first commit

ultralytics/nn/modules/__init__.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+Ultralytics modules.
+
+Example:
+    Visualize a module with Netron.
+    ```python
+    from ultralytics.nn.modules import *
+    import torch
+    import os
+
+    x = torch.ones(1, 128, 40, 40)
+    m = Conv(128, 128)
+    f = f'{m._get_name()}.onnx'
+    torch.onnx.export(m, x, f)
+    os.system(f'onnxsim {f} {f} && open {f}')
+    ```
+"""
+
+from .block import (
+    C1,
+    C2,
+    C2PSA,
+    C3,
+    C3TR,
+    CIB,
+    DFL,
+    ELAN1,
+    PSA,
+    SPP,
+    SPPELAN,
+    SPPF,
+    AConv,
+    ADown,
+    BNContrastiveHead,
+    Bottleneck,
+    BottleneckCSP,
+    C2f,
+    C2fMaskAtt,
+    ConvMaskAtt,
+    ConvMask,
+    C2fAttn,
+    C2fCIB,
+    C2fPSA,
+    C3Ghost,
+    C3k2,
+    C3x,
+    CBFuse,
+    CBLinear,
+    ContrastiveHead,
+    GhostBottleneck,
+    HGBlock,
+    HGStem,
+    ImagePoolingAttn,
+    Proto,
+    RepC3,
+    RepNCSPELAN4,
+    RepVGGDW,
+    ResNetLayer,
+    SCDown,
+    Silence,
+)
+from .conv import (
+    CBAM,
+    ChannelAttention,
+    Concat,
+    Add,
+    Max,
+    CatConv,
+    CatConvAtt,
+    Conv,
+    Conv2,
+    ConvTranspose,
+    DWConv,
+    DWConvTranspose2d,
+    Focus,
+    GhostConv,
+    LightConv,
+    RepConv,
+    SpatialAttention,
+)
+from .head import OBB, Classify, Detect, DetectBin, DetectQG, Detrack, Demask, Pose, RTDETRDecoder, Segment, WorldDetect
+from .transformer import (
+    AIFI,
+    MLP,
+    DeformableTransformerDecoder,
+    DeformableTransformerDecoderLayer,
+    LayerNorm2d,
+    MLPBlock,
+    MSDeformAttn,
+    TransformerBlock,
+    TransformerEncoderLayer,
+    TransformerLayer,
+)
+
+__all__ = (
+    "Conv",
+    "Conv2",
+    "LightConv",
+    "RepConv",
+    "DWConv",
+    "DWConvTranspose2d",
+    "ConvTranspose",
+    "Focus",
+    "GhostConv",
+    "ChannelAttention",
+    "SpatialAttention",
+    "CBAM",
+    "Concat",
+    "Add",
+    "Max",
+    "CatConv",
+    "CatConvAtt",
+    "TransformerLayer",
+    "TransformerBlock",
+    "MLPBlock",
+    "LayerNorm2d",
+    "DFL",
+    "HGBlock",
+    "HGStem",
+    "SPP",
+    "SPPF",
+    "C1",
+    "C2",
+    "C3",
+    "C2f",
+    "C2fMaskAtt",
+    "ConvMaskAtt",
+    "ConvMask",
+    "C3k2",
+    "SCDown",
+    "C2fPSA",
+    "C2PSA",
+    "C2fAttn",
+    "C3x",
+    "C3TR",
+    "C3Ghost",
+    "GhostBottleneck",
+    "Bottleneck",
+    "BottleneckCSP",
+    "Proto",
+    "Detect",
+    "DetectBin",
+    "DetectQG",
+    "Detrack",
+    "Demask",
+    "Segment",
+    "Pose",
+    "Classify",
+    "TransformerEncoderLayer",
+    "RepC3",
+    "RTDETRDecoder",
+    "AIFI",
+    "DeformableTransformerDecoder",
+    "DeformableTransformerDecoderLayer",
+    "MSDeformAttn",
+    "MLP",
+    "ResNetLayer",
+    "OBB",
+    "WorldDetect",
+    "v10Detect",
+    "ImagePoolingAttn",
+    "ContrastiveHead",
+    "BNContrastiveHead",
+    "RepNCSPELAN4",
+    "ADown",
+    "SPPELAN",
+    "CBFuse",
+    "CBLinear",
+    "Silence",
+    "AConv",
+    "ELAN1",
+    "RepVGGDW",
+    "CIB",
+    "C2fCIB",
+    "Attention",
+    "PSA",
+)

ultralytics/nn/modules/conv.py

@@ -0,0 +1,461 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""Convolution modules."""
+
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+__all__ = (
+    "Conv",
+    "Conv2",
+    "LightConv",
+    "DWConv",
+    "DWConvTranspose2d",
+    "ConvTranspose",
+    "Focus",
+    "GhostConv",
+    "ChannelAttention",
+    "SpatialAttention",
+    "CBAM",
+    "Concat",
+    "Add",
+    "Max",
+    "CatConv",
+    "CatConvAtt",
+    "RepConv",
+)
+
+
+def autopad(k, p=None, d=1):  # kernel, padding, dilation
+    """Pad to 'same' shape outputs."""
+    if d > 1:
+        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+    return p
+
+
+class Conv(nn.Module):
+    """Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
+
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        """Apply convolution, batch normalization and activation to input tensor."""
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        """Perform transposed convolution of 2D data."""
+        return self.act(self.conv(x))
+
+
+class Conv2(Conv):
+    """Simplified RepConv module with Conv fusing."""
+
+    def __init__(self, c1, c2, k=3, s=1, p=None, g=1, d=1, act=True):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__(c1, c2, k, s, p, g=g, d=d, act=act)
+        self.cv2 = nn.Conv2d(c1, c2, 1, s, autopad(1, p, d), groups=g, dilation=d, bias=False)  # add 1x1 conv
+
+    def forward(self, x):
+        """Apply convolution, batch normalization and activation to input tensor."""
+        return self.act(self.bn(self.conv(x) + self.cv2(x)))
+
+    def forward_fuse(self, x):
+        """Apply fused convolution, batch normalization and activation to input tensor."""
+        return self.act(self.bn(self.conv(x)))
+
+    def fuse_convs(self):
+        """Fuse parallel convolutions."""
+        w = torch.zeros_like(self.conv.weight.data)
+        i = [x // 2 for x in w.shape[2:]]
+        w[:, :, i[0] : i[0] + 1, i[1] : i[1] + 1] = self.cv2.weight.data.clone()
+        self.conv.weight.data += w
+        self.__delattr__("cv2")
+        self.forward = self.forward_fuse
+
+
+class LightConv(nn.Module):
+    """
+    Light convolution with args(ch_in, ch_out, kernel).
+
+    https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
+    """
+
+    def __init__(self, c1, c2, k=1, act=nn.ReLU()):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__()
+        self.conv1 = Conv(c1, c2, 1, act=False)
+        self.conv2 = DWConv(c2, c2, k, act=act)
+
+    def forward(self, x):
+        """Apply 2 convolutions to input tensor."""
+        return self.conv2(self.conv1(x))
+
+
+class DWConv(Conv):
+    """Depth-wise convolution."""
+
+    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
+        """Initialize Depth-wise convolution with given parameters."""
+        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
+
+
+class DWConvTranspose2d(nn.ConvTranspose2d):
+    """Depth-wise transpose convolution."""
+
+    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
+        """Initialize DWConvTranspose2d class with given parameters."""
+        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
+
+
+class ConvTranspose(nn.Module):
+    """Convolution transpose 2d layer."""
+
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
+        """Initialize ConvTranspose2d layer with batch normalization and activation function."""
+        super().__init__()
+        self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn)
+        self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity()
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        """Applies transposed convolutions, batch normalization and activation to input."""
+        return self.act(self.bn(self.conv_transpose(x)))
+
+    def forward_fuse(self, x):
+        """Applies activation and convolution transpose operation to input."""
+        return self.act(self.conv_transpose(x))
+
+
+class Focus(nn.Module):
+    """Focus wh information into c-space."""
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
+        """Initializes Focus object with user defined channel, convolution, padding, group and activation values."""
+        super().__init__()
+        self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
+        # self.contract = Contract(gain=2)
+
+    def forward(self, x):
+        """
+        Applies convolution to concatenated tensor and returns the output.
+
+        Input shape is (b,c,w,h) and output shape is (b,4c,w/2,h/2).
+        """
+        return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
+        # return self.conv(self.contract(x))
+
+
+class GhostConv(nn.Module):
+    """Ghost Convolution https://github.com/huawei-noah/ghostnet."""
+
+    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
+        """Initializes the GhostConv object with input channels, output channels, kernel size, stride, groups and
+        activation.
+        """
+        super().__init__()
+        c_ = c2 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
+        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
+
+    def forward(self, x):
+        """Forward propagation through a Ghost Bottleneck layer with skip connection."""
+        y = self.cv1(x)
+        return torch.cat((y, self.cv2(y)), 1)
+
+
+class RepConv(nn.Module):
+    """
+    RepConv is a basic rep-style block, including training and deploy status.
+
+    This module is used in RT-DETR.
+    Based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
+    """
+
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):
+        """Initializes Light Convolution layer with inputs, outputs & optional activation function."""
+        super().__init__()
+        assert k == 3 and p == 1
+        self.g = g
+        self.c1 = c1
+        self.c2 = c2
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+        self.bn = nn.BatchNorm2d(num_features=c1) if bn and c2 == c1 and s == 1 else None
+        self.conv1 = Conv(c1, c2, k, s, p=p, g=g, act=False)
+        self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)
+
+    def forward_fuse(self, x):
+        """Forward process."""
+        return self.act(self.conv(x))
+
+    def forward(self, x):
+        """Forward process."""
+        id_out = 0 if self.bn is None else self.bn(x)
+        return self.act(self.conv1(x) + self.conv2(x) + id_out)
+
+    def get_equivalent_kernel_bias(self):
+        """Returns equivalent kernel and bias by adding 3x3 kernel, 1x1 kernel and identity kernel with their biases."""
+        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
+        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)
+        kernelid, biasid = self._fuse_bn_tensor(self.bn)
+        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
+
+    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
+        """Pads a 1x1 tensor to a 3x3 tensor."""
+        if kernel1x1 is None:
+            return 0
+        else:
+            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
+
+    def _fuse_bn_tensor(self, branch):
+        """Generates appropriate kernels and biases for convolution by fusing branches of the neural network."""
+        if branch is None:
+            return 0, 0
+        if isinstance(branch, Conv):
+            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
+        elif isinstance(branch, nn.BatchNorm2d):
+            if not hasattr(self, "id_tensor"):
+                input_dim = self.c1 // self.g
+                kernel_value = np.zeros((self.c1, input_dim, 3, 3), dtype=np.float32)
+                for i in range(self.c1):
+                    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 fuse_convs(self):
+        """Combines two convolution layers into a single layer and removes unused attributes from the class."""
+        if hasattr(self, "conv"):
+            return
+        kernel, bias = self.get_equivalent_kernel_bias()
+        self.conv = nn.Conv2d(
+            in_channels=self.conv1.conv.in_channels,
+            out_channels=self.conv1.conv.out_channels,
+            kernel_size=self.conv1.conv.kernel_size,
+            stride=self.conv1.conv.stride,
+            padding=self.conv1.conv.padding,
+            dilation=self.conv1.conv.dilation,
+            groups=self.conv1.conv.groups,
+            bias=True,
+        ).requires_grad_(False)
+        self.conv.weight.data = kernel
+        self.conv.bias.data = bias
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("conv1")
+        self.__delattr__("conv2")
+        if hasattr(self, "nm"):
+            self.__delattr__("nm")
+        if hasattr(self, "bn"):
+            self.__delattr__("bn")
+        if hasattr(self, "id_tensor"):
+            self.__delattr__("id_tensor")
+
+
+class ChannelAttention(nn.Module):
+    """Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
+
+    def __init__(self, channels: int) -> None:
+        """Initializes the class and sets the basic configurations and instance variables required."""
+        super().__init__()
+        self.pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)
+        self.act = nn.Sigmoid()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Applies forward pass using activation on convolutions of the input, optionally using batch normalization."""
+        return x * self.act(self.fc(self.pool(x)))
+
+
+class SpatialAttention(nn.Module):
+    """Spatial-attention module."""
+
+    def __init__(self, kernel_size=7):
+        """Initialize Spatial-attention module with kernel size argument."""
+        super().__init__()
+        assert kernel_size in {3, 7}, "kernel size must be 3 or 7"
+        padding = 3 if kernel_size == 7 else 1
+        self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
+        self.act = nn.Sigmoid()
+
+    def forward(self, x):
+        """Apply channel and spatial attention on input for feature recalibration."""
+        return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1)))
+    
+class MaxAttention(nn.Module):
+    """Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
+
+    def __init__(self, channels, kernel_size=7):
+        """Initializes the class and sets the basic configurations and instance variables required."""
+        super().__init__()
+        self.pool = nn.AdaptiveMaxPool2d(1)
+        self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)
+
+        assert kernel_size in {3, 7}, "kernel size must be 3 or 7"
+        padding = 3 if kernel_size == 7 else 1
+        self.cv1 = nn.Conv2d(1, 1, kernel_size, padding=padding, bias=False)
+        self.act = nn.Sigmoid()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Applies forward pass using activation on convolutions of the input, optionally using batch normalization."""
+        x =  x * self.act(self.fc(self.pool(x)))
+        return x * self.act(self.cv1(torch.max(x, 1, keepdim=True)[0]))
+
+
+class CBAM(nn.Module):
+    """Convolutional Block Attention Module."""
+
+    def __init__(self, c1, kernel_size=7):
+        """Initialize CBAM with given input channel (c1) and kernel size."""
+        super().__init__()
+        self.channel_attention = ChannelAttention(c1)
+        self.spatial_attention = SpatialAttention(kernel_size)
+
+    def forward(self, x):
+        """Applies the forward pass through C1 module."""
+        return self.spatial_attention(self.channel_attention(x))
+
+
+class Concat(nn.Module):
+    """Concatenate a list of tensors along dimension."""
+
+    def __init__(self, dimension=1):
+        """Concatenates a list of tensors along a specified dimension."""
+        super().__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        """Forward pass for the YOLOv8 mask Proto module."""
+        return torch.cat(x, self.d)
+
+class Add(nn.Module):
+    #  Add two tensors
+    def __init__(self, arg):
+        super().__init__()
+        self.arg = arg
+
+    def forward(self, x):
+        x0 = [xi if isinstance(xi, torch.Tensor) else xi[0] for xi in x]
+        return torch.add(x0[0], x0[1])
+    
+class Max(nn.Module):
+    #  Max two tensors
+    def __init__(self, arg):
+        super().__init__()
+        self.arg = arg
+
+    def forward(self, x):
+        x0 = [xi if isinstance(xi, torch.Tensor) else xi[0] for xi in x]
+        return torch.max(x0[0], x0[1])
+    
+class CatConv(nn.Module):
+    """Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
+
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__()
+        self.d = 1
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        """Apply convolution, batch normalization and activation to input tensor."""
+        x = torch.cat(x, self.d)
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        """Perform transposed convolution of 2D data."""
+        x = torch.cat(x, self.d)
+        return self.act(self.conv(x))
+
+class SimAM(nn.Module):
+    def __init__(self, e_lambda=1e-4):
+        super(SimAM, self).__init__()
+        self.activaton = nn.Sigmoid()
+        self.e_lambda = e_lambda
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        n = w * h - 1
+        x_minus_mu_square = (x - x.mean(dim=[2, 3], keepdim=True)).pow(2)
+        y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2, 3], keepdim=True) / n + self.e_lambda)) + 0.5
+
+        return self.activaton(y)
+    
+class CatConvAtt1(nn.Module):
+        
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__()
+        self.d = 1
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+        # self.att = CBAM(c1)
+        self.att0 = CBAM(c2)
+        self.att1 = CBAM(c2)
+        self.c2 = Conv(c2, c2, 3, 1)
+
+    def forward(self, x):
+        """Apply convolution, batch normalization and activation to input tensor."""
+        x[0] = self.att0(x[0])
+        x[1] = self.att1(x[1])
+        x = torch.cat(x, self.d)
+        # x = self.att(x)
+        return self.c2(self.act(self.bn(self.conv(x))))
+
+    def forward_fuse(self, x):
+        """Perform transposed convolution of 2D data."""
+        x[0] = self.att0(x[0])
+        x[1] = self.att1(x[1])
+        x = torch.cat(x, self.d)
+        # x = self.att(x)
+        return self.c2(self.act(self.conv(x)))
+    
+class CatConvAtt(nn.Module):
+        
+    default_act = nn.SiLU()  # default activation
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__()
+        # self.att = SpatialAttention(kernel_size=3)
+        self.att = MaxAttention(c2, kernel_size=3)
+
+    def forward(self, x):
+        """Apply convolution, batch normalization and activation to input tensor."""
+        x[0] = self.att(x[0])
+        x[1] = self.att(x[1])
+        return torch.add(x[0], x[1])

ultralytics/nn/modules/head.py

@@ -0,0 +1,740 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""Model head modules."""
+
+import math
+import copy
+
+import torch
+import torch.nn as nn
+from torch.nn.init import constant_, xavier_uniform_
+
+from ultralytics.utils.tal import TORCH_1_10, dist2bbox, dist2rbox, make_anchors
+
+from .block import DFL, BNContrastiveHead, ContrastiveHead, Proto
+from .conv import Conv
+from .transformer import MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer
+from .utils import bias_init_with_prob, linear_init
+
+__all__ = "Detect", "DetectBin", "DetectQG", "Detrack", "Demask", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder"
+
+
+class Detect(nn.Module):
+    """YOLOv8 Detect head for detection models."""
+
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    end2end = False  # end2end
+    max_det = 300  # max_det
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=()):
+        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
+        super().__init__()
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16  # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+        c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
+        )
+        self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        """Concatenates and returns predicted bounding boxes and class probabilities."""
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:  # Training path
+            return x
+
+        # Inference path
+        shape = x[0].shape  # BCHW
+        x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
+        if self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}:  # avoid TF FlexSplitV ops
+            box = x_cat[:, : self.reg_max * 4]
+            cls = x_cat[:, self.reg_max * 4 :]
+        else:
+            box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
+
+        if self.export and self.format in {"tflite", "edgetpu"}:
+            # Precompute normalization factor to increase numerical stability
+            # See https://github.com/ultralytics/ultralytics/issues/7371
+            grid_h = shape[2]
+            grid_w = shape[3]
+            grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
+            norm = self.strides / (self.stride[0] * grid_size)
+            dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
+        else:
+            dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
+
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        """Initialize Detect() biases, WARNING: requires stride availability."""
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
+
+    def decode_bboxes(self, bboxes, anchors):
+        """Decode bounding boxes."""
+        return dist2bbox(bboxes, anchors, xywh=True, dim=1)
+
+
+class DetectBin(Detect):
+    """YOLOv8 Detect head for detection models."""
+
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=()):
+        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
+        super().__init__(nc, ch)
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16  # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
+        self.no = nc + self.reg_max * 4 + 1 # number of outputs per anchor
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+        c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100))  # channels
+        # cv2 for bounding box regression
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
+        )
+        # cv3 for class prediction
+        self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        # cv4 for objectness (binary classification)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, 1, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        """Concatenates and returns predicted bounding boxes, class probabilities, and objectness scores."""
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i]), self.cv4[i](x[i])), 1)
+        if self.training:  # Training path
+            return x
+
+        # Inference path
+        shape = x[0].shape  # BCHW
+        x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
+        if self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}:  # avoid TF FlexSplitV ops
+            box = x_cat[:, : self.reg_max * 4]
+            cls = x_cat[:, self.reg_max * 4: -1]
+            obj = x_cat[:, -1]
+        else:
+            box, cls, obj = x_cat.split((self.reg_max * 4, self.nc, 1), 1)
+
+        if self.export and self.format in {"tflite", "edgetpu"}:
+            # Precompute normalization factor to increase numerical stability
+            grid_h = shape[2]
+            grid_w = shape[3]
+            grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
+            norm = self.strides / (self.stride[0] * grid_size)
+            dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
+        else:
+            dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
+
+        y = torch.cat((dbox, cls.sigmoid(), obj.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+import itertools
+class DetectQG(Detect):
+    """YOLOv8 Detect head for detection models."""
+
+    dynamic = False  # force grid reconstruction
+    export = False  # export mode
+    shape = None
+    anchors = torch.empty(0)  # init
+    strides = torch.empty(0)  # init
+
+    def __init__(self, nc=80, ch=()):
+        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
+        super().__init__(nc, ch)
+        self.nc = nc  # number of classes
+        self.nl = len(ch)  # number of detection layers
+        self.reg_max = 16  # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
+        self.no = nc + self.reg_max * 4  # number of outputs per anchor
+        self.stride = torch.zeros(self.nl)  # strides computed during build
+        c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100))  # channels
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
+        )
+        self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+        output_sizes = [7, 5, 3, 3]
+        output_size_cycle = itertools.cycle(output_sizes)
+        self.qg = nn.ModuleList(QG_Modulator(channels=x, output_size=next(output_size_cycle)) for x in ch)
+
+    def forward(self, x, bboxes=None):
+        """Concatenates and returns predicted bounding boxes and class probabilities."""
+        
+        for i in range(self.nl):
+            if bboxes is None:
+                continue
+            gt_bboxes_z = bboxes.clone()
+            B, C, H, W = x[i].shape
+            y = x[i].view(-1, 2, C, H, W)
+            y_z = y[:, 0, :, :, :]
+            y_x = y[:, 1, :, :, :]
+            gt_bboxes_z[:, 1:] = bboxes[:, 1:].mul_(torch.tensor([H, W], device=bboxes.device)[[1, 0, 1, 0]]) #确认bboxes顺序
+            if C > 1:
+                x[i] = y_x
+            else:
+                x[i] = self.qg[i](y_z, y_x, gt_bboxes_z=gt_bboxes_z.to(y_z.device))
+
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        if self.training:  # Training path
+            return x
+
+        # Inference path
+        shape = x[0].shape  # BCHW
+        x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
+        if self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}:  # avoid TF FlexSplitV ops
+            box = x_cat[:, : self.reg_max * 4]
+            cls = x_cat[:, self.reg_max * 4 :]
+        else:
+            box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
+
+        if self.export and self.format in {"tflite", "edgetpu"}:
+            # Precompute normalization factor to increase numerical stability
+            # See https://github.com/ultralytics/ultralytics/issues/7371
+            grid_h = shape[2]
+            grid_w = shape[3]
+            grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
+            norm = self.strides / (self.stride[0] * grid_size)
+            dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
+        else:
+            dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
+
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        """Initialize Detect() biases, WARNING: requires stride availability."""
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
+
+    def decode_bboxes(self, bboxes, anchors):
+        """Decode bounding boxes."""
+        return dist2bbox(bboxes, anchors, xywh=True, dim=1)
+
+import torchvision.ops as ops
+import torch.nn.functional as F
+class QG_Modulator(nn.Module):
+
+    def __init__(self,
+                 channels=256,
+                 output_size=7):
+        super(QG_Modulator, self).__init__()
+        self.conv_z = Conv(channels, channels, 3)
+        self.conv_x = Conv(channels, channels, 3)
+        self.roi_align = ops.RoIAlign(output_size=output_size, spatial_scale=1, sampling_ratio=-1)
+    
+    def forward(self, feats_z, feats_x, gt_bboxes_z):
+        feats_x = feats_x.contiguous()
+        feats_z = feats_z.contiguous()
+
+        rois = gt_bboxes_z.to(feats_z.device)
+        bbox_feats = self.roi_align(feats_z, rois)
+        
+        # 深度可分离相关卷积
+        batch = bbox_feats.size(0)
+        channel = bbox_feats.size(1)
+        feats_x = feats_x.view(1, batch*channel, feats_x.size(2), feats_x.size(3))
+        bbox_feats = bbox_feats.view(batch*channel, 1, bbox_feats.size(2), bbox_feats.size(3))
+        out = F.conv2d(feats_x, bbox_feats, groups=batch*channel)
+        out = out.view(batch, channel, out.size(2), out.size(3))
+
+        return out
+    
+class QG_Modulator1(nn.Module):
+
+    def __init__(self,
+                 channels=256,
+                 output_size=7):
+        super(QG_Modulator, self).__init__()
+        self.roi_output_size = output_size
+        self.proj_modulator = nn.Conv2d(channels, channels, output_size, padding=0)
+        self.proj_out = nn.Conv2d(channels, channels, 1, padding=0)
+        self.roi_align = ops.RoIAlign(output_size=output_size, spatial_scale=1, sampling_ratio=-1)
+    
+    def forward(self, feats_z, feats_x, gt_bboxes_z):
+        rois = gt_bboxes_z.to(feats_z.device)
+        bbox_feats = self.roi_align(feats_z, rois)
+
+        bbox_feats = bbox_feats.contiguous()
+        bbox_feats = self.proj_modulator(bbox_feats)
+        feats_x = bbox_feats * feats_x
+        # # 使用 gather 或 scatter 更新 feats_x
+        # batch_indices = rois[:, 0].long()
+        # for i, batch_idx in enumerate(batch_indices):
+        #     feats_x[batch_idx, ...] = bbox_feats[i, ...] * feats_x[batch_idx, ...]
+
+        feats_x = self.proj_out(feats_x)
+        return feats_x
+
+class Detrack(Detect):
+    def __init__(self, nc=80, ch=()):
+        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
+        super().__init__(nc, ch)
+
+class Demask(nn.Module):
+    """YOLOv8 Detect head for detection models."""
+    def __init__(self, nc=80, ch=()):
+        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
+        super().__init__()
+        self.nc = 1  # 1
+        self.nl = len(ch)  # number of detection layers
+        self.export = False
+
+        c4 = max(ch[0] // 4, self.nc)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nc, 1)) for x in ch)
+
+    def forward(self, x):
+        """Concatenates and returns predicted bounding boxes and class probabilities."""
+        for i in range(self.nl):
+            x[i] = self.cv4[i](x[i])
+        if self.training:  # Training path
+            return x
+
+        # Inference path
+        y = x[0].sigmoid()
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        """Initialize Detect() biases, WARNING: requires stride availability."""
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        # for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+        #     a[-1].bias.data[:] = 1.0  # box
+        #     b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
+
+    def decode_bboxes(self, bboxes, anchors):
+        """Decode bounding boxes."""
+        return dist2bbox(bboxes, anchors, xywh=True, dim=1)
+
+class Segment(Detect):
+    """YOLOv8 Segment head for segmentation models."""
+
+    def __init__(self, nc=80, nm=32, npr=256, ch=()):
+        """Initialize the YOLO model attributes such as the number of masks, prototypes, and the convolution layers."""
+        super().__init__(nc, ch)
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
+
+        c4 = max(ch[0] // 4, self.nm)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
+
+    def forward(self, x):
+        """Return model outputs and mask coefficients if training, otherwise return outputs and mask coefficients."""
+        p = self.proto(x[0])  # mask protos
+        bs = p.shape[0]  # batch size
+
+        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = Detect.forward(self, x)
+        if self.training:
+            return x, mc, p
+        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
+
+
+class OBB(Detect):
+    """YOLOv8 OBB detection head for detection with rotation models."""
+
+    def __init__(self, nc=80, ne=1, ch=()):
+        """Initialize OBB with number of classes `nc` and layer channels `ch`."""
+        super().__init__(nc, ch)
+        self.ne = ne  # number of extra parameters
+
+        c4 = max(ch[0] // 4, self.ne)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.ne, 1)) for x in ch)
+
+    def forward(self, x):
+        """Concatenates and returns predicted bounding boxes and class probabilities."""
+        bs = x[0].shape[0]  # batch size
+        angle = torch.cat([self.cv4[i](x[i]).view(bs, self.ne, -1) for i in range(self.nl)], 2)  # OBB theta logits
+        # NOTE: set `angle` as an attribute so that `decode_bboxes` could use it.
+        angle = (angle.sigmoid() - 0.25) * math.pi  # [-pi/4, 3pi/4]
+        # angle = angle.sigmoid() * math.pi / 2  # [0, pi/2]
+        if not self.training:
+            self.angle = angle
+        x = Detect.forward(self, x)
+        if self.training:
+            return x, angle
+        return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle))
+
+    def decode_bboxes(self, bboxes, anchors):
+        """Decode rotated bounding boxes."""
+        return dist2rbox(bboxes, self.angle, anchors, dim=1)
+
+
+class Pose(Detect):
+    """YOLOv8 Pose head for keypoints models."""
+
+    def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
+        """Initialize YOLO network with default parameters and Convolutional Layers."""
+        super().__init__(nc, ch)
+        self.kpt_shape = kpt_shape  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+        self.nk = kpt_shape[0] * kpt_shape[1]  # number of keypoints total
+
+        c4 = max(ch[0] // 4, self.nk)
+        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nk, 1)) for x in ch)
+
+    def forward(self, x):
+        """Perform forward pass through YOLO model and return predictions."""
+        bs = x[0].shape[0]  # batch size
+        kpt = torch.cat([self.cv4[i](x[i]).view(bs, self.nk, -1) for i in range(self.nl)], -1)  # (bs, 17*3, h*w)
+        x = Detect.forward(self, x)
+        if self.training:
+            return x, kpt
+        pred_kpt = self.kpts_decode(bs, kpt)
+        return torch.cat([x, pred_kpt], 1) if self.export else (torch.cat([x[0], pred_kpt], 1), (x[1], kpt))
+
+    def kpts_decode(self, bs, kpts):
+        """Decodes keypoints."""
+        ndim = self.kpt_shape[1]
+        if self.export:  # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
+            y = kpts.view(bs, *self.kpt_shape, -1)
+            a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
+            if ndim == 3:
+                a = torch.cat((a, y[:, :, 2:3].sigmoid()), 2)
+            return a.view(bs, self.nk, -1)
+        else:
+            y = kpts.clone()
+            if ndim == 3:
+                y[:, 2::3] = y[:, 2::3].sigmoid()  # sigmoid (WARNING: inplace .sigmoid_() Apple MPS bug)
+            y[:, 0::ndim] = (y[:, 0::ndim] * 2.0 + (self.anchors[0] - 0.5)) * self.strides
+            y[:, 1::ndim] = (y[:, 1::ndim] * 2.0 + (self.anchors[1] - 0.5)) * self.strides
+            return y
+
+
+class Classify(nn.Module):
+    """YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
+        """Initializes YOLOv8 classification head with specified input and output channels, kernel size, stride,
+        padding, and groups.
+        """
+        super().__init__()
+        c_ = c1 # 1280  # efficientnet_b0 size
+        self.conv = Conv(c1, c_, k, s, p, g)
+        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
+        self.drop = nn.Dropout(p=0.0, inplace=True)
+        self.linear = nn.Linear(c_, c2)  # to x(b,c2)
+
+    def forward(self, x):
+        """Performs a forward pass of the YOLO model on input image data."""
+        if isinstance(x, list):
+            x = torch.cat(x, 1)
+        x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
+        return x if self.training else x.softmax(1)
+
+
+class WorldDetect(Detect):
+    def __init__(self, nc=80, embed=512, with_bn=False, ch=()):
+        """Initialize YOLOv8 detection layer with nc classes and layer channels ch."""
+        super().__init__(nc, ch)
+        c3 = max(ch[0], min(self.nc, 100))
+        self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, embed, 1)) for x in ch)
+        self.cv4 = nn.ModuleList(BNContrastiveHead(embed) if with_bn else ContrastiveHead() for _ in ch)
+
+    def forward(self, x, text):
+        """Concatenates and returns predicted bounding boxes and class probabilities."""
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv4[i](self.cv3[i](x[i]), text)), 1)
+        if self.training:
+            return x
+
+        # Inference path
+        shape = x[0].shape  # BCHW
+        x_cat = torch.cat([xi.view(shape[0], self.nc + self.reg_max * 4, -1) for xi in x], 2)
+        if self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        if self.export and self.format in {"saved_model", "pb", "tflite", "edgetpu", "tfjs"}:  # avoid TF FlexSplitV ops
+            box = x_cat[:, : self.reg_max * 4]
+            cls = x_cat[:, self.reg_max * 4 :]
+        else:
+            box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
+
+        if self.export and self.format in {"tflite", "edgetpu"}:
+            # Precompute normalization factor to increase numerical stability
+            # See https://github.com/ultralytics/ultralytics/issues/7371
+            grid_h = shape[2]
+            grid_w = shape[3]
+            grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
+            norm = self.strides / (self.stride[0] * grid_size)
+            dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
+        else:
+            dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
+
+        y = torch.cat((dbox, cls.sigmoid()), 1)
+        return y if self.export else (y, x)
+
+    def bias_init(self):
+        """Initialize Detect() biases, WARNING: requires stride availability."""
+        m = self  # self.model[-1]  # Detect() module
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
+        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
+            a[-1].bias.data[:] = 1.0  # box
+            # b[-1].bias.data[:] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
+
+
+class RTDETRDecoder(nn.Module):
+    """
+    Real-Time Deformable Transformer Decoder (RTDETRDecoder) module for object detection.
+
+    This decoder module utilizes Transformer architecture along with deformable convolutions to predict bounding boxes
+    and class labels for objects in an image. It integrates features from multiple layers and runs through a series of
+    Transformer decoder layers to output the final predictions.
+    """
+
+    export = False  # export mode
+
+    def __init__(
+        self,
+        nc=80,
+        ch=(512, 1024, 2048),
+        hd=256,  # hidden dim
+        nq=300,  # num queries
+        ndp=4,  # num decoder points
+        nh=8,  # num head
+        ndl=6,  # num decoder layers
+        d_ffn=1024,  # dim of feedforward
+        dropout=0.0,
+        act=nn.ReLU(),
+        eval_idx=-1,
+        # Training args
+        nd=100,  # num denoising
+        label_noise_ratio=0.5,
+        box_noise_scale=1.0,
+        learnt_init_query=False,
+    ):
+        """
+        Initializes the RTDETRDecoder module with the given parameters.
+
+        Args:
+            nc (int): Number of classes. Default is 80.
+            ch (tuple): Channels in the backbone feature maps. Default is (512, 1024, 2048).
+            hd (int): Dimension of hidden layers. Default is 256.
+            nq (int): Number of query points. Default is 300.
+            ndp (int): Number of decoder points. Default is 4.
+            nh (int): Number of heads in multi-head attention. Default is 8.
+            ndl (int): Number of decoder layers. Default is 6.
+            d_ffn (int): Dimension of the feed-forward networks. Default is 1024.
+            dropout (float): Dropout rate. Default is 0.
+            act (nn.Module): Activation function. Default is nn.ReLU.
+            eval_idx (int): Evaluation index. Default is -1.
+            nd (int): Number of denoising. Default is 100.
+            label_noise_ratio (float): Label noise ratio. Default is 0.5.
+            box_noise_scale (float): Box noise scale. Default is 1.0.
+            learnt_init_query (bool): Whether to learn initial query embeddings. Default is False.
+        """
+        super().__init__()
+        self.hidden_dim = hd
+        self.nhead = nh
+        self.nl = len(ch)  # num level
+        self.nc = nc
+        self.num_queries = nq
+        self.num_decoder_layers = ndl
+
+        # Backbone feature projection
+        self.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)
+        # NOTE: simplified version but it's not consistent with .pt weights.
+        # self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)
+
+        # Transformer module
+        decoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)
+        self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)
+
+        # Denoising part
+        self.denoising_class_embed = nn.Embedding(nc, hd)
+        self.num_denoising = nd
+        self.label_noise_ratio = label_noise_ratio
+        self.box_noise_scale = box_noise_scale
+
+        # Decoder embedding
+        self.learnt_init_query = learnt_init_query
+        if learnt_init_query:
+            self.tgt_embed = nn.Embedding(nq, hd)
+        self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)
+
+        # Encoder head
+        self.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))
+        self.enc_score_head = nn.Linear(hd, nc)
+        self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)
+
+        # Decoder head
+        self.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])
+        self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])
+
+        self._reset_parameters()
+
+    def forward(self, x, batch=None):
+        """Runs the forward pass of the module, returning bounding box and classification scores for the input."""
+        from ultralytics.models.utils.ops import get_cdn_group
+
+        # Input projection and embedding
+        feats, shapes = self._get_encoder_input(x)
+
+        # Prepare denoising training
+        dn_embed, dn_bbox, attn_mask, dn_meta = get_cdn_group(
+            batch,
+            self.nc,
+            self.num_queries,
+            self.denoising_class_embed.weight,
+            self.num_denoising,
+            self.label_noise_ratio,
+            self.box_noise_scale,
+            self.training,
+        )
+
+        embed, refer_bbox, enc_bboxes, enc_scores = self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)
+
+        # Decoder
+        dec_bboxes, dec_scores = self.decoder(
+            embed,
+            refer_bbox,
+            feats,
+            shapes,
+            self.dec_bbox_head,
+            self.dec_score_head,
+            self.query_pos_head,
+            attn_mask=attn_mask,
+        )
+        x = dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta
+        if self.training:
+            return x
+        # (bs, 300, 4+nc)
+        y = torch.cat((dec_bboxes.squeeze(0), dec_scores.squeeze(0).sigmoid()), -1)
+        return y if self.export else (y, x)
+
+    def _generate_anchors(self, shapes, grid_size=0.05, dtype=torch.float32, device="cpu", eps=1e-2):
+        """Generates anchor bounding boxes for given shapes with specific grid size and validates them."""
+        anchors = []
+        for i, (h, w) in enumerate(shapes):
+            sy = torch.arange(end=h, dtype=dtype, device=device)
+            sx = torch.arange(end=w, dtype=dtype, device=device)
+            grid_y, grid_x = torch.meshgrid(sy, sx, indexing="ij") if TORCH_1_10 else torch.meshgrid(sy, sx)
+            grid_xy = torch.stack([grid_x, grid_y], -1)  # (h, w, 2)
+
+            valid_WH = torch.tensor([w, h], dtype=dtype, device=device)
+            grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH  # (1, h, w, 2)
+            wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0**i)
+            anchors.append(torch.cat([grid_xy, wh], -1).view(-1, h * w, 4))  # (1, h*w, 4)
+
+        anchors = torch.cat(anchors, 1)  # (1, h*w*nl, 4)
+        valid_mask = ((anchors > eps) & (anchors < 1 - eps)).all(-1, keepdim=True)  # 1, h*w*nl, 1
+        anchors = torch.log(anchors / (1 - anchors))
+        anchors = anchors.masked_fill(~valid_mask, float("inf"))
+        return anchors, valid_mask
+
+    def _get_encoder_input(self, x):
+        """Processes and returns encoder inputs by getting projection features from input and concatenating them."""
+        # Get projection features
+        x = [self.input_proj[i](feat) for i, feat in enumerate(x)]
+        # Get encoder inputs
+        feats = []
+        shapes = []
+        for feat in x:
+            h, w = feat.shape[2:]
+            # [b, c, h, w] -> [b, h*w, c]
+            feats.append(feat.flatten(2).permute(0, 2, 1))
+            # [nl, 2]
+            shapes.append([h, w])
+
+        # [b, h*w, c]
+        feats = torch.cat(feats, 1)
+        return feats, shapes
+
+    def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
+        """Generates and prepares the input required for the decoder from the provided features and shapes."""
+        bs = feats.shape[0]
+        # Prepare input for decoder
+        anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
+        features = self.enc_output(valid_mask * feats)  # bs, h*w, 256
+
+        enc_outputs_scores = self.enc_score_head(features)  # (bs, h*w, nc)
+
+        # Query selection
+        # (bs, num_queries)
+        topk_ind = torch.topk(enc_outputs_scores.max(-1).values, self.num_queries, dim=1).indices.view(-1)
+        # (bs, num_queries)
+        batch_ind = torch.arange(end=bs, dtype=topk_ind.dtype).unsqueeze(-1).repeat(1, self.num_queries).view(-1)
+
+        # (bs, num_queries, 256)
+        top_k_features = features[batch_ind, topk_ind].view(bs, self.num_queries, -1)
+        # (bs, num_queries, 4)
+        top_k_anchors = anchors[:, topk_ind].view(bs, self.num_queries, -1)
+
+        # Dynamic anchors + static content
+        refer_bbox = self.enc_bbox_head(top_k_features) + top_k_anchors
+
+        enc_bboxes = refer_bbox.sigmoid()
+        if dn_bbox is not None:
+            refer_bbox = torch.cat([dn_bbox, refer_bbox], 1)
+        enc_scores = enc_outputs_scores[batch_ind, topk_ind].view(bs, self.num_queries, -1)
+
+        embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(bs, 1, 1) if self.learnt_init_query else top_k_features
+        if self.training:
+            refer_bbox = refer_bbox.detach()
+            if not self.learnt_init_query:
+                embeddings = embeddings.detach()
+        if dn_embed is not None:
+            embeddings = torch.cat([dn_embed, embeddings], 1)
+
+        return embeddings, refer_bbox, enc_bboxes, enc_scores
+
+    # TODO
+    def _reset_parameters(self):
+        """Initializes or resets the parameters of the model's various components with predefined weights and biases."""
+        # Class and bbox head init
+        bias_cls = bias_init_with_prob(0.01) / 80 * self.nc
+        # NOTE: the weight initialization in `linear_init` would cause NaN when training with custom datasets.
+        # linear_init(self.enc_score_head)
+        constant_(self.enc_score_head.bias, bias_cls)
+        constant_(self.enc_bbox_head.layers[-1].weight, 0.0)
+        constant_(self.enc_bbox_head.layers[-1].bias, 0.0)
+        for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
+            # linear_init(cls_)
+            constant_(cls_.bias, bias_cls)
+            constant_(reg_.layers[-1].weight, 0.0)
+            constant_(reg_.layers[-1].bias, 0.0)
+
+        linear_init(self.enc_output[0])
+        xavier_uniform_(self.enc_output[0].weight)
+        if self.learnt_init_query:
+            xavier_uniform_(self.tgt_embed.weight)
+        xavier_uniform_(self.query_pos_head.layers[0].weight)
+        xavier_uniform_(self.query_pos_head.layers[1].weight)
+        for layer in self.input_proj:
+            xavier_uniform_(layer[0].weight)

ultralytics/nn/modules/transformer.py

@@ -0,0 +1,426 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""Transformer modules."""
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.init import constant_, xavier_uniform_
+
+from .conv import Conv
+from .utils import _get_clones, inverse_sigmoid, multi_scale_deformable_attn_pytorch
+
+__all__ = (
+    "TransformerEncoderLayer",
+    "TransformerLayer",
+    "TransformerBlock",
+    "MLPBlock",
+    "LayerNorm2d",
+    "AIFI",
+    "DeformableTransformerDecoder",
+    "DeformableTransformerDecoderLayer",
+    "MSDeformAttn",
+    "MLP",
+)
+
+
+class TransformerEncoderLayer(nn.Module):
+    """Defines a single layer of the transformer encoder."""
+
+    def __init__(self, c1, cm=2048, num_heads=8, dropout=0.0, act=nn.GELU(), normalize_before=False):
+        """Initialize the TransformerEncoderLayer with specified parameters."""
+        super().__init__()
+        from ...utils.torch_utils import TORCH_1_9
+
+        if not TORCH_1_9:
+            raise ModuleNotFoundError(
+                "TransformerEncoderLayer() requires torch>=1.9 to use nn.MultiheadAttention(batch_first=True)."
+            )
+        self.ma = nn.MultiheadAttention(c1, num_heads, dropout=dropout, batch_first=True)
+        # Implementation of Feedforward model
+        self.fc1 = nn.Linear(c1, cm)
+        self.fc2 = nn.Linear(cm, c1)
+
+        self.norm1 = nn.LayerNorm(c1)
+        self.norm2 = nn.LayerNorm(c1)
+        self.dropout = nn.Dropout(dropout)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.act = act
+        self.normalize_before = normalize_before
+
+    @staticmethod
+    def with_pos_embed(tensor, pos=None):
+        """Add position embeddings to the tensor if provided."""
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
+        """Performs forward pass with post-normalization."""
+        q = k = self.with_pos_embed(src, pos)
+        src2 = self.ma(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.fc2(self.dropout(self.act(self.fc1(src))))
+        src = src + self.dropout2(src2)
+        return self.norm2(src)
+
+    def forward_pre(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
+        """Performs forward pass with pre-normalization."""
+        src2 = self.norm1(src)
+        q = k = self.with_pos_embed(src2, pos)
+        src2 = self.ma(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
+        src = src + self.dropout1(src2)
+        src2 = self.norm2(src)
+        src2 = self.fc2(self.dropout(self.act(self.fc1(src2))))
+        return src + self.dropout2(src2)
+
+    def forward(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
+        """Forward propagates the input through the encoder module."""
+        if self.normalize_before:
+            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
+        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
+
+
+class AIFI(TransformerEncoderLayer):
+    """Defines the AIFI transformer layer."""
+
+    def __init__(self, c1, cm=2048, num_heads=8, dropout=0, act=nn.GELU(), normalize_before=False):
+        """Initialize the AIFI instance with specified parameters."""
+        super().__init__(c1, cm, num_heads, dropout, act, normalize_before)
+
+    def forward(self, x):
+        """Forward pass for the AIFI transformer layer."""
+        c, h, w = x.shape[1:]
+        pos_embed = self.build_2d_sincos_position_embedding(w, h, c)
+        # Flatten [B, C, H, W] to [B, HxW, C]
+        x = super().forward(x.flatten(2).permute(0, 2, 1), pos=pos_embed.to(device=x.device, dtype=x.dtype))
+        return x.permute(0, 2, 1).view([-1, c, h, w]).contiguous()
+
+    @staticmethod
+    def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0):
+        """Builds 2D sine-cosine position embedding."""
+        assert embed_dim % 4 == 0, "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
+        grid_w = torch.arange(w, dtype=torch.float32)
+        grid_h = torch.arange(h, dtype=torch.float32)
+        grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
+        pos_dim = embed_dim // 4
+        omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
+        omega = 1.0 / (temperature**omega)
+
+        out_w = grid_w.flatten()[..., None] @ omega[None]
+        out_h = grid_h.flatten()[..., None] @ omega[None]
+
+        return torch.cat([torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], 1)[None]
+
+
+class TransformerLayer(nn.Module):
+    """Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)."""
+
+    def __init__(self, c, num_heads):
+        """Initializes a self-attention mechanism using linear transformations and multi-head attention."""
+        super().__init__()
+        self.q = nn.Linear(c, c, bias=False)
+        self.k = nn.Linear(c, c, bias=False)
+        self.v = nn.Linear(c, c, bias=False)
+        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
+        self.fc1 = nn.Linear(c, c, bias=False)
+        self.fc2 = nn.Linear(c, c, bias=False)
+
+    def forward(self, x):
+        """Apply a transformer block to the input x and return the output."""
+        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
+        return self.fc2(self.fc1(x)) + x
+
+
+class TransformerBlock(nn.Module):
+    """Vision Transformer https://arxiv.org/abs/2010.11929."""
+
+    def __init__(self, c1, c2, num_heads, num_layers):
+        """Initialize a Transformer module with position embedding and specified number of heads and layers."""
+        super().__init__()
+        self.conv = None
+        if c1 != c2:
+            self.conv = Conv(c1, c2)
+        self.linear = nn.Linear(c2, c2)  # learnable position embedding
+        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
+        self.c2 = c2
+
+    def forward(self, x):
+        """Forward propagates the input through the bottleneck module."""
+        if self.conv is not None:
+            x = self.conv(x)
+        b, _, w, h = x.shape
+        p = x.flatten(2).permute(2, 0, 1)
+        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
+
+
+class MLPBlock(nn.Module):
+    """Implements a single block of a multi-layer perceptron."""
+
+    def __init__(self, embedding_dim, mlp_dim, act=nn.GELU):
+        """Initialize the MLPBlock with specified embedding dimension, MLP dimension, and activation function."""
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass for the MLPBlock."""
+        return self.lin2(self.act(self.lin1(x)))
+
+
+class MLP(nn.Module):
+    """Implements a simple multi-layer perceptron (also called FFN)."""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        """Initialize the MLP with specified input, hidden, output dimensions and number of layers."""
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        """Forward pass for the entire MLP."""
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+
+class LayerNorm2d(nn.Module):
+    """
+    2D Layer Normalization module inspired by Detectron2 and ConvNeXt implementations.
+
+    Original implementations in
+    https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py
+    and
+    https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py.
+    """
+
+    def __init__(self, num_channels, eps=1e-6):
+        """Initialize LayerNorm2d with the given parameters."""
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x):
+        """Perform forward pass for 2D layer normalization."""
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        return self.weight[:, None, None] * x + self.bias[:, None, None]
+
+
+class MSDeformAttn(nn.Module):
+    """
+    Multiscale Deformable Attention Module based on Deformable-DETR and PaddleDetection implementations.
+
+    https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
+    """
+
+    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
+        """Initialize MSDeformAttn with the given parameters."""
+        super().__init__()
+        if d_model % n_heads != 0:
+            raise ValueError(f"d_model must be divisible by n_heads, but got {d_model} and {n_heads}")
+        _d_per_head = d_model // n_heads
+        # Better to set _d_per_head to a power of 2 which is more efficient in a CUDA implementation
+        assert _d_per_head * n_heads == d_model, "`d_model` must be divisible by `n_heads`"
+
+        self.im2col_step = 64
+
+        self.d_model = d_model
+        self.n_levels = n_levels
+        self.n_heads = n_heads
+        self.n_points = n_points
+
+        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
+        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
+        self.value_proj = nn.Linear(d_model, d_model)
+        self.output_proj = nn.Linear(d_model, d_model)
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        """Reset module parameters."""
+        constant_(self.sampling_offsets.weight.data, 0.0)
+        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
+        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+        grid_init = (
+            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+            .view(self.n_heads, 1, 1, 2)
+            .repeat(1, self.n_levels, self.n_points, 1)
+        )
+        for i in range(self.n_points):
+            grid_init[:, :, i, :] *= i + 1
+        with torch.no_grad():
+            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+        constant_(self.attention_weights.weight.data, 0.0)
+        constant_(self.attention_weights.bias.data, 0.0)
+        xavier_uniform_(self.value_proj.weight.data)
+        constant_(self.value_proj.bias.data, 0.0)
+        xavier_uniform_(self.output_proj.weight.data)
+        constant_(self.output_proj.bias.data, 0.0)
+
+    def forward(self, query, refer_bbox, value, value_shapes, value_mask=None):
+        """
+        Perform forward pass for multiscale deformable attention.
+
+        https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py
+
+        Args:
+            query (torch.Tensor): [bs, query_length, C]
+            refer_bbox (torch.Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),
+                bottom-right (1, 1), including padding area
+            value (torch.Tensor): [bs, value_length, C]
+            value_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
+            value_mask (Tensor): [bs, value_length], True for non-padding elements, False for padding elements
+
+        Returns:
+            output (Tensor): [bs, Length_{query}, C]
+        """
+        bs, len_q = query.shape[:2]
+        len_v = value.shape[1]
+        assert sum(s[0] * s[1] for s in value_shapes) == len_v
+
+        value = self.value_proj(value)
+        if value_mask is not None:
+            value = value.masked_fill(value_mask[..., None], float(0))
+        value = value.view(bs, len_v, self.n_heads, self.d_model // self.n_heads)
+        sampling_offsets = self.sampling_offsets(query).view(bs, len_q, self.n_heads, self.n_levels, self.n_points, 2)
+        attention_weights = self.attention_weights(query).view(bs, len_q, self.n_heads, self.n_levels * self.n_points)
+        attention_weights = F.softmax(attention_weights, -1).view(bs, len_q, self.n_heads, self.n_levels, self.n_points)
+        # N, Len_q, n_heads, n_levels, n_points, 2
+        num_points = refer_bbox.shape[-1]
+        if num_points == 2:
+            offset_normalizer = torch.as_tensor(value_shapes, dtype=query.dtype, device=query.device).flip(-1)
+            add = sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+            sampling_locations = refer_bbox[:, :, None, :, None, :] + add
+        elif num_points == 4:
+            add = sampling_offsets / self.n_points * refer_bbox[:, :, None, :, None, 2:] * 0.5
+            sampling_locations = refer_bbox[:, :, None, :, None, :2] + add
+        else:
+            raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {num_points}.")
+        output = multi_scale_deformable_attn_pytorch(value, value_shapes, sampling_locations, attention_weights)
+        return self.output_proj(output)
+
+
+class DeformableTransformerDecoderLayer(nn.Module):
+    """
+    Deformable Transformer Decoder Layer inspired by PaddleDetection and Deformable-DETR implementations.
+
+    https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py
+    https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/deformable_transformer.py
+    """
+
+    def __init__(self, d_model=256, n_heads=8, d_ffn=1024, dropout=0.0, act=nn.ReLU(), n_levels=4, n_points=4):
+        """Initialize the DeformableTransformerDecoderLayer with the given parameters."""
+        super().__init__()
+
+        # Self attention
+        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # Cross attention
+        self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
+        self.dropout2 = nn.Dropout(dropout)
+        self.norm2 = nn.LayerNorm(d_model)
+
+        # FFN
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.act = act
+        self.dropout3 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout4 = nn.Dropout(dropout)
+        self.norm3 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        """Add positional embeddings to the input tensor, if provided."""
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, tgt):
+        """Perform forward pass through the Feed-Forward Network part of the layer."""
+        tgt2 = self.linear2(self.dropout3(self.act(self.linear1(tgt))))
+        tgt = tgt + self.dropout4(tgt2)
+        return self.norm3(tgt)
+
+    def forward(self, embed, refer_bbox, feats, shapes, padding_mask=None, attn_mask=None, query_pos=None):
+        """Perform the forward pass through the entire decoder layer."""
+
+        # Self attention
+        q = k = self.with_pos_embed(embed, query_pos)
+        tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1), attn_mask=attn_mask)[
+            0
+        ].transpose(0, 1)
+        embed = embed + self.dropout1(tgt)
+        embed = self.norm1(embed)
+
+        # Cross attention
+        tgt = self.cross_attn(
+            self.with_pos_embed(embed, query_pos), refer_bbox.unsqueeze(2), feats, shapes, padding_mask
+        )
+        embed = embed + self.dropout2(tgt)
+        embed = self.norm2(embed)
+
+        # FFN
+        return self.forward_ffn(embed)
+
+
+class DeformableTransformerDecoder(nn.Module):
+    """
+    Implementation of Deformable Transformer Decoder based on PaddleDetection.
+
+    https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py
+    """
+
+    def __init__(self, hidden_dim, decoder_layer, num_layers, eval_idx=-1):
+        """Initialize the DeformableTransformerDecoder with the given parameters."""
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.hidden_dim = hidden_dim
+        self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx
+
+    def forward(
+        self,
+        embed,  # decoder embeddings
+        refer_bbox,  # anchor
+        feats,  # image features
+        shapes,  # feature shapes
+        bbox_head,
+        score_head,
+        pos_mlp,
+        attn_mask=None,
+        padding_mask=None,
+    ):
+        """Perform the forward pass through the entire decoder."""
+        output = embed
+        dec_bboxes = []
+        dec_cls = []
+        last_refined_bbox = None
+        refer_bbox = refer_bbox.sigmoid()
+        for i, layer in enumerate(self.layers):
+            output = layer(output, refer_bbox, feats, shapes, padding_mask, attn_mask, pos_mlp(refer_bbox))
+
+            bbox = bbox_head[i](output)
+            refined_bbox = torch.sigmoid(bbox + inverse_sigmoid(refer_bbox))
+
+            if self.training:
+                dec_cls.append(score_head[i](output))
+                if i == 0:
+                    dec_bboxes.append(refined_bbox)
+                else:
+                    dec_bboxes.append(torch.sigmoid(bbox + inverse_sigmoid(last_refined_bbox)))
+            elif i == self.eval_idx:
+                dec_cls.append(score_head[i](output))
+                dec_bboxes.append(refined_bbox)
+                break
+
+            last_refined_bbox = refined_bbox
+            refer_bbox = refined_bbox.detach() if self.training else refined_bbox
+
+        return torch.stack(dec_bboxes), torch.stack(dec_cls)

ultralytics/nn/modules/utils.py

@@ -0,0 +1,85 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""Module utils."""
+
+import copy
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.init import uniform_
+
+__all__ = "multi_scale_deformable_attn_pytorch", "inverse_sigmoid"
+
+
+def _get_clones(module, n):
+    """Create a list of cloned modules from the given module."""
+    return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
+
+
+def bias_init_with_prob(prior_prob=0.01):
+    """Initialize conv/fc bias value according to a given probability value."""
+    return float(-np.log((1 - prior_prob) / prior_prob))  # return bias_init
+
+
+def linear_init(module):
+    """Initialize the weights and biases of a linear module."""
+    bound = 1 / math.sqrt(module.weight.shape[0])
+    uniform_(module.weight, -bound, bound)
+    if hasattr(module, "bias") and module.bias is not None:
+        uniform_(module.bias, -bound, bound)
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    """Calculate the inverse sigmoid function for a tensor."""
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+def multi_scale_deformable_attn_pytorch(
+    value: torch.Tensor,
+    value_spatial_shapes: torch.Tensor,
+    sampling_locations: torch.Tensor,
+    attention_weights: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Multiscale deformable attention.
+
+    https://github.com/IDEA-Research/detrex/blob/main/detrex/layers/multi_scale_deform_attn.py
+    """
+
+    bs, _, num_heads, embed_dims = value.shape
+    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+    sampling_grids = 2 * sampling_locations - 1
+    sampling_value_list = []
+    for level, (H_, W_) in enumerate(value_spatial_shapes):
+        # bs, H_*W_, num_heads, embed_dims ->
+        # bs, H_*W_, num_heads*embed_dims ->
+        # bs, num_heads*embed_dims, H_*W_ ->
+        # bs*num_heads, embed_dims, H_, W_
+        value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
+        # bs, num_queries, num_heads, num_points, 2 ->
+        # bs, num_heads, num_queries, num_points, 2 ->
+        # bs*num_heads, num_queries, num_points, 2
+        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
+        # bs*num_heads, embed_dims, num_queries, num_points
+        sampling_value_l_ = F.grid_sample(
+            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+        )
+        sampling_value_list.append(sampling_value_l_)
+    # (bs, num_queries, num_heads, num_levels, num_points) ->
+    # (bs, num_heads, num_queries, num_levels, num_points) ->
+    # (bs, num_heads, 1, num_queries, num_levels*num_points)
+    attention_weights = attention_weights.transpose(1, 2).reshape(
+        bs * num_heads, 1, num_queries, num_levels * num_points
+    )
+    output = (
+        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+        .sum(-1)
+        .view(bs, num_heads * embed_dims, num_queries)
+    )
+    return output.transpose(1, 2).contiguous()