准备环境
git clone https://github.com/ultralytics/ultralytics.git # clone
cd ultralytics
pip install -r requirements.txt # install
pip install -e '.[dev]' # develop
pip install onnxsim修改 ultralytics/nn/modules/block.py class DFL(nn.Module):
def __init__(self, c1=16):
"""Initialize a convolutional layer with a given number of input channels."""
super().__init__()
self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
x = torch.arange(c1, dtype=torch.float)
self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1))
self.c1 = c1
def forward(self, x):
"""Applies a transformer layer on input tensor 'x' and returns a tensor."""
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, 4, self.c1, a).transpose(2, 1).softmax(1))
# return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a)修改 ultralytics/nn/modules/head.py class Detect(nn.Module):
def forward(self, x):
"""Concatenates and returns predicted bounding boxes and class probabilities."""
shape = x[0].shape # BCHW
for i in range(self.nl):
x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
if self.training:
return x
elif self.dynamic or self.shape != shape:
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
self.shape = shape
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
if self.export and self.format in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'): # avoid TF FlexSplitV ops
box = x_cat[:, :self.reg_max * 4]
cls = x_cat[:, self.reg_max * 4:]
else:
box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
# dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
dbox = self.dfl(box)
if self.export and self.format in ('tflite', 'edgetpu'):
# Normalize xywh with image size to mitigate quantization error of TFLite integer models as done in YOLOv5:
# https://github.com/ultralytics/yolov5/blob/0c8de3fca4a702f8ff5c435e67f378d1fce70243/models/tf.py#L307-L309
# See this PR for details: https://github.com/ultralytics/ultralytics/pull/1695
img_h = shape[2] * self.stride[0]
img_w = shape[3] * self.stride[0]
img_size = torch.tensor([img_w, img_h, img_w, img_h], device=dbox.device).reshape(1, 4, 1)
dbox /= img_size
x = dbox
y = cls.sigmoid()
# y = torch.cat((dbox, cls.sigmoid()), 1)
return (y, x) if self.export else (y, x)
# return y if self.export else (y, x)导出模型
yolo export model=yolov8s.pt format=onnx
onnxsim yolov8s.onnx yolov8s_sim.onnx 
sudo docker load -i ax_pulsar2_${version}.tar.gz
sudo docker run -it --net host --rm -v $PWD:/data pulsar2:${version}修改 config/yolov8s_config_b1.json 带注释的内容为自己的配置
{
"model_type": "ONNX",
"npu_mode": "NPU3", #NPU1为单核NPU，NPU2为双核NPU，NPU3为三核NPU
"quant": {
"input_configs": [
{
"tensor_name": "images", #onnx模型输入名
"calibration_dataset": "coco_10.tar", #量化数据集包
"calibration_size": 4,
"calibration_mean": [0, 0, 0],
"calibration_std": [255.0, 255.0, 255.0]
}
],
"calibration_method": "MinMax",
"precision_analysis": true,
"precision_analysis_method":"EndToEnd"
},
"input_processors": [
{
"tensor_name": "images",
"tensor_format": "BGR",
"src_format": "BGR",
"src_dtype": "U8",
"src_layout": "NHWC"
}
],
"output_processors": [
{
"tensor_name": "407", #onnx模型第一个输出头
"dst_perm": [0, 1, 3, 2]
},
{
"tensor_name": "output0", #onnx模型第二个输出头
"dst_perm": [0, 2, 1]
}
],
"compiler": {
"check": 0
}
}
转换模型
pulsar2 build --input onnx/yolov8s_sim.onnx --output_dir axmodel/ --config config/yolov8s_config_b1.json在 axmodel/ 目录下找到文件 compiled.axmodel 重命名为 yolov8s.axmodel 上传至 M4N 开发板即可使用 ax-samples 或 ax-pipeline 加载推理