Dynamic output shape is incorrect/ not same as static - tensorflow

Question

I am trying to implement a patch creation function with using tensorflow's extract_image_patches function but dynamic output shape is not same as my expectation.
Let me tell briefly what it does. Input shape is supposed to be
6000x4000. We first find its greatest common denominator. It turns out it is 3. then we pass '64' argument to our function to create patches with size of 3x64,2x64=192,128. This returns us 31x31 distinct patches. Everything works ok with static output, but when it comes to dynamic output things are not ok. I could not find which part caused a different dynamic output.
# input_shape_inbuild: (None, 6000, 4000, 1)
# ---LAYER---
# Input Size: (None, 6000, 4000, 1)
# Patch Size: (x,y) = 192, 128
# Aspect ratio: (3, 2)
!wget https://www.fujifilm.com/products/digital_cameras/x/fujifilm_x_pro2/sample_images/img/index/ff_x_pro2_001.JPG
img = cv2.imread('ff_x_pro2_001.JPG', 0)
img = tf.reshape(img, [1,img.shape[0],img.shape[1],1])
***tensorflow takes images as (y, x)
so a 6000x4000 im is given as tf.func(4000, 6000)
# Here I define custom layer in tensorflow.
class create_patches(Layer):
def __init__(self, patchMultiplier):
super(create_patches, self).__init__()
self.patchMultiplier = patchMultiplier
def build(self, input_shape):
print('input_shape_inbuild: ', input_shape)
def aspect_ratio(width, height):
#find greatest common divider of input_shape
def gcd(x, y):
while y != 0:
(x, y) = (y, x % y)
return x
r = gcd(width, height)
x = int(width/r)
y = int(height/r)
return x, y
self.aspect_ratio = aspect_ratio(input_shape[1], input_shape[2])
self.patchSize_x = self.aspect_ratio[0] * self.patchMultiplier
self.patchSize_y = self.aspect_ratio[1] * self.patchMultiplier
def call(self, inputs):
print('---LAYER---')
print('Input Size:', inputs._keras_shape)
print('Patch Size: (x,y) = {}, {}'.format(self.patchSize_x, self.patchSize_y))
print('Aspect ratio: {}'.format(self.aspect_ratio))
print('---LAYER---')
#call tf.extract_image_patches to return it.
out = tf.extract_image_patches(images=inputs,
ksizes=[1, self.patchSize_y, self.patchSize_x, 1],
strides=[1, self.patchSize_y, self.patchSize_x, 1],
rates=[1, 1, 1, 1],
padding='VALID')
return out
def compute_output_shape(self, input_shape):
"""
ksize_cols = patchSize_x
ksize_rows = patchSize_y
"""
#output shape=[batch, out_rows, out_cols, ksize_rows * ksize_cols * depth]
"""
shape = (self.patchSize_x, self.patchSize_y,
(input_shape[1]/self.patchSize_x) * (input_shape[2]/self.patchSize_y))
"""
shape =(input_shape[0],
input_shape[1]/self.patchSize_x, # patch row count
input_shape[2]/self.patchSize_y, # patch col count
self.patchSize_x * self.patchSize_y) # patch pixel count
return shape
#here is input with 6000x4000 pixels.
input_shape_1 = Input(shape=(6000, 4000, 1))
#here I fed input to my custom layer.
x1 = create_patches(64)(input_shape_1)
print('Output shape: ', x1.shape)
# here I build a model to see static output
f = K.function([input_shape_1], [x1])
import numpy as np
#result = f([np.random.randint(256, size=(1,4000,6000,1))])
result = f([img])
#print(result)
result = np.array(result)
# [batch, out_rows, out_cols, ksize_rows * ksize_cols * depth]
# Result shape: (1, 1, 125, 125, 1536)
print('Result shape: ', result.shape, '\n\n')
#print(result[:, :, :, 0].shape)
here is output I get.
input_shape_inbuild: (None, 6000, 4000, 1)
---LAYER---
Input Size: (None, 6000, 4000, 1)
Patch Size: (x,y) = 192, 128
Aspect ratio: (3, 2)
---LAYER---
Output shape: (?, 46, 20, 24576)
Result shape: (1, 1, 31, 31, 24576)
#####Result Shape is as I expected but at output shape I could not resolve where 46 and 20 come from. Could you tell me why it is like this?