EPSILON = 1e-10 # attention fusion strategy, average based on weight maps # (注意力融合策略，基于权重图的平均值) def attention_fusion_weight(tensor1, tensor2): # avg, max, nuclear (平均、最大值、核) f_spatial = spatial_fusion(tensor1, tensor2) tensor_f = f_spatial return tensor_f # 空间融合 def spatial_fusion(tensor1, tensor2, spatial_type='mean'): shape = tensor1.size() # calculate spatial attention (计算空间注意力) spatial1 = spatial_attention(tensor1, spatial_type) spatial2 = spatial_attention(tensor2, spatial_type) # get weight map, soft-max (获取权重图，soft-max) spatial_w1 = torch.exp(spatial1) / (torch.exp(spatial1) + torch.exp(spatial2) + EPSILON) spatial_w2 = torch.exp(spatial2) / (torch.exp(spatial1) + torch.exp(spatial2) + EPSILON) spatial_w1 = spatial_w1.repeat(1, shape[1], 1, 1) spatial_w2 = spatial_w2.repeat(1, shape[1], 1, 1) tensor_f = spatial_w1 * tensor1 + spatial_w2 * tensor2 return tensor_f # spatial attention # (空间注意) def spatial_attention(tensor, spatial_type='mean'): if spatial_type == 'mean': spatial = tensor.mean(dim=1, keepdim=True) elif spatial_type == 'sum': spatial = tensor.sum(dim=1, keepdim=True) return spatial将上述代码用伪代码表示

对于注意力融合策略，基于权重图的平均值，伪代码如下：

EPSILON = 1e-10

# 计算注意力融合权重
def attention_fusion_weight(tensor1, tensor2):
    f_spatial = spatial_fusion(tensor1, tensor2)
    tensor_f = f_spatial
    return tensor_f

# 空间融合
def spatial_fusion(tensor1, tensor2, spatial_type='mean'):
    shape = tensor1.size()
    # 计算空间注意力
    spatial1 = spatial_attention(tensor1, spatial_type)
    spatial2 = spatial_attention(tensor2, spatial_type)
    # 获取权重图，soft-max
    spatial_w1 = torch.exp(spatial1) / (torch.exp(spatial1) + torch.exp(spatial2) + EPSILON)
    spatial_w2 = torch.exp(spatial2) / (torch.exp(spatial1) + torch.exp(spatial2) + EPSILON)
    spatial_w1 = spatial_w1.repeat(1, shape[1], 1, 1)
    spatial_w2 = spatial_w2.repeat(1, shape[1], 1, 1)
    tensor_f = spatial_w1 * tensor1 + spatial_w2 * tensor2
    return tensor_f

# 空间注意力
def spatial_attention(tensor, spatial_type='mean'):
    if spatial_type == 'mean':
        spatial = tensor.mean(dim=1, keepdim=True)
    elif spatial_type == 'sum':
        spatial = tensor.sum(dim=1, keepdim=True)
    return spatial