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[Fix] Fix wrong condition judgment in `analyze_logs.py` and prevent empty curve. (#510) 

* [Fix] Analyze logs with single training iteration

* [Fix] Make `plot_curve` with 'accuracy' metric

* [Fix] Assert length of training log records of each epoch is larger than 1

Co-authored-by: Ma Zerun <mzr1996@163.com>

Co-authored-by: Ma Zerun <mzr1996@163.com>
pull/518/head
imyhxy 2021-11-03 11:37:51 +08:00 committed by GitHub
parent b486bb23f8
commit abb5a222cf
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1 changed files with 6 additions and 5 deletions
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11
tools/analysis_tools/analyze_logs.py
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@ -57,11 +57,9 @@ def plot_curve(log_dicts, args):
f'{args.json_logs[i]} does not contain metric {metric} '
f'in train mode')
if 'mAP' in metric:
xs = np.arange(1, max(epochs) + 1)
ys = []
for epoch in epochs:
ys += log_dict[epoch][metric]
if any(m in metric for m in ('mAP', 'accuracy')):
xs = epochs
ys = [log_dict[e][metric] for e in xs]
ax = plt.gca()
ax.set_xticks(xs)
plt.xlabel('epoch')
@ -74,6 +72,9 @@ def plot_curve(log_dicts, args):
iters = log_dict[epoch]['iter']
if log_dict[epoch]['mode'][-1] == 'val':
iters = iters[:-1]
assert len(iters) > 0, (
'The training log is empty, please try to reduce the '
'interval of log in config file.')
xs.append(
np.array(iters) + (epoch - 1) * num_iters_per_epoch)
ys.append(np.array(log_dict[epoch][metric][:len(iters)]))