yolov9_pytorch

hubconf.py

+import torch
+
+
+def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+    """Creates or loads a YOLO model
+
+    Arguments:
+        name (str): model name 'yolov3' or path 'path/to/best.pt'
+        pretrained (bool): load pretrained weights into the model
+        channels (int): number of input channels
+        classes (int): number of model classes
+        autoshape (bool): apply YOLO .autoshape() wrapper to model
+        verbose (bool): print all information to screen
+        device (str, torch.device, None): device to use for model parameters
+
+    Returns:
+        YOLO model
+    """
+    from pathlib import Path
+
+    from models.common import AutoShape, DetectMultiBackend
+    from models.experimental import attempt_load
+    from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
+    from utils.downloads import attempt_download
+    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
+    from utils.torch_utils import select_device
+
+    if not verbose:
+        LOGGER.setLevel(logging.WARNING)
+    check_requirements(exclude=('opencv-python', 'tensorboard', 'thop'))
+    name = Path(name)
+    path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name  # checkpoint path
+    try:
+        device = select_device(device)
+        if pretrained and channels == 3 and classes == 80:
+            try:
+                model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
+                if autoshape:
+                    if model.pt and isinstance(model.model, ClassificationModel):
+                        LOGGER.warning('WARNING ⚠️ YOLO ClassificationModel is not yet AutoShape compatible. '
+                                       'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
+                    elif model.pt and isinstance(model.model, SegmentationModel):
+                        LOGGER.warning('WARNING ⚠️ YOLO SegmentationModel is not yet AutoShape compatible. '
+                                       'You will not be able to run inference with this model.')
+                    else:
+                        model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
+            except Exception:
+                model = attempt_load(path, device=device, fuse=False)  # arbitrary model
+        else:
+            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0]  # model.yaml path
+            model = DetectionModel(cfg, channels, classes)  # create model
+            if pretrained:
+                ckpt = torch.load(attempt_download(path), map_location=device)  # load
+                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
+                csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors'])  # intersect
+                model.load_state_dict(csd, strict=False)  # load
+                if len(ckpt['model'].names) == classes:
+                    model.names = ckpt['model'].names  # set class names attribute
+        if not verbose:
+            LOGGER.setLevel(logging.INFO)  # reset to default
+        return model.to(device)
+
+    except Exception as e:
+        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
+        s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
+        raise Exception(s) from e
+
+
+def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
+    # YOLO custom or local model
+    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
+
+
+if __name__ == '__main__':
+    import argparse
+    from pathlib import Path
+
+    import numpy as np
+    from PIL import Image
+
+    from utils.general import cv2, print_args
+
+    # Argparser
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, default='yolo', help='model name')
+    opt = parser.parse_args()
+    print_args(vars(opt))
+
+    # Model
+    model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
+    # model = custom(path='path/to/model.pt')  # custom
+
+    # Images
+    imgs = [
+        'data/images/zidane.jpg',  # filename
+        Path('data/images/zidane.jpg'),  # Path
+        'https://ultralytics.com/images/zidane.jpg',  # URI
+        cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
+        Image.open('data/images/bus.jpg'),  # PIL
+        np.zeros((320, 640, 3))]  # numpy
+
+    # Inference
+    results = model(imgs, size=320)  # batched inference
+
+    # Results
+    results.print()
+    results.save()

models/__init__.py

+# init
\ No newline at end of file

models/common.py

+import ast
+import contextlib
+import json
+import math
+import platform
+import warnings
+import zipfile
+from collections import OrderedDict, namedtuple
+from copy import copy
+from pathlib import Path
+from urllib.parse import urlparse
+
+from typing import Optional
+
+import cv2
+import numpy as np
+import pandas as pd
+import requests
+import torch
+import torch.nn as nn
+from IPython.display import display
+from PIL import Image
+from torch.cuda import amp
+
+from utils import TryExcept
+from utils.dataloaders import exif_transpose, letterbox
+from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
+                           increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes,
+                           xywh2xyxy, xyxy2xywh, yaml_load)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import copy_attr, smart_inference_mode
+
+
+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):
+        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):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+
+class AConv(nn.Module):
+    def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        self.cv1 = Conv(c1, c2, 3, 2, 1)
+
+    def forward(self, x):
+        x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
+        return self.cv1(x)
+
+
+class ADown(nn.Module):
+    def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        self.c = c2 // 2
+        self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)
+        self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)
+
+    def forward(self, x):
+        x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
+        x1,x2 = x.chunk(2, 1)
+        x1 = self.cv1(x1)
+        x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)
+        x2 = self.cv2(x2)
+        return torch.cat((x1, x2), 1)
+
+
+class RepConvN(nn.Module):
+    """RepConv 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
+    """
+    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):
+        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 = 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):
+        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 _avg_to_3x3_tensor(self, avgp):
+        channels = self.c1
+        groups = self.g
+        kernel_size = avgp.kernel_size
+        input_dim = channels // groups
+        k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
+        k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
+        return k
+
+    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, 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):
+        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 SP(nn.Module):
+    def __init__(self, k=3, s=1):
+        super(SP, self).__init__()
+        self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
+
+    def forward(self, x):
+        return self.m(x)
+
+
+class MP(nn.Module):
+    # Max pooling
+    def __init__(self, k=2):
+        super(MP, self).__init__()
+        self.m = nn.MaxPool2d(kernel_size=k, stride=k)
+
+    def forward(self, x):
+        return self.m(x)
+
+
+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):
+        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):
+        return self.act(self.bn(self.conv_transpose(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
+        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
+        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
+
+
+class DFL(nn.Module):
+    # DFL module
+    def __init__(self, c1=17):
+        super().__init__()
+        self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
+        self.conv.weight.data[:] = nn.Parameter(torch.arange(c1, dtype=torch.float).view(1, c1, 1, 1)) # / 120.0
+        self.c1 = c1
+        # self.bn = nn.BatchNorm2d(4)
+
+    def forward(self, x):
+        b, c, a = x.shape  # batch, channels, anchors
+        return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)
+        # return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a)
+
+
+class BottleneckBase(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(1, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class RBottleneckBase(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 1), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class RepNRBottleneckBase(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 1), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = RepConvN(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class Bottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class RepNBottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expand
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = RepConvN(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class Res(nn.Module):
+    # ResNet bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super(Res, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_, c_, 3, 1, g=g)
+        self.cv3 = Conv(c_, c2, 1, 1)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
+
+
+class RepNRes(nn.Module):
+    # ResNet bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super(RepNRes, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = RepConvN(c_, c_, 3, 1, g=g)
+        self.cv3 = Conv(c_, c2, 1, 1)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
+
+
+class BottleneckCSP(nn.Module):
+    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+        self.cv4 = Conv(2 * c_, c2, 1, 1)
+        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
+        self.act = nn.SiLU()
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        y1 = self.cv3(self.m(self.cv1(x)))
+        y2 = self.cv2(x)
+        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
+
+
+class CSP(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class RepNCSP(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(RepNBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class CSPBase(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(BottleneckBase(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class SPP(nn.Module):
+    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+    def __init__(self, c1, c2, k=(5, 9, 13)):
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+        
+class ASPP(torch.nn.Module):
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        kernel_sizes = [1, 3, 3, 1]
+        dilations = [1, 3, 6, 1]
+        paddings = [0, 3, 6, 0]
+        self.aspp = torch.nn.ModuleList()
+        for aspp_idx in range(len(kernel_sizes)):
+            conv = torch.nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_sizes[aspp_idx],
+                stride=1,
+                dilation=dilations[aspp_idx],
+                padding=paddings[aspp_idx],
+                bias=True)
+            self.aspp.append(conv)
+        self.gap = torch.nn.AdaptiveAvgPool2d(1)
+        self.aspp_num = len(kernel_sizes)
+        for m in self.modules():
+            if isinstance(m, torch.nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                m.bias.data.fill_(0)
+
+    def forward(self, x):
+        avg_x = self.gap(x)
+        out = []
+        for aspp_idx in range(self.aspp_num):
+            inp = avg_x if (aspp_idx == self.aspp_num - 1) else x
+            out.append(F.relu_(self.aspp[aspp_idx](inp)))
+        out[-1] = out[-1].expand_as(out[-2])
+        out = torch.cat(out, dim=1)
+        return out
+
+
+class SPPCSPC(nn.Module):
+    # CSP SPP https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
+        super(SPPCSPC, self).__init__()
+        c_ = int(2 * c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(c_, c_, 3, 1)
+        self.cv4 = Conv(c_, c_, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+        self.cv5 = Conv(4 * c_, c_, 1, 1)
+        self.cv6 = Conv(c_, c_, 3, 1)
+        self.cv7 = Conv(2 * c_, c2, 1, 1)
+
+    def forward(self, x):
+        x1 = self.cv4(self.cv3(self.cv1(x)))
+        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
+        y2 = self.cv2(x)
+        return self.cv7(torch.cat((y1, y2), dim=1))
+
+
+class SPPF(nn.Module):
+    # Spatial Pyramid Pooling - Fast (SPPF) layer by Glenn Jocher
+    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * 4, c2, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+        # self.m = SoftPool2d(kernel_size=k, stride=1, padding=k // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
+
+
+import torch.nn.functional as F
+from torch.nn.modules.utils import _pair
+    
+    
+class ReOrg(nn.Module):
+    # yolo
+    def __init__(self):
+        super(ReOrg, self).__init__()
+
+    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+        return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
+
+
+class Contract(nn.Module):
+    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
+        s = self.gain
+        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
+        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
+        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
+        s = self.gain
+        x = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)
+        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
+        return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)
+
+
+class Concat(nn.Module):
+    # Concatenate a list of tensors along dimension
+    def __init__(self, dimension=1):
+        super().__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+class Shortcut(nn.Module):
+    def __init__(self, dimension=0):
+        super(Shortcut, self).__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return x[0]+x[1]
+    
+    
+class Silence(nn.Module):
+    def __init__(self):
+        super(Silence, self).__init__()
+    def forward(self, x):    
+        return x
+
+
+##### GELAN #####        
+        
+class SPPELAN(nn.Module):
+    # spp-elan
+    def __init__(self, c1, c2, c3):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        self.c = c3
+        self.cv1 = Conv(c1, c3, 1, 1)
+        self.cv2 = SP(5)
+        self.cv3 = SP(5)
+        self.cv4 = SP(5)
+        self.cv5 = Conv(4*c3, c2, 1, 1)
+
+    def forward(self, x):
+        y = [self.cv1(x)]
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3, self.cv4])
+        return self.cv5(torch.cat(y, 1))
+        
+        
+class RepNCSPELAN4(nn.Module):
+    # csp-elan
+    def __init__(self, c1, c2, c3, c4, c5=1):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        self.c = c3//2
+        self.cv1 = Conv(c1, c3, 1, 1)
+        self.cv2 = nn.Sequential(RepNCSP(c3//2, c4, c5), Conv(c4, c4, 3, 1))
+        self.cv3 = nn.Sequential(RepNCSP(c4, c4, c5), Conv(c4, c4, 3, 1))
+        self.cv4 = Conv(c3+(2*c4), c2, 1, 1)
+
+    def forward(self, x):
+        y = list(self.cv1(x).chunk(2, 1))
+        y.extend((m(y[-1])) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+
+    def forward_split(self, x):
+        y = list(self.cv1(x).split((self.c, self.c), 1))
+        y.extend(m(y[-1]) for m in [self.cv2, self.cv3])
+        return self.cv4(torch.cat(y, 1))
+
+#################
+
+
+##### YOLOR #####
+
+class ImplicitA(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitA, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, std=.02)        
+
+    def forward(self, x):
+        return self.implicit + x
+
+
+class ImplicitM(nn.Module):
+    def __init__(self, channel):
+        super(ImplicitM, self).__init__()
+        self.channel = channel
+        self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
+        nn.init.normal_(self.implicit, mean=1., std=.02)        
+
+    def forward(self, x):
+        return self.implicit * x
+
+#################
+
+
+##### CBNet #####
+
+class CBLinear(nn.Module):
+    def __init__(self, c1, c2s, k=1, s=1, p=None, g=1):  # ch_in, ch_outs, kernel, stride, padding, groups
+        super(CBLinear, self).__init__()
+        self.c2s = c2s
+        self.conv = nn.Conv2d(c1, sum(c2s), k, s, autopad(k, p), groups=g, bias=True)
+
+    def forward(self, x):
+        outs = self.conv(x).split(self.c2s, dim=1)
+        return outs
+
+class CBFuse(nn.Module):
+    def __init__(self, idx):
+        super(CBFuse, self).__init__()
+        self.idx = idx
+
+    def forward(self, xs):
+        target_size = xs[-1].shape[2:]
+        res = [F.interpolate(x[self.idx[i]], size=target_size, mode='nearest') for i, x in enumerate(xs[:-1])]
+        out = torch.sum(torch.stack(res + xs[-1:]), dim=0)
+        return out
+
+#################
+
+
+class DetectMultiBackend(nn.Module):
+    # YOLO MultiBackend class for python inference on various backends
+    def __init__(self, weights='yolo.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
+        # Usage:
+        #   PyTorch:              weights = *.pt
+        #   TorchScript:                    *.torchscript
+        #   ONNX Runtime:                   *.onnx
+        #   ONNX OpenCV DNN:                *.onnx --dnn
+        #   OpenVINO:                       *_openvino_model
+        #   CoreML:                         *.mlmodel
+        #   TensorRT:                       *.engine
+        #   TensorFlow SavedModel:          *_saved_model
+        #   TensorFlow GraphDef:            *.pb
+        #   TensorFlow Lite:                *.tflite
+        #   TensorFlow Edge TPU:            *_edgetpu.tflite
+        #   PaddlePaddle:                   *_paddle_model
+        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
+
+        super().__init__()
+        w = str(weights[0] if isinstance(weights, list) else weights)
+        pt, jit, onnx, onnx_end2end, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
+        fp16 &= pt or jit or onnx or engine  # FP16
+        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
+        stride = 32  # default stride
+        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
+        if not (pt or triton):
+            w = attempt_download(w)  # download if not local
+
+        if pt:  # PyTorch
+            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
+            stride = max(int(model.stride.max()), 32)  # model stride
+            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+            model.half() if fp16 else model.float()
+            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
+        elif jit:  # TorchScript
+            LOGGER.info(f'Loading {w} for TorchScript inference...')
+            extra_files = {'config.txt': ''}  # model metadata
+            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
+            model.half() if fp16 else model.float()
+            if extra_files['config.txt']:  # load metadata dict
+                d = json.loads(extra_files['config.txt'],
+                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
+                                                      for k, v in d.items()})
+                stride, names = int(d['stride']), d['names']
+        elif dnn:  # ONNX OpenCV DNN
+            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
+            check_requirements('opencv-python>=4.5.4')
+            net = cv2.dnn.readNetFromONNX(w)
+        elif onnx:  # ONNX Runtime
+            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
+            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
+            import onnxruntime
+            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
+            session = onnxruntime.InferenceSession(w, providers=providers)
+            output_names = [x.name for x in session.get_outputs()]
+            meta = session.get_modelmeta().custom_metadata_map  # metadata
+            if 'stride' in meta:
+                stride, names = int(meta['stride']), eval(meta['names'])
+        elif xml:  # OpenVINO
+            LOGGER.info(f'Loading {w} for OpenVINO inference...')
+            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            from openvino.runtime import Core, Layout, get_batch
+            ie = Core()
+            if not Path(w).is_file():  # if not *.xml
+                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
+            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            if network.get_parameters()[0].get_layout().empty:
+                network.get_parameters()[0].set_layout(Layout("NCHW"))
+            batch_dim = get_batch(network)
+            if batch_dim.is_static:
+                batch_size = batch_dim.get_length()
+            executable_network = ie.compile_model(network, device_name="CPU")  # device_name="MYRIAD" for Intel NCS2
+            stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
+        elif engine:  # TensorRT
+            LOGGER.info(f'Loading {w} for TensorRT inference...')
+            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
+            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
+            if device.type == 'cpu':
+                device = torch.device('cuda:0')
+            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
+            logger = trt.Logger(trt.Logger.INFO)
+            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
+                model = runtime.deserialize_cuda_engine(f.read())
+            context = model.create_execution_context()
+            bindings = OrderedDict()
+            output_names = []
+            fp16 = False  # default updated below
+            dynamic = False
+            for i in range(model.num_bindings):
+                name = model.get_binding_name(i)
+                dtype = trt.nptype(model.get_binding_dtype(i))
+                if model.binding_is_input(i):
+                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
+                        dynamic = True
+                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
+                    if dtype == np.float16:
+                        fp16 = True
+                else:  # output
+                    output_names.append(name)
+                shape = tuple(context.get_binding_shape(i))
+                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
+                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
+            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
+            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
+        elif coreml:  # CoreML
+            LOGGER.info(f'Loading {w} for CoreML inference...')
+            import coremltools as ct
+            model = ct.models.MLModel(w)
+        elif saved_model:  # TF SavedModel
+            LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
+            import tensorflow as tf
+            keras = False  # assume TF1 saved_model
+            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
+        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
+            LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
+            import tensorflow as tf
+
+            def wrap_frozen_graph(gd, inputs, outputs):
+                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
+                ge = x.graph.as_graph_element
+                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
+
+            def gd_outputs(gd):
+                name_list, input_list = [], []
+                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
+                    name_list.append(node.name)
+                    input_list.extend(node.input)
+                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
+
+            gd = tf.Graph().as_graph_def()  # TF GraphDef
+            with open(w, 'rb') as f:
+                gd.ParseFromString(f.read())
+            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
+        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
+            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
+                from tflite_runtime.interpreter import Interpreter, load_delegate
+            except ImportError:
+                import tensorflow as tf
+                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
+            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
+                LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
+                delegate = {
+                    'Linux': 'libedgetpu.so.1',
+                    'Darwin': 'libedgetpu.1.dylib',
+                    'Windows': 'edgetpu.dll'}[platform.system()]
+                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
+            else:  # TFLite
+                LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
+                interpreter = Interpreter(model_path=w)  # load TFLite model
+            interpreter.allocate_tensors()  # allocate
+            input_details = interpreter.get_input_details()  # inputs
+            output_details = interpreter.get_output_details()  # outputs
+            # load metadata
+            with contextlib.suppress(zipfile.BadZipFile):
+                with zipfile.ZipFile(w, "r") as model:
+                    meta_file = model.namelist()[0]
+                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
+                    stride, names = int(meta['stride']), meta['names']
+        elif tfjs:  # TF.js
+            raise NotImplementedError('ERROR: YOLO TF.js inference is not supported')
+        elif paddle:  # PaddlePaddle
+            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
+            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
+            import paddle.inference as pdi
+            if not Path(w).is_file():  # if not *.pdmodel
+                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
+            weights = Path(w).with_suffix('.pdiparams')
+            config = pdi.Config(str(w), str(weights))
+            if cuda:
+                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
+            predictor = pdi.create_predictor(config)
+            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
+            output_names = predictor.get_output_names()
+        elif triton:  # NVIDIA Triton Inference Server
+            LOGGER.info(f'Using {w} as Triton Inference Server...')
+            check_requirements('tritonclient[all]')
+            from utils.triton import TritonRemoteModel
+            model = TritonRemoteModel(url=w)
+            nhwc = model.runtime.startswith("tensorflow")
+        else:
+            raise NotImplementedError(f'ERROR: {w} is not a supported format')
+
+        # class names
+        if 'names' not in locals():
+            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
+        if names[0] == 'n01440764' and len(names) == 1000:  # ImageNet
+            names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']  # human-readable names
+
+        self.__dict__.update(locals())  # assign all variables to self
+
+    def forward(self, im, augment=False, visualize=False):
+        # YOLO MultiBackend inference
+        b, ch, h, w = im.shape  # batch, channel, height, width
+        if self.fp16 and im.dtype != torch.float16:
+            im = im.half()  # to FP16
+        if self.nhwc:
+            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
+
+        if self.pt:  # PyTorch
+            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
+        elif self.jit:  # TorchScript
+            y = self.model(im)
+        elif self.dnn:  # ONNX OpenCV DNN
+            im = im.cpu().numpy()  # torch to numpy
+            self.net.setInput(im)
+            y = self.net.forward()
+        elif self.onnx:  # ONNX Runtime
+            im = im.cpu().numpy()  # torch to numpy
+            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
+        elif self.xml:  # OpenVINO
+            im = im.cpu().numpy()  # FP32
+            y = list(self.executable_network([im]).values())
+        elif self.engine:  # TensorRT
+            if self.dynamic and im.shape != self.bindings['images'].shape:
+                i = self.model.get_binding_index('images')
+                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
+                self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
+                for name in self.output_names:
+                    i = self.model.get_binding_index(name)
+                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
+            s = self.bindings['images'].shape
+            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
+            self.binding_addrs['images'] = int(im.data_ptr())
+            self.context.execute_v2(list(self.binding_addrs.values()))
+            y = [self.bindings[x].data for x in sorted(self.output_names)]
+        elif self.coreml:  # CoreML
+            im = im.cpu().numpy()
+            im = Image.fromarray((im[0] * 255).astype('uint8'))
+            # im = im.resize((192, 320), Image.ANTIALIAS)
+            y = self.model.predict({'image': im})  # coordinates are xywh normalized
+            if 'confidence' in y:
+                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
+                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
+                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
+            else:
+                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
+        elif self.paddle:  # PaddlePaddle
+            im = im.cpu().numpy().astype(np.float32)
+            self.input_handle.copy_from_cpu(im)
+            self.predictor.run()
+            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
+        elif self.triton:  # NVIDIA Triton Inference Server
+            y = self.model(im)
+        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
+            im = im.cpu().numpy()
+            if self.saved_model:  # SavedModel
+                y = self.model(im, training=False) if self.keras else self.model(im)
+            elif self.pb:  # GraphDef
+                y = self.frozen_func(x=self.tf.constant(im))
+            else:  # Lite or Edge TPU
+                input = self.input_details[0]
+                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
+                if int8:
+                    scale, zero_point = input['quantization']
+                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
+                self.interpreter.set_tensor(input['index'], im)
+                self.interpreter.invoke()
+                y = []
+                for output in self.output_details:
+                    x = self.interpreter.get_tensor(output['index'])
+                    if int8:
+                        scale, zero_point = output['quantization']
+                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
+                    y.append(x)
+            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
+            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels
+
+        if isinstance(y, (list, tuple)):
+            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
+        else:
+            return self.from_numpy(y)
+
+    def from_numpy(self, x):
+        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
+
+    def warmup(self, imgsz=(1, 3, 640, 640)):
+        # Warmup model by running inference once
+        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
+        if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
+            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
+            for _ in range(2 if self.jit else 1):  #
+                self.forward(im)  # warmup
+
+    @staticmethod
+    def _model_type(p='path/to/model.pt'):
+        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
+        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
+        from export import export_formats
+        from utils.downloads import is_url
+        sf = list(export_formats().Suffix)  # export suffixes
+        if not is_url(p, check=False):
+            check_suffix(p, sf)  # checks
+        url = urlparse(p)  # if url may be Triton inference server
+        types = [s in Path(p).name for s in sf]
+        types[8] &= not types[9]  # tflite &= not edgetpu
+        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
+        return types + [triton]
+
+    @staticmethod
+    def _load_metadata(f=Path('path/to/meta.yaml')):
+        # Load metadata from meta.yaml if it exists
+        if f.exists():
+            d = yaml_load(f)
+            return d['stride'], d['names']  # assign stride, names
+        return None, None
+
+
+class AutoShape(nn.Module):
+    # YOLO input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    agnostic = False  # NMS class-agnostic
+    multi_label = False  # NMS multiple labels per box
+    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+    max_det = 1000  # maximum number of detections per image
+    amp = False  # Automatic Mixed Precision (AMP) inference
+
+    def __init__(self, model, verbose=True):
+        super().__init__()
+        if verbose:
+            LOGGER.info('Adding AutoShape... ')
+        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
+        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
+        self.pt = not self.dmb or model.pt  # PyTorch model
+        self.model = model.eval()
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.inplace = False  # Detect.inplace=False for safe multithread inference
+            m.export = True  # do not output loss values
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        from models.yolo import Detect, Segment
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            if isinstance(m, (Detect, Segment)):
+                for k in 'stride', 'anchor_grid', 'stride_grid', 'grid':
+                    x = getattr(m, k)
+                    setattr(m, k, list(map(fn, x))) if isinstance(x, (list, tuple)) else setattr(m, k, fn(x))
+        return self
+
+    @smart_inference_mode()
+    def forward(self, ims, size=640, augment=False, profile=False):
+        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
+        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
+        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
+        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
+        #   numpy:           = np.zeros((640,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
+        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        dt = (Profile(), Profile(), Profile())
+        with dt[0]:
+            if isinstance(size, int):  # expand
+                size = (size, size)
+            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
+            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
+            if isinstance(ims, torch.Tensor):  # torch
+                with amp.autocast(autocast):
+                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
+
+            # Pre-process
+            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
+            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
+            for i, im in enumerate(ims):
+                f = f'image{i}'  # filename
+                if isinstance(im, (str, Path)):  # filename or uri
+                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+                    im = np.asarray(exif_transpose(im))
+                elif isinstance(im, Image.Image):  # PIL Image
+                    im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
+                files.append(Path(f).with_suffix('.jpg').name)
+                if im.shape[0] < 5:  # image in CHW
+                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
+                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
+                s = im.shape[:2]  # HWC
+                shape0.append(s)  # image shape
+                g = max(size) / max(s)  # gain
+                shape1.append([int(y * g) for y in s])
+                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
+            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
+            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
+            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
+            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
+
+        with amp.autocast(autocast):
+            # Inference
+            with dt[1]:
+                y = self.model(x, augment=augment)  # forward
+
+            # Post-process
+            with dt[2]:
+                y = non_max_suppression(y if self.dmb else y[0],
+                                        self.conf,
+                                        self.iou,
+                                        self.classes,
+                                        self.agnostic,
+                                        self.multi_label,
+                                        max_det=self.max_det)  # NMS
+                for i in range(n):
+                    scale_boxes(shape1, y[i][:, :4], shape0[i])
+
+            return Detections(ims, y, files, dt, self.names, x.shape)
+
+
+class Detections:
+    # YOLO detections class for inference results
+    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
+        super().__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
+        self.ims = ims  # list of images as numpy arrays
+        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
+        self.names = names  # class names
+        self.files = files  # image filenames
+        self.times = times  # profiling times
+        self.xyxy = pred  # xyxy pixels
+        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
+        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
+        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
+        self.n = len(self.pred)  # number of images (batch size)
+        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
+        self.s = tuple(shape)  # inference BCHW shape
+
+    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
+        s, crops = '', []
+        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
+            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
+            if pred.shape[0]:
+                for c in pred[:, -1].unique():
+                    n = (pred[:, -1] == c).sum()  # detections per class
+                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                s = s.rstrip(', ')
+                if show or save or render or crop:
+                    annotator = Annotator(im, example=str(self.names))
+                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
+                        label = f'{self.names[int(cls)]} {conf:.2f}'
+                        if crop:
+                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
+                        else:  # all others
+                            annotator.box_label(box, label if labels else '', color=colors(cls))
+                    im = annotator.im
+            else:
+                s += '(no detections)'
+
+            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
+            if show:
+                display(im) if is_notebook() else im.show(self.files[i])
+            if save:
+                f = self.files[i]
+                im.save(save_dir / f)  # save
+                if i == self.n - 1:
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
+            if render:
+                self.ims[i] = np.asarray(im)
+        if pprint:
+            s = s.lstrip('\n')
+            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
+        if crop:
+            if save:
+                LOGGER.info(f'Saved results to {save_dir}\n')
+            return crops
+
+    @TryExcept('Showing images is not supported in this environment')
+    def show(self, labels=True):
+        self._run(show=True, labels=labels)  # show results
+
+    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
+        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
+
+    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
+        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
+
+    def render(self, labels=True):
+        self._run(render=True, labels=labels)  # render results
+        return self.ims
+
+    def pandas(self):
+        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+        new = copy(self)  # return copy
+        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
+        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
+        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
+            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+        return new
+
+    def tolist(self):
+        # return a list of Detections objects, i.e. 'for result in results.tolist():'
+        r = range(self.n)  # iterable
+        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
+        # for d in x:
+        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
+        return x
+
+    def print(self):
+        LOGGER.info(self.__str__())
+
+    def __len__(self):  # override len(results)
+        return self.n
+
+    def __str__(self):  # override print(results)
+        return self._run(pprint=True)  # print results
+
+    def __repr__(self):
+        return f'YOLO {self.__class__} instance\n' + self.__str__()
+
+
+class Proto(nn.Module):
+    # YOLO mask Proto module for segmentation models
+    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
+        super().__init__()
+        self.cv1 = Conv(c1, c_, k=3)
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        self.cv2 = Conv(c_, c_, k=3)
+        self.cv3 = Conv(c_, c2)
+
+    def forward(self, x):
+        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
+
+
+class UConv(nn.Module):
+    def __init__(self, c1, c_=256, c2=256):  # ch_in, number of protos, number of masks
+        super().__init__()
+        
+        self.cv1 = Conv(c1, c_, k=3)
+        self.cv2 = nn.Conv2d(c_, c2, 1, 1)
+        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+
+    def forward(self, x):
+        return self.up(self.cv2(self.cv1(x)))
+
+
+class Classify(nn.Module):
+    # YOLO 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):  # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__()
+        c_ = 1280  # efficientnet_b0 size
+        self.conv = Conv(c1, c_, k, s, autopad(k, 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):
+        if isinstance(x, list):
+            x = torch.cat(x, 1)
+        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))

models/detect/gelan-c.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   # detect
+   [[15, 18, 21], 1, DDetect, [nc]],  # DDetect(P3, P4, P5)
+  ]

models/detect/gelan-e.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   [-1, 1, Silence, []],
+  
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]],  # 3
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 4-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]],  # 5
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 6-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 7
+
+   # avg-conv down
+   [-1, 1, ADown, [1024]],  # 8-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 9
+   
+   # routing
+   [1, 1, CBLinear, [[64]]], # 10
+   [3, 1, CBLinear, [[64, 128]]], # 11
+   [5, 1, CBLinear, [[64, 128, 256]]], # 12
+   [7, 1, CBLinear, [[64, 128, 256, 512]]], # 13
+   [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]], # 14
+  
+   # conv down fuse
+   [0, 1, Conv, [64, 3, 2]],  # 15-P1/2
+   [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]], # 16
+
+   # conv down fuse
+   [-1, 1, Conv, [128, 3, 2]],  # 17-P2/4
+   [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]], # 18  
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]],  # 19
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [256]],  # 20-P3/8
+   [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]], # 21  
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]],  # 22
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [512]],  # 23-P4/16
+   [[13, 14, -1], 1, CBFuse, [[3, 3]]], # 24 
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 25
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [1024]],  # 26-P5/32
+   [[14, -1], 1, CBFuse, [[4]]], # 27
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 28
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [28, 1, SPPELAN, [512, 256]],  # 29
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 25], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 32
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 22], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]],  # 35 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 32], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 38 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 29], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]],  # 41 (P5/32-large)
+
+   # detect
+   [[35, 38, 41], 1, DDetect, [nc]],  # Detect(P3, P4, P5)
+  ]

models/detect/gelan.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   # detect
+   [[15, 18, 21], 1, DDetect, [nc]],  # Detect(P3, P4, P5)
+  ]

models/detect/yolov7-af.yaml

+# YOLOv7
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.  # model depth multiple
+width_multiple: 1.  # layer channel multiple
+anchors: 3
+
+# YOLOv7 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+  
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
+   [-1, 1, Conv, [64, 3, 1]],
+   
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
+   [-1, 1, Conv, [64, 1, 1]],
+   [-2, 1, Conv, [64, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]],  # 11
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]],  # 24
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 37
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-3, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 50
+  ]
+
+# yolov7 head
+head:
+  [[-1, 1, SPPCSPC, [512]], # 51
+  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [37, 1, Conv, [256, 1, 1]], # route backbone P4
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 63
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [24, 1, Conv, [128, 1, 1]], # route backbone P3
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [128, 1, 1]], # 75
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3, 63], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 88
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3, 51], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [512, 1, 1]],
+   [-2, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]], # 101
+   
+   [75, 1, Conv, [256, 3, 1]],
+   [88, 1, Conv, [512, 3, 1]],
+   [101, 1, Conv, [1024, 3, 1]],
+
+   [[102, 103, 104], 1, Detect, [nc]],  # Detect(P3, P4, P5)
+  ]

models/detect/yolov9-c.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# YOLOv9 backbone
+backbone:
+  [
+   [-1, 1, Silence, []],  
+   
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 3
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 4-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 5
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 6-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 7
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 8-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 9
+  ]
+
+# YOLOv9 head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 10
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 7], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 13
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 5], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 16 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 13], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 19 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 22 (P5/32-large)
+   
+   
+   # multi-level reversible auxiliary branch
+   
+   # routing
+   [5, 1, CBLinear, [[256]]], # 23
+   [7, 1, CBLinear, [[256, 512]]], # 24
+   [9, 1, CBLinear, [[256, 512, 512]]], # 25
+   
+   # conv down
+   [0, 1, Conv, [64, 3, 2]],  # 26-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 27-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 28
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [256]],  # 29-P3/8
+   [[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30  
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 31
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [512]],  # 32-P4/16
+   [[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33 
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 34
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [512]],  # 35-P5/32
+   [[25, -1], 1, CBFuse, [[2]]], # 36
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 37
+   
+   
+   
+   # detection head
+
+   # detect
+   [[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)
+  ]

models/detect/yolov9-e.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# YOLOv9 backbone
+backbone:
+  [
+   [-1, 1, Silence, []],
+  
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]],  # 3
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 4-P3/8
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]],  # 5
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 6-P4/16
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 7
+
+   # avg-conv down
+   [-1, 1, ADown, [1024]],  # 8-P5/32
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 9
+   
+   # routing
+   [1, 1, CBLinear, [[64]]], # 10
+   [3, 1, CBLinear, [[64, 128]]], # 11
+   [5, 1, CBLinear, [[64, 128, 256]]], # 12
+   [7, 1, CBLinear, [[64, 128, 256, 512]]], # 13
+   [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]], # 14
+  
+   # conv down
+   [0, 1, Conv, [64, 3, 2]],  # 15-P1/2
+   [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]], # 16
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 17-P2/4
+   [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]], # 18  
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]],  # 19
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [256]],  # 20-P3/8
+   [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]], # 21  
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]],  # 22
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [512]],  # 23-P4/16
+   [[13, 14, -1], 1, CBFuse, [[3, 3]]], # 24 
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 25
+
+   # avg-conv down fuse
+   [-1, 1, ADown, [1024]],  # 26-P5/32
+   [[14, -1], 1, CBFuse, [[4]]], # 27
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]],  # 28
+  ]
+
+# YOLOv9 head
+head:
+  [
+   # multi-level auxiliary branch  
+  
+   # elan-spp block
+   [9, 1, SPPELAN, [512, 256]],  # 29
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 7], 1, Concat, [1]],  # cat backbone P4
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 32
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 5], 1, Concat, [1]],  # cat backbone P3
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]],  # 35
+   
+   
+   
+   # main branch  
+   
+   # elan-spp block
+   [28, 1, SPPELAN, [512, 256]],  # 36
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 25], 1, Concat, [1]],  # cat backbone P4
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 39
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 22], 1, Concat, [1]],  # cat backbone P3
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]],  # 42 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 39], 1, Concat, [1]],  # cat head P4
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]],  # 45 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 36], 1, Concat, [1]],  # cat head P5
+
+   # csp-elan block
+   [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]],  # 48 (P5/32-large)
+
+   # detect
+   [[35, 32, 29, 42, 45, 48], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)
+  ]

models/detect/yolov9.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# YOLOv9 backbone
+backbone:
+  [
+   [-1, 1, Silence, []],  
+   
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 3
+
+   # conv down
+   [-1, 1, Conv, [256, 3, 2]],  # 4-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 5
+
+   # conv down
+   [-1, 1, Conv, [512, 3, 2]],  # 6-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 7
+
+   # conv down
+   [-1, 1, Conv, [512, 3, 2]],  # 8-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 9
+  ]
+
+# YOLOv9 head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 10
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 7], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 13
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 5], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 16 (P3/8-small)
+
+   # conv-down merge
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 13], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 19 (P4/16-medium)
+
+   # conv-down merge
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 22 (P5/32-large)
+   
+   # routing
+   [5, 1, CBLinear, [[256]]], # 23
+   [7, 1, CBLinear, [[256, 512]]], # 24
+   [9, 1, CBLinear, [[256, 512, 512]]], # 25
+   
+   # conv down
+   [0, 1, Conv, [64, 3, 2]],  # 26-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 27-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 28
+
+   # conv down fuse
+   [-1, 1, Conv, [256, 3, 2]],  # 29-P3/8
+   [[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30  
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 31
+
+   # conv down fuse
+   [-1, 1, Conv, [512, 3, 2]],  # 32-P4/16
+   [[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33 
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 34
+
+   # conv down fuse
+   [-1, 1, Conv, [512, 3, 2]],  # 35-P5/32
+   [[25, -1], 1, CBFuse, [[2]]], # 36
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 37
+
+   # detect
+   [[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)
+  ]

models/experimental.py

+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from utils.downloads import attempt_download
+
+
+class Sum(nn.Module):
+    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+    def __init__(self, n, weight=False):  # n: number of inputs
+        super().__init__()
+        self.weight = weight  # apply weights boolean
+        self.iter = range(n - 1)  # iter object
+        if weight:
+            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights
+
+    def forward(self, x):
+        y = x[0]  # no weight
+        if self.weight:
+            w = torch.sigmoid(self.w) * 2
+            for i in self.iter:
+                y = y + x[i + 1] * w[i]
+        else:
+            for i in self.iter:
+                y = y + x[i + 1]
+        return y
+
+
+class MixConv2d(nn.Module):
+    # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch_out, kernel, stride, ch_strategy
+        super().__init__()
+        n = len(k)  # number of convolutions
+        if equal_ch:  # equal c_ per group
+            i = torch.linspace(0, n - 1E-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
+        else:  # equal weight.numel() per group
+            b = [c2] + [0] * n
+            a = np.eye(n + 1, n, k=-1)
+            a -= np.roll(a, 1, axis=1)
+            a *= np.array(k) ** 2
+            a[0] = 1
+            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
+
+        self.m = nn.ModuleList([
+            nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+
+
+class Ensemble(nn.ModuleList):
+    # Ensemble of models
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, augment=False, profile=False, visualize=False):
+        y = [module(x, augment, profile, visualize)[0] for module in self]
+        # y = torch.stack(y).max(0)[0]  # max ensemble
+        # y = torch.stack(y).mean(0)  # mean ensemble
+        y = torch.cat(y, 1)  # nms ensemble
+        return y, None  # inference, train output
+
+
+class ORT_NMS(torch.autograd.Function):
+    '''ONNX-Runtime NMS operation'''
+    @staticmethod
+    def forward(ctx,
+                boxes,
+                scores,
+                max_output_boxes_per_class=torch.tensor([100]),
+                iou_threshold=torch.tensor([0.45]),
+                score_threshold=torch.tensor([0.25])):
+        device = boxes.device
+        batch = scores.shape[0]
+        num_det = random.randint(0, 100)
+        batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device)
+        idxs = torch.arange(100, 100 + num_det).to(device)
+        zeros = torch.zeros((num_det,), dtype=torch.int64).to(device)
+        selected_indices = torch.cat([batches[None], zeros[None], idxs[None]], 0).T.contiguous()
+        selected_indices = selected_indices.to(torch.int64)
+        return selected_indices
+
+    @staticmethod
+    def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold):
+        return g.op("NonMaxSuppression", boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
+
+
+class TRT_NMS(torch.autograd.Function):
+    '''TensorRT NMS operation'''
+    @staticmethod
+    def forward(
+        ctx,
+        boxes,
+        scores,
+        background_class=-1,
+        box_coding=1,
+        iou_threshold=0.45,
+        max_output_boxes=100,
+        plugin_version="1",
+        score_activation=0,
+        score_threshold=0.25,
+    ):
+
+        batch_size, num_boxes, num_classes = scores.shape
+        num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32)
+        det_boxes = torch.randn(batch_size, max_output_boxes, 4)
+        det_scores = torch.randn(batch_size, max_output_boxes)
+        det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32)
+        return num_det, det_boxes, det_scores, det_classes
+
+    @staticmethod
+    def symbolic(g,
+                 boxes,
+                 scores,
+                 background_class=-1,
+                 box_coding=1,
+                 iou_threshold=0.45,
+                 max_output_boxes=100,
+                 plugin_version="1",
+                 score_activation=0,
+                 score_threshold=0.25):
+        out = g.op("TRT::EfficientNMS_TRT",
+                   boxes,
+                   scores,
+                   background_class_i=background_class,
+                   box_coding_i=box_coding,
+                   iou_threshold_f=iou_threshold,
+                   max_output_boxes_i=max_output_boxes,
+                   plugin_version_s=plugin_version,
+                   score_activation_i=score_activation,
+                   score_threshold_f=score_threshold,
+                   outputs=4)
+        nums, boxes, scores, classes = out
+        return nums, boxes, scores, classes
+
+
+class ONNX_ORT(nn.Module):
+    '''onnx module with ONNX-Runtime NMS operation.'''
+    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None, n_classes=80):
+        super().__init__()
+        self.device = device if device else torch.device("cpu")
+        self.max_obj = torch.tensor([max_obj]).to(device)
+        self.iou_threshold = torch.tensor([iou_thres]).to(device)
+        self.score_threshold = torch.tensor([score_thres]).to(device)
+        self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic
+        self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
+                                           dtype=torch.float32,
+                                           device=self.device)
+        self.n_classes=n_classes
+
+    def forward(self, x):
+        ## https://github.com/thaitc-hust/yolov9-tensorrt/blob/main/torch2onnx.py
+        ## thanks https://github.com/thaitc-hust
+        if isinstance(x, list):  ## yolov9-c.pt and yolov9-e.pt return list
+            x = x[1]
+        x = x.permute(0, 2, 1)
+        bboxes_x = x[..., 0:1]
+        bboxes_y = x[..., 1:2]
+        bboxes_w = x[..., 2:3]
+        bboxes_h = x[..., 3:4]
+        bboxes = torch.cat([bboxes_x, bboxes_y, bboxes_w, bboxes_h], dim = -1)
+        bboxes = bboxes.unsqueeze(2) # [n_batch, n_bboxes, 4] -> [n_batch, n_bboxes, 1, 4]
+        obj_conf = x[..., 4:]
+        scores = obj_conf
+        bboxes @= self.convert_matrix
+        max_score, category_id = scores.max(2, keepdim=True)
+        dis = category_id.float() * self.max_wh
+        nmsbox = bboxes + dis
+        max_score_tp = max_score.transpose(1, 2).contiguous()
+        selected_indices = ORT_NMS.apply(nmsbox, max_score_tp, self.max_obj, self.iou_threshold, self.score_threshold)
+        X, Y = selected_indices[:, 0], selected_indices[:, 2]
+        selected_boxes = bboxes[X, Y, :]
+        selected_categories = category_id[X, Y, :].float()
+        selected_scores = max_score[X, Y, :]
+        X = X.unsqueeze(1).float()
+        return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1)
+
+
+class ONNX_TRT(nn.Module):
+    '''onnx module with TensorRT NMS operation.'''
+    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None, n_classes=80):
+        super().__init__()
+        assert max_wh is None
+        self.device = device if device else torch.device('cpu')
+        self.background_class = -1,
+        self.box_coding = 1,
+        self.iou_threshold = iou_thres
+        self.max_obj = max_obj
+        self.plugin_version = '1'
+        self.score_activation = 0
+        self.score_threshold = score_thres
+        self.n_classes=n_classes
+
+    def forward(self, x):
+        ## https://github.com/thaitc-hust/yolov9-tensorrt/blob/main/torch2onnx.py
+        ## thanks https://github.com/thaitc-hust
+        if isinstance(x, list):  ## yolov9-c.pt and yolov9-e.pt return list
+            x = x[1]
+        x = x.permute(0, 2, 1)
+        bboxes_x = x[..., 0:1]
+        bboxes_y = x[..., 1:2]
+        bboxes_w = x[..., 2:3]
+        bboxes_h = x[..., 3:4]
+        bboxes = torch.cat([bboxes_x, bboxes_y, bboxes_w, bboxes_h], dim = -1)
+        bboxes = bboxes.unsqueeze(2) # [n_batch, n_bboxes, 4] -> [n_batch, n_bboxes, 1, 4]
+        obj_conf = x[..., 4:]
+        scores = obj_conf
+        num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(bboxes, scores, self.background_class, self.box_coding,
+                                                                    self.iou_threshold, self.max_obj,
+                                                                    self.plugin_version, self.score_activation,
+                                                                    self.score_threshold)
+        return num_det, det_boxes, det_scores, det_classes
+
+class End2End(nn.Module):
+    '''export onnx or tensorrt model with NMS operation.'''
+    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None, n_classes=80):
+        super().__init__()
+        device = device if device else torch.device('cpu')
+        assert isinstance(max_wh,(int)) or max_wh is None
+        self.model = model.to(device)
+        self.model.model[-1].end2end = True
+        self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT
+        self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device, n_classes)
+        self.end2end.eval()
+
+    def forward(self, x):
+        x = self.model(x)
+        x = self.end2end(x)
+        return x
+
+
+def attempt_load(weights, device=None, inplace=True, fuse=True):
+    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    from models.yolo import Detect, Model
+
+    model = Ensemble()
+    for w in weights if isinstance(weights, list) else [weights]:
+        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
+        ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
+
+        # Model compatibility updates
+        if not hasattr(ckpt, 'stride'):
+            ckpt.stride = torch.tensor([32.])
+        if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
+            ckpt.names = dict(enumerate(ckpt.names))  # convert to dict
+
+        model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
+
+    # Module compatibility updates
+    for m in model.modules():
+        t = type(m)
+        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
+            m.inplace = inplace  # torch 1.7.0 compatibility
+            # if t is Detect and not isinstance(m.anchor_grid, list):
+            #    delattr(m, 'anchor_grid')
+            #    setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
+        elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
+            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
+
+    # Return model
+    if len(model) == 1:
+        return model[-1]
+
+    # Return detection ensemble
+    print(f'Ensemble created with {weights}\n')
+    for k in 'names', 'nc', 'yaml':
+        setattr(model, k, getattr(model[0], k))
+    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
+    assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
+    return model

models/hub/anchors.yaml

+# YOLOv3 & YOLOv5
+# Default anchors for COCO data
+
+
+# P5 -------------------------------------------------------------------------------------------------------------------
+# P5-640:
+anchors_p5_640:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+
+# P6 -------------------------------------------------------------------------------------------------------------------
+# P6-640:  thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11,  21,19,  17,41,  43,32,  39,70,  86,64,  65,131,  134,130,  120,265,  282,180,  247,354,  512,387
+anchors_p6_640:
+  - [9,11,  21,19,  17,41]  # P3/8
+  - [43,32,  39,70,  86,64]  # P4/16
+  - [65,131,  134,130,  120,265]  # P5/32
+  - [282,180,  247,354,  512,387]  # P6/64
+
+# P6-1280:  thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792
+anchors_p6_1280:
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [436,615,  739,380,  925,792]  # P6/64
+
+# P6-1920:  thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41,  67,59,  57,141,  144,103,  129,227,  270,205,  209,452,  455,396,  358,812,  653,922,  1109,570,  1387,1187
+anchors_p6_1920:
+  - [28,41,  67,59,  57,141]  # P3/8
+  - [144,103,  129,227,  270,205]  # P4/16
+  - [209,452,  455,396,  358,812]  # P5/32
+  - [653,922,  1109,570,  1387,1187]  # P6/64
+
+
+# P7 -------------------------------------------------------------------------------------------------------------------
+# P7-640:  thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11,  13,30,  29,20,  30,46,  61,38,  39,92,  78,80,  146,66,  79,163,  149,150,  321,143,  157,303,  257,402,  359,290,  524,372
+anchors_p7_640:
+  - [11,11,  13,30,  29,20]  # P3/8
+  - [30,46,  61,38,  39,92]  # P4/16
+  - [78,80,  146,66,  79,163]  # P5/32
+  - [149,150,  321,143,  157,303]  # P6/64
+  - [257,402,  359,290,  524,372]  # P7/128
+
+# P7-1280:  thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22,  54,36,  32,77,  70,83,  138,71,  75,173,  165,159,  148,334,  375,151,  334,317,  251,626,  499,474,  750,326,  534,814,  1079,818
+anchors_p7_1280:
+  - [19,22,  54,36,  32,77]  # P3/8
+  - [70,83,  138,71,  75,173]  # P4/16
+  - [165,159,  148,334,  375,151]  # P5/32
+  - [334,317,  251,626,  499,474]  # P6/64
+  - [750,326,  534,814,  1079,818]  # P7/128
+
+# P7-1920:  thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34,  81,55,  47,115,  105,124,  207,107,  113,259,  247,238,  222,500,  563,227,  501,476,  376,939,  749,711,  1126,489,  801,1222,  1618,1227
+anchors_p7_1920:
+  - [29,34,  81,55,  47,115]  # P3/8
+  - [105,124,  207,107,  113,259]  # P4/16
+  - [247,238,  222,500,  563,227]  # P5/32
+  - [501,476,  376,939,  749,711]  # P6/64
+  - [1126,489,  801,1222,  1618,1227]  # P7/128

models/hub/yolov3-spp.yaml

+# YOLOv3
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
+  ]
+
+# YOLOv3-SPP head
+head:
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, SPP, [512, [5, 9, 13]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
+
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]

models/hub/yolov3-tiny.yaml

+# YOLOv3
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+anchors:
+  - [10,14, 23,27, 37,58]  # P4/16
+  - [81,82, 135,169, 344,319]  # P5/32
+
+# YOLOv3-tiny backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [16, 3, 1]],  # 0
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 1-P1/2
+   [-1, 1, Conv, [32, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 3-P2/4
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 5-P3/8
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 7-P4/16
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 9-P5/32
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]],  # 11
+   [-1, 1, nn.MaxPool2d, [2, 1, 0]],  # 12
+  ]
+
+# YOLOv3-tiny head
+head:
+  [[-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Conv, [256, 3, 1]],  # 19 (P4/16-medium)
+
+   [[19, 15], 1, Detect, [nc, anchors]],  # Detect(P4, P5)
+  ]

models/hub/yolov3.yaml

+# YOLOv3
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
+  ]
+
+# YOLOv3 head
+head:
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
+
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]

models/panoptic/gelan-c-pan.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   # panoptic
+   [[15, 18, 21], 1, Panoptic, [nc, 93, 32, 256]],  # Panoptic(P3, P4, P5)
+  ]

models/panoptic/yolov7-af-pan.yaml

+# YOLOv7
+
+# Parameters
+nc: 80  # number of classes
+sem_nc: 93  # number of stuff classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+anchors: 3
+
+# YOLOv7 backbone
+backbone:
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+  
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
+   [-1, 1, Conv, [64, 3, 1]],
+   
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
+   [-1, 1, Conv, [64, 1, 1]],
+   [-2, 1, Conv, [64, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]],  # 11
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]],  # 24
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 37
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-3, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 50
+  ]
+
+# yolov7 head
+head:
+  [[-1, 1, SPPCSPC, [512]], # 51
+  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [37, 1, Conv, [256, 1, 1]], # route backbone P4
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 63
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [24, 1, Conv, [128, 1, 1]], # route backbone P3
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [128, 1, 1]], # 75
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3, 63], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 88
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3, 51], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [512, 1, 1]],
+   [-2, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]], # 101
+   
+   [75, 1, Conv, [256, 3, 1]],
+   [88, 1, Conv, [512, 3, 1]],
+   [101, 1, Conv, [1024, 3, 1]],
+
+   [[102, 103, 104], 1, Panoptic, [nc, 93, 32, 256]],  # Panoptic(P3, P4, P5)
+  ]

models/segment/gelan-c-dseg.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   [15, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 22   
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [-1, 1, Conv, [256, 3, 1]], # 24
+
+   # segment
+   [[15, 18, 21, 24], 1, DSegment, [nc, 32, 256]],  # Segment(P3, P4, P5)
+  ]

models/segment/gelan-c-seg.yaml

+# YOLOv9
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+#activation: nn.LeakyReLU(0.1)
+#activation: nn.ReLU()
+
+# anchors
+anchors: 3
+
+# gelan backbone
+backbone:
+  [
+   # conv down
+   [-1, 1, Conv, [64, 3, 2]],  # 0-P1/2
+
+   # conv down
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+
+   # elan-1 block
+   [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 2
+
+   # avg-conv down
+   [-1, 1, ADown, [256]],  # 3-P3/8
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 4
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 5-P4/16
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 6
+
+   # avg-conv down
+   [-1, 1, ADown, [512]],  # 7-P5/32
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 8
+  ]
+
+# gelan head
+head:
+  [
+   # elan-spp block
+   [-1, 1, SPPELAN, [512, 256]],  # 9
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 12
+
+   # up-concat merge
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 15 (P3/8-small)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [256]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P4
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 18 (P4/16-medium)
+
+   # avg-conv-down merge
+   [-1, 1, ADown, [512]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+
+   # elan-2 block
+   [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 21 (P5/32-large)
+
+   # segment
+   [[15, 18, 21], 1, Segment, [nc, 32, 256]],  # Segment(P3, P4, P5)
+  ]

models/segment/yolov7-af-seg.yaml

+# YOLOv7
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+anchors: 3
+
+# YOLOv7 backbone
+backbone:
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+  
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
+   [-1, 1, Conv, [64, 3, 1]],
+   
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
+   [-1, 1, Conv, [64, 1, 1]],
+   [-2, 1, Conv, [64, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]],  # 11
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]],  # 24
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 37
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-3, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [1024, 1, 1]],  # 50
+  ]
+
+# yolov7 head
+head:
+  [[-1, 1, SPPCSPC, [512]], # 51
+  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [37, 1, Conv, [256, 1, 1]], # route backbone P4
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 63
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [24, 1, Conv, [128, 1, 1]], # route backbone P3
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [128, 1, 1]], # 75
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [128, 1, 1]],
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, -3, 63], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [256, 1, 1]], # 88
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, -3, 51], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [512, 1, 1]],
+   [-2, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
+   [-1, 1, Conv, [512, 1, 1]], # 101
+   
+   [75, 1, Conv, [256, 3, 1]],
+   [88, 1, Conv, [512, 3, 1]],
+   [101, 1, Conv, [1024, 3, 1]],
+
+   [[102, 103, 104], 1, Segment, [nc, 32, 256]],  # Segment(P3, P4, P5)
+  ]