Model报错: must return a relationship instance

本文介绍如何在Laravel模型中处理`hasMany`关系的逻辑错误,并提供了一种通过`get()`方法转换查询以避免报错的方法。作者详细展示了如何将条件判断转换为关系实例,确保在不同情况下的正确调用。

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模型里面有一个方法是这样的:

public function adjustbills()
    {
        if($this->user_combined == '1'){
            $invoiceNumberArr = ['xxx'];
            return Adjustbill::whereIn('invoice_number',$invoiceNumberArr)->where('user_id', $this->user_id);
        }else{
            return $this->hasMany(Adjustbill::class, 'invoice_number', 'invoice_number')->where('user_id', $this->user_id);
        }
    }

当调用方式是Model->adjustbills的时候,

  1. 要是走到了下面的判断,不会报错
  2. 要是走到了上面的判断, 会报错must return a relationship instance, 因为laravel默认会用到下面的hasMany的关系, 如果没有的话就会报错.

解决方法:

  1. 想办法把上面的逻辑修改成下面的那种关系方式
  2. 调用方式用:Model->adjustbills->get()的方式
### YOLOv11 SPPF Module Implementation and Usage The Spatial Pyramid Pooling Fixed (SPPF) module is a key component of the YOLO family, including YOLOv11. It enhances feature extraction by aggregating multi-scale information through pooling operations at different scales. In this context, replacing or modifying the SPPF with advanced modules like AIFI from RT-DETR can lead to improved performance in certain scenarios. #### Overview of SPPF In traditional implementations, the SPPF layer performs spatial pyramid pooling on input features using multiple max-pooling layers with varying kernel sizes but fixed strides. This allows it to capture richer contextual information across various scales while maintaining computational efficiency. However, as noted in recent advancements[^2], simply substituting components such as SPPF may not always result in accuracy improvements due to differences between datasets and model architectures. #### Code Example for Standard SPPF Layer Below demonstrates how an SPPF might be implemented within PyTorch: ```python import torch.nn as nn class SPPF(nn.Module): def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = nn.Conv2d(c1, c_, 1, 1) self.cv2 = nn.Conv2d(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1) def forward(self, x): x = self.cv1(x) y1 = self.m(x) y2 = self.m(y1) y3 = self.m(y2) return self.cv2(torch.cat([x, y1, y2, y3], dim=1)) ``` This code defines a standard SPPF block where `c1` represents the number of input channels, `c2` denotes output channels after processing via convolutional transformations (`cv1`, `cv2`) alongside concatenation steps involving pooled outputs generated under progressively larger receptive fields defined by parameter 'k'. #### Integration With Advanced Modules Like AIFI From RT-Detr When integrating more sophisticated elements into existing frameworks—such as incorporating AIFI post-processing stages following initial convolutions—it becomes crucially important both theoretically align these additions correctly according architecture design principles outlined earlier works [^1]. 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