calc DeepLab receptive field

calc.py

#!/usr/bin/env python
# net architecture of http://ccvl.stat.ucla.edu/ccvl/DeepLab/train.prototxt
# format: [kernel, stride]
deep_lab_vgg = [
    # 1
    [3, 1], 
    [3, 1], 
    [2, 2],
    # 2
    [3, 1], 
    [3, 1], 
    [2, 2],
    # 3
    [3, 1], 
    [3, 1], 
    [3, 1], 
    [2, 2],
    # 4
    [3, 1], 
    [3, 1], 
    [3, 1], 
    [2, 1],
    # 5
    [5, 1], 
    [5, 1],
    [5, 1], 
    [3, 1],
    # fc6, kernel=4, hole=4
    [13, 1]
]

# K: composed kernel, also the receptive field 
# S: composed stride
K, S = 1, 1
for i in range(len(deep_lab_vgg)):
    k, s = deep_lab_vgg[i]
    K = (k-1) * S + K
    S = S * s
    print 'layer:', i, deep_lab_vgg[i], '[', K, S, ']'

P.S.:
receptive filed of hole_conv:
r.f. = kernel + (kernel-1)*(hole-1)

Hi
The deeplab paper said "In the case of the VGG-16 net we consider, its receptive field is 224x224 (with zero-padding) and 404x404 pixels if the net is applied convolutionally" and "We have addressed this practical problem by spatially subsampling (by simple decimation) the first FC layer to 4x4 (or 3x3) spatial size. This has reduced the receptive field of the network down to 128x128 (with zero-padding) or 308x308 (in convolutional mode) "
How do they calculate and get the results of "224x224" and "128x128" ? What does "with zero-padding" mean? Zero-padding doesn't influence receptive field, does it?
I guess you guys might have answers. Thanks a lot!

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