src/nn/backbone/csp_resnet.py

"""
https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.6/ppdet/modeling/backbones/cspresnet.py

Copyright(c) 2023 lyuwenyu. All Rights Reserved.
"""

from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F

from ...core import register
from .common import get_activation

__all__ = ["CSPResNet"]


donwload_url = {
    "s": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_s_pretrained_from_paddle.pth",
    "m": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_m_pretrained_from_paddle.pth",
    "l": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_l_pretrained_from_paddle.pth",
    "x": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_x_pretrained_from_paddle.pth",
}


class ConvBNLayer(nn.Module):
    def __init__(self, ch_in, ch_out, filter_size=3, stride=1, groups=1, padding=0, act=None):
        super().__init__()
        self.conv = nn.Conv2d(
            ch_in, ch_out, filter_size, stride, padding, groups=groups, bias=False
        )
        self.bn = nn.BatchNorm2d(ch_out)
        self.act = get_activation(act)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv(x)
        x = self.bn(x)
        x = self.act(x)
        return x


class RepVggBlock(nn.Module):
    def __init__(self, ch_in, ch_out, act="relu", alpha: bool = False):
        super().__init__()
        self.ch_in = ch_in
        self.ch_out = ch_out
        self.conv1 = ConvBNLayer(ch_in, ch_out, 3, stride=1, padding=1, act=None)
        self.conv2 = ConvBNLayer(ch_in, ch_out, 1, stride=1, padding=0, act=None)
        self.act = get_activation(act)

        if alpha:
            self.alpha = nn.Parameter(
                torch.ones(
                    1,
                )
            )
        else:
            self.alpha = None

    def forward(self, x):
        if hasattr(self, "conv"):
            y = self.conv(x)
        else:
            if self.alpha:
                y = self.conv1(x) + self.alpha * self.conv2(x)
            else:
                y = self.conv1(x) + self.conv2(x)
        y = self.act(y)
        return y

    def convert_to_deploy(self):
        if not hasattr(self, "conv"):
            self.conv = nn.Conv2d(self.ch_in, self.ch_out, 3, 1, padding=1)

        kernel, bias = self.get_equivalent_kernel_bias()
        self.conv.weight.data = kernel
        self.conv.bias.data = bias

    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)

        if self.alpha:
            return kernel3x3 + self.alpha * self._pad_1x1_to_3x3_tensor(
                kernel1x1
            ), bias3x3 + self.alpha * bias1x1
        else:
            return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1), bias3x3 + bias1x1

    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return F.pad(kernel1x1, [1, 1, 1, 1])

    def _fuse_bn_tensor(self, branch: ConvBNLayer):
        if branch is None:
            return 0, 0
        kernel = branch.conv.weight
        running_mean = branch.norm.running_mean
        running_var = branch.norm.running_var
        gamma = branch.norm.weight
        beta = branch.norm.bias
        eps = branch.norm.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std


class BasicBlock(nn.Module):
    def __init__(self, ch_in, ch_out, act="relu", shortcut=True, use_alpha=False):
        super().__init__()
        assert ch_in == ch_out
        self.conv1 = ConvBNLayer(ch_in, ch_out, 3, stride=1, padding=1, act=act)
        self.conv2 = RepVggBlock(ch_out, ch_out, act=act, alpha=use_alpha)
        self.shortcut = shortcut

    def forward(self, x):
        y = self.conv1(x)
        y = self.conv2(y)
        if self.shortcut:
            return x + y
        else:
            return y


class EffectiveSELayer(nn.Module):
    """Effective Squeeze-Excitation
    From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
    """

    def __init__(self, channels, act="hardsigmoid"):
        super(EffectiveSELayer, self).__init__()
        self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
        self.act = get_activation(act)

    def forward(self, x: torch.Tensor):
        x_se = x.mean((2, 3), keepdim=True)
        x_se = self.fc(x_se)
        x_se = self.act(x_se)
        return x * x_se


class CSPResStage(nn.Module):
    def __init__(self, block_fn, ch_in, ch_out, n, stride, act="relu", attn="eca", use_alpha=False):
        super().__init__()
        ch_mid = (ch_in + ch_out) // 2
        if stride == 2:
            self.conv_down = ConvBNLayer(ch_in, ch_mid, 3, stride=2, padding=1, act=act)
        else:
            self.conv_down = None
        self.conv1 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
        self.conv2 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
        self.blocks = nn.Sequential(
            *[
                block_fn(ch_mid // 2, ch_mid // 2, act=act, shortcut=True, use_alpha=use_alpha)
                for i in range(n)
            ]
        )
        if attn:
            self.attn = EffectiveSELayer(ch_mid, act="hardsigmoid")
        else:
            self.attn = None

        self.conv3 = ConvBNLayer(ch_mid, ch_out, 1, act=act)

    def forward(self, x):
        if self.conv_down is not None:
            x = self.conv_down(x)
        y1 = self.conv1(x)
        y2 = self.blocks(self.conv2(x))
        y = torch.concat([y1, y2], dim=1)
        if self.attn is not None:
            y = self.attn(y)
        y = self.conv3(y)
        return y


@register()
class CSPResNet(nn.Module):
    layers = [3, 6, 6, 3]
    channels = [64, 128, 256, 512, 1024]
    model_cfg = {
        "s": {
            "depth_mult": 0.33,
            "width_mult": 0.50,
        },
        "m": {
            "depth_mult": 0.67,
            "width_mult": 0.75,
        },
        "l": {
            "depth_mult": 1.00,
            "width_mult": 1.00,
        },
        "x": {
            "depth_mult": 1.33,
            "width_mult": 1.25,
        },
    }

    def __init__(
        self,
        name: str,
        act="silu",
        return_idx=[1, 2, 3],
        use_large_stem=True,
        use_alpha=False,
        pretrained=False,
    ):
        super().__init__()
        depth_mult = self.model_cfg[name]["depth_mult"]
        width_mult = self.model_cfg[name]["width_mult"]

        channels = [max(round(c * width_mult), 1) for c in self.channels]
        layers = [max(round(l * depth_mult), 1) for l in self.layers]
        act = get_activation(act)

        if use_large_stem:
            self.stem = nn.Sequential(
                OrderedDict(
                    [
                        (
                            "conv1",
                            ConvBNLayer(3, channels[0] // 2, 3, stride=2, padding=1, act=act),
                        ),
                        (
                            "conv2",
                            ConvBNLayer(
                                channels[0] // 2, channels[0] // 2, 3, stride=1, padding=1, act=act
                            ),
                        ),
                        (
                            "conv3",
                            ConvBNLayer(
                                channels[0] // 2, channels[0], 3, stride=1, padding=1, act=act
                            ),
                        ),
                    ]
                )
            )
        else:
            self.stem = nn.Sequential(
                OrderedDict(
                    [
                        (
                            "conv1",
                            ConvBNLayer(3, channels[0] // 2, 3, stride=2, padding=1, act=act),
                        ),
                        (
                            "conv2",
                            ConvBNLayer(
                                channels[0] // 2, channels[0], 3, stride=1, padding=1, act=act
                            ),
                        ),
                    ]
                )
            )

        n = len(channels) - 1
        self.stages = nn.Sequential(
            OrderedDict(
                [
                    (
                        str(i),
                        CSPResStage(
                            BasicBlock,
                            channels[i],
                            channels[i + 1],
                            layers[i],
                            2,
                            act=act,
                            use_alpha=use_alpha,
                        ),
                    )
                    for i in range(n)
                ]
            )
        )

        self._out_channels = channels[1:]
        self._out_strides = [4 * 2**i for i in range(n)]
        self.return_idx = return_idx

        if pretrained:
            if isinstance(pretrained, bool) or "http" in pretrained:
                state = torch.hub.load_state_dict_from_url(donwload_url[name], map_location="cpu")
            else:
                state = torch.load(pretrained, map_location="cpu")
            self.load_state_dict(state)
            print(f"Load CSPResNet_{name} state_dict")

    def forward(self, x):
        x = self.stem(x)
        outs = []
        for idx, stage in enumerate(self.stages):
            x = stage(x)
            if idx in self.return_idx:
                outs.append(x)

        return outs