Managed to create Human Activity Recognition (HAR) model using Keras, and successfully converted to kmodel for K210 MCU. However.... The inference is still off, why oh why?

HAR-Maix-Infer.py

import image
import KPU as kpu
import lcd

lcd.clear()
lcd.draw_string(100,112,"Loading model...")

print('Loading model...')
task = kpu.load('/sd/HAR.kmodel')

# Sample data
data = [[22, 20, 15,  0],
       [14, 20, 15,  0],
       [13, 30, 23,  0],
       [17,  9, 11,  0],
       [ 8, 30, 21,  0],
       [22, 30, 19,  0],
       [ 5, 24, 18,  0],
       [17, 19, 19,  0],
       [ 8, 30, 27,  0],
       [14, 13, 17,  0],
       [13, 28, 29,  0],
       [21, 24, 14,  0],
       [ 5, 25, 19,  0],
       [14, 19, 17,  0],
       [11, 30, 27,  0],
       [17, 13, 17,  0],
       [15, 24, 27,  0],
       [17, 14, 13,  0],
       [13, 25, 20,  0],
       [23, 18, 14,  0],
       [17, 19, 15,  0],
       [12, 30, 24,  0],
       [15, 10, 12,  0],
       [10, 29, 23,  0],
       [18, 28, 17,  0],
       [ 7, 23, 19,  0],
       [15, 18, 19,  0],
       [ 5, 30, 25,  0],
       [17, 15, 19,  0],
       [10, 25, 21,  0],
       [14, 19, 19,  0],
       [18, 20, 14,  0],
       [17, 30, 23,  0],
       [18,  9,  8,  0],
       [12, 30, 21,  0],
       [18, 28, 17,  0],
       [ 8, 27, 20,  0],
       [17, 18, 19,  0],
       [20, 27, 14,  0],
       [22, 30, 22,  0],
       [17,  8,  8,  0],
       [11, 30, 15,  0],
       [22, 30, 20,  0],
       [13, 22, 19,  0],
       [17, 19, 14,  0],
       [14, 30, 23,  0],
       [12, 27, 29,  0],
       [22, 18, 14,  0],
       [ 5, 25, 21,  0],
       [22, 18, 18,  0],
       [23, 30, 22,  0],
       [17, 17, 19,  0],
       [ 9, 30, 10,  0],
       [27, 30, 20,  0],
       [13, 21, 19,  0],
       [17, 17, 17,  0],
       [10, 30, 27,  0],
       [14, 14, 17,  0],
       [13, 27, 29,  0],
       [18, 15, 12,  0],
       [ 4, 24, 20,  0],
       [17, 17, 20,  0],
       [ 4, 30, 22,  0],
       [15, 15, 19,  0],
       [11, 24, 28,  0],
       [20, 12, 13,  0],
       [ 5, 24, 20,  0],
       [19, 18, 17,  0],
       [ 9, 30, 13,  0],
       [24, 27, 21,  0],
       [18,  9,  4,  0],
       [12, 28,  7,  0],
       [23, 30, 23,  0],
       [18, 20, 18,  0],
       [19, 17, 19,  0],
       [15,  8,  4,  0],
       [13, 23, 27,  0],
       [18, 29, 22,  0],
       [17, 22, 13,  0],
       [ 9, 21, 21,  0]]

# Preparing dummy 'image' for input as MaixPy expect image data
dummy = image.Image()
inputData = dummy.to_grayscale(1)
inputData = inputData.resize(4, 80)

# Fill the data to dummy image
for rowIdx, (x, y, z, a) in enumerate(data):
    inputData.set_pixel(0, rowIdx, x)#(x,x,x))
    inputData.set_pixel(1, rowIdx, y)#(y,y,y))
    inputData.set_pixel(2, rowIdx, z)#(z,z,z))
    inputData.set_pixel(3, rowIdx, a)#(a,a,a))
    #print("orig: {}, {}, {}, {}".format(x, y, z, a))
    #print("dest: {}, {}, {}, {}".format(inputData.get_pixel(0, rowIdx), inputData.get_pixel(1, rowIdx), inputData.get_pixel(2, rowIdx), inputData.get_pixel(3, rowIdx)))

# Dump to serial to check they're same
# for row in range(80):
#     print("Row: {}, {}, {}, {}".format(inputData.get_pixel(0, row), inputData.get_pixel(1, row), inputData.get_pixel(2, row), inputData.get_pixel(3, row)))

# print("image w: {}, h:{}".format(inputData.width(), inputData.height()))

lcd.display(inputData)

# Prepare data for AI buffer
inputData.pix_to_ai()

# Forward
fmap = kpu.forward(task, inputData)
plist = fmap[:]
print(plist)

pmax=max(plist)					#get max probability
max_index=plist.index(pmax)
lcd.draw_string(0,0,"%d: %.3f"%(max_index,pmax),lcd.WHITE,lcd.BLACK)	

Model file (.kmodel) is here: https://drive.google.com/file/d/1u8MOzAccrY_vG5QreSqNeQRI014x-mkn/view?usp=sharing

The issue is that, the output is always similar as:
(0.05627171, 0.01401903, 0.3184637, 0.4632558, 0.1154114, 0.03257844)
regardless the input. As we can see that 3rd index (0.4632558 ) is always bigger means the prediction always the same.

I also add put the issue here: https://bbs.sipeed.com/t/topic/863 with the hope anyone can help.