@@ -29,6 +29,12 @@ def process_checkpoint(in_file, out_file):
     # remove optimizer for smaller file size
     if 'optimizer' in checkpoint:
         del checkpoint['optimizer']
+
+    # remove ema state_dict
+    for key in list(checkpoint['state_dict']):
+        if key.startswith('ema_'):
+            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':
import argparse
import glob
import json
import os.path as osp
import shutil
import subprocess
from collections import OrderedDict

import mmcv
import torch
import yaml


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 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') + '-{}.pth'.format(sha[:8])
    subprocess.Popen(['mv', out_file, final_file])
    return final_file


def get_final_epoch(config):
    cfg = mmcv.Config.fromfile('./configs/' + config)
    return cfg.runner.max_epochs


def get_real_epoch(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


def get_final_results(log_json_path, epoch, results_lut):
    result_dict = dict()
    with open(log_json_path, 'r') as f:
        for line in 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


def get_dataset_name(config):
    # If there are more dataset, add here.
    name_map = dict(
        CityscapesDataset='Cityscapes',
        CocoDataset='COCO',
        DeepFashionDataset='Deep Fashion',
        LVISV05Dataset='LVIS v0.5',
        LVISV1Dataset='LVIS v1',
        VOCDataset='Pascal VOC',
        WIDERFaceDataset='WIDER Face')
    cfg = mmcv.Config.fromfile('./configs/' + config)
    return name_map[cfg.dataset_type]


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
        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
        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}))
        pwc_model_info['Results'] = results

        link_string = 'https://download.openmmlab.com/mmdetection/v2.0/'
        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')

    args = parser.parse_args()
    return args


def main():
    args = parse_args()
    models_root = args.root
    models_out = args.out
    mmcv.mkdir_or_exist(models_out)

    # find all models in the root directory to be gathered
    raw_configs = list(mmcv.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)
        # check whether the exps is finished
        final_epoch = get_final_epoch(used_config)
        final_model = 'epoch_{}.pth'.format(final_epoch)
        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
        log_json_path = list(
            sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
        log_txt_path = list(sorted(glob.glob(osp.join(exp_dir, '*.log'))))[-1]
        cfg = mmcv.Config.fromfile('./configs/' + used_config)
        results_lut = cfg.evaluation.metric
        if not isinstance(results_lut, list):
            results_lut = [results_lut]
        # case when using VOC, the evaluation key is only 'mAP'
        results_lut = [key + '_mAP' for key in results_lut if 'mAP' not in key]
        model_performance = get_final_results(log_json_path, final_epoch,
                                              results_lut)

        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,
                log_json_path=osp.split(log_json_path)[-1]))

    # publish model for each checkpoint
    publish_model_infos = []
    for model in model_infos:
        model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
        mmcv.mkdir_or_exist(model_publish_dir)

        model_name = osp.split(model['config'])[-1].split('.')[0]

        model_name += '_' + model['model_time']
        publish_model_path = osp.join(model_publish_dir, model_name)
        trained_model_path = osp.join(models_root, model['config'],
                                      'epoch_{}.pth'.format(model['epochs']))

        # convert model
        final_model_path = process_checkpoint(trained_model_path,
                                              publish_model_path)

        # copy log
        shutil.copy(
            osp.join(models_root, model['config'], model['log_json_path']),
            osp.join(model_publish_dir, f'{model_name}.log.json'))
        shutil.copy(
            osp.join(models_root, model['config'],
                     model['log_json_path'].rstrip('.json')),
            osp.join(model_publish_dir, f'{model_name}.log'))

        # 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_cconfig_path = osp.split(config_path)[-1]
        shutil.copy(config_path,
                    osp.join(model_publish_dir, target_cconfig_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')
    mmcv.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()

Kind reminder: create a new PR to support it in publish_model.py