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Code Issues Projects Releases Wiki Activity

support iter mode in gather_models (#7740)

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update

remove print
pull/7641/head
Cedric Luo 3 years ago committed by GitHub
parent 20c0852ab6
commit 623d9465ad
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1 changed files with 85 additions and 38 deletions
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  1. 123
      .dev_scripts/gather_models.py

123
.dev_scripts/gather_models.py
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@ -48,44 +48,85 @@ def process_checkpoint(in_file, out_file):
return final_file
def get_final_epoch(config):
def is_by_epoch(config):
cfg = mmcv.Config.fromfile('./configs/' + config)
return cfg.runner.max_epochs
return cfg.runner.type == 'EpochBasedRunner'
def get_best_epoch(exp_dir):
best_epoch_full_path = list(
def get_final_epoch_or_iter(config):
cfg = mmcv.Config.fromfile('./configs/' + config)
if cfg.runner.type == 'EpochBasedRunner':
return cfg.runner.max_epochs
else:
return cfg.runner.max_iters
def get_best_epoch_or_iter(exp_dir):
best_epoch_iter_full_path = list(
sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1]
best_epoch_model_path = best_epoch_full_path.split('/')[-1]
best_epoch = best_epoch_model_path.split('_')[-1].split('.')[0]
return best_epoch_model_path, int(best_epoch)
best_epoch_or_iter_model_path = best_epoch_iter_full_path.split('/')[-1]
best_epoch_or_iter = best_epoch_or_iter_model_path.\
split('_')[-1].split('.')[0]
return best_epoch_or_iter_model_path, int(best_epoch_or_iter)
def get_real_epoch(config):
def get_real_epoch_or_iter(config):
cfg = mmcv.Config.fromfile('./configs/' + config)
epoch = cfg.runner.max_epochs
if cfg.data.train.type == 'RepeatDataset':
epoch *= cfg.data.train.times
return epoch
if cfg.runner.type == 'EpochBasedRunner':
epoch = cfg.runner.max_epochs
if cfg.data.train.type == 'RepeatDataset':
epoch *= cfg.data.train.times
return epoch
else:
return cfg.runner.max_iters
def get_final_results(log_json_path, epoch, results_lut):
def get_final_results(log_json_path,
epoch_or_iter,
results_lut,
by_epoch=True):
result_dict = dict()
last_val_line = None
last_train_line = None
last_val_line_idx = -1
last_train_line_idx = -1
with open(log_json_path, 'r') as f:
for line in f.readlines():
for i, line in enumerate(f.readlines()):
log_line = json.loads(line)
if 'mode' not in log_line.keys():
continue
if log_line['mode'] == 'train' and log_line['epoch'] == epoch:
result_dict['memory'] = log_line['memory']
if log_line['mode'] == 'val' and log_line['epoch'] == epoch:
result_dict.update({
key: log_line[key]
for key in results_lut if key in log_line
})
return result_dict
if by_epoch:
if (log_line['mode'] == 'train'
and log_line['epoch'] == epoch_or_iter):
result_dict['memory'] = log_line['memory']
if (log_line['mode'] == 'val'
and log_line['epoch'] == epoch_or_iter):
result_dict.update({
key: log_line[key]
for key in results_lut if key in log_line
})
return result_dict
else:
if log_line['mode'] == 'train':
last_train_line_idx = i
last_train_line = log_line
if log_line and log_line['mode'] == 'val':
last_val_line_idx = i
last_val_line = log_line
# bug: max_iters = 768, last_train_line['iter'] = 750
assert last_val_line_idx == last_train_line_idx + 1, \
'Log file is incomplete'
result_dict['memory'] = last_train_line['memory']
result_dict.update({
key: last_val_line[key]
for key in results_lut if key in last_val_line
})
return result_dict
def get_dataset_name(config):
@ -116,10 +157,12 @@ def convert_model_info_to_pwc(model_infos):
# get metadata
memory = round(model['results']['memory'] / 1024, 1)
epochs = get_real_epoch(model['config'])
meta_data = OrderedDict()
meta_data['Training Memory (GB)'] = memory
meta_data['Epochs'] = epochs
if 'epochs' in model:
meta_data['Epochs'] = get_real_epoch_or_iter(model['config'])
else:
meta_data['Iterations'] = get_real_epoch_or_iter(model['config'])
pwc_model_info['Metadata'] = meta_data
# get dataset name
@ -200,12 +243,14 @@ def main():
model_infos = []
for used_config in used_configs:
exp_dir = osp.join(models_root, used_config)
by_epoch = is_by_epoch(used_config)
# check whether the exps is finished
if args.best is True:
final_model, final_epoch = get_best_epoch(exp_dir)
final_model, final_epoch_or_iter = get_best_epoch_or_iter(exp_dir)
else:
final_epoch = get_final_epoch(used_config)
final_model = 'epoch_{}.pth'.format(final_epoch)
final_epoch_or_iter = get_final_epoch_or_iter(used_config)
final_model = '{}_{}.pth'.format('epoch' if by_epoch else 'iter',
final_epoch_or_iter)
model_path = osp.join(exp_dir, final_model)
# skip if the model is still training
@ -225,21 +270,23 @@ def main():
for i, key in enumerate(results_lut):
if 'mAP' not in key and 'PQ' not in key:
results_lut[i] = key + 'm_AP'
model_performance = get_final_results(log_json_path, final_epoch,
results_lut)
model_performance = get_final_results(log_json_path,
final_epoch_or_iter, results_lut,
by_epoch)
if model_performance is None:
continue
model_time = osp.split(log_txt_path)[-1].split('.')[0]
model_infos.append(
dict(
config=used_config,
results=model_performance,
epochs=final_epoch,
model_time=model_time,
final_model=final_model,
log_json_path=osp.split(log_json_path)[-1]))
model_info = dict(
config=used_config,
results=model_performance,
model_time=model_time,
final_model=final_model,
log_json_path=osp.split(log_json_path)[-1])
model_info['epochs' if by_epoch else 'iterations'] =\
final_epoch_or_iter
model_infos.append(model_info)
# publish model for each checkpoint
publish_model_infos = []

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