def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
logger.info('MMDetection Version: {}'.format(__version__))
logger.info('Config: {}'.format(cfg.text))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
# model.CLASSES = ('破洞', '污渍', '三丝', '结头', '花板跳', '百脚', '毛粒', '粗经',
# '松经', '断经', '吊经', '粗维', '纬缩', '浆斑', '整经结', '星跳', '跳花', '断氨纶', '色差档',
# '磨痕', '死皱')
train_detector(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None):
logger = get_root_logger(cfg.log_level)
# start training
if distributed:
_dist_train(
model,
dataset,
cfg,
validate=validate,
logger=logger,
timestamp=timestamp)
else:
_non_dist_train(
model,
dataset,
cfg,
validate=validate,
logger=logger,
timestamp=timestamp)
def main():
# 读取命令行的参数
args = parse_args()
# 读取配置文件
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
# 如果命令行中没有设定工作空间，就按照默认的——work_dir = './work_dirs/cascade_rcnn_r50_fpn_1x'；如果有输入就更新
if args.work_dir is not None:
cfg.work_dir = args.work_dir
# 如果是在预训练的基础上继续训练，那就更新cfg，否则就按照默认的resume_from = None
if args.resume_from is not None:
cfg.resume_from = args.resume_from
# 输入的gpu数量来设置
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text)
# init distributed env first, since logger depends on the dist info.
# 如果不设置分布式的，那么distributed的值为false
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# 核心调用 build_detector,get_dataset,train_detector
# train函数的核心 —— 调用build_detector()来创建模型，将config配置文件中的数据加载到建立的模型中去，返回的是对应的网络实例化的对象
model = build_detector(
# 获得config文件的model配置数据，train的配置数据，test的配置数据
cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# 注册数据集，获得cfg中的data字典其中的train字段，也为字典类型
# 返回是一个dict，有数据集相关的数据和datasets所有的数据集标签。
train_dataset = get_dataset(cfg.data.train)
# 开始训练
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
version = local_metadata.get('version', None)
if version is None or version < 2:
# the key is different in early versions
# In version < 2, ModulatedDeformConvPack
# loads previous benchmark models.
if (prefix + 'conv_offset.weight' not in state_dict
and prefix[:-1] + '_offset.weight' in state_dict):
state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
prefix[:-1] + '_offset.weight')
if (prefix + 'conv_offset.bias' not in state_dict
and prefix[:-1] + '_offset.bias' in state_dict):
state_dict[prefix +
'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
'_offset.bias')
if version is not None and version > 1:
from mmdet.apis import get_root_logger
logger = get_root_logger()
logger.info(
'ModulatedDeformConvPack {} is upgraded to version 2.'.format(
prefix.rstrip('.')))
super()._load_from_state_dict(state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
error_msgs)
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.dcn is not None:
for m in self.modules():
if isinstance(m, Bottleneck) and hasattr(
m, 'conv2_offset'):
constant_init(m.conv2_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
else:
raise TypeError('pretrained must be a str or None')
def train(args):
cfg = Config.fromfile(args.config)
cfg.data.workers_per_gpu = args.workers_per_gpu
cfg.data.imgs_per_gpu = args.imgs_per_gpu
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
else:
args.work_dir = cfg.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
torch.save(model, args.work_dir + '/' + args.model_name + '.pth')
return
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
args.resume_from = "/home/ubuntu/code/fengda/MaskTrackRCNN/pretrained_models/epoch_12.pth"
# network_data = torch.load(args.resume_from)
load_checkpoint(model, args.resume_from)
# model.eval()
# for param in model.parameters():
# param.requires_grad = False
model.load_flow()
# model.flow_head.train()
# for param in model.flow_head.parameters():
# param.requires_grad = True
# get dataset
train_dataset = get_dataset(cfg.data.train)
print("len of dataset: {}.".format(len(train_dataset)))
# train
train_flownet(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
cfg.work_dir = cfg.work_dir + '_' + time.strftime('Time_%m%d_%H%M%S',
time.localtime())
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# log cfg
logger.info('training config:{}\n'.format(pprint.pformat(cfg._cfg_dict)))
# log git hash
logger.info('git hash: {}'.format(get_git_hash()))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = get_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
classes=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
# 在图片输入尺度固定时开启，可以加速.一般都是关的，只有在固定尺度的网络如SSD512中才开启；
# ？ cv's model in general input is fixed ?
# should test;
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
# 创建工作目录存放训练文件，如果不键入，会自动按照py配置文件生成对应的目录
cfg.work_dir = args.work_dir
if args.resume_from is not None:
# 断点继续训练的权值文件
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# ipdb.set_trace(context=35)
# 搭建模型
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# 将训练配置传入
train_dataset = build_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in checkpoints as meta data
# 要注意的是，以前发布的模型是不存这个类别等信息的，
# 用的默认COCO或者VOC参数，所以如果用以前训练好的模型检测时会提醒warning一下，无伤大雅
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES # model的CLASSES属性本来没有的，但是python不用提前声明，再赋值的时候自动定义变量
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
# parse arguments
args = parse_args()
cfg = Config.fromfile(args.config)
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.fp16:
cfg.fp16 = {'loss_scale': 512.}
if args.workers is not None:
cfg.data.workers_per_gpu = args.workers
cfg.gpus = args.gpus
if args.autoscale_lr:
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8.
ops.sys_print('Args:\n--', args)
ops.sys_print('Configs:\n--', cfg)
# init distributed env, logger and random seeds
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# build model
model = build_detector(
cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# build dataset
train_dataset = build_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
cfg.checkpoint_config.meta = {
'mmdet_version': mmdet.__version__,
'config': cfg.text,
'CLASSES': train_dataset.CLASSES}
model.CLASSES = train_dataset.CLASSES
# run training
train_detector(
model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def set_configuration(self, cfg_in):
cfg = self.get_configuration()
cfg.merge_config(cfg_in)
self._config_file = str(cfg.get_value("config_file"))
self._seed_weights = str(cfg.get_value("seed_weights"))
self._train_directory = str(cfg.get_value("train_directory"))
self._output_directory = str(cfg.get_value("output_directory"))
self._gpu_count = int(cfg.get_value("gpu_count"))
self._integer_labels = strtobool(cfg.get_value("integer_labels"))
self._launcher = str(cfg.get_value("launcher"))
self._validate = strtobool(cfg.get_value("validate"))
self._training_data = []
self._cfg = Config.fromfile(self._config_file)
if self._cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if self._train_directory is not None:
self._cfg.work_dir = self._train_directory
self._groundtruth_store = os.path.join(self._train_directory,
self._tmp_annotation_file)
if not os.path.exists(self._train_directory):
os.mkdir(self._train_directory)
else:
self._groundtruth_store = self._tmp_annotation_file
if self._seed_weights is not None:
self._cfg.resume_from = self._seed_weights
if self._gpu_count > 0:
self._cfg.gpus = self._gpu_count
else:
self._cfg.gpus = torch.cuda.device_count()
if self._cfg.checkpoint_config is not None:
self._cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=self._cfg.text)
if self._launcher == 'none':
self._distributed = False
else:
self._distributed = True
init_dist(self._launcher, **self._cfg.dist_params)
self._logger = get_root_logger(self._cfg.log_level)
self._logger.info('Distributed training: {}'.format(self._distributed))
if self._random_seed is not "none":
logger.info('Set random seed to {}'.format(self._random_seed))
set_random_seed(int(self._random_seed))
self._model = build_detector(self._cfg.model,
train_cfg=self._cfg.train_cfg,
test_cfg=self._cfg.test_cfg)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# print('cfg.data.train:',cfg.data.train)
train_dataset = build_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES
print('model.CLASSES:', model.CLASSES)
train_detector(
model,
train_dataset,
cfg,
distributed=distributed, #default : False
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg,
global_cfg=cfg)
train_dataset = [build_dataset(c) for c in cfg.data.train] \
if type(cfg.data.train) == list else \
build_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES
if hasattr(train_dataset, 'CLASSES') else None)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES if hasattr(train_dataset,
'CLASSES') else None
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
multitask=type(train_dataset) == list,
vis=True)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
cfg.work_dir = args.work_dir
cfg.gpus = args.gpus
if args.resume_from is not None:
cfg.resume_from = args.resume_from
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
train_dataset = build_dataset(cfg.data.train)
model = torch.nn.parallel.DistributedDataParallel(
model.cuda(), find_unused_parameters=True, device_ids=[args.local_rank], output_device=args.local_rank)
print(model)
print("Model have {} paramerters.".format(sum(x.numel() for x in model.parameters()) / 1e6))
print("Model have {} backbone.".format(sum(x.numel() for x in model.module.backbone.parameters()) / 1e6))
print("Model have {} neck.".format(sum(x.numel() for x in model.module.neck.parameters()) / 1e6))
print("Model have {} head.".format(sum(x.numel() for x in model.module.bbox_head.parameters()) / 1e6))
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES
train_detector(
model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.save_dir is not None:
try:
os.makedirs(args.save_dir)
except OSError:
pass
cfg.work_dir = args.save_dir
# import pdb; pdb.set_trace()
if args.load_from is not None:
cfg.resume_from = args.load_from
cfg.load_from = args.load_from
logger = get_root_logger(cfg.log_level)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
cfg.data.train.pipeline = cfg.train_pipeline
cfg.data.train.classes_to_load = args.classes
datasets = [build_dataset(cfg.data.train)]
cfg.train_cfg.rcnn.assigner.pos_iou_thr = args.fg_iou_thr
cfg.train_cfg.rcnn.assigner.min_pos_iou = args.fg_iou_thr
cfg.train_cfg.rcnn.assigner.neg_iou_thr = args.bg_iou_thr
cfg.data.imgs_per_gpu = 8
if 'val' in args.data_split:
cfg.data.val.pipeline = cfg.train_pipeline
cfg.data.val.classes_to_load = args.classes
datasets = [build_dataset(cfg.data.val)]
elif 'test' in args.data_split:
cfg.data.test.pipeline = cfg.train_pipeline
cfg.data.test.classes_to_load = args.classes
datasets = [build_dataset(cfg.data.test)]
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
extract_feats(model,
datasets,
cfg,
args.save_dir,
data_split=args.data_split,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = get_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
# 训练完成后做一次评估
import os.path as osp
checkpoint = osp.join(cfg.work_dir, 'latest.pth')
out = osp.join(cfg.work_dir, 'val_cropped_dets.pkl')
_do_dota_eval(args.config, checkpoint, out)
def main():
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
import torch.distributed as dist
dist.init_process_group('gloo', init_method='file:///tmp/somefile', rank=0, world_size=1)
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
train_dataset = get_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES
train_detector(
model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# get dataset
# from mmdet.models.decision_net.utils import modify_cfg
# video_name = '01c783268c'
# cfg.data.train = modify_cfg(cfg, video_name)
train_dataset = get_dataset(cfg.data.train)
val_dataset = get_dataset(cfg.data.val)
print("len of dataset: {}.".format(len(train_dataset)))
# train
train_detector(model, [train_dataset, val_dataset],
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def load_network(self):
from mmcv import Config
self._cfg = Config.fromfile(self._train_config)
if self._cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if self._train_directory is not None:
self._cfg.work_dir = self._train_directory
if self._seed_weights is not None:
self._cfg.load_from = self._seed_weights
if self._gpu_count is not None and self._gpu_count > 0:
self._cfg.gpus = self._gpu_count
flux_factor = self._images_per_gpu * self._gpu_count
self._cfg.optimizer['lr'] = self._cfg.optimizer['lr'] * flux_factor
if self._cfg.checkpoint_config is not None:
from mmdet import __version__
self._cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=self._cfg.text)
if self._launcher == 'none':
self._distributed = False
else:
self._distributed = True
from mmcv.runner import init_dist
init_dist(self._launcher, **self._cfg.dist_params)
from mmdet.apis import get_root_logger
self._logger = get_root_logger(self._cfg.log_level)
self._logger.info('Distributed training: {}'.format(self._distributed))
if self._random_seed is not 'none':
self._logger.info('Set random seed to {}'.format(
self._random_seed))
from mmdet.apis import set_random_seed
if isinstance(self._random_seed, int):
set_random_seed(int(self._random_seed))
from mmdet.models import build_detector
if self._cfg.model['pretrained'] is not None:
if not os.path.exists(self._cfg.model['pretrained']):
dirname = os.path.dirname(self._config_file)
relpath = os.path.join(dirname, self._cfg.model['pretrained'])
if os.path.exists(relpath):
self._cfg.model['pretrained'] = relpath
self._model = build_detector(self._cfg.model,
train_cfg=self._cfg.train_cfg,
test_cfg=self._cfg.test_cfg)
def main():
# ipdb.set_trace()
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark 在图片输入尺度固定时开启，可以加速
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
# 创建工作目录存放训练文件，如果不键入，会自动按照py配置文件生成对应的目录
cfg.work_dir = args.work_dir
if args.resume_from is not None:
# 断点继续训练的权值文件
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# if cfg.checkpoint_config is not None:
# # save mmdet version in checkpoints as meta data
# cfg.checkpoint_config.meta = dict(
# mmdet_version=__version__, config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# 模型的build和inference一样，就不多说了
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# ipdb.set_trace()
# 注意传入的是cfg.data.train
train_dataset = get_dataset(cfg.data.train)
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = get_dataset(cfg.data.train)
if hasattr(cfg, 'data2') and hasattr(cfg.data2, 'train'):
train_dataset2 = get_dataset(cfg.data2.train)
else:
train_dataset2 = None
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
train_dataset2=train_dataset2)
def main():
args = parse_args() # 解析命令行参数
cfg = Config.fromfile(args.config) # 读取配置文件
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir # checkpoint save path
if args.resume_from is not None:
cfg.resume_from = args.resume_from # checkpoint resume from path
cfg.gpus = args.gpus # gpus numbers
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none': # if laucher == none , then distributed == False means no distributed training.
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = get_dataset(
cfg.data.train
) # get dataset param: train(a dict containing configs) return a specific dataset class
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES # a tuple containing class names
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
fb_cfg = mmcv_config.fromfile(args.fb_cfg)
_space = fb_cfg.search_space
# base = _space['base']
# depth = _space['depth']
# space = _space['space']
model_cfg = mmcv_config.fromfile(args.model_cfg)
# set cudnn_benchmark
if model_cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
model_cfg.work_dir = args.work_dir
if args.resume_from is not None:
model_cfg.resume_from = args.resume_from
model_cfg.gpus = args.gpus
if model_cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
model_cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=model_cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **model_cfg.dist_params)
# init logger before other steps
logger = get_root_logger(model_cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = detection(mmcv_config(model_cfg['model_cfg']),
mmcv_config(model_cfg['train_cfg']),
mmcv_config(model_cfg['test_cfg']), _space,
args.theta_txt)
print(model)
train_dataset = get_dataset(model_cfg.data.train)
train_detector(model,
train_dataset,
model_cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__, config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# 首先要先注册 BACKBONES、 NECKS、 ROI_EXTRACTORS、 HEADS、 DETECTORS、
# 然后 BACKBONES.register_module（class SSDVGG） @HEADS.register_module(class AnchorHead)
# @HEADS.register_module(class SSDHead) @DETECTORS.register_module(class SingleStageDetector)
# 最后 build_detector() 相当于SingleStageDetector(**args)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
train_dataset = get_dataset(cfg.data.train)
train_detector(
model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# if args.aux_cls_weight is not None:
# cfg.train_cfg.aux.cls_weight = args.aux_cls_weight
# if args.aux_reg_weight is not None:
# cfg.train_cfg.aux.reg_weight = args.aux_reg_weight
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = get_dataset(cfg.data.train)
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set num_classes
cfg.model.bbox_head['num_classes'] = len(
load_classes(cfg.data_root + 'ocr.names')) + 1
print('num_classes: %d' % (cfg.model.bbox_head['num_classes']))
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
cfg.gpus = args.gpus
if cfg.checkpoint_config is not None:
# save mmdet version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = obj_from_dict(cfg.data.train, datasets)
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def copy_synthesised_weights(model,
filename,
dataset_name='voc',
split='65_15'):
logger = get_root_logger('INFO')
checkpoint = torch.load(filename, map_location='cpu')
if isinstance(checkpoint, OrderedDict):
state_dict = checkpoint
elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
raise RuntimeError(
'No state_dict found in checkpoint file {}'.format(filename))
if hasattr(model, 'module'):
own_state = model.module.state_dict()
else:
own_state = model.state_dict()
unseen_class_inds = get_unseen_class_ids(dataset=dataset_name, split=split)
seen_bg_weights = own_state['bbox_head.fc_cls.weight'][0].data.cpu().numpy(
).copy()
seen_bg_bias = own_state['bbox_head.fc_cls.bias'][0].data.cpu().numpy(
).copy()
own_state['bbox_head.fc_cls.bias'][unseen_class_inds] = state_dict[
'fc1.bias'][1:]
own_state['bbox_head.fc_cls.weight'][unseen_class_inds] = state_dict[
'fc1.weight'][1:]
alpha1 = 0.35
alpha2 = 0.65
own_state['bbox_head.fc_cls.bias'][0] = alpha1 * own_state[
'bbox_head.fc_cls.bias'][0] + alpha2 * state_dict['fc1.bias'][0]
own_state['bbox_head.fc_cls.weight'][0] = alpha1 * own_state[
'bbox_head.fc_cls.weight'][0] + alpha2 * state_dict['fc1.weight'][0]
logger.info(
f'{dataset_name} {unseen_class_inds.shape} seenbg: {alpha1} synbg: {alpha2} copied classifier weights from {filename} \n'
)
return seen_bg_weights, seen_bg_bias
def main():
"""Start train."""
args = parse_args()
if args.config.endswith(".json"):
load_method = mmcv.load
mmcv.load = json_to_dict
cfg = mmcv.Config.fromfile(args.config)
mmcv.load = load_method
else:
cfg = mmcv.Config.fromfile(args.config)
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
# init distributed env， logger and model.
init_dist('pytorch', **cfg.dist_params)
logger = get_root_logger(cfg.log_level)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=True,
logger=logger)
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.features.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.Linear):
normal_init(m, std=0.01)
else:
raise TypeError('pretrained must be a str or None')
for m in self.extra.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution='uniform')
constant_init(self.l2_norm, self.l2_norm.scale)
def set_configuration( self, cfg_in ):
cfg = self.get_configuration()
cfg.merge_config( cfg_in )
self._config_file = str( cfg.get_value( "config_file" ) )
self._seed_weights = str( cfg.get_value( "seed_weights" ) )
self._train_directory = str( cfg.get_value( "train_directory" ) )
self._output_directory = str( cfg.get_value( "output_directory" ) )
self._gpu_count = int( cfg.get_value( "gpu_count" ) )
self._integer_labels = strtobool( cfg.get_value( "integer_labels" ) )
self._launcher = str( cfg.get_value( "launcher" ) )
self._validate = strtobool( cfg.get_value( "validate" ) )
self._training_data = []
from mmcv import Config
self._cfg = Config.fromfile( self._config_file )
if self._cfg.get( 'cudnn_benchmark', False ):
torch.backends.cudnn.benchmark = True
if self._train_directory is not None:
self._cfg.work_dir = self._train_directory
self._groundtruth_store = os.path.join(
self._train_directory, self._tmp_annotation_file )
if not os.path.exists( self._train_directory ):
os.mkdir( self._train_directory )
else:
self._groundtruth_store = self._tmp_annotation_file
if self._seed_weights is not None:
self._cfg.resume_from = self._seed_weights
if self._gpu_count > 0:
self._cfg.gpus = self._gpu_count
else:
self._cfg.gpus = torch.cuda.device_count()
if self._cfg.checkpoint_config is not None:
from mmdet import __version__
self._cfg.checkpoint_config.meta = dict(
mmdet_version=__version__, config=self._cfg.text )
if self._launcher == 'none':
self._distributed = False
else:
self._distributed = True
from mmdet.apis import init_dist
init_dist( self._launcher, **self._cfg.dist_params )
from mmdet.apis import get_root_logger
self._logger = get_root_logger( self._cfg.log_level )
self._logger.info( 'Distributed training: {}'.format( self._distributed ) )
if self._random_seed is not "none":
logger.info( 'Set random seed to {}'.format( self._random_seed ) )
from mmdet.apis import set_random_seed
set_random_seed( int( self._random_seed ) )
from mmdet.models import build_detector
self._model = build_detector(
self._cfg.model, train_cfg=self._cfg.train_cfg, test_cfg=self._cfg.test_cfg )