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""" | ||
some instructions | ||
1. Fill the models that needs to be checked in the modelzoo_dict | ||
2. Arange the structure of the directory as follows, the script will find the | ||
corresponding config itself: | ||
model_dir/model_family/checkpoints | ||
e.g.: models/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth | ||
models/faster_rcnn/faster_rcnn_r101_fpn_1x_coco_20200130-047c8118.pth | ||
3. Excute the batch_test.sh | ||
""" | ||
|
||
import argparse | ||
import json | ||
import os | ||
import subprocess | ||
|
||
import mmcv | ||
import torch | ||
from mmcv import Config, get_logger | ||
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel | ||
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, | ||
wrap_fp16_model) | ||
|
||
from mmdet.apis import multi_gpu_test, single_gpu_test | ||
from mmdet.datasets import (build_dataloader, build_dataset, | ||
replace_ImageToTensor) | ||
from mmdet.models import build_detector | ||
|
||
modelzoo_dict = { | ||
'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py': { | ||
'bbox': 0.374 | ||
}, | ||
'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py': { | ||
'bbox': 0.382, | ||
'segm': 0.347 | ||
}, | ||
'configs/rpn/rpn_r50_fpn_1x_coco.py': { | ||
'AR@1000': 0.582 | ||
} | ||
} | ||
|
||
|
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def parse_args(): | ||
parser = argparse.ArgumentParser( | ||
description='The script used for checking the correctness \ | ||
of batch inference') | ||
parser.add_argument('model_dir', help='directory of models') | ||
parser.add_argument( | ||
'json_out', help='the output json records test information like mAP') | ||
parser.add_argument( | ||
'--launcher', | ||
choices=['none', 'pytorch', 'slurm', 'mpi'], | ||
default='none', | ||
help='job launcher') | ||
parser.add_argument('--local_rank', type=int, default=0) | ||
args = parser.parse_args() | ||
if 'LOCAL_RANK' not in os.environ: | ||
os.environ['LOCAL_RANK'] = str(args.local_rank) | ||
return args | ||
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||
|
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def check_finish(all_model_dict, result_file): | ||
# check if all models are checked | ||
tested_cfgs = [] | ||
with open(result_file, 'r+') as f: | ||
for line in f: | ||
line = json.loads(line) | ||
tested_cfgs.append(line['cfg']) | ||
is_finish = True | ||
for cfg in sorted(all_model_dict.keys()): | ||
if cfg not in tested_cfgs: | ||
return cfg | ||
if is_finish: | ||
with open(result_file, 'a+') as f: | ||
f.write('finished\n') | ||
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|
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def dump_dict(record_dict, json_out): | ||
# dump result json dict | ||
with open(json_out, 'a+') as f: | ||
mmcv.dump(record_dict, f, file_format='json') | ||
f.write('\n') | ||
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def main(): | ||
args = parse_args() | ||
# touch the output json if not exist | ||
with open(args.json_out, 'a+'): | ||
pass | ||
# init distributed env first, since logger depends on the dist | ||
# info. | ||
if args.launcher == 'none': | ||
distributed = False | ||
else: | ||
distributed = True | ||
init_dist(args.launcher, backend='nccl') | ||
rank, world_size = get_dist_info() | ||
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logger = get_logger('root') | ||
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# read info of checkpoints and config | ||
result_dict = dict() | ||
for model_family_dir in os.listdir(args.model_dir): | ||
for model in os.listdir( | ||
os.path.join(args.model_dir, model_family_dir)): | ||
# cpt: rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth | ||
# cfg: rpn_r50_fpn_1x_coco.py | ||
cfg = model.split('.')[0][:-18] + '.py' | ||
cfg_path = os.path.join('configs', model_family_dir, cfg) | ||
assert os.path.isfile( | ||
cfg_path), f'{cfg_path} is not valid config path' | ||
cpt_path = os.path.join(args.model_dir, model_family_dir, model) | ||
result_dict[cfg_path] = cpt_path | ||
assert cfg_path in modelzoo_dict, f'please fill the ' \ | ||
f'performance of cfg: {cfg_path}' | ||
cfg = check_finish(result_dict, args.json_out) | ||
cpt = result_dict[cfg] | ||
try: | ||
cfg_name = cfg | ||
logger.info(f'evaluate {cfg}') | ||
record = dict(cfg=cfg, cpt=cpt) | ||
cfg = Config.fromfile(cfg) | ||
# cfg.data.test.ann_file = 'data/val_0_10.json' | ||
# set cudnn_benchmark | ||
if cfg.get('cudnn_benchmark', False): | ||
torch.backends.cudnn.benchmark = True | ||
cfg.model.pretrained = None | ||
if cfg.model.get('neck'): | ||
if isinstance(cfg.model.neck, list): | ||
for neck_cfg in cfg.model.neck: | ||
if neck_cfg.get('rfp_backbone'): | ||
if neck_cfg.rfp_backbone.get('pretrained'): | ||
neck_cfg.rfp_backbone.pretrained = None | ||
elif cfg.model.neck.get('rfp_backbone'): | ||
if cfg.model.neck.rfp_backbone.get('pretrained'): | ||
cfg.model.neck.rfp_backbone.pretrained = None | ||
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# in case the test dataset is concatenated | ||
if isinstance(cfg.data.test, dict): | ||
cfg.data.test.test_mode = True | ||
elif isinstance(cfg.data.test, list): | ||
for ds_cfg in cfg.data.test: | ||
ds_cfg.test_mode = True | ||
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# build the dataloader | ||
samples_per_gpu = 2 # hack test with 2 image per gpu | ||
if samples_per_gpu > 1: | ||
# Replace 'ImageToTensor' to 'DefaultFormatBundle' | ||
cfg.data.test.pipeline = replace_ImageToTensor( | ||
cfg.data.test.pipeline) | ||
dataset = build_dataset(cfg.data.test) | ||
data_loader = build_dataloader( | ||
dataset, | ||
samples_per_gpu=samples_per_gpu, | ||
workers_per_gpu=cfg.data.workers_per_gpu, | ||
dist=distributed, | ||
shuffle=False) | ||
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# build the model and load checkpoint | ||
cfg.model.train_cfg = None | ||
model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) | ||
fp16_cfg = cfg.get('fp16', None) | ||
if fp16_cfg is not None: | ||
wrap_fp16_model(model) | ||
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checkpoint = load_checkpoint(model, cpt, map_location='cpu') | ||
# old versions did not save class info in checkpoints, | ||
# this walkaround is for backward compatibility | ||
if 'CLASSES' in checkpoint['meta']: | ||
model.CLASSES = checkpoint['meta']['CLASSES'] | ||
else: | ||
model.CLASSES = dataset.CLASSES | ||
|
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if not distributed: | ||
model = MMDataParallel(model, device_ids=[0]) | ||
outputs = single_gpu_test(model, data_loader) | ||
else: | ||
model = MMDistributedDataParallel( | ||
model.cuda(), | ||
device_ids=[torch.cuda.current_device()], | ||
broadcast_buffers=False) | ||
outputs = multi_gpu_test(model, data_loader, 'tmp') | ||
if rank == 0: | ||
ref_mAP_dict = modelzoo_dict[cfg_name] | ||
metrics = list(ref_mAP_dict.keys()) | ||
metrics = [ | ||
m if m != 'AR@1000' else 'proposal_fast' for m in metrics | ||
] | ||
eval_results = dataset.evaluate(outputs, metrics) | ||
print(eval_results) | ||
for metric in metrics: | ||
if metric == 'proposal_fast': | ||
ref_metric = modelzoo_dict[cfg_name]['AR@1000'] | ||
eval_metric = eval_results['AR@1000'] | ||
else: | ||
ref_metric = modelzoo_dict[cfg_name][metric] | ||
eval_metric = eval_results[f'{metric}_mAP'] | ||
if abs(ref_metric - eval_metric) > 0.003: | ||
record['is_normal'] = False | ||
dump_dict(record, args.json_out) | ||
check_finish(result_dict, args.json_out) | ||
except Exception as e: | ||
logger.error(f'rank: {rank} test fail with error: {e}') | ||
record['terminate'] = True | ||
dump_dict(record, args.json_out) | ||
check_finish(result_dict, args.json_out) | ||
# hack there to throw some error to prevent hang out | ||
subprocess.call('xxx') | ||
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if __name__ == '__main__': | ||
main() |
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export PYTHONPATH=${PWD} | ||
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partition=$1 | ||
model_dir=$2 | ||
json_out=$3 | ||
job_name=batch_test | ||
gpus=8 | ||
gpu_per_node=8 | ||
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touch $json_out | ||
lastLine=$(tail -n 1 $json_out) | ||
while [ "$lastLine" != "finished" ] | ||
do | ||
srun -p ${partition} --gres=gpu:${gpu_per_node} -n${gpus} --ntasks-per-node=${gpu_per_node} \ | ||
--job-name=${job_name} --kill-on-bad-exit=1 \ | ||
python .dev_scripts/batch_test.py $model_dir $json_out --launcher='slurm' | ||
lastLine=$(tail -n 1 $json_out) | ||
echo $lastLine | ||
done |
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import argparse | ||
import os | ||
import os.path as osp | ||
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import mmcv | ||
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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') | ||
parser.add_argument( | ||
'--model-options', | ||
nargs='+', | ||
help='custom options to special model benchmark') | ||
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args = parser.parse_args() | ||
return args | ||
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basic_arch_root = [ | ||
'atss', 'cascade_rcnn', 'cascade_rpn', 'centripetalnet', 'cornernet', | ||
'detectors', 'detr', 'double_heads', 'dynamic_rcnn', 'faster_rcnn', 'fcos', | ||
'foveabox', 'fp16', 'free_anchor', 'fsaf', 'gfl', 'ghm', 'grid_rcnn', | ||
'guided_anchoring', 'htc', 'libra_rcnn', 'mask_rcnn', 'ms_rcnn', | ||
'nas_fcos', 'paa', 'pisa', 'point_rend', 'reppoints', 'retinanet', 'rpn', | ||
'sabl', 'ssd', 'tridentnet', 'vfnet', 'yolact', 'yolo', 'sparse_rcnn', | ||
'scnet' | ||
] | ||
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datasets_root = [ | ||
'wider_face', 'pascal_voc', 'cityscapes', 'lvis', 'deepfashion' | ||
] | ||
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data_pipeline_root = ['albu_example', 'instaboost'] | ||
|
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nn_module_root = [ | ||
'carafe', 'dcn', 'empirical_attention', 'gcnet', 'gn', 'gn+ws', 'hrnet', | ||
'pafpn', 'nas_fpn', 'regnet', 'resnest', 'res2net', 'groie' | ||
] | ||
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benchmark_pool = [ | ||
'configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py', | ||
'configs/atss/atss_r50_fpn_1x_coco.py', | ||
'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py', | ||
'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', | ||
'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py', | ||
'configs/centripetalnet/' | ||
'centripetalnet_hourglass104_mstest_16x6_210e_coco.py', | ||
'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py', | ||
'configs/cornernet/' | ||
'cornernet_hourglass104_mstest_8x6_210e_coco.py', # special | ||
'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/detectors/detectors_htc_r50_1x_coco.py', | ||
'configs/detr/detr_r50_8x2_150e_coco.py', | ||
'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py', | ||
'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py', | ||
'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py', # noqa | ||
'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_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/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py', | ||
'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py', | ||
'configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py', | ||
'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', | ||
'configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py', | ||
'configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py', | ||
'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', | ||
'configs/fsaf/fsaf_r50_fpn_1x_coco.py', | ||
'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py', | ||
'configs/gfl/gfl_r50_fpn_1x_coco.py', | ||
'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py', | ||
'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py', | ||
'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py', | ||
'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', | ||
'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py', | ||
'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py', | ||
'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py', | ||
'configs/htc/htc_r50_fpn_1x_coco.py', | ||
'configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py', | ||
'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py', | ||
'configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py', | ||
'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py', | ||
'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py', | ||
'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py', | ||
'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py', | ||
'configs/paa/paa_r50_fpn_1x_coco.py', | ||
'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py', | ||
'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py', | ||
'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py', | ||
'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py', | ||
'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py', | ||
'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py', | ||
'configs/resnest/' | ||
'mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py', | ||
'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py', | ||
'configs/rpn/rpn_r50_fpn_1x_coco.py', | ||
'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py', | ||
'configs/ssd/ssd300_coco.py', | ||
'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py', | ||
'configs/vfnet/vfnet_r50_fpn_1x_coco.py', | ||
'configs/yolact/yolact_r50_1x8_coco.py', | ||
'configs/yolo/yolov3_d53_320_273e_coco.py', | ||
'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py', | ||
'configs/scnet/scnet_r50_fpn_1x_coco.py' | ||
] | ||
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def main(): | ||
args = parse_args() | ||
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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 | ||
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special_model = args.model_options | ||
if special_model is not None: | ||
benchmark_type += special_model | ||
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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) | ||
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print(f'Totally found {len(benchmark_configs)} configs to benchmark') | ||
config_dicts = dict(models=benchmark_configs) | ||
mmcv.dump(config_dicts, 'regression_test_configs.json') | ||
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if __name__ == '__main__': | ||
main() |
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import argparse | ||
import os | ||
import os.path as osp | ||
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import mmcv | ||
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def parse_args(): | ||
parser = argparse.ArgumentParser( | ||
description='Convert benchmark model json to script') | ||
parser.add_argument( | ||
'json_path', type=str, help='json path output by benchmark_filter') | ||
parser.add_argument('partition', type=str, help='slurm partition name') | ||
parser.add_argument( | ||
'--max-keep-ckpts', | ||
type=int, | ||
default=1, | ||
help='The maximum checkpoints to keep') | ||
parser.add_argument( | ||
'--run', action='store_true', help='run script directly') | ||
parser.add_argument( | ||
'--out', type=str, help='path to save model benchmark script') | ||
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args = parser.parse_args() | ||
return args | ||
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def main(): | ||
args = parse_args() | ||
if args.out: | ||
out_suffix = args.out.split('.')[-1] | ||
assert args.out.endswith('.sh'), \ | ||
f'Expected out file path suffix is .sh, but get .{out_suffix}' | ||
assert args.out or args.run, \ | ||
('Please specify at least one operation (save/run/ the ' | ||
'script) with the argument "--out" or "--run"') | ||
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json_data = mmcv.load(args.json_path) | ||
model_cfgs = json_data['models'] | ||
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partition = args.partition # cluster name | ||
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root_name = './tools' | ||
train_script_name = osp.join(root_name, 'slurm_train.sh') | ||
# stdout is no output | ||
stdout_cfg = '>/dev/null' | ||
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max_keep_ckpts = args.max_keep_ckpts | ||
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commands = [] | ||
for i, cfg in enumerate(model_cfgs): | ||
# print cfg name | ||
echo_info = f'echo \'{cfg}\' &' | ||
commands.append(echo_info) | ||
commands.append('\n') | ||
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fname, _ = osp.splitext(osp.basename(cfg)) | ||
out_fname = osp.join(root_name, fname) | ||
# default setting | ||
command_info = f'GPUS=8 GPUS_PER_NODE=8 ' \ | ||
f'CPUS_PER_TASK=2 {train_script_name} ' | ||
command_info += f'{partition} ' | ||
command_info += f'{fname} ' | ||
command_info += f'{cfg} ' | ||
command_info += f'{out_fname} ' | ||
if max_keep_ckpts: | ||
command_info += f'--cfg-options ' \ | ||
f'checkpoint_config.max_keep_ckpts=' \ | ||
f'{max_keep_ckpts}' + ' ' | ||
command_info += f'{stdout_cfg} &' | ||
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commands.append(command_info) | ||
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if i < len(model_cfgs): | ||
commands.append('\n') | ||
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command_str = ''.join(commands) | ||
if args.out: | ||
with open(args.out, 'w') as f: | ||
f.write(command_str) | ||
if args.run: | ||
os.system(command_str) | ||
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if __name__ == '__main__': | ||
main() |
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import argparse | ||
import glob | ||
import os.path as osp | ||
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import mmcv | ||
from gather_models import get_final_results | ||
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try: | ||
import xlrd | ||
except ImportError: | ||
xlrd = None | ||
try: | ||
import xlutils | ||
from xlutils.copy import copy | ||
except ImportError: | ||
xlutils = None | ||
|
||
|
||
def parse_args(): | ||
parser = argparse.ArgumentParser( | ||
description='Gather benchmarked models metric') | ||
parser.add_argument( | ||
'root', | ||
type=str, | ||
help='root path of benchmarked models to be gathered') | ||
parser.add_argument( | ||
'benchmark_json', type=str, help='json path of benchmark models') | ||
parser.add_argument( | ||
'--out', type=str, help='output path of gathered metrics to be stored') | ||
parser.add_argument( | ||
'--not-show', action='store_true', help='not show metrics') | ||
parser.add_argument( | ||
'--excel', type=str, help='input path of excel to be recorded') | ||
parser.add_argument( | ||
'--ncol', type=int, help='Number of column to be modified or appended') | ||
|
||
args = parser.parse_args() | ||
return args | ||
|
||
|
||
if __name__ == '__main__': | ||
args = parse_args() | ||
|
||
if args.excel: | ||
assert args.ncol, 'Please specify "--excel" and "--ncol" ' \ | ||
'at the same time' | ||
if xlrd is None: | ||
raise RuntimeError( | ||
'xlrd is not installed,' | ||
'Please use “pip install xlrd==1.2.0” to install') | ||
if xlutils is None: | ||
raise RuntimeError( | ||
'xlutils is not installed,' | ||
'Please use “pip install xlutils==2.0.0” to install') | ||
readbook = xlrd.open_workbook(args.excel) | ||
sheet = readbook.sheet_by_name('Sheet1') | ||
sheet_info = {} | ||
total_nrows = sheet.nrows | ||
for i in range(3, sheet.nrows): | ||
sheet_info[sheet.row_values(i)[0]] = i | ||
xlrw = copy(readbook) | ||
table = xlrw.get_sheet(0) | ||
|
||
root_path = args.root | ||
metrics_out = args.out | ||
benchmark_json_path = args.benchmark_json | ||
model_configs = mmcv.load(benchmark_json_path)['models'] | ||
|
||
result_dict = {} | ||
for config in model_configs: | ||
config_name = osp.split(config)[-1] | ||
config_name = osp.splitext(config_name)[0] | ||
result_path = osp.join(root_path, config_name) | ||
if osp.exists(result_path): | ||
# 1 read config | ||
cfg = mmcv.Config.fromfile(config) | ||
total_epochs = cfg.runner.max_epochs | ||
final_results = cfg.evaluation.metric | ||
if not isinstance(final_results, list): | ||
final_results = [final_results] | ||
final_results_out = [] | ||
for key in final_results: | ||
if 'proposal_fast' in key: | ||
final_results_out.append('AR@1000') # RPN | ||
elif 'mAP' not in key: | ||
final_results_out.append(key + '_mAP') | ||
|
||
# 2 determine whether total_epochs ckpt exists | ||
ckpt_path = f'epoch_{total_epochs}.pth' | ||
if osp.exists(osp.join(result_path, ckpt_path)): | ||
log_json_path = list( | ||
sorted(glob.glob(osp.join(result_path, '*.log.json'))))[-1] | ||
|
||
# 3 read metric | ||
model_performance = get_final_results(log_json_path, | ||
total_epochs, | ||
final_results_out) | ||
if model_performance is None: | ||
print(f'log file error: {log_json_path}') | ||
continue | ||
for performance in model_performance: | ||
if performance in ['AR@1000', 'bbox_mAP', 'segm_mAP']: | ||
metric = round(model_performance[performance] * 100, 1) | ||
model_performance[performance] = metric | ||
result_dict[config] = model_performance | ||
|
||
# update and append excel content | ||
if args.excel: | ||
if 'AR@1000' in model_performance: | ||
metrics = f'{model_performance["AR@1000"]}(AR@1000)' | ||
elif 'segm_mAP' in model_performance: | ||
metrics = f'{model_performance["bbox_mAP"]}/' \ | ||
f'{model_performance["segm_mAP"]}' | ||
else: | ||
metrics = f'{model_performance["bbox_mAP"]}' | ||
|
||
row_num = sheet_info.get(config, None) | ||
if row_num: | ||
table.write(row_num, args.ncol, metrics) | ||
else: | ||
table.write(total_nrows, 0, config) | ||
table.write(total_nrows, args.ncol, metrics) | ||
total_nrows += 1 | ||
|
||
else: | ||
print(f'{config} not exist: {ckpt_path}') | ||
else: | ||
print(f'not exist: {config}') | ||
|
||
# 4 save or print results | ||
if metrics_out: | ||
mmcv.mkdir_or_exist(metrics_out) | ||
mmcv.dump(result_dict, osp.join(metrics_out, 'model_metric_info.json')) | ||
if not args.not_show: | ||
print('===================================') | ||
for config_name, metrics in result_dict.items(): | ||
print(config_name, metrics) | ||
print('===================================') | ||
if args.excel: | ||
filename, sufflx = osp.splitext(args.excel) | ||
xlrw.save(f'{filename}_o{sufflx}') | ||
print(f'>>> Output {filename}_o{sufflx}') |
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import argparse | ||
import glob | ||
import json | ||
import os.path as osp | ||
import shutil | ||
import subprocess | ||
|
||
import mmcv | ||
import torch | ||
|
||
|
||
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): | ||
cfg = mmcv.Config.fromfile('./configs/' + config) | ||
return cfg.total_epochs | ||
|
||
|
||
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 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')) | ||
|
||
|
||
if __name__ == '__main__': | ||
main() |
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yapf -r -i mmdet/ configs/ tests/ tools/ | ||
isort -rc mmdet/ configs/ tests/ tools/ | ||
flake8 . |
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# Contributor Covenant Code of Conduct | ||
|
||
## Our Pledge | ||
|
||
In the interest of fostering an open and welcoming environment, we as | ||
contributors and maintainers pledge to making participation in our project and | ||
our community a harassment-free experience for everyone, regardless of age, body | ||
size, disability, ethnicity, sex characteristics, gender identity and expression, | ||
level of experience, education, socio-economic status, nationality, personal | ||
appearance, race, religion, or sexual identity and orientation. | ||
|
||
## Our Standards | ||
|
||
Examples of behavior that contributes to creating a positive environment | ||
include: | ||
|
||
* Using welcoming and inclusive language | ||
* Being respectful of differing viewpoints and experiences | ||
* Gracefully accepting constructive criticism | ||
* Focusing on what is best for the community | ||
* Showing empathy towards other community members | ||
|
||
Examples of unacceptable behavior by participants include: | ||
|
||
* The use of sexualized language or imagery and unwelcome sexual attention or | ||
advances | ||
* Trolling, insulting/derogatory comments, and personal or political attacks | ||
* Public or private harassment | ||
* Publishing others' private information, such as a physical or electronic | ||
address, without explicit permission | ||
* Other conduct which could reasonably be considered inappropriate in a | ||
professional setting | ||
|
||
## Our Responsibilities | ||
|
||
Project maintainers are responsible for clarifying the standards of acceptable | ||
behavior and are expected to take appropriate and fair corrective action in | ||
response to any instances of unacceptable behavior. | ||
|
||
Project maintainers have the right and responsibility to remove, edit, or | ||
reject comments, commits, code, wiki edits, issues, and other contributions | ||
that are not aligned to this Code of Conduct, or to ban temporarily or | ||
permanently any contributor for other behaviors that they deem inappropriate, | ||
threatening, offensive, or harmful. | ||
|
||
## Scope | ||
|
||
This Code of Conduct applies both within project spaces and in public spaces | ||
when an individual is representing the project or its community. Examples of | ||
representing a project or community include using an official project e-mail | ||
address, posting via an official social media account, or acting as an appointed | ||
representative at an online or offline event. Representation of a project may be | ||
further defined and clarified by project maintainers. | ||
|
||
## Enforcement | ||
|
||
Instances of abusive, harassing, or otherwise unacceptable behavior may be | ||
reported by contacting the project team at chenkaidev@gmail.com. All | ||
complaints will be reviewed and investigated and will result in a response that | ||
is deemed necessary and appropriate to the circumstances. The project team is | ||
obligated to maintain confidentiality with regard to the reporter of an incident. | ||
Further details of specific enforcement policies may be posted separately. | ||
|
||
Project maintainers who do not follow or enforce the Code of Conduct in good | ||
faith may face temporary or permanent repercussions as determined by other | ||
members of the project's leadership. | ||
|
||
## Attribution | ||
|
||
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, | ||
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html | ||
|
||
[homepage]: https://www.contributor-covenant.org | ||
|
||
For answers to common questions about this code of conduct, see | ||
https://www.contributor-covenant.org/faq |
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We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline. |
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blank_issues_enabled: false | ||
|
||
contact_links: | ||
- name: Common Issues | ||
url: https://mmdetection.readthedocs.io/en/latest/faq.html | ||
about: Check if your issue already has solutions | ||
- name: MMDetection Documentation | ||
url: https://mmdetection.readthedocs.io/en/latest/ | ||
about: Check if your question is answered in docs |
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---|---|---|
@@ -0,0 +1,47 @@ | ||
--- | ||
name: Error report | ||
about: Create a report to help us improve | ||
title: '' | ||
labels: '' | ||
assignees: '' | ||
|
||
--- | ||
|
||
Thanks for your error report and we appreciate it a lot. | ||
|
||
**Checklist** | ||
|
||
1. I have searched related issues but cannot get the expected help. | ||
2. I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help. | ||
3. The bug has not been fixed in the latest version. | ||
|
||
**Describe the bug** | ||
A clear and concise description of what the bug is. | ||
|
||
**Reproduction** | ||
|
||
1. What command or script did you run? | ||
|
||
```none | ||
A placeholder for the command. | ||
``` | ||
|
||
2. Did you make any modifications on the code or config? Did you understand what you have modified? | ||
3. What dataset did you use? | ||
|
||
**Environment** | ||
|
||
1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here. | ||
2. You may add addition that may be helpful for locating the problem, such as | ||
- How you installed PyTorch [e.g., pip, conda, source] | ||
- Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) | ||
|
||
**Error traceback** | ||
If applicable, paste the error trackback here. | ||
|
||
```none | ||
A placeholder for trackback. | ||
``` | ||
|
||
**Bug fix** | ||
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! |
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---|---|---|
@@ -0,0 +1,22 @@ | ||
--- | ||
name: Feature request | ||
about: Suggest an idea for this project | ||
title: '' | ||
labels: '' | ||
assignees: '' | ||
|
||
--- | ||
|
||
**Describe the feature** | ||
|
||
**Motivation** | ||
A clear and concise description of the motivation of the feature. | ||
Ex1. It is inconvenient when [....]. | ||
Ex2. There is a recent paper [....], which is very helpful for [....]. | ||
|
||
**Related resources** | ||
If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful. | ||
|
||
**Additional context** | ||
Add any other context or screenshots about the feature request here. | ||
If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated. |
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---|---|---|
@@ -0,0 +1,8 @@ | ||
--- | ||
name: General questions | ||
about: Ask general questions to get help | ||
title: '' | ||
labels: '' | ||
assignees: '' | ||
|
||
--- |
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---|---|---|
@@ -0,0 +1,68 @@ | ||
--- | ||
name: Reimplementation Questions | ||
about: Ask about questions during model reimplementation | ||
title: '' | ||
labels: 'reimplementation' | ||
assignees: '' | ||
|
||
--- | ||
|
||
**Notice** | ||
|
||
There are several common situations in the reimplementation issues as below | ||
|
||
1. Reimplement a model in the model zoo using the provided configs | ||
2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets) | ||
3. Reimplement a custom model but all the components are implemented in MMDetection | ||
4. Reimplement a custom model with new modules implemented by yourself | ||
|
||
There are several things to do for different cases as below. | ||
|
||
- For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue. | ||
- For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write. | ||
- One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemented code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you. | ||
|
||
**Checklist** | ||
|
||
1. I have searched related issues but cannot get the expected help. | ||
2. The issue has not been fixed in the latest version. | ||
|
||
**Describe the issue** | ||
|
||
A clear and concise description of what the problem you meet and what have you done. | ||
|
||
**Reproduction** | ||
|
||
1. What command or script did you run? | ||
|
||
```none | ||
A placeholder for the command. | ||
``` | ||
|
||
2. What config dir you run? | ||
|
||
```none | ||
A placeholder for the config. | ||
``` | ||
|
||
3. Did you make any modifications on the code or config? Did you understand what you have modified? | ||
4. What dataset did you use? | ||
|
||
**Environment** | ||
|
||
1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here. | ||
2. You may add addition that may be helpful for locating the problem, such as | ||
1. How you installed PyTorch [e.g., pip, conda, source] | ||
2. Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) | ||
|
||
**Results** | ||
|
||
If applicable, paste the related results here, e.g., what you expect and what you get. | ||
|
||
```none | ||
A placeholder for results comparison | ||
``` | ||
|
||
**Issue fix** | ||
|
||
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! |
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name: build | ||
|
||
on: [push, pull_request] | ||
|
||
jobs: | ||
lint: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: actions/checkout@v2 | ||
- name: Set up Python 3.7 | ||
uses: actions/setup-python@v2 | ||
with: | ||
python-version: 3.7 | ||
- name: Install pre-commit hook | ||
run: | | ||
pip install pre-commit | ||
pre-commit install | ||
- name: Linting | ||
run: pre-commit run --all-files | ||
- name: Check docstring coverage | ||
run: | | ||
pip install interrogate | ||
interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmdet | ||
build_cpu: | ||
runs-on: ubuntu-latest | ||
strategy: | ||
matrix: | ||
python-version: [3.7] | ||
torch: [1.3.1, 1.5.1, 1.6.0] | ||
include: | ||
- torch: 1.3.1 | ||
torchvision: 0.4.2 | ||
mmcv: "latest+torch1.3.0+cpu" | ||
- torch: 1.5.1 | ||
torchvision: 0.6.1 | ||
mmcv: "latest+torch1.5.0+cpu" | ||
- torch: 1.6.0 | ||
torchvision: 0.7.0 | ||
mmcv: "latest+torch1.6.0+cpu" | ||
steps: | ||
- uses: actions/checkout@v2 | ||
- name: Set up Python ${{ matrix.python-version }} | ||
uses: actions/setup-python@v2 | ||
with: | ||
python-version: ${{ matrix.python-version }} | ||
- name: Install Pillow | ||
run: pip install Pillow==6.2.2 | ||
if: ${{matrix.torchvision == '0.4.2'}} | ||
- name: Install PyTorch | ||
run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html | ||
- name: Install MMCV | ||
run: | | ||
pip install mmcv-full==${{matrix.mmcv}} -f https://download.openmmlab.com/mmcv/dist/index.html --use-deprecated=legacy-resolver | ||
python -c 'import mmcv; print(mmcv.__version__)' | ||
- name: Install unittest dependencies | ||
run: pip install -r requirements/tests.txt -r requirements/optional.txt | ||
- name: Build and install | ||
run: rm -rf .eggs && pip install -e . | ||
- name: Run unittests and generate coverage report | ||
run: | | ||
coverage run --branch --source mmdet -m pytest tests/ | ||
coverage xml | ||
coverage report -m | ||
build_cuda: | ||
runs-on: ubuntu-latest | ||
|
||
env: | ||
CUDA: 10.1.105-1 | ||
CUDA_SHORT: 10.1 | ||
UBUNTU_VERSION: ubuntu1804 | ||
strategy: | ||
matrix: | ||
python-version: [3.7] | ||
torch: [1.3.1, 1.5.1+cu101, 1.6.0+cu101] | ||
include: | ||
- torch: 1.3.1 | ||
torchvision: 0.4.2 | ||
mmcv: "latest+torch1.3.0+cu101" | ||
- torch: 1.5.1+cu101 | ||
torchvision: 0.6.1+cu101 | ||
mmcv: "latest+torch1.5.0+cu101" | ||
- torch: 1.6.0+cu101 | ||
torchvision: 0.7.0+cu101 | ||
mmcv: "latest+torch1.6.0+cu101" | ||
- torch: 1.6.0+cu101 | ||
torchvision: 0.7.0+cu101 | ||
mmcv: "latest+torch1.6.0+cu101" | ||
python-version: 3.6 | ||
- torch: 1.6.0+cu101 | ||
torchvision: 0.7.0+cu101 | ||
mmcv: "latest+torch1.6.0+cu101" | ||
python-version: 3.8 | ||
|
||
steps: | ||
- uses: actions/checkout@v2 | ||
- name: Set up Python ${{ matrix.python-version }} | ||
uses: actions/setup-python@v2 | ||
with: | ||
python-version: ${{ matrix.python-version }} | ||
- name: Install CUDA | ||
run: | | ||
export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb | ||
wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER} | ||
sudo dpkg -i ${INSTALLER} | ||
wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub | ||
sudo apt-key add 7fa2af80.pub | ||
sudo apt update -qq | ||
sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-} | ||
sudo apt clean | ||
export CUDA_HOME=/usr/local/cuda-${CUDA_SHORT} | ||
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH} | ||
export PATH=${CUDA_HOME}/bin:${PATH} | ||
- name: Install Pillow | ||
run: pip install Pillow==6.2.2 | ||
if: ${{matrix.torchvision < 0.5}} | ||
- name: Install PyTorch | ||
run: pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html | ||
- name: Install mmdet dependencies | ||
run: | | ||
pip install mmcv-full==${{matrix.mmcv}} -f https://download.openmmlab.com/mmcv/dist/index.html --use-deprecated=legacy-resolver | ||
pip install -r requirements.txt | ||
python -c 'import mmcv; print(mmcv.__version__)' | ||
- name: Build and install | ||
run: | | ||
rm -rf .eggs | ||
python setup.py check -m -s | ||
TORCH_CUDA_ARCH_LIST=7.0 pip install . | ||
- name: Run unittests and generate coverage report | ||
run: | | ||
coverage run --branch --source mmdet -m pytest tests/ | ||
coverage xml | ||
coverage report -m | ||
- name: Upload coverage to Codecov | ||
uses: codecov/codecov-action@v1.0.10 | ||
with: | ||
file: ./coverage.xml | ||
flags: unittests | ||
env_vars: OS,PYTHON | ||
name: codecov-umbrella | ||
fail_ci_if_error: false |
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name: build_pat | ||
|
||
on: push | ||
|
||
jobs: | ||
build_parrots: | ||
runs-on: ubuntu-latest | ||
container: | ||
image: ghcr.io/sunnyxiaohu/parrots-mmcv:1.2.1 | ||
credentials: | ||
username: sunnyxiaohu | ||
password: ${{secrets.CR_PAT}} | ||
|
||
steps: | ||
- uses: actions/checkout@v2 | ||
- name: Install mmdet dependencies | ||
run: | | ||
git clone https://github.com/open-mmlab/mmcv.git && cd mmcv | ||
MMCV_WITH_OPS=1 python setup.py install | ||
cd .. && rm -rf mmcv | ||
python -c 'import mmcv; print(mmcv.__version__)' | ||
pip install -r requirements.txt | ||
- name: Build and install | ||
run: rm -rf .eggs && pip install -e . |
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name: deploy | ||
|
||
on: push | ||
|
||
jobs: | ||
build-n-publish: | ||
runs-on: ubuntu-latest | ||
if: startsWith(github.event.ref, 'refs/tags') | ||
steps: | ||
- uses: actions/checkout@v2 | ||
- name: Set up Python 3.7 | ||
uses: actions/setup-python@v2 | ||
with: | ||
python-version: 3.7 | ||
- name: Install torch | ||
run: pip install torch | ||
- name: Install wheel | ||
run: pip install wheel | ||
- name: Build MMDetection | ||
run: python setup.py sdist bdist_wheel | ||
- name: Publish distribution to PyPI | ||
run: | | ||
pip install twine | ||
twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }} |
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# Byte-compiled / optimized / DLL files | ||
__pycache__/ | ||
*.py[cod] | ||
*$py.class | ||
|
||
# C extensions | ||
*.so | ||
|
||
# Distribution / packaging | ||
.Python | ||
build/ | ||
develop-eggs/ | ||
dist/ | ||
downloads/ | ||
eggs/ | ||
.eggs/ | ||
lib/ | ||
lib64/ | ||
parts/ | ||
sdist/ | ||
var/ | ||
wheels/ | ||
*.egg-info/ | ||
.installed.cfg | ||
*.egg | ||
MANIFEST | ||
|
||
# PyInstaller | ||
# Usually these files are written by a python script from a template | ||
# before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
*.manifest | ||
*.spec | ||
|
||
# Installer logs | ||
pip-log.txt | ||
pip-delete-this-directory.txt | ||
|
||
# Unit test / coverage reports | ||
htmlcov/ | ||
.tox/ | ||
.coverage | ||
.coverage.* | ||
.cache | ||
nosetests.xml | ||
coverage.xml | ||
*.cover | ||
.hypothesis/ | ||
.pytest_cache/ | ||
|
||
# Translations | ||
*.mo | ||
*.pot | ||
|
||
# Django stuff: | ||
*.log | ||
local_settings.py | ||
db.sqlite3 | ||
|
||
# Flask stuff: | ||
instance/ | ||
.webassets-cache | ||
|
||
# Scrapy stuff: | ||
.scrapy | ||
|
||
# Sphinx documentation | ||
docs/_build/ | ||
|
||
# PyBuilder | ||
target/ | ||
|
||
# Jupyter Notebook | ||
.ipynb_checkpoints | ||
|
||
# pyenv | ||
.python-version | ||
|
||
# celery beat schedule file | ||
celerybeat-schedule | ||
|
||
# SageMath parsed files | ||
*.sage.py | ||
|
||
# Environments | ||
.env | ||
.venv | ||
env/ | ||
venv/ | ||
ENV/ | ||
env.bak/ | ||
venv.bak/ | ||
|
||
# Spyder project settings | ||
.spyderproject | ||
.spyproject | ||
|
||
# Rope project settings | ||
.ropeproject | ||
|
||
# mkdocs documentation | ||
/site | ||
|
||
# mypy | ||
.mypy_cache/ | ||
|
||
data/ | ||
data | ||
.vscode | ||
.idea | ||
.DS_Store | ||
|
||
# custom | ||
*.pkl | ||
*.pkl.json | ||
*.log.json | ||
work_dirs/ | ||
|
||
# Pytorch | ||
*.pth | ||
*.py~ | ||
*.sh~ |
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repos: | ||
- repo: https://gitlab.com/pycqa/flake8.git | ||
rev: 3.8.3 | ||
hooks: | ||
- id: flake8 | ||
- repo: https://github.com/asottile/seed-isort-config | ||
rev: v2.2.0 | ||
hooks: | ||
- id: seed-isort-config | ||
- repo: https://github.com/timothycrosley/isort | ||
rev: 4.3.21 | ||
hooks: | ||
- id: isort | ||
- repo: https://github.com/pre-commit/mirrors-yapf | ||
rev: v0.30.0 | ||
hooks: | ||
- id: yapf | ||
- repo: https://github.com/pre-commit/pre-commit-hooks | ||
rev: v3.1.0 | ||
hooks: | ||
- id: trailing-whitespace | ||
- id: check-yaml | ||
- id: end-of-file-fixer | ||
- id: requirements-txt-fixer | ||
- id: double-quote-string-fixer | ||
- id: check-merge-conflict | ||
- id: fix-encoding-pragma | ||
args: ["--remove"] | ||
- id: mixed-line-ending | ||
args: ["--fix=lf"] | ||
- repo: https://github.com/jumanjihouse/pre-commit-hooks | ||
rev: 2.1.4 | ||
hooks: | ||
- id: markdownlint | ||
args: ["-r", "~MD002,~MD013,~MD024,~MD029,~MD033,~MD034,~MD036"] | ||
- repo: https://github.com/myint/docformatter | ||
rev: v1.3.1 | ||
hooks: | ||
- id: docformatter | ||
args: ["--in-place", "--wrap-descriptions", "79"] |
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version: 2 | ||
|
||
python: | ||
version: 3.7 | ||
install: | ||
- requirements: requirements/docs.txt | ||
- requirements: requirements/readthedocs.txt |
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Copyright 2018-2019 Open-MMLab. All rights reserved. | ||
|
||
Apache License | ||
Version 2.0, January 2004 | ||
http://www.apache.org/licenses/ | ||
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||
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END OF TERMS AND CONDITIONS | ||
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APPENDIX: How to apply the Apache License to your work. | ||
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To apply the Apache License to your work, attach the following | ||
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Copyright 2018-2019 Open-MMLab. | ||
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Licensed under the Apache License, Version 2.0 (the "License"); | ||
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Unless required by applicable law or agreed to in writing, software | ||
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<div align="center"> | ||
<img src="resources/mmdet-logo.png" width="600"/> | ||
</div> | ||
|
||
**News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155). | ||
|
||
Documentation: https://mmdetection.readthedocs.io/ | ||
|
||
## Introduction | ||
|
||
English | [简体中文](README_zh-CN.md) | ||
|
||
MMDetection is an open source object detection toolbox based on PyTorch. It is | ||
a part of the [OpenMMLab](https://openmmlab.com/) project. | ||
|
||
The master branch works with **PyTorch 1.3+**. | ||
The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage. | ||
|
||
![demo image](resources/coco_test_12510.jpg) | ||
|
||
### Major features | ||
|
||
- **Modular Design** | ||
|
||
We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. | ||
|
||
- **Support of multiple frameworks out of box** | ||
|
||
The toolbox directly supports popular and contemporary detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc. | ||
|
||
- **High efficiency** | ||
|
||
All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). | ||
|
||
- **State of the art** | ||
|
||
The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. | ||
|
||
Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox. | ||
|
||
## License | ||
|
||
This project is released under the [Apache 2.0 license](LICENSE). | ||
|
||
## Changelog | ||
|
||
v2.11.0 was released in 01/04/2021. | ||
Please refer to [changelog.md](docs/changelog.md) for details and release history. | ||
A comparison between v1.x and v2.0 codebases can be found in [compatibility.md](docs/compatibility.md). | ||
|
||
## Benchmark and model zoo | ||
|
||
Results and models are available in the [model zoo](docs/model_zoo.md). | ||
|
||
Supported backbones: | ||
|
||
- [x] ResNet (CVPR'2016) | ||
- [x] ResNeXt (CVPR'2017) | ||
- [x] VGG (ICLR'2015) | ||
- [x] HRNet (CVPR'2019) | ||
- [x] RegNet (CVPR'2020) | ||
- [x] Res2Net (TPAMI'2020) | ||
- [x] ResNeSt (ArXiv'2020) | ||
|
||
Supported methods: | ||
|
||
- [x] [RPN (NeurIPS'2015)](configs/rpn) | ||
- [x] [Fast R-CNN (ICCV'2015)](configs/fast_rcnn) | ||
- [x] [Faster R-CNN (NeurIPS'2015)](configs/faster_rcnn) | ||
- [x] [Mask R-CNN (ICCV'2017)](configs/mask_rcnn) | ||
- [x] [Cascade R-CNN (CVPR'2018)](configs/cascade_rcnn) | ||
- [x] [Cascade Mask R-CNN (CVPR'2018)](configs/cascade_rcnn) | ||
- [x] [SSD (ECCV'2016)](configs/ssd) | ||
- [x] [RetinaNet (ICCV'2017)](configs/retinanet) | ||
- [x] [GHM (AAAI'2019)](configs/ghm) | ||
- [x] [Mask Scoring R-CNN (CVPR'2019)](configs/ms_rcnn) | ||
- [x] [Double-Head R-CNN (CVPR'2020)](configs/double_heads) | ||
- [x] [Hybrid Task Cascade (CVPR'2019)](configs/htc) | ||
- [x] [Libra R-CNN (CVPR'2019)](configs/libra_rcnn) | ||
- [x] [Guided Anchoring (CVPR'2019)](configs/guided_anchoring) | ||
- [x] [FCOS (ICCV'2019)](configs/fcos) | ||
- [x] [RepPoints (ICCV'2019)](configs/reppoints) | ||
- [x] [Foveabox (TIP'2020)](configs/foveabox) | ||
- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor) | ||
- [x] [NAS-FPN (CVPR'2019)](configs/nas_fpn) | ||
- [x] [ATSS (CVPR'2020)](configs/atss) | ||
- [x] [FSAF (CVPR'2019)](configs/fsaf) | ||
- [x] [PAFPN (CVPR'2018)](configs/pafpn) | ||
- [x] [Dynamic R-CNN (ECCV'2020)](configs/dynamic_rcnn) | ||
- [x] [PointRend (CVPR'2020)](configs/point_rend) | ||
- [x] [CARAFE (ICCV'2019)](configs/carafe/README.md) | ||
- [x] [DCNv2 (CVPR'2019)](configs/dcn/README.md) | ||
- [x] [Group Normalization (ECCV'2018)](configs/gn/README.md) | ||
- [x] [Weight Standardization (ArXiv'2019)](configs/gn+ws/README.md) | ||
- [x] [OHEM (CVPR'2016)](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py) | ||
- [x] [Soft-NMS (ICCV'2017)](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py) | ||
- [x] [Generalized Attention (ICCV'2019)](configs/empirical_attention/README.md) | ||
- [x] [GCNet (ICCVW'2019)](configs/gcnet/README.md) | ||
- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md) | ||
- [x] [InstaBoost (ICCV'2019)](configs/instaboost/README.md) | ||
- [x] [GRoIE (ICPR'2020)](configs/groie/README.md) | ||
- [x] [DetectoRS (ArXix'2020)](configs/detectors/README.md) | ||
- [x] [Generalized Focal Loss (NeurIPS'2020)](configs/gfl/README.md) | ||
- [x] [CornerNet (ECCV'2018)](configs/cornernet/README.md) | ||
- [x] [Side-Aware Boundary Localization (ECCV'2020)](configs/sabl/README.md) | ||
- [x] [YOLOv3 (ArXiv'2018)](configs/yolo/README.md) | ||
- [x] [PAA (ECCV'2020)](configs/paa/README.md) | ||
- [x] [YOLACT (ICCV'2019)](configs/yolact/README.md) | ||
- [x] [CentripetalNet (CVPR'2020)](configs/centripetalnet/README.md) | ||
- [x] [VFNet (ArXix'2020)](configs/vfnet/README.md) | ||
- [x] [DETR (ECCV'2020)](configs/detr/README.md) | ||
- [x] [CascadeRPN (NeurIPS'2019)](configs/cascade_rpn/README.md) | ||
- [x] [SCNet (AAAI'2021)](configs/scnet/README.md) | ||
|
||
Some other methods are also supported in [projects using MMDetection](./docs/projects.md). | ||
|
||
## Installation | ||
|
||
Please refer to [get_started.md](docs/get_started.md) for installation. | ||
|
||
## Getting Started | ||
|
||
Please see [get_started.md](docs/get_started.md) for the basic usage of MMDetection. | ||
We provide [colab tutorial](demo/MMDet_Tutorial.ipynb), and full guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with new dataset](docs/2_new_data_model.md) for beginners. | ||
There are also tutorials for [finetuning models](docs/tutorials/finetune.md), [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [useful tools](docs/useful_tools.md). | ||
|
||
Please refer to [FAQ](docs/faq.md) for frequently asked questions. | ||
|
||
## Contributing | ||
|
||
We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. | ||
|
||
## Acknowledgement | ||
|
||
MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. | ||
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. | ||
|
||
## Citation | ||
|
||
If you use this toolbox or benchmark in your research, please cite this project. | ||
|
||
``` | ||
@article{mmdetection, | ||
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, | ||
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and | ||
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and | ||
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and | ||
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and | ||
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong | ||
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, | ||
journal= {arXiv preprint arXiv:1906.07155}, | ||
year={2019} | ||
} | ||
``` | ||
|
||
## Projects in OpenMMLab | ||
|
||
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. | ||
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. | ||
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. | ||
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. | ||
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. | ||
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. | ||
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. | ||
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. | ||
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. |
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<div align="center"> | ||
<img src="resources/mmdet-logo.png" width="600"/> | ||
</div> | ||
|
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**新闻**: 我们在 [ArXiv](https://arxiv.org/abs/1906.07155) 上公开了技术报告。 | ||
|
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文档: https://mmdetection.readthedocs.io/ | ||
|
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## 简介 | ||
|
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[English](README.md) | 简体中文 | ||
|
||
MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。 | ||
|
||
主分支代码目前支持 PyTorch 1.3 以上的版本。 | ||
|
||
v1.x 的历史版本支持 PyTorch 1.1 到 1.4,但是我们强烈建议用户使用新的 2.x 的版本,新的版本速度更快,性能更高,有更优雅的代码设计,对用户使用也更加友好。 | ||
|
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![demo image](resources/coco_test_12510.jpg) | ||
|
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### 主要特性 | ||
|
||
- **模块化设计** | ||
|
||
MMDetection 将检测框架解耦成不同的模块组件,通过组合不同的模块组件,用户可以便捷地构建自定义的检测模型 | ||
|
||
- **丰富的即插即用的算法和模型** | ||
|
||
MMDetection 支持了众多主流的和最新的检测算法,例如 Faster R-CNN,Mask R-CNN,RetinaNet 等。 | ||
|
||
- **速度快** | ||
|
||
基本的框和 mask 操作都实现了 GPU 版本,训练速度比其他代码库更快或者相当,包括 [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) 和 [SimpleDet](https://github.com/TuSimple/simpledet)。 | ||
|
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- **性能高** | ||
|
||
MMDetection 这个算法库源自于 COCO 2018 目标检测竞赛的冠军团队 *MMDet* 团队开发的代码,我们在之后持续进行了改进和提升。 | ||
|
||
除了 MMDetection 之外,我们还开源了计算机视觉基础库 [MMCV](https://github.com/open-mmlab/mmcv),MMCV 是 MMDetection 的主要依赖。 | ||
|
||
## 开源许可证 | ||
|
||
该项目采用 [Apache 2.0 开源许可证](LICENSE)。 | ||
|
||
## 更新日志 | ||
|
||
最新的月度版本 v2.11.0 在 2021.04.01 发布。 | ||
如果想了解更多版本更新细节和历史信息,请阅读[更新日志](docs/changelog.md)。 | ||
在[兼容性说明文档](docs/compatibility.md)中我们提供了 1.x 和 2.0 版本的详细比较。 | ||
|
||
## 基准测试和模型库 | ||
|
||
测试结果和模型可以在[模型库](docs/model_zoo.md)中找到。 | ||
|
||
已支持的骨干网络: | ||
|
||
- [x] ResNet (CVPR'2016) | ||
- [x] ResNeXt (CVPR'2017) | ||
- [x] VGG (ICLR'2015) | ||
- [x] HRNet (CVPR'2019) | ||
- [x] RegNet (CVPR'2020) | ||
- [x] Res2Net (TPAMI'2020) | ||
- [x] ResNeSt (ArXiv'2020) | ||
|
||
已支持的算法: | ||
|
||
- [x] [RPN (NeurIPS'2015)](configs/rpn) | ||
- [x] [Fast R-CNN (ICCV'2015)](configs/fast_rcnn) | ||
- [x] [Faster R-CNN (NeurIPS'2015)](configs/faster_rcnn) | ||
- [x] [Mask R-CNN (ICCV'2017)](configs/mask_rcnn) | ||
- [x] [Cascade R-CNN (CVPR'2018)](configs/cascade_rcnn) | ||
- [x] [Cascade Mask R-CNN (CVPR'2018)](configs/cascade_rcnn) | ||
- [x] [SSD (ECCV'2016)](configs/ssd) | ||
- [x] [RetinaNet (ICCV'2017)](configs/retinanet) | ||
- [x] [GHM (AAAI'2019)](configs/ghm) | ||
- [x] [Mask Scoring R-CNN (CVPR'2019)](configs/ms_rcnn) | ||
- [x] [Double-Head R-CNN (CVPR'2020)](configs/double_heads) | ||
- [x] [Hybrid Task Cascade (CVPR'2019)](configs/htc) | ||
- [x] [Libra R-CNN (CVPR'2019)](configs/libra_rcnn) | ||
- [x] [Guided Anchoring (CVPR'2019)](configs/guided_anchoring) | ||
- [x] [FCOS (ICCV'2019)](configs/fcos) | ||
- [x] [RepPoints (ICCV'2019)](configs/reppoints) | ||
- [x] [Foveabox (TIP'2020)](configs/foveabox) | ||
- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor) | ||
- [x] [NAS-FPN (CVPR'2019)](configs/nas_fpn) | ||
- [x] [ATSS (CVPR'2020)](configs/atss) | ||
- [x] [FSAF (CVPR'2019)](configs/fsaf) | ||
- [x] [PAFPN (CVPR'2018)](configs/pafpn) | ||
- [x] [Dynamic R-CNN (ECCV'2020)](configs/dynamic_rcnn) | ||
- [x] [PointRend (CVPR'2020)](configs/point_rend) | ||
- [x] [CARAFE (ICCV'2019)](configs/carafe/README.md) | ||
- [x] [DCNv2 (CVPR'2019)](configs/dcn/README.md) | ||
- [x] [Group Normalization (ECCV'2018)](configs/gn/README.md) | ||
- [x] [Weight Standardization (ArXiv'2019)](configs/gn+ws/README.md) | ||
- [x] [OHEM (CVPR'2016)](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py) | ||
- [x] [Soft-NMS (ICCV'2017)](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py) | ||
- [x] [Generalized Attention (ICCV'2019)](configs/empirical_attention/README.md) | ||
- [x] [GCNet (ICCVW'2019)](configs/gcnet/README.md) | ||
- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md) | ||
- [x] [InstaBoost (ICCV'2019)](configs/instaboost/README.md) | ||
- [x] [GRoIE (ICPR'2020)](configs/groie/README.md) | ||
- [x] [DetectoRS (ArXix'2020)](configs/detectors/README.md) | ||
- [x] [Generalized Focal Loss (NeurIPS'2020)](configs/gfl/README.md) | ||
- [x] [CornerNet (ECCV'2018)](configs/cornernet/README.md) | ||
- [x] [Side-Aware Boundary Localization (ECCV'2020)](configs/sabl/README.md) | ||
- [x] [YOLOv3 (ArXiv'2018)](configs/yolo/README.md) | ||
- [x] [PAA (ECCV'2020)](configs/paa/README.md) | ||
- [x] [YOLACT (ICCV'2019)](configs/yolact/README.md) | ||
- [x] [CentripetalNet (CVPR'2020)](configs/centripetalnet/README.md) | ||
- [x] [VFNet (ArXix'2020)](configs/vfnet/README.md) | ||
- [x] [DETR (ECCV'2020)](configs/detr/README.md) | ||
- [x] [CascadeRPN (NeurIPS'2019)](configs/cascade_rpn/README.md) | ||
- [x] [SCNet (AAAI'2021)](configs/scnet/README.md) | ||
|
||
我们在[基于 MMDetection 的项目](./docs/projects.md)中列举了一些其他的支持的算法。 | ||
|
||
## 安装 | ||
|
||
请参考[快速入门文档](docs/get_started.md)进行安装。 | ||
|
||
## 快速入门 | ||
|
||
请参考[快速入门文档](docs/get_started.md)学习 MMDetection 的基本使用。 | ||
我们提供了 [colab 教程](demo/MMDet_Tutorial.ipynb),也为新手提供了完整的运行教程,分别针对[已有数据集](docs/1_exist_data_model.md)和[新数据集](docs/2_new_data_model.md) 完整的使用指南 | ||
|
||
我们也提供了一些进阶教程,内容覆盖了 [finetune 模型](docs/tutorials/finetune.md),[增加新数据集支持](docs/tutorials/new_dataset.md),[设计新的数据预处理流程](docs/tutorials/data_pipeline.md),[增加自定义模型](ocs/tutorials/customize_models.md),[增加自定义的运行时配置](docs/tutorials/customize_runtime.md),[常用工具和脚本](docs/useful_tools.md)。 | ||
|
||
如果遇到问题,请参考 [FAQ 页面](docs/faq.md)。 | ||
|
||
## 贡献指南 | ||
|
||
我们感谢所有的贡献者为改进和提升 MMDetection 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。 | ||
|
||
## 致谢 | ||
|
||
MMDetection 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者,以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具,供用户复现已有算法并开发自己的新模型,从而不断为开源社区提供贡献。 | ||
|
||
## 引用 | ||
|
||
如果你在研究中使用了本项目的代码或者性能基准,请参考如下 bibtex 引用 MMDetection。 | ||
|
||
``` | ||
@article{mmdetection, | ||
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, | ||
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and | ||
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and | ||
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and | ||
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and | ||
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong | ||
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, | ||
journal= {arXiv preprint arXiv:1906.07155}, | ||
year={2019} | ||
} | ||
``` | ||
|
||
## OpenMMLab 的其他项目 | ||
|
||
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库 | ||
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱 | ||
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱 | ||
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台 | ||
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱 | ||
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱 | ||
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台 | ||
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱 | ||
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱 |
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## imTED | ||
|
||
Code of [Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection](https://arxiv.org/abs/2205.09613). | ||
|
||
The Code is based on [mmdetection](https://github.com/open-mmlab/mmdetection), please refer to [get_started.md](docs/en/get_started.md) and [MMDET_README.md](MMDET_README.md) to set up the environment and prepare the data. | ||
|
||
## Config Files and Performance | ||
|
||
We provide 9 configuration files in the configs directory. | ||
|
||
| Config File | Backbone | Epochs | Box AP | Mask AP | | ||
| :--------------------------------------------------------------------------------: | :---------: | :-------: | :---------: | :-------: | | ||
| configs/imted/imted_faster_rcnn_vit_small_3x_coco.py | ViT-S | 36 | 48.2 | | | ||
| configs/imted/imted_faster_rcnn_vit_base_3x_coco.py | ViT-B | 36 | 52.9 | | | ||
| configs/imted/imted_faster_rcnn_vit_large_3x_coco.py | ViT-L | 36 | 55.4 | | | ||
| configs/imted/imted_mask_rcnn_vit_small_3x_coco.py | ViT-S | 36 | 48.7 | 42.7 | | ||
| configs/imted/imted_mask_rcnn_vit_base_3x_coco.py | ViT-B | 36 | 53.3 | 46.4 | | ||
| configs/imted/imted_mask_rcnn_vit_large_3x_coco.py | ViT-L | 36 | 55.5 | 48.1 | | ||
| configs/imted/few_shot/imted_faster_rcnn_vit_base_2x_base_training_coco.py | ViT-B | 24 | 50.6 | | | ||
| configs/imted/few_shot/imted_faster_rcnn_vit_base_2x_finetuning_10shot_coco.py | ViT-B | 108 | 22.5 | | | ||
| configs/imted/few_shot/imted_faster_rcnn_vit_base_2x_finetuning_30shot_coco.py | ViT-B | 108 | 30.2 | | | ||
|
||
## MAE Pre-training | ||
|
||
The pre-trained model is trained with the [official MAE code](https://github.com/facebookresearch/mae). | ||
For ViT-S, we use a 4-layer decoder with dimension 256 for 800 epochs of pre-training. | ||
For ViT-B, we use an 8-layer decoder with dimension 512 for 1600 epochs of pre-training. Pre-trained weights can be downloaded from the [official MAE weight](https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base_full.pth). | ||
For ViT-L, we use an 8-layer decoder with dimension 512 for 1600 epochs of pre-training. Pre-trained weights can be downloaded from the [official MAE weight](https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large_full.pth). | ||
|
||
## Last Step before Training | ||
For all experiments, remember to modify the path of pre-trained weights in the configuration files, e.g. configs/imted/imted_faster_rcnn_vit_small_3x_coco.py. | ||
|
||
For few-shot experiments, please refer to [FsDet](https://github.com/ucbdrive/few-shot-object-detection/blob/master/datasets/README.md#:~:text=2%2C%20and%203.-,COCO%3A,-cocosplit/%0A%20%20datasplit/%0A%20%20%20%20trainvalno5k) for data preparation. Remember to modify the path of json in the configuration files, e.g. configs/imted/few_shot/imted_faster_rcnn_vit_base_2x_base_training_coco.py. | ||
|
||
## Training with 8 GPUs | ||
|
||
```bash | ||
tools/dist_train.sh "path/to/config/file.py" 8 | ||
``` |
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dataset_type = 'CityscapesDataset' | ||
data_root = 'data/cityscapes/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True), | ||
dict( | ||
type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(2048, 1024), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=1, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type='RepeatDataset', | ||
times=8, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/instancesonly_filtered_gtFine_train.json', | ||
img_prefix=data_root + 'leftImg8bit/train/', | ||
pipeline=train_pipeline)), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/instancesonly_filtered_gtFine_val.json', | ||
img_prefix=data_root + 'leftImg8bit/val/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/instancesonly_filtered_gtFine_test.json', | ||
img_prefix=data_root + 'leftImg8bit/test/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(interval=1, metric='bbox') |
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dataset_type = 'CityscapesDataset' | ||
data_root = 'data/cityscapes/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | ||
dict( | ||
type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(2048, 1024), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=1, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type='RepeatDataset', | ||
times=8, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/instancesonly_filtered_gtFine_train.json', | ||
img_prefix=data_root + 'leftImg8bit/train/', | ||
pipeline=train_pipeline)), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/instancesonly_filtered_gtFine_val.json', | ||
img_prefix=data_root + 'leftImg8bit/val/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/instancesonly_filtered_gtFine_test.json', | ||
img_prefix=data_root + 'leftImg8bit/test/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(metric=['bbox', 'segm']) |
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dataset_type = 'CocoDataset' | ||
data_root = 'data/coco/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True), | ||
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(1333, 800), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_train2017.json', | ||
img_prefix=data_root + 'train2017/', | ||
pipeline=train_pipeline), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(interval=1, metric='bbox') |
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dataset_type = 'CocoDataset' | ||
data_root = 'data/coco/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | ||
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(1333, 800), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_train2017.json', | ||
img_prefix=data_root + 'train2017/', | ||
pipeline=train_pipeline), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(metric=['bbox', 'segm']) |
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dataset_type = 'CocoDataset' | ||
data_root = 'data/coco/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), | ||
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='SegRescale', scale_factor=1 / 8), | ||
dict(type='DefaultFormatBundle'), | ||
dict( | ||
type='Collect', | ||
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(1333, 800), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_train2017.json', | ||
img_prefix=data_root + 'train2017/', | ||
seg_prefix=data_root + 'stuffthingmaps/train2017/', | ||
pipeline=train_pipeline), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(metric=['bbox', 'segm']) |
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# dataset settings | ||
dataset_type = 'DeepFashionDataset' | ||
data_root = 'data/DeepFashion/In-shop/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | ||
dict(type='Resize', img_scale=(750, 1101), keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(750, 1101), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
imgs_per_gpu=2, | ||
workers_per_gpu=1, | ||
train=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', | ||
img_prefix=data_root + 'Img/', | ||
pipeline=train_pipeline, | ||
data_root=data_root), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', | ||
img_prefix=data_root + 'Img/', | ||
pipeline=test_pipeline, | ||
data_root=data_root), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + | ||
'annotations/DeepFashion_segmentation_gallery.json', | ||
img_prefix=data_root + 'Img/', | ||
pipeline=test_pipeline, | ||
data_root=data_root)) | ||
evaluation = dict(interval=5, metric=['bbox', 'segm']) |
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_base_ = 'coco_instance.py' | ||
dataset_type = 'LVISV05Dataset' | ||
data_root = 'data/lvis_v0.5/' | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
_delete_=True, | ||
type='ClassBalancedDataset', | ||
oversample_thr=1e-3, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/lvis_v0.5_train.json', | ||
img_prefix=data_root + 'train2017/')), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/lvis_v0.5_val.json', | ||
img_prefix=data_root + 'val2017/'), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/lvis_v0.5_val.json', | ||
img_prefix=data_root + 'val2017/')) | ||
evaluation = dict(metric=['bbox', 'segm']) |
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_base_ = 'coco_instance.py' | ||
dataset_type = 'LVISV1Dataset' | ||
data_root = 'data/lvis_v1/' | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
_delete_=True, | ||
type='ClassBalancedDataset', | ||
oversample_thr=1e-3, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/lvis_v1_train.json', | ||
img_prefix=data_root)), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/lvis_v1_val.json', | ||
img_prefix=data_root), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/lvis_v1_val.json', | ||
img_prefix=data_root)) | ||
evaluation = dict(metric=['bbox', 'segm']) |
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# dataset settings | ||
dataset_type = 'VOCDataset' | ||
data_root = 'data/VOCdevkit/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True), | ||
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(1000, 600), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=32), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type='RepeatDataset', | ||
times=3, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=[ | ||
data_root + 'VOC2007/ImageSets/Main/trainval.txt', | ||
data_root + 'VOC2012/ImageSets/Main/trainval.txt' | ||
], | ||
img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'], | ||
pipeline=train_pipeline)), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', | ||
img_prefix=data_root + 'VOC2007/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', | ||
img_prefix=data_root + 'VOC2007/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(interval=1, metric='mAP') |
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# dataset settings | ||
dataset_type = 'WIDERFaceDataset' | ||
data_root = 'data/WIDERFace/' | ||
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) | ||
train_pipeline = [ | ||
dict(type='LoadImageFromFile', to_float32=True), | ||
dict(type='LoadAnnotations', with_bbox=True), | ||
dict( | ||
type='PhotoMetricDistortion', | ||
brightness_delta=32, | ||
contrast_range=(0.5, 1.5), | ||
saturation_range=(0.5, 1.5), | ||
hue_delta=18), | ||
dict( | ||
type='Expand', | ||
mean=img_norm_cfg['mean'], | ||
to_rgb=img_norm_cfg['to_rgb'], | ||
ratio_range=(1, 4)), | ||
dict( | ||
type='MinIoURandomCrop', | ||
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), | ||
min_crop_size=0.3), | ||
dict(type='Resize', img_scale=(300, 300), keep_ratio=False), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(300, 300), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=False), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
data = dict( | ||
samples_per_gpu=60, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type='RepeatDataset', | ||
times=2, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'train.txt', | ||
img_prefix=data_root + 'WIDER_train/', | ||
min_size=17, | ||
pipeline=train_pipeline)), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'val.txt', | ||
img_prefix=data_root + 'WIDER_val/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'val.txt', | ||
img_prefix=data_root + 'WIDER_val/', | ||
pipeline=test_pipeline)) |
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checkpoint_config = dict(interval=1) | ||
# yapf:disable | ||
log_config = dict( | ||
interval=50, | ||
hooks=[ | ||
dict(type='TextLoggerHook'), | ||
# dict(type='TensorboardLoggerHook') | ||
]) | ||
# yapf:enable | ||
custom_hooks = [dict(type='NumClassCheckHook')] | ||
|
||
dist_params = dict(backend='nccl') | ||
log_level = 'INFO' | ||
load_from = None | ||
resume_from = None | ||
workflow = [('train', 1)] |
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# model settings | ||
model = dict( | ||
type='CascadeRCNN', | ||
pretrained='torchvision://resnet50', | ||
backbone=dict( | ||
type='ResNet', | ||
depth=50, | ||
num_stages=4, | ||
out_indices=(0, 1, 2, 3), | ||
frozen_stages=1, | ||
norm_cfg=dict(type='BN', requires_grad=True), | ||
norm_eval=True, | ||
style='pytorch'), | ||
neck=dict( | ||
type='FPN', | ||
in_channels=[256, 512, 1024, 2048], | ||
out_channels=256, | ||
num_outs=5), | ||
rpn_head=dict( | ||
type='RPNHead', | ||
in_channels=256, | ||
feat_channels=256, | ||
anchor_generator=dict( | ||
type='AnchorGenerator', | ||
scales=[8], | ||
ratios=[0.5, 1.0, 2.0], | ||
strides=[4, 8, 16, 32, 64]), | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[.0, .0, .0, .0], | ||
target_stds=[1.0, 1.0, 1.0, 1.0]), | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), | ||
roi_head=dict( | ||
type='CascadeRoIHead', | ||
num_stages=3, | ||
stage_loss_weights=[1, 0.5, 0.25], | ||
bbox_roi_extractor=dict( | ||
type='SingleRoIExtractor', | ||
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | ||
out_channels=256, | ||
featmap_strides=[4, 8, 16, 32]), | ||
bbox_head=[ | ||
dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
reg_class_agnostic=True, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', | ||
use_sigmoid=False, | ||
loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, | ||
loss_weight=1.0)), | ||
dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.05, 0.05, 0.1, 0.1]), | ||
reg_class_agnostic=True, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', | ||
use_sigmoid=False, | ||
loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, | ||
loss_weight=1.0)), | ||
dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.033, 0.033, 0.067, 0.067]), | ||
reg_class_agnostic=True, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', | ||
use_sigmoid=False, | ||
loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) | ||
], | ||
mask_roi_extractor=dict( | ||
type='SingleRoIExtractor', | ||
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), | ||
out_channels=256, | ||
featmap_strides=[4, 8, 16, 32]), | ||
mask_head=dict( | ||
type='FCNMaskHead', | ||
num_convs=4, | ||
in_channels=256, | ||
conv_out_channels=256, | ||
num_classes=80, | ||
loss_mask=dict( | ||
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), | ||
# model training and testing settings | ||
train_cfg=dict( | ||
rpn=dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.7, | ||
neg_iou_thr=0.3, | ||
min_pos_iou=0.3, | ||
match_low_quality=True, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=256, | ||
pos_fraction=0.5, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=False), | ||
allowed_border=0, | ||
pos_weight=-1, | ||
debug=False), | ||
rpn_proposal=dict( | ||
nms_pre=2000, | ||
max_per_img=2000, | ||
nms=dict(type='nms', iou_threshold=0.7), | ||
min_bbox_size=0), | ||
rcnn=[ | ||
dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.5, | ||
neg_iou_thr=0.5, | ||
min_pos_iou=0.5, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
mask_size=28, | ||
pos_weight=-1, | ||
debug=False), | ||
dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.6, | ||
neg_iou_thr=0.6, | ||
min_pos_iou=0.6, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
mask_size=28, | ||
pos_weight=-1, | ||
debug=False), | ||
dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.7, | ||
neg_iou_thr=0.7, | ||
min_pos_iou=0.7, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
mask_size=28, | ||
pos_weight=-1, | ||
debug=False) | ||
]), | ||
test_cfg=dict( | ||
rpn=dict( | ||
nms_pre=1000, | ||
max_per_img=1000, | ||
nms=dict(type='nms', iou_threshold=0.7), | ||
min_bbox_size=0), | ||
rcnn=dict( | ||
score_thr=0.05, | ||
nms=dict(type='nms', iou_threshold=0.5), | ||
max_per_img=100, | ||
mask_thr_binary=0.5))) |
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# model settings | ||
model = dict( | ||
type='CascadeRCNN', | ||
pretrained='torchvision://resnet50', | ||
backbone=dict( | ||
type='ResNet', | ||
depth=50, | ||
num_stages=4, | ||
out_indices=(0, 1, 2, 3), | ||
frozen_stages=1, | ||
norm_cfg=dict(type='BN', requires_grad=True), | ||
norm_eval=True, | ||
style='pytorch'), | ||
neck=dict( | ||
type='FPN', | ||
in_channels=[256, 512, 1024, 2048], | ||
out_channels=256, | ||
num_outs=5), | ||
rpn_head=dict( | ||
type='RPNHead', | ||
in_channels=256, | ||
feat_channels=256, | ||
anchor_generator=dict( | ||
type='AnchorGenerator', | ||
scales=[8], | ||
ratios=[0.5, 1.0, 2.0], | ||
strides=[4, 8, 16, 32, 64]), | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[.0, .0, .0, .0], | ||
target_stds=[1.0, 1.0, 1.0, 1.0]), | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), | ||
roi_head=dict( | ||
type='CascadeRoIHead', | ||
num_stages=3, | ||
stage_loss_weights=[1, 0.5, 0.25], | ||
bbox_roi_extractor=dict( | ||
type='SingleRoIExtractor', | ||
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | ||
out_channels=256, | ||
featmap_strides=[4, 8, 16, 32]), | ||
bbox_head=[ | ||
dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
reg_class_agnostic=True, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', | ||
use_sigmoid=False, | ||
loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, | ||
loss_weight=1.0)), | ||
dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.05, 0.05, 0.1, 0.1]), | ||
reg_class_agnostic=True, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', | ||
use_sigmoid=False, | ||
loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, | ||
loss_weight=1.0)), | ||
dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.033, 0.033, 0.067, 0.067]), | ||
reg_class_agnostic=True, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', | ||
use_sigmoid=False, | ||
loss_weight=1.0), | ||
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) | ||
]), | ||
# model training and testing settings | ||
train_cfg=dict( | ||
rpn=dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.7, | ||
neg_iou_thr=0.3, | ||
min_pos_iou=0.3, | ||
match_low_quality=True, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=256, | ||
pos_fraction=0.5, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=False), | ||
allowed_border=0, | ||
pos_weight=-1, | ||
debug=False), | ||
rpn_proposal=dict( | ||
nms_pre=2000, | ||
max_per_img=2000, | ||
nms=dict(type='nms', iou_threshold=0.7), | ||
min_bbox_size=0), | ||
rcnn=[ | ||
dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.5, | ||
neg_iou_thr=0.5, | ||
min_pos_iou=0.5, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
pos_weight=-1, | ||
debug=False), | ||
dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.6, | ||
neg_iou_thr=0.6, | ||
min_pos_iou=0.6, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
pos_weight=-1, | ||
debug=False), | ||
dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.7, | ||
neg_iou_thr=0.7, | ||
min_pos_iou=0.7, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
pos_weight=-1, | ||
debug=False) | ||
]), | ||
test_cfg=dict( | ||
rpn=dict( | ||
nms_pre=1000, | ||
max_per_img=1000, | ||
nms=dict(type='nms', iou_threshold=0.7), | ||
min_bbox_size=0), | ||
rcnn=dict( | ||
score_thr=0.05, | ||
nms=dict(type='nms', iou_threshold=0.5), | ||
max_per_img=100))) |
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# model settings | ||
model = dict( | ||
type='FastRCNN', | ||
pretrained='torchvision://resnet50', | ||
backbone=dict( | ||
type='ResNet', | ||
depth=50, | ||
num_stages=4, | ||
out_indices=(0, 1, 2, 3), | ||
frozen_stages=1, | ||
norm_cfg=dict(type='BN', requires_grad=True), | ||
norm_eval=True, | ||
style='pytorch'), | ||
neck=dict( | ||
type='FPN', | ||
in_channels=[256, 512, 1024, 2048], | ||
out_channels=256, | ||
num_outs=5), | ||
roi_head=dict( | ||
type='StandardRoIHead', | ||
bbox_roi_extractor=dict( | ||
type='SingleRoIExtractor', | ||
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | ||
out_channels=256, | ||
featmap_strides=[4, 8, 16, 32]), | ||
bbox_head=dict( | ||
type='Shared2FCBBoxHead', | ||
in_channels=256, | ||
fc_out_channels=1024, | ||
roi_feat_size=7, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
reg_class_agnostic=False, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
loss_bbox=dict(type='L1Loss', loss_weight=1.0))), | ||
# model training and testing settings | ||
train_cfg=dict( | ||
rcnn=dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.5, | ||
neg_iou_thr=0.5, | ||
min_pos_iou=0.5, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
pos_weight=-1, | ||
debug=False)), | ||
test_cfg=dict( | ||
rcnn=dict( | ||
score_thr=0.05, | ||
nms=dict(type='nms', iou_threshold=0.5), | ||
max_per_img=100))) |
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@@ -0,0 +1,112 @@ | ||
# model settings | ||
norm_cfg = dict(type='BN', requires_grad=False) | ||
model = dict( | ||
type='FasterRCNN', | ||
pretrained='open-mmlab://detectron2/resnet50_caffe', | ||
backbone=dict( | ||
type='ResNet', | ||
depth=50, | ||
num_stages=3, | ||
strides=(1, 2, 2), | ||
dilations=(1, 1, 1), | ||
out_indices=(2, ), | ||
frozen_stages=1, | ||
norm_cfg=norm_cfg, | ||
norm_eval=True, | ||
style='caffe'), | ||
rpn_head=dict( | ||
type='RPNHead', | ||
in_channels=1024, | ||
feat_channels=1024, | ||
anchor_generator=dict( | ||
type='AnchorGenerator', | ||
scales=[2, 4, 8, 16, 32], | ||
ratios=[0.5, 1.0, 2.0], | ||
strides=[16]), | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[.0, .0, .0, .0], | ||
target_stds=[1.0, 1.0, 1.0, 1.0]), | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | ||
loss_bbox=dict(type='L1Loss', loss_weight=1.0)), | ||
roi_head=dict( | ||
type='StandardRoIHead', | ||
shared_head=dict( | ||
type='ResLayer', | ||
depth=50, | ||
stage=3, | ||
stride=2, | ||
dilation=1, | ||
style='caffe', | ||
norm_cfg=norm_cfg, | ||
norm_eval=True), | ||
bbox_roi_extractor=dict( | ||
type='SingleRoIExtractor', | ||
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), | ||
out_channels=1024, | ||
featmap_strides=[16]), | ||
bbox_head=dict( | ||
type='BBoxHead', | ||
with_avg_pool=True, | ||
roi_feat_size=7, | ||
in_channels=2048, | ||
num_classes=80, | ||
bbox_coder=dict( | ||
type='DeltaXYWHBBoxCoder', | ||
target_means=[0., 0., 0., 0.], | ||
target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
reg_class_agnostic=False, | ||
loss_cls=dict( | ||
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
loss_bbox=dict(type='L1Loss', loss_weight=1.0))), | ||
# model training and testing settings | ||
train_cfg=dict( | ||
rpn=dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.7, | ||
neg_iou_thr=0.3, | ||
min_pos_iou=0.3, | ||
match_low_quality=True, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=256, | ||
pos_fraction=0.5, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=False), | ||
allowed_border=0, | ||
pos_weight=-1, | ||
debug=False), | ||
rpn_proposal=dict( | ||
nms_pre=12000, | ||
max_per_img=2000, | ||
nms=dict(type='nms', iou_threshold=0.7), | ||
min_bbox_size=0), | ||
rcnn=dict( | ||
assigner=dict( | ||
type='MaxIoUAssigner', | ||
pos_iou_thr=0.5, | ||
neg_iou_thr=0.5, | ||
min_pos_iou=0.5, | ||
match_low_quality=False, | ||
ignore_iof_thr=-1), | ||
sampler=dict( | ||
type='RandomSampler', | ||
num=512, | ||
pos_fraction=0.25, | ||
neg_pos_ub=-1, | ||
add_gt_as_proposals=True), | ||
pos_weight=-1, | ||
debug=False)), | ||
test_cfg=dict( | ||
rpn=dict( | ||
nms_pre=6000, | ||
max_per_img=1000, | ||
nms=dict(type='nms', iou_threshold=0.7), | ||
min_bbox_size=0), | ||
rcnn=dict( | ||
score_thr=0.05, | ||
nms=dict(type='nms', iou_threshold=0.5), | ||
max_per_img=100))) |
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