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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os
import os.path as osp
import shutil
import subprocess
import time
from collections import OrderedDict
import torch
import yaml
from mmengine.config import Config
from mmengine.fileio import dump
from mmengine.utils import mkdir_or_exist, scandir
def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
class OrderedDumper(Dumper):
pass
def _dict_representer(dumper, data):
return dumper.represent_mapping(
yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items())
OrderedDumper.add_representer(OrderedDict, _dict_representer)
return yaml.dump(data, stream, OrderedDumper, **kwds)
def process_checkpoint(in_file, out_file):
checkpoint = torch.load(in_file, map_location='cpu')
# remove optimizer for smaller file size
if 'optimizer' in checkpoint:
del checkpoint['optimizer']
if 'message_hub' in checkpoint:
del checkpoint['message_hub']
if 'ema_state_dict' in checkpoint:
del checkpoint['ema_state_dict']
for key in list(checkpoint['state_dict']):
if key.startswith('data_preprocessor'):
checkpoint['state_dict'].pop(key)
elif 'priors_base_sizes' in key:
checkpoint['state_dict'].pop(key)
elif 'grid_offset' in key:
checkpoint['state_dict'].pop(key)
elif 'prior_inds' in key:
checkpoint['state_dict'].pop(key)
# if it is necessary to remove some sensitive data in checkpoint['meta'],
# add the code here.
if torch.__version__ >= '1.6':
torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
else:
torch.save(checkpoint, out_file)
sha = subprocess.check_output(['sha256sum', out_file]).decode()
final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
subprocess.Popen(['mv', out_file, final_file])
return final_file
def is_by_epoch(config):
cfg = Config.fromfile('./configs/' + config)
return cfg.train_cfg.type == 'EpochBasedTrainLoop'
def get_final_epoch_or_iter(config):
cfg = Config.fromfile('./configs/' + config)
if cfg.train_cfg.type == 'EpochBasedTrainLoop':
return cfg.train_cfg.max_epochs
else:
return cfg.train_cfg.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_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_or_iter(config):
cfg = Config.fromfile('./configs/' + config)
if cfg.train_cfg.type == 'EpochBasedTrainLoop':
epoch = cfg.train_cfg.max_epochs
return epoch
else:
return cfg.runner.max_iters
def get_final_results(log_json_path,
epoch_or_iter,
results_lut='coco/bbox_mAP',
by_epoch=True):
result_dict = dict()
with open(log_json_path) as f:
r = f.readlines()[-1]
last_metric = r.split(',')[0].split(': ')[-1].strip()
result_dict[results_lut] = last_metric
return result_dict
def get_dataset_name(config):
# If there are more dataset, add here.
name_map = dict(
CityscapesDataset='Cityscapes',
CocoDataset='COCO',
PoseCocoDataset='COCO Person',
YOLOv5CocoDataset='COCO',
CocoPanopticDataset='COCO',
YOLOv5DOTADataset='DOTA 1.0',
DeepFashionDataset='Deep Fashion',
LVISV05Dataset='LVIS v0.5',
LVISV1Dataset='LVIS v1',
VOCDataset='Pascal VOC',
YOLOv5VOCDataset='Pascal VOC',
WIDERFaceDataset='WIDER Face',
OpenImagesDataset='OpenImagesDataset',
OpenImagesChallengeDataset='OpenImagesChallengeDataset')
cfg = Config.fromfile('./configs/' + config)
return name_map[cfg.dataset_type]
def find_last_dir(model_dir):
dst_times = []
for time_stamp in os.scandir(model_dir):
if osp.isdir(time_stamp):
dst_time = time.mktime(
time.strptime(time_stamp.name, '%Y%m%d_%H%M%S'))
dst_times.append([dst_time, time_stamp.name])
return max(dst_times, key=lambda x: x[0])[1]
def convert_model_info_to_pwc(model_infos):
pwc_files = {}
for model in model_infos:
cfg_folder_name = osp.split(model['config'])[-2]
pwc_model_info = OrderedDict()
pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0]
pwc_model_info['In Collection'] = 'Please fill in Collection name'
pwc_model_info['Config'] = osp.join('configs', model['config'])
# get metadata
meta_data = OrderedDict()
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
dataset_name = get_dataset_name(model['config'])
# get results
results = []
# if there are more metrics, add here.
if 'bbox_mAP' in model['results']:
metric = round(model['results']['bbox_mAP'] * 100, 1)
results.append(
OrderedDict(
Task='Object Detection',
Dataset=dataset_name,
Metrics={'box AP': metric}))
if 'segm_mAP' in model['results']:
metric = round(model['results']['segm_mAP'] * 100, 1)
results.append(
OrderedDict(
Task='Instance Segmentation',
Dataset=dataset_name,
Metrics={'mask AP': metric}))
if 'PQ' in model['results']:
metric = round(model['results']['PQ'], 1)
results.append(
OrderedDict(
Task='Panoptic Segmentation',
Dataset=dataset_name,
Metrics={'PQ': metric}))
pwc_model_info['Results'] = results
link_string = 'https://download.openmmlab.com/mmyolo/v0/'
link_string += '{}/{}'.format(model['config'].rstrip('.py'),
osp.split(model['model_path'])[-1])
pwc_model_info['Weights'] = link_string
if cfg_folder_name in pwc_files:
pwc_files[cfg_folder_name].append(pwc_model_info)
else:
pwc_files[cfg_folder_name] = [pwc_model_info]
return pwc_files
def parse_args():
parser = argparse.ArgumentParser(description='Gather benchmarked models')
parser.add_argument(
'root',
type=str,
help='root path of benchmarked models to be gathered')
parser.add_argument(
'out', type=str, help='output path of gathered models to be stored')
parser.add_argument(
'--best',
action='store_true',
help='whether to gather the best model.')
args = parser.parse_args()
return args
# TODO: Refine
def main():
args = parse_args()
models_root = args.root
models_out = args.out
mkdir_or_exist(models_out)
# find all models in the root directory to be gathered
raw_configs = list(scandir('./configs', '.py', recursive=True))
# filter configs that is not trained in the experiments dir
used_configs = []
for raw_config in raw_configs:
if osp.exists(osp.join(models_root, raw_config)):
used_configs.append(raw_config)
print(f'Find {len(used_configs)} models to be gathered')
# find final_ckpt and log file for trained each config
# and parse the best performance
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_or_iter = get_best_epoch_or_iter(exp_dir)
else:
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
if not osp.exists(model_path):
continue
# get the latest logs
latest_exp_name = find_last_dir(exp_dir)
latest_exp_json = osp.join(exp_dir, latest_exp_name, 'vis_data',
latest_exp_name + '.json')
model_performance = get_final_results(
latest_exp_json, final_epoch_or_iter, by_epoch=by_epoch)
if model_performance is None:
continue
model_info = dict(
config=used_config,
results=model_performance,
final_model=final_model,
latest_exp_json=latest_exp_json,
latest_exp_name=latest_exp_name)
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 = []
for model in model_infos:
model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
mkdir_or_exist(model_publish_dir)
model_name = osp.split(model['config'])[-1].split('.')[0]
model_name += '_' + model['latest_exp_name']
publish_model_path = osp.join(model_publish_dir, model_name)
trained_model_path = osp.join(models_root, model['config'],
model['final_model'])
# convert model
final_model_path = process_checkpoint(trained_model_path,
publish_model_path)
# copy log
shutil.copy(model['latest_exp_json'],
osp.join(model_publish_dir, f'{model_name}.log.json'))
# copy config to guarantee reproducibility
config_path = model['config']
config_path = osp.join(
'configs',
config_path) if 'configs' not in config_path else config_path
target_config_path = osp.split(config_path)[-1]
shutil.copy(config_path, osp.join(model_publish_dir,
target_config_path))
model['model_path'] = final_model_path
publish_model_infos.append(model)
models = dict(models=publish_model_infos)
print(f'Totally gathered {len(publish_model_infos)} models')
dump(models, osp.join(models_out, 'model_info.json'))
pwc_files = convert_model_info_to_pwc(publish_model_infos)
for name in pwc_files:
with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f:
ordered_yaml_dump(pwc_files[name], f, encoding='utf-8')
if __name__ == '__main__':
main()
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