I have spent 2 weeks to deploy the environment without success. Could you please pay for help to solve the problem? My email is [email protected]

.dev_scripts/benchmark_filter.py

@@ -0,0 +1,121 @@
+import argparse
+import os
+import os.path as osp
+
+import mmcv
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Filter configs to train')
+    parser.add_argument(
+        '--basic-arch',
+        action='store_true',
+        help='to train models in basic arch')
+    parser.add_argument(
+        '--datasets', action='store_true', help='to train models in dataset')
+    parser.add_argument(
+        '--data-pipeline',
+        action='store_true',
+        help='to train models related to data pipeline, e.g. augmentations')
+    parser.add_argument(
+        '--nn-module',
+        action='store_true',
+        help='to train models related to neural network modules')
+
+    args = parser.parse_args()
+    return args
+
+
+basic_arch_root = [
+    'cascade_rcnn', 'double_heads', 'fcos', 'foveabox', 'free_anchor',
+    'grid_rcnn', 'guided_anchoring', 'htc', 'libra_rcnn', 'atss', 'mask_rcnn',
+    'ms_rcnn', 'nas_fpn', 'reppoints', 'retinanet', 'ssd', 'gn', 'ghm', 'fsaf'
+]
+
+datasets_root = ['wider_face', 'pascal_voc', 'cityscapes', 'mask_rcnn']
+
+data_pipeline_root = [
+    'albu_example', 'instaboost', 'ssd', 'mask_rcnn', 'nas_fpn'
+]
+
+nn_module_root = [
+    'carafe', 'dcn', 'empirical_attention', 'gcnet', 'gn+ws', 'hrnet', 'pafpn',
+    'nas_fpn'
+]
+
+benchmark_pool = [
+    'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py',
+    'configs/htc/htc_r50_fpn_1x_coco.py',
+    'ghm/retinanet_ghm_r50_fpn_1x_coco.py',
+    'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py',
+    'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py',
+    'configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py',
+    'configs/rpn/rpn_r50_fpn_1x_coco.py',
+    'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py',
+    'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py',
+    'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py',
+    'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py',
+    'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py',
+    'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py',
+    'configs/ssd/ssd300_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py',  # noqa
+    'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py',
+    'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py',
+    'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py',
+    'configs/fsaf/fsaf_r50_fpn_1x_coco.py',
+    'configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py',
+    'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py',
+    'configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py',
+    'configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py',
+    'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
+    'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py',
+    'configs/wider_face/ssd300_wider_face.py',
+    'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py',
+    'configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py',
+    'configs/atss/atss_r50_fpn_1x_coco.py',
+    'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py',
+    'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py',
+    'configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py',
+    'configs/pascal_voc/ssd300_voc0712.py',
+    'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py',
+    'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py',
+    'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
+]
+
+
+def main():
+    args = parse_args()
+
+    benchmark_type = []
+    if args.basic_arch:
+        benchmark_type += basic_arch_root
+    if args.datasets:
+        benchmark_type += datasets_root
+    if args.data_pipeline:
+        benchmark_type += data_pipeline_root
+    if args.nn_module:
+        benchmark_type += nn_module_root
+
+    config_dpath = 'configs/'
+    benchmark_configs = []
+    for cfg_root in benchmark_type:
+        cfg_dir = osp.join(config_dpath, cfg_root)
+        configs = os.scandir(cfg_dir)
+        for cfg in configs:
+            config_path = osp.join(cfg_dir, cfg.name)
+            if (config_path in benchmark_pool
+                    and config_path not in benchmark_configs):
+                benchmark_configs.append(config_path)
+
+    print(f'Totally found {len(benchmark_configs)} configs to benchmark')
+    config_dicts = dict(models=benchmark_configs)
+    mmcv.dump(config_dicts, 'regression_test_configs.json')
+
+
+if __name__ == '__main__':
+    main()

.dev_scripts/gather_models.py

@@ -0,0 +1,170 @@
+import argparse
+import glob
+import json
+import os.path as osp
+import shutil
+import subprocess
+
+import mmcv
+import torch
+
+# build schedule look-up table to automatically find the final model
+SCHEDULES_LUT = {
+    '1x': 12,
+    '2x': 24,
+    '20e': 20,
+    '3x': 36,
+    '4x': 48,
+    '24e': 24,
+    '6x': 73
+}
+RESULTS_LUT = ['bbox_mAP', 'segm_mAP']
+
+
+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.
+    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):
+    if config.find('grid_rcnn') != -1 and config.find('2x') != -1:
+        # grid_rcnn 2x trains 25 epochs
+        return 25
+
+    for schedule_name, epoch_num in SCHEDULES_LUT.items():
+        if config.find(schedule_name) != -1:
+            return epoch_num
+
+
+def get_final_results(log_json_path, epoch):
+    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 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 logs
+        log_json_path = glob.glob(osp.join(exp_dir, '*.log.json'))[0]
+        log_txt_path = glob.glob(osp.join(exp_dir, '*.log'))[0]
+        model_performance = get_final_results(log_json_path, final_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,
+                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)
+    mmcv.dump(models, osp.join(models_out, 'model_info.json'))
+
+
+if __name__ == '__main__':
+    main()

.dev_scripts/linter.sh

@@ -0,0 +1,3 @@
+yapf -r -i --style .style.yapf mmdet/ configs/ tests/ tools/
+isort -rc mmdet/ configs/ tests/ tools/
+flake8 .

.gitignore

@@ -104,9 +104,10 @@ venv.bak/
 .mypy_cache/
 
 mmdet/version.py
-data
+./data/
 .vscode
 .idea
+.DS_Store
 
 # custom
 *.pkl
@@ -116,3 +117,5 @@ work_dirs/
 
 # Pytorch
 *.pth
+*.py~
+*.sh~

.isort.cfg

@@ -3,6 +3,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet
-known_third_party = asynctest,cityscapesscripts,cv2,matplotlib,mmcv,numpy,onnx,pycocotools,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
+known_third_party = PIL,asynctest,cityscapesscripts,cv2,matplotlib,mmcv,numpy,onnx,pycocotools,pytest,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://gitlab.com/pycqa/flake8.git
-    rev: 3.7.9
+    rev: 3.8.0
     hooks:
       - id: flake8
   - repo: https://github.com/asottile/seed-isort-config
@@ -10,7 +10,7 @@ repos:
   - repo: https://github.com/timothycrosley/isort
     rev: 4.3.21
     hooks:
-        - id: isort
+      - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
     rev: v0.29.0
     hooks:
@@ -23,5 +23,6 @@ repos:
       - id: end-of-file-fixer
       - id: requirements-txt-fixer
       - id: double-quote-string-fixer
+      - id: check-merge-conflict
       - id: fix-encoding-pragma
         args: ["--remove"]

.readthedocs.yml

@@ -0,0 +1,7 @@
+version: 2
+
+python:
+    version: 3.7
+    install:
+      - requirements: requirements/docs.txt
+      - requirements: requirements/readthedocs.txt