转换模型时遇到“Unsupported slice step!”

在将 YOLOv5 模型从 ONNX 转换为模型时遇到“Unsupported slice step!”错误，这通常是因为当前不支持某些特定的 ONNX 操作或参数配置，尤其是带有非标准切片步长的操作。

解决方案

修改模型或代码:
- 尝试修改模型中的切片操作，使其使用支持的步长（通常是步长为1）。
- 检查 YOLOv5 的代码，特别是数据预处理或网络结构中的切片操作，调整这些操作以兼容。
使用 ONNX Simplifier:
- 尝试使用 ONNX Simplifier 来简化 ONNX 模型，这有时可以去除或替换不兼容的操作。
```
pip install onnx-simplifier
python -m onnxsim your_model.onnx simplified_model.onnx
```
手动调整 Focus模型r:
- 使用 ONNX 图形编辑器（如 Netron）来检查并手动调整不兼容的操作。### 具体步骤

安装 ONNX Simplifier 并简化模型:

pip install onnx-simplifier
python -m onnxsim yolov5_model.onnx yolov5_model_simplified.onnx

转换为 NCNN 模型:

onnx2ncnn yolov5_model_simplified.onnx yolov5_model.param yolov5_model.bin

手动调整 Focus模型:

在 models 文件夹下的 common.py 文件中找到并修改 Focus 类，以避免使用不受支持的 slice 步长。原始代码如下：

class Focus(nn.Module):# Focus wh information into c-spacedef __init__(self, c1, c2, k=1):super(Focus, self).__init__()self.conv = Conv(c1 * 4, c2, k, 1)def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))

将上述代码修改为如下代码：

class Focus(nn.Module):# Focus wh information into c-spacedef __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groupssuper(Focus, self).__init__()self.conv = Conv(c1 * 4, c2, k, s, p, g, act)with torch.no_grad():self.conv1 = nn.Conv2d(c1, c1, (2, 2), groups=c1, bias=False, stride=(2, 2))self.conv1.weight[:, :, 0, 0] = 1self.conv1.weight[:, :, 0, 1] = 0self.conv1.weight[:, :, 1, 0] = 0self.conv1.weight[:, :, 1, 1] = 0self.conv2 = nn.Conv2d(c1, c1, (2, 2), groups=c1, bias=False, stride=(2, 2))self.conv2.weight[:, :, 0, 0] = 0self.conv2.weight[:, :, 0, 1] = 0self.conv2.weight[:, :, 1, 0] = 1self.conv2.weight[:, :, 1, 1] = 0self.conv3 = nn.Conv2d(c1, c1, (2, 2), groups=c1, bias=False, stride=(2, 2))self.conv3.weight[:, :, 0, 0] = 0self.conv3.weight[:, :, 0, 1] = 1self.conv3.weight[:, :, 1, 0] = 0self.conv3.weight[:, :, 1, 1] = 0self.conv4 = nn.Conv2d(c1, c1, (2, 2), groups=c1, bias=False, stride=(2, 2))self.conv4.weight[:, :, 0, 0] = 0self.conv4.weight[:, :, 0, 1] = 0self.conv4.weight[:, :, 1, 0] = 0self.conv4.weight[:, :, 1, 1] = 1def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)return self.conv(torch.cat([self.conv1(x), self.conv2(x), self.conv3(x), self.conv4(x)], 1))