blob: 0319e8390e8507948dc9a09c6a9db9eb944031b1 [file] [log] [blame] [edit]
#!/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()