
+        p4 = self.smooth1_(p4)
+
+        f3 = self.latlayer2_(f3)
+        p3 = self._upsample_add(p4, f3)
+        p3 = self.smooth2_(p3)
+
+        f2 = self.latlayer3_(f2)
+        p2 = self._upsample_add(p3, f2)
+        p2 = self.smooth3_(p2)
+
+        p3 = self._upsample(p3, p2)
+        p4 = self._upsample(p4, p2)
+        p5 = self._upsample(p5, p2)
+
+        fuse = paddle.concat([p2, p3, p4, p5], axis=1)
+        return fuse