yolov6/models/efficientrep.py

from torch import nn
from yolov6.layers.common import RepVGGBlock, RepBlock, SimSPPF


class EfficientRep(nn.Module):
    '''EfficientRep Backbone
    EfficientRep is handcrafted by hardware-aware neural network design.
    With rep-style struct, EfficientRep is friendly to high-computation hardware(e.g. GPU).
    '''

    def __init__(
        self,
        in_channels=3,
        channels_list=None,
        num_repeats=None,
    ):
        super().__init__()

        assert channels_list is not None
        assert num_repeats is not None

        self.stem = RepVGGBlock(
            in_channels=in_channels,
            out_channels=channels_list[0],
            kernel_size=3,
            stride=2
        )

        self.ERBlock_2 = nn.Sequential(
            RepVGGBlock(
                in_channels=channels_list[0],
                out_channels=channels_list[1],
                kernel_size=3,
                stride=2
            ),
            RepBlock(
                in_channels=channels_list[1],
                out_channels=channels_list[1],
                n=num_repeats[1]
            )
        )

        self.ERBlock_3 = nn.Sequential(
            RepVGGBlock(
                in_channels=channels_list[1],
                out_channels=channels_list[2],
                kernel_size=3,
                stride=2
            ),
            RepBlock(
                in_channels=channels_list[2],
                out_channels=channels_list[2],
                n=num_repeats[2]
            )
        )

        self.ERBlock_4 = nn.Sequential(
            RepVGGBlock(
                in_channels=channels_list[2],
                out_channels=channels_list[3],
                kernel_size=3,
                stride=2
            ),
            RepBlock(
                in_channels=channels_list[3],
                out_channels=channels_list[3],
                n=num_repeats[3]
            )
        )

        self.ERBlock_5 = nn.Sequential(
            RepVGGBlock(
                in_channels=channels_list[3],
                out_channels=channels_list[4],
                kernel_size=3,
                stride=2,
            ),
            RepBlock(
                in_channels=channels_list[4],
                out_channels=channels_list[4],
                n=num_repeats[4]
            ),
            SimSPPF(
                in_channels=channels_list[4],
                out_channels=channels_list[4],
                kernel_size=5
            )
        )

    def forward(self, x):

        outputs = []
        x = self.stem(x)
        x = self.ERBlock_2(x)
        x = self.ERBlock_3(x)
        outputs.append(x)
        x = self.ERBlock_4(x)
        outputs.append(x)
        x = self.ERBlock_5(x)
        outputs.append(x)

        return tuple(outputs)