# [filter size, stride, padding]
# Assume the two dimensions are the same
# Each kernel requires the following parameters:
# - k_i: kernel size
# - s_i: stride
# - p_i: padding (if padding is uneven, right padding will higher than left padding; "SAME" option in tensorflow)
#
# Each layer i requires the following parameters to be fully represented:
# - n_i: number of feature (data layer has n_1 = imagesize )
# - j_i: distance (projected to image pixel distance) between center of two adjacent features
# - r_i: receptive field of a feature in layer i
# - start_i: position of the first feature's receptive field in layer i (idx start from 0, negative means the center fall into padding)

import math

convnet = [[11, 4, 0], [3, 2, 0], [5, 1, 2], [3, 2, 0], [3, 1, 1], [3, 1, 1], [3, 1, 1], [3, 2, 0], [6, 1, 0],
           [1, 1, 0]]
layer_names = ['conv1', 'pool1', 'conv2', 'pool2', 'conv3', 'conv4', 'conv5', 'pool5', 'fc6-conv', 'fc7-conv']
imsize = 227


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 = []
if __name__ == '__main__':
	# 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, 0.5]
	printLayer(currentLayer, "input image")
	for i in range(len(convnet)):
		currentLayer = outFromIn(convnet[i], currentLayer)
		layerInfos.append(currentLayer)
		printLayer(currentLayer, layer_names[i])
	print ("------------------------")
	layer_name = raw_input("Layer name where the feature in: ")
	layer_idx = layer_names.index(layer_name)
	idx_x = int(raw_input("index of the feature in x dimension (from 0)"))
	idx_y = int(raw_input("index of the feature in y dimension (from 0)"))

	n = layerInfos[layer_idx][0]
	j = layerInfos[layer_idx][1]
	r = layerInfos[layer_idx][2]
	start = layerInfos[layer_idx][3]
	assert (idx_x < n)
	assert (idx_y < n)

	print ("receptive field: (%s, %s)" % (r, r))
	print ("center: (%s, %s)" % (start + idx_x * j, start + idx_y * j))