​

​

​

import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from mmcv.cnn import ConvModule, build_norm_layer
__all__ = ('Low_FAM', 'Low_IFM', 'Split', 'SimConv', 'Low_LAF', 'Inject', 'RepBlock', 'High_FAM', 'High_IFM', 'High_LAF')
class High_LAF(nn.Module):
    def forward(self, x1, x2):
        if torch.onnx.is_in_onnx_export():
            self.pool = onnx_AdaptiveAvgPool2d
        else:
            self.pool = nn.functional.adaptive_avg_pool2d
        N, C, H, W = x2.shape
        # output_size = np.array([H, W])
        output_size = [H, W]
        x1 = self.pool(x1, output_size)
        return torch.cat([x1, x2], 1)
class High_IFM(nn.Module):
    def __init__(self, block_num, embedding_dim, key_dim, num_heads,
                 mlp_ratio=4., attn_ratio=2., drop=0., attn_drop=0., drop_path=0.,
                 norm_cfg=dict(type='BN', requires_grad=True),
                 act_layer=nn.ReLU6):
        super().__init__()
        self.block_num = block_num
        drop_path = [x.item() for x in torch.linspace(0, drop_path[0], drop_path[1])] # 0.1, 2
        self.transformer_blocks = nn.ModuleList()
        for i in range(self.block_num):
            self.transformer_blocks.append(top_Block(
                    embedding_dim, key_dim=key_dim, num_heads=num_heads,
                    mlp_ratio=mlp_ratio, attn_ratio=attn_ratio,
                    drop=drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                    norm_cfg=norm_cfg, act_layer=act_layer))
    def forward(self, x):
        # token * N 
        for i in range(self.block_num):
            x = self.transformer_blocks[i](x)
        return x
class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, drop=0.,
                 norm_cfg=dict(type='BN', requires_grad=True)):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = Conv2d_BN(in_features, hidden_features, norm_cfg=norm_cfg)
        self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, 1, 1, bias=True, groups=hidden_features)
        self.act = act_layer()
        self.fc2 = Conv2d_BN(hidden_features, out_features, norm_cfg=norm_cfg)
        self.drop = nn.Dropout(drop)
    def forward(self, x):
        x = self.fc1(x)
        x = self.dwconv(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x
class top_Block(nn.Module):
    def __init__(self, dim, key_dim, num_heads, mlp_ratio=4., attn_ratio=2., drop=0.,
                 drop_path=0., act_layer=nn.ReLU, norm_cfg=dict(type='BN2d', requires_grad=True)):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio
        self.attn = Attention(dim, key_dim=key_dim, num_heads=num_heads, attn_ratio=attn_ratio, activation=act_layer,
                              norm_cfg=norm_cfg)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop,
                       norm_cfg=norm_cfg)
    def forward(self, x1):
        x1 = x1 + self.drop_path(self.attn(x1))
        x1 = x1 + self.drop_path(self.mlp(x1))
        return x1
def drop_path(x, drop_prob: float = 0., training: bool = False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output
class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob
    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)
class Attention(torch.nn.Module):
    def __init__(self, dim, key_dim, num_heads,
                 attn_ratio=4,
                 activation=None,
                 norm_cfg=dict(type='BN', requires_grad=True), ):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads  # num_head key_dim
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        self.to_q = Conv2d_BN(dim, nh_kd, 1, norm_cfg=norm_cfg)
        self.to_k = Conv2d_BN(dim, nh_kd, 1, norm_cfg=norm_cfg)
        self.to_v = Conv2d_BN(dim, self.dh, 1, norm_cfg=norm_cfg)
        self.proj = torch.nn.Sequential(activation(), Conv2d_BN(
                self.dh, dim, bn_weight_init=0, norm_cfg=norm_cfg))
    def forward(self, x):  # x (B,N,C)
        B, C, H, W = get_shape(x)
        qq = self.to_q(x).reshape(B, self.num_heads, self.key_dim, H * W).permute(0, 1, 3, 2)
        kk = self.to_k(x).reshape(B, self.num_heads, self.key_dim, H * W)
        vv = self.to_v(x).reshape(B, self.num_heads, self.d, H * W).permute(0, 1, 3, 2)
        attn = torch.matmul(qq, kk)
        attn = attn.softmax(dim=-1)  # dim = k
        xx = torch.matmul(attn, vv)
        xx = xx.permute(0, 1, 3, 2).reshape(B, self.dh, H, W)
        xx = self.proj(xx)
        return xx
def get_shape(tensor):
    shape = tensor.shape
    if torch.onnx.is_in_onnx_export():
        shape = [i.cpu().numpy() for i in shape]
    return shape
class Conv2d_BN(nn.Sequential):
    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
                 groups=1, bn_weight_init=1,
                 norm_cfg=dict(type='BN', requires_grad=True)):
        super().__init__()
        self.inp_channel = a
        self.out_channel = b
        self.ks = ks
        self.pad = pad
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.add_module('c', nn.Conv2d(
                a, b, ks, stride, pad, dilation, groups, bias=False))
        bn = build_norm_layer(norm_cfg, b)[1]
        nn.init.constant_(bn.weight, bn_weight_init)
        nn.init.constant_(bn.bias, 0)
        self.add_module('bn', bn)
class High_FAM(nn.Module):
    def __init__(self, stride, pool_mode='onnx'):
        super().__init__()
        self.stride = stride
        if pool_mode == 'torch':
            self.pool = nn.functional.adaptive_avg_pool2d
        elif pool_mode == 'onnx':
            self.pool = onnx_AdaptiveAvgPool2d
    def forward(self, inputs):
        B, C, H, W = get_shape(inputs[-1])
        H = (H - 1) // self.stride + 1
        W = (W - 1) // self.stride + 1
        # output_size = np.array([H, W])
        output_size = [H, W]
        if not hasattr(self, 'pool'):
            self.pool = nn.functional.adaptive_avg_pool2d
        if torch.onnx.is_in_onnx_export():
            self.pool = onnx_AdaptiveAvgPool2d
        out = [self.pool(inp, output_size) for inp in inputs]
        return torch.cat(out, dim=1)
class RepVGGBlock(nn.Module):
    '''RepVGGBlock is a basic rep-style block, including training and deploy status
    This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
    '''
    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
        super(RepVGGBlock, self).__init__()
        """ Initialization of the class.
        Args:
            in_channels (int): Number of channels in the input image
            out_channels (int): Number of channels produced by the convolution
            kernel_size (int or tuple): Size of the convolving kernel
            stride (int or tuple, optional): Stride of the convolution. Default: 1
            padding (int or tuple, optional): Zero-padding added to both sides of
                the input. Default: 1
            dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
            groups (int, optional): Number of blocked connections from input
                channels to output channels. Default: 1
            padding_mode (string, optional): Default: 'zeros'
            deploy: Whether to be deploy status or training status. Default: False
            use_se: Whether to use se. Default: False
        """
        self.deploy = deploy
        self.groups = groups
        self.in_channels = in_channels
        self.out_channels = out_channels
        assert kernel_size == 3
        assert padding == 1
        padding_11 = padding - kernel_size // 2
        self.nonlinearity = nn.ReLU()
        if use_se:
            raise NotImplementedError("se block not supported yet")
        else:
            self.se = nn.Identity()
        if deploy:
            self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                                         stride=stride,
                                         padding=padding, dilation=dilation, groups=groups, bias=True,
                                         padding_mode=padding_mode)
        else:
            self.rbr_identity = nn.BatchNorm2d(
                    num_features=in_channels) if out_channels == in_channels and stride == 1 else None
            self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                                     stride=stride, padding=padding, groups=groups)
            self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
                                   padding=padding_11, groups=groups)
    def forward(self, inputs):
        '''Forward process'''
        if hasattr(self, 'rbr_reparam'):
            return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
        if self.rbr_identity is None:
            id_out = 0
        else:
            id_out = self.rbr_identity(inputs)
        return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))
    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
    def _fuse_bn_tensor(self, branch):
        if branch is None:
            return 0, 0
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            running_mean = branch.bn.running_mean
            running_var = branch.bn.running_var
            gamma = branch.bn.weight
            beta = branch.bn.bias
            eps = branch.bn.eps
        else:
            assert isinstance(branch, nn.BatchNorm2d)
            if not hasattr(self, 'id_tensor'):
                input_dim = self.in_channels // self.groups
                kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
                for i in range(self.in_channels):
                    kernel_value[i, i % input_dim, 1, 1] = 1
                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
            kernel = self.id_tensor
            running_mean = branch.running_mean
            running_var = branch.running_var
            gamma = branch.weight
            beta = branch.bias
            eps = branch.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std
    def switch_to_deploy(self):
        if hasattr(self, 'rbr_reparam'):
            return
        kernel, bias = self.get_equivalent_kernel_bias()
        self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels,
                                     out_channels=self.rbr_dense.conv.out_channels,
                                     kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,
                                     padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation,
                                     groups=self.rbr_dense.conv.groups, bias=True)
        self.rbr_reparam.weight.data = kernel
        self.rbr_reparam.bias.data = bias
        for para in self.parameters():
            para.detach_()
        self.__delattr__('rbr_dense')
        self.__delattr__('rbr_1x1')
        if hasattr(self, 'rbr_identity'):
            self.__delattr__('rbr_identity')
        if hasattr(self, 'id_tensor'):
            self.__delattr__('id_tensor')
        self.deploy = True
class RepBlock(nn.Module):
    '''
        RepBlock is a stage block with rep-style basic block
    '''
    def __init__(self, in_channels, out_channels, n=1, block=RepVGGBlock, basic_block=RepVGGBlock):
        super().__init__()
        self.conv1 = block(in_channels, out_channels)
        self.block = nn.Sequential(*(block(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
        '''
        if block == BottleRep:
            self.conv1 = BottleRep(in_channels, out_channels, basic_block=basic_block, weight=True)
            n = n // 2
            self.block = nn.Sequential(
                    *(BottleRep(out_channels, out_channels, basic_block=basic_block, weight=True) for _ in
                      range(n - 1))) if n > 1 else None
        '''
    def forward(self, x):
        x = self.conv1(x)
        if self.block is not None:
            x = self.block(x)
        return x
class Inject(nn.Module):
    def __init__(
            self,
            inp: int,
            oup: int,
            global_index: int,
            norm_cfg=dict(type='BN', requires_grad=True),
            activations=nn.ReLU6,
            global_inp=None,
    ) -> None:
        super().__init__()
        self.global_index = global_index
        self.norm_cfg = norm_cfg
        if not global_inp:
            global_inp = inp
        self.local_embedding = ConvModule(inp, oup, kernel_size=1, norm_cfg=self.norm_cfg, act_cfg=None)
        self.global_embedding = ConvModule(global_inp, oup, kernel_size=1, norm_cfg=self.norm_cfg, act_cfg=None)
        self.global_act = ConvModule(global_inp, oup, kernel_size=1, norm_cfg=self.norm_cfg, act_cfg=None)
        self.act = h_sigmoid()
    def forward(self, x_l, x_g):
        '''
        x_g: global features
        x_l: local features
        '''
        x_g = x_g[self.global_index]
        B, C, H, W = x_l.shape
        g_B, g_C, g_H, g_W = x_g.shape
        use_pool = H < g_H
        local_feat = self.local_embedding(x_l)
        global_act = self.global_act(x_g)
        global_feat = self.global_embedding(x_g)
        if use_pool:
            avg_pool = get_avg_pool()
            # output_size = np.array([H, W])
            output_size = [H, W]
            sig_act = avg_pool(global_act, output_size)
            global_feat = avg_pool(global_feat, output_size)
        else:
            sig_act = F.interpolate(self.act(global_act), size=(H, W), mode='bilinear', align_corners=False)
            global_feat = F.interpolate(global_feat, size=(H, W), mode='bilinear', align_corners=False)
        out = local_feat * sig_act + global_feat
        return out
class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)
    def forward(self, x):
        return self.relu(x + 3) / 6
def get_avg_pool():
    if torch.onnx.is_in_onnx_export():
        avg_pool = onnx_AdaptiveAvgPool2d
    else:
        avg_pool = nn.functional.adaptive_avg_pool2d
    return avg_pool
class Low_LAF(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.cv1 = SimConv(in_channels, out_channels, 1, 1)
        self.cv_fuse = SimConv(round(out_channels * 2.5), out_channels, 1, 1)
        self.downsample = nn.functional.adaptive_avg_pool2d
    def forward(self, x):
        N, C, H, W = x[1].shape
        # output_size = np.array([H, W])
        output_size = [H, W]
        if torch.onnx.is_in_onnx_export():
            self.downsample = onnx_AdaptiveAvgPool2d
            output_size = np.array([H, W])
        x0 = self.downsample(x[0], output_size)
        x1 = self.cv1(x[1])
        x2 = F.interpolate(x[2], size=(H, W), mode='bilinear', align_corners=False)
        return self.cv_fuse(torch.cat((x0, x1, x2), dim=1))
class SimConv(nn.Module):
    '''Normal Conv with ReLU VAN_activation'''
    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False, padding=None):
        super().__init__()
        if padding is None:
            padding = kernel_size // 2
        self.conv = nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                stride=stride,
                padding=padding,
                groups=groups,
                bias=bias,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = nn.ReLU()
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))
    def forward_fuse(self, x):
        return self.act(self.conv(x))
class Split(nn.Module):
    def __init__(self, trans_channels):
        super().__init__()
        self.trans_channels = trans_channels
    def forward(self, x):
        return x.split(self.trans_channels, dim=1)
class Low_IFM(nn.Module):
    def __init__(self, in_channels, embed_dims, fuse_block_num, out_channels):
        super().__init__()
        self.conv1 = Conv(in_channels, embed_dims, kernel_size=1, stride=1, padding=0)
        self.block = nn.ModuleList([RepVGGBlock(embed_dims, embed_dims) for _ in range(fuse_block_num)]) if fuse_block_num > 0 else nn.Identity
        self.conv2 = Conv(embed_dims, out_channels, kernel_size=1, stride=1, padding=0)
    def forward(self, x):
        x = self.conv1(x)
        for b in self.block:
            x = b(x)
        out = self.conv2(x)
        return out
class Low_FAM(nn.Module):
    def __init__(self):
        super().__init__()
        self.avg_pool = nn.functional.adaptive_avg_pool2d
    def forward(self, x):
        x_l, x_m, x_s, x_n = x
        B, C, H, W = x_s.shape
        # output_size = np.array([H, W])
        output_size = [H, W]
        if torch.onnx.is_in_onnx_export():
            self.avg_pool = onnx_AdaptiveAvgPool2d
        x_l = self.avg_pool(x_l, output_size)
        x_m = self.avg_pool(x_m, output_size)
        x_n = F.interpolate(x_n, size=(H, W), mode='bilinear', align_corners=False)
        out = torch.cat([x_l, x_m, x_s, x_n], 1)
        return out
def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1, bias=False):
    '''Basic cell for rep-style block, including conv and bn'''
    result = nn.Sequential()
    result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                        kernel_size=kernel_size, stride=stride, padding=padding, groups=groups,
                                        bias=bias))
    result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
    return result
class Conv(nn.Module):
    '''Normal Conv with SiLU VAN_activation'''
    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False, padding=None):
        super().__init__()
        if padding is None:
            padding = kernel_size // 2
        self.conv = nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                stride=stride,
                padding=padding,
                groups=groups,
                bias=bias,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = nn.SiLU()
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))
def onnx_AdaptiveAvgPool2d(x, output_size):
    stride_size = np.floor(np.array(x.shape[-2:]) / output_size).astype(np.int32)
    kernel_size = np.array(x.shape[-2:]) - (output_size - 1) * stride_size
    avg = nn.AvgPool2d(kernel_size=list(kernel_size), stride=list(stride_size))
    x = avg(x)
    return x

​

  # --------------GOLD-YOLO--------------
        elif m in (nn.Conv2d, SimConv):
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(c2 * width, 8)
            args = [c1, c2, *args[1:]]
        elif m in (High_IFM, ):
            c2 = args[1]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(c2 * width, 8)
            args = [args[0], c2, *args[2:]]
        elif m in (Low_FAM, High_FAM, High_LAF):
            c2 = sum(ch[x] for x in f)
        elif m is Low_IFM:
            c1, c2 = ch[f], args[2]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, *args[:-1], c2]
        elif m is Low_LAF:
            c1, c2 = ch[f[1]], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, c2, *args[1:]]
        elif m is Inject:
            global_index = args[1]
            c1, c2 = ch[f[1]][global_index], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, c2, global_index]
        elif m is RepBlock:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            nums_repeat = max(round(args[1] * depth), 1) if args[1] > 1 else args[1]  # depth gain
            args = [c1, c2, nums_repeat]
        elif m is Split:
            goldyolo = True
            c2 = []
            for arg in args:
                if arg != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                    c2.append(make_divisible(min(arg, max_channels) * width, 8))
            args = [c2]
        # --------------GOLD-YOLO--------------

        if m in [Inject, High_LAF]:
            # input nums
            m_.input_nums = len(f)
        else:
            m_.input_nums = 1

            try:
                if m.input_nums > 1:
                    # input nums more than one
                    x = m(*x)  # run
                else:
                    x = m(x)
            except AttributeError:
                # AttributeError: 'Conv' object has no attribute 'input_nums'
                x = m(x)

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
# YOLO11n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  - [-1, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
  - [[2, 4, 6, -1], 1, Low_FAM, []]
  - [-1, 1, Low_IFM, [96, 3, 768]]
  - [-1, 1, Split, [512, 256]] # 13-low_global_info
  - [10, 1, SimConv, [512, 1, 1]] # 14-c5_half
  - [[4, 6, -1], 1, Low_LAF, [512]]
  - [[-1, 13], 1, Inject, [512, 0]]
  - [-1, 1, RepBlock, [512, 12]] # 17-p4
  - [-1, 1, SimConv, [256, 1, 1]] # 18-p4_half
  - [[2, 4, -1], 1, Low_LAF, [256]]
  - [[-1, 13], 1, Inject, [256, 1]]
  - [-1, 1, RepBlock, [256, 12]] # 21-p3
  - [[-1, 17, 10], 1, High_FAM, [1, 'torch']]
  - [-1, 1, High_IFM, [2, 1792, 8, 4, 1, 2, 0, 0, [0.1, 2]]]
  - [-1, 1, nn.Conv2d, [1536, 1, 1, 0]]
  - [-1, 1, Split, [512, 1024]] # 25-high_global_info
  - [[21, 18], 1, High_LAF, []]
  - [[-1, 25], 1, Inject, [512, 0]]
  - [-1, 1, RepBlock, [512, 12]] # 28-n4
  - [[-1, 14], 1, High_LAF, []]
  - [[-1, 25], 1, Inject, [1024, 1]]
  - [-1, 1, RepBlock, [1024, 12]] # 31-n5
  - [[21, 28, 31], 1, Detect, [nc]]  # Detect(P3, N4, N5)

import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO
if __name__ == '__main__':
    model = YOLO("替换你的模型yaml文件路径")
    model.load('yolov8n.pt') # 我这里用的n的权重文件，大家可以自行替换自己的版本的
    model.train(data=r'替换你的数据集yaml文件地址',
                cache=False,
                imgsz=640,
                epochs=150,
                batch=16,
                close_mosaic=0,
                workers=0,
                device=0,
                optimizer='SGD', # using SGD
                amp=False,# close amp
                )