[LAB 10 · 从 RGB 图像训练小型检测器](/course/notebooks/a14237bfa9dd413d959ab4e458e11f42.html)

# 哪些锚框受哪一种监督 · 实现代码
[独立阅读](/course/notebook-blocks/b6e5552805db40308895eb423c00fff1.html)
主线阅读与优先实验

RGB图像、单特征层锚框检测器、分类/位置损失、NMS、独立检测评价
展开完整实现代码
```
%%writefile -a lab10_detector.py
def anchors_grid(grid=12,sizes=(.23,.37)):
    boxes=[]
    for y in range(grid):
        for x in range(grid):
            for s in sizes:boxes.append([(x+.5)/grid-s/2,(y+.5)/grid-s/2,(x+.5)/grid+s/2,(y+.5)/grid+s/2])
    return torch.tensor(boxes)

def encode(anchors,boxes):
    ac=(anchors[:,:2]+anchors[:,2:])/2;aw=anchors[:,2:]-anchors[:,:2]
    bc=(boxes[:,:2]+boxes[:,2:])/2;bw=boxes[:,2:]-boxes[:,:2]
    return torch.cat([(bc-ac)/aw,torch.log((bw/aw).clamp(min=1e-6))],1)

def decode(anchors,offset):
    ac=(anchors[:,:2]+anchors[:,2:])/2;aw=anchors[:,2:]-anchors[:,:2]
    bc=ac+offset[:,:2]*aw;bw=aw*torch.exp(offset[:,2:].clamp(-3,3))
    return torch.cat([bc-bw/2,bc+bw/2],1).clamp(0,1)

def match_targets(anchors,target):
    """-1 ignore, 0 background, 1/2 object. Each GT gets a distinct forced anchor."""
    classes=torch.zeros(len(anchors),dtype=torch.long);offsets=torch.zeros_like(anchors)
    if not len(target['boxes']): return classes,offsets
    overlap=iou_matrix(anchors,target['boxes']);best,best_gt=overlap.max(1)
    classes[(best>=.25)&(best<.45)]=-1
    positive=best>=.45
    # Greedy global maximum, invalidate its anchor and GT; guarantees all GT assigned.
    work=overlap.clone()
    for _ in range(len(target['boxes'])):
        flat=int(work.argmax()); a=flat//work.shape[1];g=flat%work.shape[1]
        positive[a]=True;best_gt[a]=g;work[a,:]=-1;work[:,g]=-1
    classes[positive]=target['labels'][best_gt[positive]]
    offsets[positive]=encode(anchors[positive],target['boxes'][best_gt[positive]])
    return classes,offsets


```