| #!/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() |