更换文档检测模型

paddle_detection/ppdet/modeling/necks/channel_mapper.py

@@ -0,0 +1,122 @@
+# Copyright (c) 2023 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.
+"""
+this code is base on mmdet: git@github.com:open-mmlab/mmdetection.git
+"""
+import paddle.nn as nn
+
+from ppdet.core.workspace import register, serializable
+from ..backbones.hrnet import ConvNormLayer
+from ..shape_spec import ShapeSpec
+from ..initializer import xavier_uniform_, constant_
+
+__all__ = ['ChannelMapper']
+
+
+@register
+@serializable
+class ChannelMapper(nn.Layer):
+    """Channel Mapper to reduce/increase channels of backbone features.
+
+    This is used to reduce/increase channels of backbone features.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale).
+        kernel_size (int, optional): kernel_size for reducing channels (used
+            at each scale). Default: 3.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        act_cfg (dict, optional): Config dict for activation layer in
+            ConvModule. Default: dict(type='ReLU').
+        num_outs (int, optional): Number of output feature maps. There
+            would be extra_convs when num_outs larger than the length
+            of in_channels.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+        
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 norm_type="gn",
+                 norm_groups=32,
+                 act='relu',
+                 num_outs=None,
+                 init_cfg=dict(
+                     type='Xavier', layer='Conv2d', distribution='uniform')):
+        super(ChannelMapper, self).__init__()
+        assert isinstance(in_channels, list)
+        self.extra_convs = None
+        if num_outs is None:
+            num_outs = len(in_channels)
+        self.convs = nn.LayerList()
+        for in_channel in in_channels:
+            self.convs.append(
+                ConvNormLayer(
+                    ch_in=in_channel,
+                    ch_out=out_channels,
+                    filter_size=kernel_size,
+                    norm_type='gn',
+                    norm_groups=32,
+                    act=act))
+
+        if num_outs > len(in_channels):
+            self.extra_convs = nn.LayerList()
+            for i in range(len(in_channels), num_outs):
+                if i == len(in_channels):
+                    in_channel = in_channels[-1]
+                else:
+                    in_channel = out_channels
+                self.extra_convs.append(
+                    ConvNormLayer(
+                        ch_in=in_channel,
+                        ch_out=out_channels,
+                        filter_size=3,
+                        stride=2,
+                        norm_type='gn',
+                        norm_groups=32,
+                        act=act))
+        self.init_weights()
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == len(self.convs)
+        outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
+        if self.extra_convs:
+            for i in range(len(self.extra_convs)):
+                if i == 0:
+                    outs.append(self.extra_convs[0](inputs[-1]))
+                else:
+                    outs.append(self.extra_convs[i](outs[-1]))
+        return tuple(outs)
+
+    @property
+    def out_shape(self):
+        return [
+            ShapeSpec(
+                channels=self.out_channel, stride=1. / s)
+            for s in self.spatial_scales
+        ]
+
+    def init_weights(self):
+        """Initialize the transformer weights."""
+        for p in self.parameters():
+            if p.rank() > 1:
+                xavier_uniform_(p)
+                if hasattr(p, 'bias') and p.bias is not None:
+                    constant_(p.bais)