import sys
import argparse
import numpy as np
import tensorflow as tf
relative_tol = 1e-6
absolute_tol = 1e-9
error_tol = 1e-4
def verify_result(tiling_key, output, golden):
if tiling_key <= 4:
output_type = np.float32
else:
output_type = np.int32
output = np.fromfile(output, dtype=output_type).reshape(-1)
golden = np.fromfile(golden, dtype=output_type).reshape(-1)
different_element_results = np.isclose(output,
golden,
rtol=relative_tol,
atol=absolute_tol,
equal_nan=True)
different_element_indexes = np.where(different_element_results == False)[0]
for index in range(len(different_element_indexes)):
real_index = different_element_indexes[index]
golden_data = golden[real_index]
output_data = output[real_index]
print(
"data index: %06d, expected: %-.9f, actual: %-.9f, rdiff: %-.6f" %
(real_index, golden_data, output_data,
abs(output_data - golden_data) / golden_data))
if index == 100:
break
error_ratio = float(different_element_indexes.size) / golden.size
return error_ratio <= error_tol
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-key', type=int, default=1, choices=range(1, 13))
parser.add_argument('output', type=str)
parser.add_argument('golden', type=str)
args = parser.parse_args()
try:
res = verify_result(args.key, args.output, args.golden)
if not res:
raise ValueError("[ERROR] result error")
else:
print("test pass!")
except Exception as e:
print(e)
sys.exit(1)