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| import torch | |
| from torch import nn | |
| from yolov6.layers.common import RepBlock, SimConv, Transpose | |
| class RepPANNeck(nn.Module): | |
| """RepPANNeck Module | |
| EfficientRep is the default backbone of this model. | |
| RepPANNeck has the balance of feature fusion ability and hardware efficiency. | |
| """ | |
| def __init__( | |
| self, | |
| channels_list=None, | |
| num_repeats=None | |
| ): | |
| super().__init__() | |
| assert channels_list is not None | |
| assert num_repeats is not None | |
| self.Rep_p4 = RepBlock( | |
| in_channels=channels_list[3] + channels_list[5], | |
| out_channels=channels_list[5], | |
| n=num_repeats[5], | |
| ) | |
| self.Rep_p3 = RepBlock( | |
| in_channels=channels_list[2] + channels_list[6], | |
| out_channels=channels_list[6], | |
| n=num_repeats[6] | |
| ) | |
| self.Rep_n3 = RepBlock( | |
| in_channels=channels_list[6] + channels_list[7], | |
| out_channels=channels_list[8], | |
| n=num_repeats[7], | |
| ) | |
| self.Rep_n4 = RepBlock( | |
| in_channels=channels_list[5] + channels_list[9], | |
| out_channels=channels_list[10], | |
| n=num_repeats[8] | |
| ) | |
| self.reduce_layer0 = SimConv( | |
| in_channels=channels_list[4], | |
| out_channels=channels_list[5], | |
| kernel_size=1, | |
| stride=1 | |
| ) | |
| self.upsample0 = Transpose( | |
| in_channels=channels_list[5], | |
| out_channels=channels_list[5], | |
| ) | |
| self.reduce_layer1 = SimConv( | |
| in_channels=channels_list[5], | |
| out_channels=channels_list[6], | |
| kernel_size=1, | |
| stride=1 | |
| ) | |
| self.upsample1 = Transpose( | |
| in_channels=channels_list[6], | |
| out_channels=channels_list[6] | |
| ) | |
| self.downsample2 = SimConv( | |
| in_channels=channels_list[6], | |
| out_channels=channels_list[7], | |
| kernel_size=3, | |
| stride=2 | |
| ) | |
| self.downsample1 = SimConv( | |
| in_channels=channels_list[8], | |
| out_channels=channels_list[9], | |
| kernel_size=3, | |
| stride=2 | |
| ) | |
| def forward(self, input): | |
| (x2, x1, x0) = input | |
| fpn_out0 = self.reduce_layer0(x0) | |
| upsample_feat0 = self.upsample0(fpn_out0) | |
| f_concat_layer0 = torch.cat([upsample_feat0, x1], 1) | |
| f_out0 = self.Rep_p4(f_concat_layer0) | |
| fpn_out1 = self.reduce_layer1(f_out0) | |
| upsample_feat1 = self.upsample1(fpn_out1) | |
| f_concat_layer1 = torch.cat([upsample_feat1, x2], 1) | |
| pan_out2 = self.Rep_p3(f_concat_layer1) | |
| down_feat1 = self.downsample2(pan_out2) | |
| p_concat_layer1 = torch.cat([down_feat1, fpn_out1], 1) | |
| pan_out1 = self.Rep_n3(p_concat_layer1) | |
| down_feat0 = self.downsample1(pan_out1) | |
| p_concat_layer2 = torch.cat([down_feat0, fpn_out0], 1) | |
| pan_out0 = self.Rep_n4(p_concat_layer2) | |
| outputs = [pan_out2, pan_out1, pan_out0] | |
| return outputs | |