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yolov9_pytorch

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hubconf.py 0 → 100644
View file @ f969ca34
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 0 → 100644
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# init
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models/common.py 0 → 100644
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models/detect/gelan-c.yaml 0 → 100644
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# 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 0 → 100644
View file @ f969ca34
# 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 0 → 100644
View file @ f969ca34
# 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 0 → 100644
View file @ f969ca34
# 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 0 → 100644
View file @ f969ca34
# 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 0 → 100644
View file @ f969ca34
# 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)
]
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