blob: dcca6fdc648db9a2bd1fd98bfa271a63fe47f6a0 [file] [log] [blame]
#!/usr/bin/env python
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#
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#
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"""
parse mxnet output log into a markdown table
"""
import argparse
import sys
import re
parser = argparse.ArgumentParser(description='Parse mxnet output log')
parser.add_argument('logfile', nargs=1, type=str,
help = 'the log file for parsing')
parser.add_argument('--format', type=str, default='markdown',
choices = ['markdown', 'none'],
help = 'the format of the parsed outout')
parser.add_argument('--metric-names', type=str, nargs="+", default = ['accuracy'],
help='names of metrics in log which should be parsed')
args = parser.parse_args()
with open(args.logfile[0]) as f:
lines = f.readlines()
res = [re.compile('.*Epoch\[(\d+)\] Train-'+s+'.*=([.\d]+)') for s in args.metric_names]\
+ [re.compile('.*Epoch\[(\d+)\] Validation-'+s+'.*=([.\d]+)') for s in args.metric_names]\
+ [re.compile('.*Epoch\[(\d+)\] Time.*=([.\d]+)')]
data = {}
for l in lines:
i = 0
for r in res:
m = r.match(l)
if m is not None:
break
i += 1
if m is None:
continue
assert len(m.groups()) == 2
epoch = int(m.groups()[0])
val = float(m.groups()[1])
if epoch not in data:
data[epoch] = [0] * len(res) * 2
data[epoch][i*2] += val
data[epoch][i*2+1] += 1
if args.format == 'markdown':
print("| epoch | " + " | ".join(['train-'+s for s in args.metric_names]) + " | " + " | ".join(['val-'+s for s in args.metric_names]) + " | time |")
print("| --- "*(len(res)+1) + "|")
for k, v in data.items():
print(f"| {k+1:2d} | "\
+ " | ".join([f"{(v[2*j]/v[2*j+1])}" for j in range(2*len(args.metric_names))])\
+ f" | {v[-2]/v[-1]:.1f} |")
elif args.format == 'none':
print("\t".join(['epoch'] + ['train-' + s for s in args.metric_names] + ['val-' + s for s in args.metric_names] + ['time']))
for k, v in data.items():
print("\t".join([f"{k+1:2d}"] + [f"{v[2*j]/v[2*j+1]}" for j in range(2*len(args.metric_names))] + [f"{v[-2]/v[-1]:.1f}"]))