从一张图到两个视图 · 实现代码

def make_data(seed=SEED):
    rng=np.random.default_rng(seed)
    yy,xx=np.mgrid[:16,:16]
    images=[]; shape=[]; color=[]; split=[]
    # Stratified before view construction. One independently generated source per ID.
    for s in range(3):
        for c in range(2):
            for i in range(140):
                cx,cy=rng.uniform(6,10,2); r=rng.uniform(3.0,4.7)
                dx,dy=xx-cx,yy-cy
                if s==0: mask=dx*dx+dy*dy<=r*r
                elif s==1: mask=np.maximum(abs(dx),abs(dy))<=r
                else: mask=abs(dx)+abs(dy)<=r*1.35
                # Red and blue have equal channel sums: grayscale erases hue by design.
                rgb=np.array([.9,.15,.15] if c==0 else [.15,.15,.9])
                bg=rng.uniform(.015,.075,(16,16,1))
                im=np.where(mask[...,None],rgb*rng.uniform(.8,1),bg)
                im+=rng.normal(0,.012,im.shape)
                images.append(np.clip(im,0,1).transpose(2,0,1))
                shape.append(s); color.append(c); split.append(0 if i<100 else 1)
    x=np.asarray(images,dtype='float32'); split=np.asarray(split)
    ids=np.arange(len(x)); train=ids[split==0]; test=ids[split==1]
    assert set(train).isdisjoint(test)
    assert len({hashlib.sha256(v.tobytes()).hexdigest() for v in x})==len(x)
    return torch.from_numpy(x),np.asarray(shape),np.asarray(color),train,test

def views(x, destroy_color=False):
    # Independent transforms of each source; no labels/scene attributes are read.
    a=x.clone(); n=len(a)
    shifts=torch.randint(-1,2,(n,2))
    for i,(dy,dx) in enumerate(shifts.tolist()):
        a[i]=torch.roll(a[i],(dy,dx),(-2,-1))
    a=a*torch.empty(n,1,1,1).uniform_(.8,1.2)
    a=a+torch.randn_like(a)*.025
    # The ablation changes only the hue swap. Every run consumes this RNG draw.
    swap=torch.rand(n)<.5
    if destroy_color: a[swap]=a[swap][:,[2,1,0]]
    return a.clamp(0,1)