Get receptive field size of a neuron

calculate_receptive_field_size.py

'''
Code from https://medium.com/mlreview/a-guide-to-receptive-field-arithmetic-for-convolutional-neural-networks-e0f514068807
'''
import math
# [kernel_size, stride, pad_size], ...
convnet =   [[8, 1, 0], [2, 2, 0],
             [5, 1, 0], [2, 2, 0],
             [3, 1, 0], [2, 2, 0],
             [2, 1, 0], [2, 2, 0]
            ]

layer_names = ['conv1', 'pool1', 'conv2', 'pool2', 'conv3', 'pool3', 'conv4', 'pool4']
imsize = 150

def outFromIn(conv, layerIn):
    n_in = layerIn[0]
    j_in = layerIn[1]
    r_in = layerIn[2]
    start_in = layerIn[3]
    k = conv[0]
    s = conv[1]
    p = conv[2]

    n_out = math.floor((n_in - k + 2*p)/s) + 1
    actualP = (n_out-1)*s - n_in + k 
    pR = math.ceil(actualP/2)
    pL = math.floor(actualP/2)

    j_out = j_in * s
    r_out = r_in + (k - 1)*j_in
    start_out = start_in + ((k-1)/2 - pL)*j_in
    return n_out, j_out, r_out, start_out

def printLayer(layer, layer_name):
    print(layer_name + ":")
    print("\t n features: %s \n \t jump: %s \n \t receptive size: %s \t start: %s " % (layer[0], layer[1], layer[2], layer[3]))

layerInfos = []

#first layer is the data layer (image) with n_0 = image size; j_0 = 1; r_0 = 1; and start_0 = 0.5
print ("-------Net summary------")
currentLayer = [imsize, 1, 1, 0]
printLayer(currentLayer, "input image")

for i in range(len(convnet)):
    currentLayer = outFromIn(convnet[i], currentLayer)
    layerInfos.append(currentLayer)
    printLayer(currentLayer, layer_names[i])
    print ("------------------------")