CMSIS_5/CMSIS/NN/Scripts/NNFunctions/fully_connected_opt_weight_generation.py

#!/usr/bin/env python

import numpy as np

def convert_to_x4_q7_weights(weights):
    [r, h, w, c] = weights.shape
    weights = np.reshape(weights, (r, h*w*c))
    num_of_rows = r
    num_of_cols = h*w*c
    new_weights = np.copy(weights)
    new_weights = np.reshape(new_weights, (r*h*w*c))
    counter = 0
    for i in range(int(num_of_rows)/4):
      # we only need to do the re-ordering for every 4 rows
      row_base = 4*i
      for j in range (int(num_of_cols)/4):
        # for each 4 entries
        column_base = 4*j
        new_weights[counter]   =  weights[row_base  ][column_base  ]
        new_weights[counter+1] =  weights[row_base+1][column_base  ]
        new_weights[counter+2] =  weights[row_base  ][column_base+2]
        new_weights[counter+3] =  weights[row_base+1][column_base+2]
        new_weights[counter+4] =  weights[row_base+2][column_base  ]
        new_weights[counter+5] =  weights[row_base+3][column_base  ]
        new_weights[counter+6] =  weights[row_base+2][column_base+2]
        new_weights[counter+7] =  weights[row_base+3][column_base+2]

        new_weights[counter+8] =  weights[row_base  ][column_base+1]
        new_weights[counter+9] =  weights[row_base+1][column_base+1]
        new_weights[counter+10] = weights[row_base  ][column_base+3]
        new_weights[counter+11] = weights[row_base+1][column_base+3]
        new_weights[counter+12] = weights[row_base+2][column_base+1]
        new_weights[counter+13] = weights[row_base+3][column_base+1]
        new_weights[counter+14] = weights[row_base+2][column_base+3]
        new_weights[counter+15] = weights[row_base+3][column_base+3]
        counter = counter + 16
      # the remaining ones are in order
      for j in range((int)(num_of_cols-num_of_cols%4), int(num_of_cols)):
        new_weights[counter] = weights[row_base][j]
        new_weights[counter+1] = weights[row_base+1][j]
        new_weights[counter+2] = weights[row_base+2][j]
        new_weights[counter+3] = weights[row_base+3][j]
        counter = counter + 4
    return new_weights

def convert_to_x4_q15_weights(weights):
    [r, h, w, c] = weights.shape
    weights = np.reshape(weights, (r, h*w*c))
    num_of_rows = r
    num_of_cols = h*w*c
    new_weights = np.copy(weights)
    new_weights = np.reshape(new_weights, (r*h*w*c))
    counter = 0
    for i in range(int(num_of_rows)/4):
      # we only need to do the re-ordering for every 4 rows
      row_base = 4*i
      for j in range (int(num_of_cols)/2):
        # for each 2 entries
        column_base = 2*j
        new_weights[counter]   =  weights[row_base  ][column_base  ]
        new_weights[counter+1] =  weights[row_base  ][column_base+1]
        new_weights[counter+2] =  weights[row_base+1][column_base  ]
        new_weights[counter+3] =  weights[row_base+1][column_base+1]
        new_weights[counter+4] =  weights[row_base+2][column_base  ]
        new_weights[counter+5] =  weights[row_base+2][column_base+1]
        new_weights[counter+6] =  weights[row_base+3][column_base  ]
        new_weights[counter+7] =  weights[row_base+3][column_base+1]

        counter = counter + 8
      # the remaining ones are in order
      for j in range((int)(num_of_cols-num_of_cols%2), int(num_of_cols)):
        new_weights[counter] = weights[row_base][j]
        new_weights[counter+1] = weights[row_base+1][j]
        new_weights[counter+2] = weights[row_base+2][j]
        new_weights[counter+3] = weights[row_base+3][j]
        counter = counter + 4
    return new_weights

def convert_q7_q15_weights(weights):
    [r, h, w, c] = weights.shape
    weights = np.reshape(weights, (r, h*w*c))
    num_of_rows = r
    num_of_cols = h*w*c
    new_weights = np.copy(weights)
    new_weights = np.reshape(new_weights, (r*h*w*c))
    counter = 0
    for i in range(int(num_of_rows)/4):
      # we only need to do the re-ordering for every 4 rows
      row_base = 4*i
      for j in range (int(num_of_cols)/2):
        # for each 2 entries
        column_base = 2*j
        new_weights[counter]   =  weights[row_base  ][column_base  ]
        new_weights[counter+1] =  weights[row_base+1][column_base  ]
        new_weights[counter+2] =  weights[row_base  ][column_base+1]
        new_weights[counter+3] =  weights[row_base+1][column_base+1]
        new_weights[counter+4] =  weights[row_base+2][column_base  ]
        new_weights[counter+5] =  weights[row_base+3][column_base  ]
        new_weights[counter+6] =  weights[row_base+2][column_base+1]
        new_weights[counter+7] =  weights[row_base+3][column_base+1]

        counter = counter + 8
      # the remaining ones are in order
      for j in range((int)(num_of_cols-num_of_cols%2), int(num_of_cols)):
        new_weights[counter] = weights[row_base][j]
        new_weights[counter+1] = weights[row_base+1][j]
        new_weights[counter+2] = weights[row_base+2][j]
        new_weights[counter+3] = weights[row_base+3][j]
        counter = counter + 4
    return new_weights

# input dimensions
vec_dim = 127
row_dim = 127

weight = np.zeros((row_dim,vec_dim), dtype=int)

# generate random inputs
for i in range(row_dim):
  for j in range(vec_dim):
    weight[i][j] = np.random.randint(256)-128

weight = np.reshape(weight, (row_dim, vec_dim, 1, 1))

outfile = open("../Ref_Implementations/fully_connected_testing_weights.h", "w")
outfile.write("#define IP2_WEIGHT {")
weight.tofile(outfile,sep=",",format="%d")
outfile.write("}\n\n")

new_weight = convert_to_x4_q7_weights(weight)
outfile.write("#define IP4_WEIGHT {")
new_weight.tofile(outfile,sep=",",format="%d")
outfile.write("}\n\n")

new_weight = convert_q7_q15_weights(weight)
outfile.write("#define IP4_q7_q15_WEIGHT {")
new_weight.tofile(outfile,sep=",",format="%d")
outfile.write("}\n\n")

new_weight = convert_to_x4_q15_weights(weight)
outfile.write("#define IP4_WEIGHT_Q15 {")
new_weight.tofile(outfile,sep=",",format="%d")
outfile.write("}\n\n")


outfile.close()