衡量神经网络的常见参数 FLOPs与参数数量计算方法总结

绝地灬酷狼 2023-03-13 06:39 183阅读 0赞

FLOPs衡量神将网络中前向传播的运算次数，FLOPs越小，则计算速度越快。  
参数数量衡量神经网络中所包含参数的数量，参数数量越小，模型体积越小，更容易部署，二者计算方法如下所示：

参数数量更容易计算，只需要衡量，神经网络中有多少个待定参数即可，以卷积神经网络为例，假设卷积核大小为k × k，输入通道为 i，输出通道为o，输出图像尺寸为t ×t,则：  
每进行一次卷积操作，或一个卷积层，需要的参数数量为:  
k×k×i×o + o 后面的加是加上bias，可看下图conv1为例：  
注意 conv1的卷积核大小为11×11，可自行计算出相同结果  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzM3OTI5ODI0_size_16_color_FFFFFF_t_70]  
在考虑FLOPs时，同样以卷积计算为例，可参考下述代码计算过程：

input_shape = (3,300,300) # Format:(channels, rows,cols)
    conv_filter = (64,3,3,3)  # Format: (num_filters, channels, rows, cols)
    stride = 1
    padding = 1
    activation = 'relu'
    
    n = conv_filter[1] * conv_filter[2] * conv_filter[3]  # vector_length
    flops_per_instance = n + (n-1)    # general defination for number of flops (n: multiplications and n-1: additions)
    
    num_instances_per_filter = (( input_shape[1] - conv_filter[2] + 2*padding) / stride ) + 1  # for rows
    num_instances_per_filter *= (( input_shape[1] - conv_filter[2] + 2*padding) / stride ) + 1 # multiplying with cols
    
    flops_per_filter = num_instances_per_filter * flops_per_instance
    total_flops_per_layer = flops_per_filter * conv_filter[0]    # multiply with number of filters
    
    if activation == 'relu':
        # Here one can add number of flops required
        # Relu takes 1 comparison and 1 multiplication
        # Assuming for Relu: number of flops equal to length of input vector
        total_flops_per_layer += conv_filter[0]*input_shape[1]*input_shape[2]
    
    
    print(total_flops_per_layer)

上式中，与下列计算结果等效：  
即，没进行一次卷积，计算量大小如下：  
k×k×i×t×t×o + (k×k×i-1)×t×t×o

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