计算各种网络结构的卷积层和返回的感受野

计算各种网络结构的卷积层和返回的感受野的代码

在卷积神经网络中，感受野的定义是卷积神经网络每一层输出的特征图（Feature Map）上的像素点在原始图像上映射的区域大小。
input field size = （output field size - 1）* stride - 2*padding + kernel size

# -*- coding: UTF-8 -*-net_struct = {'alexnet': {'net':[[11,4,0],[3,2,0],[5,1,2],[3,2,0],[3,1,1],[3,1,1],[3,1,1],[3,2,0]],                   'name':['conv1','pool1','conv2','pool2','conv3','conv4','conv5','pool5']},       'vgg16': {'net':[[3,1,1],[3,1,1],[2,2,0],[3,1,1],[3,1,1],[2,2,0],[3,1,1],[3,1,1],[3,1,1],                        [2,2,0],[3,1,1],[3,1,1],[3,1,1],[2,2,0],[3,1,1],[3,1,1],[3,1,1],[2,2,0]],                 'name':['conv1_1','conv1_2','pool1','conv2_1','conv2_2','pool2','conv3_1','conv3_2',                         'conv3_3', 'pool3','conv4_1','conv4_2','conv4_3','pool4','conv5_1','conv5_2','conv5_3','pool5']},       'zf-5':{'net': [[7,2,3],[3,2,1],[5,2,2],[3,2,1],[3,1,1],[3,1,1],[3,1,1]],               'name': ['conv1','pool1','conv2','pool2','conv3','conv4','conv5']}}imsize = 224def outFromIn(isz,net,layernum):#从前往后算输出维度    totstride = 1    insize = isz    for layer in range(layernum):        fsize,stride,pad = net[layer]        outsize = (insize - fsize + 2*pad) / stride +1        insize = outsize        totstride = totstride * stride    return outsize,totstridedef inFromOut(net,layernum):#从后往前算感受野 返回该层元素在原始图片中的感受野    RF = 1    for layer in reversed(range(layernum)):        fsize,stride,pad = net[layer]        RF = ((RF - 1)*stride) + fsize    return RFif __name__ == '__main__':    print "layer output sizes given image = %dx%d" % (imsize,imsize)    for net in net_struct.keys():        print '*********net structure name is %s********'%net        for i in range(len(net_struct[net]['net'])):            p = outFromIn(imsize,net_struct[net]['net'],i+1)            rf = inFromOut(net_struct[net]['net'],i+1)            print "Layer Name = %s,Output size = %3d,Stride = %3d,RF size = %3d "%(net_struct[net]['name'][i],p[0],p[1],rf)

输出结果

Layer Name = conv1_1,Output size = 224,Stride =   1,RF size =   3 Layer Name = conv1_2,Output size = 224,Stride =   1,RF size =   5 Layer Name = pool1,Output size = 112,Stride =   2,RF size =   6 Layer Name = conv2_1,Output size = 112,Stride =   2,RF size =  10 Layer Name = conv2_2,Output size = 112,Stride =   2,RF size =  14 Layer Name = pool2,Output size =  56,Stride =   4,RF size =  16 Layer Name = conv3_1,Output size =  56,Stride =   4,RF size =  24 Layer Name = conv3_2,Output size =  56,Stride =   4,RF size =  32 Layer Name = conv3_3,Output size =  56,Stride =   4,RF size =  40 Layer Name = pool3,Output size =  28,Stride =   8,RF size =  44 Layer Name = conv4_1,Output size =  28,Stride =   8,RF size =  60 Layer Name = conv4_2,Output size =  28,Stride =   8,RF size =  76 Layer Name = conv4_3,Output size =  28,Stride =   8,RF size =  92 Layer Name = pool4,Output size =  14,Stride =  16,RF size = 100 Layer Name = conv5_1,Output size =  14,Stride =  16,RF size = 132 Layer Name = conv5_2,Output size =  14,Stride =  16,RF size = 164 Layer Name = conv5_3,Output size =  14,Stride =  16,RF size = 196 Layer Name = pool5,Output size =   7,Stride =  32,RF size = 212 *********net structure name is zf-5********Layer Name = conv1,Output size = 112,Stride =   2,RF size =   7 Layer Name = pool1,Output size =  56,Stride =   4,RF size =  11 Layer Name = conv2,Output size =  28,Stride =   8,RF size =  27 Layer Name = pool2,Output size =  14,Stride =  16,RF size =  43 Layer Name = conv3,Output size =  14,Stride =  16,RF size =  75 Layer Name = conv4,Output size =  14,Stride =  16,RF size = 107 Layer Name = conv5,Output size =  14,Stride =  16,RF size = 139 *********net structure name is alexnet********Layer Name = conv1,Output size =  54,Stride =   4,RF size =  11 Layer Name = pool1,Output size =  26,Stride =   8,RF size =  19 Layer Name = conv2,Output size =  26,Stride =   8,RF size =  51 Layer Name = pool2,Output size =  12,Stride =  16,RF size =  67 Layer Name = conv3,Output size =  12,Stride =  16,RF size =  99 Layer Name = conv4,Output size =  12,Stride =  16,RF size = 131 Layer Name = conv5,Output size =  12,Stride =  16,RF size = 163 Layer Name = pool5,Output size =   5,Stride =  32,RF size = 195