更换文档检测模型

paddle_detection/ppdet/modeling/backbones/senet.py

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+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
+#   
+# 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
+
+from ppdet.core.workspace import register, serializable
+from .resnet import ResNet, Blocks, BasicBlock, BottleNeck
+from ..shape_spec import ShapeSpec
+from .name_adapter import NameAdapter
+
+__all__ = ['SENet', 'SERes5Head']
+
+
+@register
+@serializable
+class SENet(ResNet):
+    __shared__ = ['norm_type']
+
+    def __init__(self,
+                 depth=50,
+                 variant='b',
+                 lr_mult_list=[1.0, 1.0, 1.0, 1.0],
+                 groups=1,
+                 base_width=64,
+                 norm_type='bn',
+                 norm_decay=0,
+                 freeze_norm=True,
+                 freeze_at=0,
+                 return_idx=[0, 1, 2, 3],
+                 dcn_v2_stages=[-1],
+                 std_senet=True,
+                 num_stages=4):
+        """
+        Squeeze-and-Excitation Networks, see https://arxiv.org/abs/1709.01507
+        
+        Args:
+            depth (int): SENet depth, should be 50, 101, 152
+            variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
+            lr_mult_list (list): learning rate ratio of different resnet stages(2,3,4,5),
+                                 lower learning rate ratio is need for pretrained model 
+                                 got using distillation(default as [1.0, 1.0, 1.0, 1.0]).
+            groups (int): group convolution cardinality
+            base_width (int): base width of each group convolution
+            norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
+            norm_decay (float): weight decay for normalization layer weights
+            freeze_norm (bool): freeze normalization layers
+            freeze_at (int): freeze the backbone at which stage
+            return_idx (list): index of the stages whose feature maps are returned
+            dcn_v2_stages (list): index of stages who select deformable conv v2
+            std_senet (bool): whether use senet, default True
+            num_stages (int): total num of stages
+        """
+
+        super(SENet, self).__init__(
+            depth=depth,
+            variant=variant,
+            lr_mult_list=lr_mult_list,
+            ch_in=128,
+            groups=groups,
+            base_width=base_width,
+            norm_type=norm_type,
+            norm_decay=norm_decay,
+            freeze_norm=freeze_norm,
+            freeze_at=freeze_at,
+            return_idx=return_idx,
+            dcn_v2_stages=dcn_v2_stages,
+            std_senet=std_senet,
+            num_stages=num_stages)
+
+
+@register
+class SERes5Head(nn.Layer):
+    def __init__(self,
+                 depth=50,
+                 variant='b',
+                 lr_mult=1.0,
+                 groups=1,
+                 base_width=64,
+                 norm_type='bn',
+                 norm_decay=0,
+                 dcn_v2=False,
+                 freeze_norm=False,
+                 std_senet=True):
+        """
+        SERes5Head layer
+
+        Args:
+            depth (int): SENet depth, should be 50, 101, 152
+            variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
+            lr_mult (list): learning rate ratio of SERes5Head, default as 1.0.
+            groups (int): group convolution cardinality
+            base_width (int): base width of each group convolution
+            norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
+            norm_decay (float): weight decay for normalization layer weights
+            dcn_v2_stages (list): index of stages who select deformable conv v2
+            std_senet (bool): whether use senet, default True
+            
+        """
+        super(SERes5Head, self).__init__()
+        ch_out = 512
+        ch_in = 256 if depth < 50 else 1024
+        na = NameAdapter(self)
+        block = BottleNeck if depth >= 50 else BasicBlock
+        self.res5 = Blocks(
+            block,
+            ch_in,
+            ch_out,
+            count=3,
+            name_adapter=na,
+            stage_num=5,
+            variant=variant,
+            groups=groups,
+            base_width=base_width,
+            lr=lr_mult,
+            norm_type=norm_type,
+            norm_decay=norm_decay,
+            freeze_norm=freeze_norm,
+            dcn_v2=dcn_v2,
+            std_senet=std_senet)
+        self.ch_out = ch_out * block.expansion
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(
+            channels=self.ch_out,
+            stride=16, )]
+
+    def forward(self, roi_feat):
+        y = self.res5(roi_feat)
+        return y