def set_configuration( self, cfg_in ):
cfg = self.get_configuration()
cfg.merge_config( cfg_in )
self._net_config = str( cfg.get_value( "net_config" ) )
self._weight_file = str( cfg.get_value( "weight_file" ) )
self._class_names = str( cfg.get_value( "class_names" ) )
self._thresh = float( cfg.get_value( "thresh" ) )
self._gpu_index = str( cfg.get_value( "gpu_index" ) )
from mmdet.models import build_detector
from mmcv.runner import load_checkpoint
self._cfg = mmcv.Config.fromfile( self._net_config )
self._cfg.model.pretrained = None
self._model = build_detector( self._cfg.model, test_cfg=self._cfg.test_cfg )
_ = load_checkpoint( self._model, self._weight_file )
def main(args):
cfg = Config.fromfile(args.config)
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.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
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)
# if cfg.weights2d_path[0] is not None and cfg.load_from is None:
# expand_sd = pretrain2d_to_3d(model, cfg.weights2d_path[0])
# model.load_state_dict(expand_sd, strict=False)
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
validate = cfg.get('evaluation', False)
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=validate,
logger=logger)
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
if cfg.model.get('neck'):
if cfg.model.neck.get('rfp_backbone'):
if cfg.model.neck.rfp_backbone.get('pretrained'):
cfg.model.neck.rfp_backbone.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
print(f"loaded checkpoint from {args.checkpoint}")
if args.fuse_conv_bn:
model = fuse_module(model)
# 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
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
args.show_score_thr)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print(f'\nwriting results to {args.out}')
mmcv.dump(outputs, args.out)
kwargs = {} if args.options is None else args.options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, **kwargs)
def measure_inference_speed(cfg, checkpoint, max_iter, log_interval,
is_fuse_conv_bn):
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# build the dataloader
samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
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=1,
# Because multiple processes will occupy additional CPU resources,
# FPS statistics will be more unstable when workers_per_gpu is not 0.
# It is reasonable to set workers_per_gpu to 0.
workers_per_gpu=0,
dist=True,
shuffle=False)
# 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)
load_checkpoint(model, checkpoint, map_location='cpu')
if is_fuse_conv_bn:
model = fuse_conv_bn(model)
model = MMDistributedDataParallel(model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
model.eval()
# the first several iterations may be very slow so skip them
num_warmup = 5
pure_inf_time = 0
fps = 0
# benchmark with 2000 image and take the average
for i, data in enumerate(data_loader):
torch.cuda.synchronize()
start_time = time.perf_counter()
with torch.no_grad():
model(return_loss=False, rescale=True, **data)
torch.cuda.synchronize()
elapsed = time.perf_counter() - start_time
if i >= num_warmup:
pure_inf_time += elapsed
if (i + 1) % log_interval == 0:
fps = (i + 1 - num_warmup) / pure_inf_time
print(
f'Done image [{i + 1:<3}/ {max_iter}], '
f'fps: {fps:.1f} img / s, '
f'times per image: {1000 / fps:.1f} ms / img',
flush=True)
if (i + 1) == max_iter:
fps = (i + 1 - num_warmup) / pure_inf_time
print(
f'Overall fps: {fps:.1f} img / s, '
f'times per image: {1000 / fps:.1f} ms / img',
flush=True)
break
return fps
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.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)
datasets = [build_dataset(cfg.data.train)]
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
if cfg.load_from:
checkpoint = load_checkpoint(model, cfg.load_from, map_location='cpu')
model.CLASSES = datasets[0].CLASSES
if cfg.load_from:
checkpoint = load_checkpoint(model, cfg.load_from, map_location='cpu')
model.CLASSES = datasets[0].CLASSES
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)
data_loader = build_dataloader(datasets[0],
imgs_per_gpu=cfg.data.imgs_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
num_gpus=cfg.gpus,
dist=False,
shuffle=False)
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
model.train()
if hasattr(model, 'module'):
model_load = model.module
optimizer_all = obj_from_dict(cfg.optimizer, torch.optim,
dict(params=model_load.parameters()))
optimizer = obj_from_dict(cfg.optimizer, torch.optim,
dict(params=model_load.agg.parameters()))
check_video = None
start_epoch = 0
meta = None
epoch = start_epoch
vis = visdom.Visdom(env='fuse_c')
loss_cls_window = vis.line(X=torch.zeros((1, )).cpu(),
Y=torch.zeros((1)).cpu(),
opts=dict(xlabel='minibatches',
ylabel='Loss of classification',
title='Loss of classification ',
legend=['Loss of classification']))
loss_init_window = vis.line(X=torch.zeros((1, )).cpu(),
Y=torch.zeros((1)).cpu(),
opts=dict(xlabel='minibatches',
ylabel='Loss of init reppoint',
title='Loss of init reppoint',
legend=['Loss of init reppoint']))
loss_refine_window = vis.line(X=torch.zeros((1, )).cpu(),
Y=torch.zeros((1)).cpu(),
opts=dict(xlabel='minibatches',
ylabel='Loss of refine reppoint',
title='Loss of refine reppoint',
legend=['Loss of refine reppoint'
]))
loss_total_window = vis.line(X=torch.zeros((1, )).cpu(),
Y=torch.zeros((1)).cpu(),
opts=dict(xlabel='minibatches',
ylabel='Loss all',
title='Loss all',
legend=['Loss all']))
loss_trans_window = vis.line(X=torch.zeros((1, )).cpu(),
Y=torch.zeros((1)).cpu(),
opts=dict(xlabel='minibatches',
ylabel='Loss trans',
title='Loss trans',
legend=['Loss trans']))
training_sample = 0
for e in range(cfg.total_epochs):
i = 0
if epoch % 1 == 0:
if meta is None:
meta = dict(epoch=epoch + 1, iter=i)
else:
meta.update(epoch=epoch + 1, iter=i)
checkpoint = {
'meta': meta,
'state_dict': weights_to_cpu(model.state_dict())
}
print()
if optimizer_all is not None:
checkpoint['optimizer'] = optimizer_all.state_dict()
if not os.path.exists(cfg.work_dir):
os.mkdir(cfg.work_dir)
filename = os.path.join(cfg.work_dir, 'epoch_{}.pth'.format(epoch))
torch.save(checkpoint, filename)
for i, data in enumerate(data_loader):
# if len(data['gt_bboxes'].data[0][0]) == 0:
# continue
optimizer.zero_grad()
optimizer_all.zero_grad()
reference_id = (data['img_meta'].data[0][0]['filename'].split('/')
[-1]).split('.')[0]
video_id = data['img_meta'].data[0][0]['filename'].split('/')[-2]
print('start image:', data['img_meta'].data[0][0]['filename'])
print('end image:', data['img_meta'].data[-1][-1]['filename'])
# print(len(data['img'].data),len(data['img'].data[0]))
# exit()
for m in range(len(data['img_meta'].data)):
start_name = data['img_meta'].data[m][0]['filename'].split(
'/')[-2]
# print(data['img_meta'].data[m][0]['filename'])
for n in range(len(data['img_meta'].data[m])):
check_name = data['img_meta'].data[m][n]['filename'].split(
'/')[-2]
# print(data['img_meta'].data[m][n]['filename'])
if start_name != check_name:
print('end of video')
data['img_meta'].data[m][n] = data['img_meta'].data[m][
0]
data['gt_bboxes'].data[m][n] = data['gt_bboxes'].data[
m][0]
data['gt_labels'].data[m][n] = data['gt_labels'].data[
m][0]
data['img'].data[m][n] = data['img'].data[m][0]
# losses,loss_trans=model(return_loss=True, **data)
losses = model(return_loss=True, **data)
# print(losses)
if isinstance(losses, list):
loss_all = []
log = []
for p in range(len(losses)):
# print(p)
# print(losses[p])
loss, log_var = parse_losses(losses[p])
loss_all.append(loss)
log.append(log_var)
else:
losses, log_vars = parse_losses(losses)
if isinstance(losses, list):
losses = loss_all[0] + 0.5 * loss_all[1] + 0.5 * loss_all[
2] + 0.5 * loss_all[3]
losses = losses / 2.5
# print(loss_trans.shape)
# loss_trans=torch.mean(loss_trans)*0.1
# losses=losses+loss_trans
# if losses.item()>10:
# losses.backward(retain_graph=False)
# optimizer.zero_grad()
# continue
losses.backward()
if epoch < 10:
optimizer.step()
else:
optimizer_all.step()
# if training_sample<700:
# optimizer.step()
# else:
# optimizer_all.step()
# print('transform kernel check',model.module.agg.trans_kernel.sum().item())
log_vars = log[0]
vis.line(X=torch.ones(1).cpu() * training_sample,
Y=(log_vars['loss_cls']) * torch.ones(1).cpu(),
win=loss_cls_window,
update='append')
vis.line(X=torch.ones(1).cpu() * training_sample,
Y=(log_vars['loss_pts_init']) * torch.ones(1).cpu(),
win=loss_init_window,
update='append')
vis.line(X=torch.ones(1).cpu() * training_sample,
Y=(log_vars['loss_pts_refine']) * torch.ones(1).cpu(),
win=loss_refine_window,
update='append')
vis.line(X=torch.ones(1).cpu() * training_sample,
Y=(losses).item() * torch.ones(1).cpu(),
win=loss_total_window,
update='append')
# vis.line(
# X=torch.ones(1).cpu() * training_sample,
# Y=loss_trans.item() * torch.ones(1).cpu(),
# win=loss_trans_window,
# update='append')
print('agg')
print('epoch:',epoch,'index:',i,'video_id:',video_id,'reference_id:',reference_id, \
'loss_cls:',log_vars['loss_cls'],'loss_init_box:',log_vars['loss_pts_init'], \
'loss_refine_box:',log_vars['loss_pts_refine'])
log_vars = log[1]
print('refer')
print('epoch:',epoch,'index:',i,'video_id:',video_id,'reference_id:',reference_id, \
'loss_cls:',log_vars['loss_cls'],'loss_init_box:',log_vars['loss_pts_init'], \
'loss_refine_box:',log_vars['loss_pts_refine'])
log_vars = log[2]
print('support')
print('epoch:',epoch,'index:',i,'video_id:',video_id,'reference_id:',reference_id, \
'loss_cls:',log_vars['loss_cls'],'loss_init_box:',log_vars['loss_pts_init'], \
'loss_refine_box:',log_vars['loss_pts_refine'])
training_sample += 1
# if i % 300 == 0:
# if meta is None:
# meta = dict(epoch=epoch + 1, iter=i)
# else:
# meta.update(epoch=epoch + 1, iter=i)
# checkpoint = {
# 'meta': meta,
# 'state_dict': weights_to_cpu(model.state_dict())
# }
# if optimizer_all is not None:
# checkpoint['optimizer'] = optimizer_all.state_dict()
# if not os.path.exists(cfg.work_dir):
# os.mkdir(cfg.work_dir)
# filename=os.path.join(cfg.work_dir,'epoch_{}_{}.pth'.format(epoch,i))
# torch.save(checkpoint,filename)
epoch += 1
def main(): # noqa: C901
"""Start test."""
args = parse_args()
if args.work_dir is not None:
mmcv.mkdir_or_exist(args.work_dir)
if args.tmpdir is None:
args.tmpdir = osp.join(args.work_dir, 'tmp_dir')
mmcv.mkdir_or_exist(args.tmpdir)
if args.out is None:
args.out = osp.join(args.work_dir, 'result.pkl')
if args.checkpoint is None:
args.checkpoint = osp.join(args.work_dir, 'latest.pth')
fps_file = osp.join(args.work_dir, 'fps.pkl')
mAP_file = osp.join(args.work_dir, 'mAP.pkl')
else:
mAP_file, fps_file = None, None
if args.checkpoint is None:
raise ValueError('Checkpoint file cannot be empty.')
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)
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
cfg.data.test.test_mode = True
if args.dist:
init_dist('pytorch', **cfg.dist_params)
# build the dataloader
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=True,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, map_location='cpu')
model.CLASSES = dataset.CLASSES
if args.dist:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
else:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, fps_file)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
assert not isinstance(outputs[0], dict)
result_files = results2json(dataset, outputs, args.out)
coco_eval(result_files,
eval_types,
dataset.coco,
dump_file=mAP_file)
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show \
or args.show_dir, \
('Please specify at least one operation (save/eval/format/show the '
'results / save the results) with the argument "--out", "--eval"'
', "--format-only", "--show" or "--show-dir"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# import modules from string list.
if cfg.get('custom_imports', None):
from mmcv.utils import import_modules_from_strings
import_modules_from_strings(**cfg['custom_imports'])
# 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
# in case the test dataset is concatenated
if isinstance(cfg.data.val, dict):
cfg.data.val.test_mode = True
elif isinstance(cfg.data.val, list):
for ds_cfg in cfg.data.val:
ds_cfg.test_mode = True
# 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)
# build the dataloader
samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
if samples_per_gpu > 1:
# Replace 'ImageToTensor' to 'DefaultFormatBundle'
cfg.data.val.pipeline = replace_ImageToTensor(cfg.data.val.pipeline)
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.fuse_conv_bn:
model = fuse_conv_bn(model)
# 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
if not distributed:
model = MMDataParallel(model, device_ids=[0])
# import pickle
# outputs = pickle.load(open('kitti.pkl','rb'))
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
args.show_score_thr)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print(f'\nwriting results to {args.out}')
mmcv.dump(outputs, args.out)
kwargs = {} if args.eval_options is None else args.eval_options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
eval_kwargs = cfg.get('evaluation', {}).copy()
# hard-code way to remove EvalHook args
for key in ['interval', 'tmpdir', 'start', 'gpu_collect']:
eval_kwargs.pop(key, None)
eval_kwargs.update(dict(metric=args.eval, **kwargs))
print(dataset.evaluate(outputs, **eval_kwargs))
pred_img_path = os.path.join(output_dir, pred_img_name)
# Ensure path exists.
if not os.path.exists(pred_list_path):
os.makedirs(pred_list_path)
if not os.path.exists(pred_img_path):
os.makedirs(pred_img_path)
cfg = mmcv.Config.fromfile(
'/nfs/project/libo_i/mmdetection/configs/KITTI/cascade_mask_rcnn_x101_64x4d_fpn_1x.py'
)
cfg.model.pretrained = None
# construct the model and load checkpoint
model = build_detector(cfg.model, test_cfg=cfg.test_cfg)
_ = load_checkpoint(
model,
'/nfs/project/libo_i/mmdetection/work_dirs/kitti_cascade_mask_rcnn_x101_64x4d_fpn_1x/epoch_24.pth'
)
for ind, result in enumerate(
inference_detector(
model,
list(
map(lambda item: os.path.join(test_image_path, item),
test_image_list)),
cfg,
device='cuda:0')):
text_save_name = "{}.txt".format(test_image_list[ind][:-4])
text_save_path = os.path.join(pred_list_path, text_save_name)
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.imgs_per_gpu > 0:
cfg.data.imgs_per_gpu = args.imgs_per_gpu
# 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)
if hasattr(cfg, 'fuse') and cfg.fuse:
train_dataset = get_dataset(cfg.datasets[0].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
datasets = list()
for flow in cfg.workflow:
mode, epoches = flow
cur_datasets = list()
for dataset_cfg in cfg.datasets:
if hasattr(dataset_cfg, mode):
cur_datasets.append(get_dataset(getattr(dataset_cfg,
mode)))
datasets.append(ConcatDataset(cur_datasets))
val_dataset = None
if cfg.data.train.get('val_every', None):
val_dataset = list()
for dataset_cfg in cfg.datasets:
if hasattr(dataset_cfg, 'val'):
val_dataset.append(get_dataset(dataset_cfg.val))
val_dataset = ConcatDataset(val_dataset)
if hasattr(cfg.model,
'smpl_head') and cfg.model.smpl_head.loss_cfg.get(
'adversarial_cfg', False):
train_adv_smpl_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
create_dummy=args.create_dummy,
val_dataset=val_dataset,
load_pretrain=args.load_pretrain,
)
else:
train_smpl_detector_fuse(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
create_dummy=args.create_dummy,
val_dataset=val_dataset,
load_pretrain=args.load_pretrain)
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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# log some basic info
logger.info('Distributed training: {}'.format(distributed))
logger.info('MMDetection Version: {}'.format(__version__))
logger.info('Config:\n{}'.format(cfg.text))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}, deterministic: {}'.format(
args.seed, args.deterministic))
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.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,
timestamp=timestamp)
def test_sparse_rcnn_forward():
config_path = 'sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py'
model = _get_detector_cfg(config_path)
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
detector.init_weights()
input_shape = (1, 3, 100, 100)
mm_inputs = _demo_mm_inputs(input_shape, num_items=[5])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train with non-empty truth batch
detector.train()
gt_bboxes = mm_inputs['gt_bboxes']
gt_bboxes = [item for item in gt_bboxes]
gt_labels = mm_inputs['gt_labels']
gt_labels = [item for item in gt_labels]
losses = detector.forward(imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
detector.forward_dummy(imgs)
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_bboxes = mm_inputs['gt_bboxes']
gt_bboxes = [item for item in gt_bboxes]
gt_labels = mm_inputs['gt_labels']
gt_labels = [item for item in gt_labels]
losses = detector.forward(imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
rescale=True,
return_loss=False)
batch_results.append(result)
# test empty proposal in roi_head
with torch.no_grad():
# test no proposal in the whole batch
detector.roi_head.simple_test([imgs[0][None, :]], torch.empty(
(1, 0, 4)), torch.empty((1, 100, 4)), [img_metas[0]],
torch.ones((1, 4)))
def test_two_stage_forward(cfg_file):
models_with_semantic = [
'htc/htc_r50_fpn_1x_coco.py',
'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py',
'scnet/scnet_r50_fpn_20e_coco.py',
]
if cfg_file in models_with_semantic:
with_semantic = True
else:
with_semantic = False
model = _get_detector_cfg(cfg_file)
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
# Save cost
if cfg_file in [
'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
]:
model.roi_head.bbox_head.num_classes = 80
model.roi_head.bbox_head.loss_cls.num_classes = 80
model.roi_head.mask_head.num_classes = 80
model.test_cfg.rcnn.score_thr = 0.05
model.test_cfg.rcnn.max_per_img = 100
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 128, 128)
# Test forward train with a non-empty truth batch
mm_inputs = _demo_mm_inputs(input_shape,
num_items=[10],
with_semantic=with_semantic)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
loss.requires_grad_(True)
assert float(loss.item()) > 0
loss.backward()
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(input_shape,
num_items=[0],
with_semantic=with_semantic)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
loss.requires_grad_(True)
assert float(loss.item()) > 0
loss.backward()
# Test RoI forward train with an empty proposals
if cfg_file in [
'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py' # noqa: E501
]:
mm_inputs.pop('gt_semantic_seg')
feature = detector.extract_feat(imgs[0][None, :])
losses = detector.roi_head.forward_train(feature, img_metas,
[torch.empty(
(0, 5))], **mm_inputs)
assert isinstance(losses, dict)
# Test forward test
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
cascade_models = [
'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
'htc/htc_r50_fpn_1x_coco.py',
'scnet/scnet_r50_fpn_20e_coco.py',
]
# test empty proposal in roi_head
with torch.no_grad():
# test no proposal in the whole batch
detector.simple_test(imgs[0][None, :], [img_metas[0]],
proposals=[torch.empty((0, 4))])
# test no proposal of aug
features = detector.extract_feats([imgs[0][None, :]] * 2)
detector.roi_head.aug_test(features, [torch.empty((0, 4))] * 2,
[[img_metas[0]]] * 2)
# test rcnn_test_cfg is None
if cfg_file not in cascade_models:
feature = detector.extract_feat(imgs[0][None, :])
bboxes, scores = detector.roi_head.simple_test_bboxes(
feature, [img_metas[0]], [torch.empty((0, 4))], None)
assert all([bbox.shape == torch.Size((0, 4)) for bbox in bboxes])
assert all([
score.shape == torch.Size(
(0, detector.roi_head.bbox_head.fc_cls.out_features))
for score in scores
])
# test no proposal in the some image
x1y1 = torch.randint(1, 100, (10, 2)).float()
# x2y2 must be greater than x1y1
x2y2 = x1y1 + torch.randint(1, 100, (10, 2))
detector.simple_test(
imgs[0][None, :].repeat(2, 1, 1, 1), [img_metas[0]] * 2,
proposals=[torch.empty((0, 4)),
torch.cat([x1y1, x2y2], dim=-1)])
# test no proposal of aug
detector.roi_head.aug_test(
features, [torch.cat([x1y1, x2y2], dim=-1),
torch.empty((0, 4))], [[img_metas[0]]] * 2)
# test rcnn_test_cfg is None
if cfg_file not in cascade_models:
feature = detector.extract_feat(imgs[0][None, :].repeat(
2, 1, 1, 1))
bboxes, scores = detector.roi_head.simple_test_bboxes(
feature, [img_metas[0]] * 2,
[torch.empty((0, 4)),
torch.cat([x1y1, x2y2], dim=-1)], None)
assert bboxes[0].shape == torch.Size((0, 4))
assert scores[0].shape == torch.Size(
(0, detector.roi_head.bbox_head.fc_cls.out_features))
def test_two_stage_forward(cfg_file):
models_with_semantic = [
'htc/htc_r50_fpn_1x_coco.py',
'scnet/scnet_r50_fpn_20e_coco.py',
]
if cfg_file in models_with_semantic:
with_semantic = True
else:
with_semantic = False
model = _get_detector_cfg(cfg_file)
model['pretrained'] = None
# Save cost
if cfg_file in [
'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
]:
model.roi_head.bbox_head.num_classes = 80
model.roi_head.bbox_head.loss_cls.num_classes = 80
model.roi_head.mask_head.num_classes = 80
model.test_cfg.rcnn.score_thr = 0.05
model.test_cfg.rcnn.max_per_img = 100
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 256, 256)
# Test forward train with a non-empty truth batch
mm_inputs = _demo_mm_inputs(input_shape,
num_items=[10],
with_semantic=with_semantic)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
loss.requires_grad_(True)
assert float(loss.item()) > 0
loss.backward()
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(input_shape,
num_items=[0],
with_semantic=with_semantic)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
loss.requires_grad_(True)
assert float(loss.item()) > 0
loss.backward()
# Test forward test
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
def main():
args = parse_args()
assert args.out or args.show or args.json_out, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
while not osp.isfile(args.checkpoint):
print('Waiting for {} to exist...'.format(args.checkpoint))
time.sleep(60)
checkpoint = load_checkpoint(model, args.checkpoint, 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
# assert not distributed
if not distributed:
model = MMDataParallel(model, device_ids=[0])
# data_loader.dataset.img_infos = data_loader.dataset.img_infos[:10]
outputs = single_gpu_test(model, data_loader)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
if dataset.ann_file == 'data/coco/annotations/image_info_test-dev2017.json':
result_files = results2json_segm(dataset,
outputs,
args.out,
dump=True)
else:
result_files = results2json_segm(dataset,
outputs,
args.out,
dump=False)
if 'lvis' in dataset.ann_file: ## an ugly fix to make it compatible with coco eval
from lvis import LVISEval
lvisEval = LVISEval(cfg.data.test.ann_file,
result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
#fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(
iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset,
outputs_,
result_file,
dump=False)
coco_eval(result_files, eval_types, dataset.coco)
##eval on lvis-77######
cfg.data.test.ann_file = 'data/lvis/lvis_v0.5_val_cocofied.json'
cfg.data.test.img_prefix = 'data/lvis/val2017/'
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
# data_loader.dataset.img_infos = data_loader.dataset.img_infos[:10]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset,
outputs,
'xxx',
dump=False)
from lvis import LVISEval
lvisEval = LVISEval(
'data/lvis/lvis_v0.5_val_cocofied.json', result_files,
'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(
iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset,
outputs_,
result_file,
dump=False)
coco_eval(result_files, eval_types, dataset.coco)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
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)
def test_config_build_detector():
"""
Test that all detection models defined in the configs can be initialized.
"""
from xdoctest.utils import import_module_from_path
from mmdet.models import build_detector
config_dpath = _get_config_directory()
print('Found config_dpath = {!r}'.format(config_dpath))
# import glob
# config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
# config_names = [relpath(p, config_dpath) for p in config_fpaths]
# Only tests a representative subset of configurations
config_names = [
# 'dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/faster_rcnn_dpool_r50_fpn_1x.py',
'dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py',
# 'dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/faster_rcnn_mdpool_r50_fpn_1x.py',
# 'dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py',
# 'dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py',
# ---
# 'htc/htc_x101_32x4d_fpn_20e_16gpu.py',
'htc/htc_without_semantic_r50_fpn_1x.py',
# 'htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py',
# 'htc/htc_x101_64x4d_fpn_20e_16gpu.py',
# 'htc/htc_r50_fpn_1x.py',
# 'htc/htc_r101_fpn_20e.py',
# 'htc/htc_r50_fpn_20e.py',
# ---
'cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py',
# 'cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py',
# ---
# 'scratch/scratch_faster_rcnn_r50_fpn_gn_6x.py',
# 'scratch/scratch_mask_rcnn_r50_fpn_gn_6x.py',
# ---
# 'grid_rcnn/grid_rcnn_gn_head_x101_32x4d_fpn_2x.py',
'grid_rcnn/grid_rcnn_gn_head_r50_fpn_2x.py',
# ---
'double_heads/dh_faster_rcnn_r50_fpn_1x.py',
# ---
'empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py',
# 'empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x.py',
# 'empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x.py',
# 'empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py',
# ---
# 'ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py',
# 'ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py',
# 'ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py',
# ---
# 'guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py',
# 'guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py',
# 'guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py',
# 'guided_anchoring/ga_fast_r50_caffe_fpn_1x.py',
# 'guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py',
# 'guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py',
# 'guided_anchoring/ga_faster_r50_caffe_fpn_1x.py',
'guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py',
# ---
'foveabox/fovea_r50_fpn_4gpu_1x.py',
# 'foveabox/fovea_align_gn_ms_r101_fpn_4gpu_2x.py',
# 'foveabox/fovea_align_gn_r50_fpn_4gpu_2x.py',
# 'foveabox/fovea_align_gn_r101_fpn_4gpu_2x.py',
'foveabox/fovea_align_gn_ms_r50_fpn_4gpu_2x.py',
# ---
# 'hrnet/cascade_rcnn_hrnetv2p_w32_20e.py',
# 'hrnet/mask_rcnn_hrnetv2p_w32_1x.py',
# 'hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e.py',
# 'hrnet/htc_hrnetv2p_w32_20e.py',
# 'hrnet/faster_rcnn_hrnetv2p_w18_1x.py',
# 'hrnet/mask_rcnn_hrnetv2p_w18_1x.py',
# 'hrnet/faster_rcnn_hrnetv2p_w32_1x.py',
# 'hrnet/faster_rcnn_hrnetv2p_w40_1x.py',
'hrnet/fcos_hrnetv2p_w32_gn_1x_4gpu.py',
# ---
# 'gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py',
# 'gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py',
'gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py',
# 'gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py',
# ---
# 'wider_face/ssd300_wider_face.py',
# ---
'pascal_voc/ssd300_voc.py',
'pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py',
'pascal_voc/ssd512_voc.py',
# ---
# 'gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x.py',
# 'gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x.py',
# 'gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x.py',
# 'gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x.py',
'gcnet/mask_rcnn_r50_fpn_sbn_1x.py',
# ---
'gn/mask_rcnn_r50_fpn_gn_contrib_2x.py',
# 'gn/mask_rcnn_r50_fpn_gn_2x.py',
# 'gn/mask_rcnn_r101_fpn_gn_2x.py',
# ---
# 'reppoints/reppoints_moment_x101_dcn_fpn_2x.py',
'reppoints/reppoints_moment_r50_fpn_2x.py',
# 'reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py',
'reppoints/reppoints_partial_minmax_r50_fpn_1x.py',
'reppoints/bbox_r50_grid_center_fpn_1x.py',
# 'reppoints/reppoints_moment_r101_dcn_fpn_2x.py',
# 'reppoints/reppoints_moment_r101_fpn_2x_mt.py',
# 'reppoints/reppoints_moment_r50_fpn_2x_mt.py',
'reppoints/reppoints_minmax_r50_fpn_1x.py',
# 'reppoints/reppoints_moment_r50_fpn_1x.py',
# 'reppoints/reppoints_moment_r101_fpn_2x.py',
# 'reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py',
'reppoints/bbox_r50_grid_fpn_1x.py',
# ---
# 'fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py',
# 'fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py',
'fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py',
# ---
'albu_example/mask_rcnn_r50_fpn_1x.py',
# ---
'libra_rcnn/libra_faster_rcnn_r50_fpn_1x.py',
# 'libra_rcnn/libra_retinanet_r50_fpn_1x.py',
# 'libra_rcnn/libra_faster_rcnn_r101_fpn_1x.py',
# 'libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x.py',
# 'libra_rcnn/libra_fast_rcnn_r50_fpn_1x.py',
# ---
# 'ghm/retinanet_ghm_r50_fpn_1x.py',
# ---
# 'fp16/retinanet_r50_fpn_fp16_1x.py',
'fp16/mask_rcnn_r50_fpn_fp16_1x.py',
'fp16/faster_rcnn_r50_fpn_fp16_1x.py'
]
print('Using {} config files'.format(len(config_names)))
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
config_mod = import_module_from_path(config_fpath)
config_mod.model
config_mod.train_cfg
config_mod.test_cfg
print('Building detector, config_fpath = {!r}'.format(config_fpath))
# Remove pretrained keys to allow for testing in an offline environment
if 'pretrained' in config_mod.model:
config_mod.model['pretrained'] = None
detector = build_detector(config_mod.model,
train_cfg=config_mod.train_cfg,
test_cfg=config_mod.test_cfg)
assert detector is not None
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# import modules from string list.
if cfg.get('custom_imports', None):
from mmcv.utils import import_modules_from_strings
import_modules_from_strings(**cfg['custom_imports'])
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(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)
# re-set gpu_ids with distributed training mode
_, world_size = get_dist_info()
cfg.gpu_ids = range(world_size)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
meta['config'] = cfg.pretty_text
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(args.config)
model = build_detector(
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
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__ + get_git_hash()[:7],
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
def main():
args = parse_args()
args.eval = ['bbox'] # NOTE v-qiaofl added.
assert args.out or args.eval or args.format_only or args.show, \
('Please specify at least one operation (save/eval/format/show the '
'results) with the argument "--out", "--eval", "--format_only" '
'or "--show"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
args.checkpoint = '/data/liqiaofei/projects/od/work_dirs2/reppoints_moment_r50_fpn_1x/epoch_12.pth'
args.config = '/home/liqiaofei/mmdetection-v1.1.0/configs/reppoints/reppoints_moment_r50_fpn_1x.py'
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, 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
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
kwargs = {} if args.options is None else args.options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, **kwargs)
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
cfg.train_cfg.rcnn.sampler.add_gt_as_proposals = False # Actually it doesn't matter.
if args.ckpt:
cfg.resume_from = args.ckpt
if args.imgs_per_gpu > 0:
cfg.data.imgs_per_gpu = args.imgs_per_gpu
if args.nms_thr:
cfg.test_cfg.rcnn.nms.iou_thr = args.nms_thr
FOCAL_LENGTH = cfg.get('FOCAL_LENGTH', 1000)
# 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.datasets[0].train)
train_dataset = get_dataset(cfg.datasets[1].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
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, lambda x: x, optimizer, cfg.work_dir, cfg.log_level)
runner.resume(cfg.resume_from)
model = runner.model
# ONLY FOR DEBUG
# print('remove DDP for debug!')
# model = model._modules['module']
model.eval()
dataset_cfg = eval_dataset_mapper[args.dataset]
dataset_cfg.update(cfg.common_val_cfg)
dataset_cfg.pop('max_samples')
dataset = get_dataset(dataset_cfg)
# dataset.debugging = True
shuffle = False if args.dataset in stable_list else True
data_loader = build_dataloader_fuse(
dataset,
1,
0,
cfg.gpus,
dist=False,
shuffle=shuffle,
drop_last=False,
)
dump_dir = os.path.join(cfg.work_dir, f'eval_{args.dataset}')
os.makedirs(dump_dir, exist_ok=True)
if args.viz_dir:
os.makedirs(args.viz_dir, exist_ok=True)
eval_handler = eval_handler_mapper[args.dataset](
writer=tqdm.write,
viz_dir=args.viz_dir,
FOCAL_LENGTH=FOCAL_LENGTH,
work_dir=cfg.work_dir) # type: EvalHandler
with torch.no_grad():
for i, data_batch in enumerate(tqdm(data_loader)):
file_name = data_batch['img_meta'].data[0][0]['file_name']
try:
bbox_results, pred_results = model(**data_batch,
return_loss=False,
use_gt_bboxes=args.use_gt)
pred_results['bboxes'] = bbox_results
if args.paper_dir:
os.makedirs(args.paper_dir, exist_ok=True)
img = denormalize(data_batch['img'].data[0][0].numpy())
verts = pred_results['pred_vertices'] + pred_results[
'pred_translation']
dump_folder = osp.join(args.paper_dir, file_name)
os.makedirs(dump_folder, exist_ok=True)
plt.imsave(osp.join(dump_folder, 'img.png'), img)
for obj_i, vert in enumerate(verts):
nr.save_obj(osp.join(dump_folder, f'{obj_i}.obj'),
vert,
torch.tensor(smpl.faces.astype(np.int64)))
save_pack = eval_handler(data_batch,
pred_results,
use_gt=args.use_gt)
save_pack.update({'bbox_results': pred_results['bboxes']})
if args.dump_pkl:
with open(
osp.join(dump_dir,
f"{save_pack['file_name']}.pkl"),
'wb') as f:
pickle.dump(save_pack, f)
except Exception as e:
tqdm.write(f"Fail on {file_name}")
tqdm.write(str(e))
eval_handler.finalize()
def main():
args = parse_args()
assert args.out or args.show or args.json_out, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
# cfg.data.test.test_mode = False
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
## uncomment to only eval on first 100 imgs
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
print('load model from {}'.format(cfg.load_from))
# checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
checkpoint = load_checkpoint(model, cfg.load_from, 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
# set0 = mmcv.load('../liyu_mmdet/set0.pkl')
# set1 = mmcv.load('../liyu_mmdet/set1.pkl')
# set2 = mmcv.load('../liyu_mmdet/set2.pkl')
# set3 = mmcv.load('../liyu_mmdet/set3.pkl')
# set4 = mmcv.load('../liyu_mmdet/set4.pkl')
# set5 = mmcv.load('../liyu_mmdet/set5.pkl')
# set6 = mmcv.load('../liyu_mmdet/set6.pkl')
# set7 = mmcv.load('../liyu_mmdet/set7.pkl')
# set0 = mmcv.load('./set0.pkl')
# set1 = mmcv.load('./set1.pkl')
# set2 = mmcv.load('./set2.pkl')
# set3 = mmcv.load('./set3.pkl')
# set4 = mmcv.load('./set4.pkl')
# set5 = mmcv.load('./set5.pkl')
# set6 = mmcv.load('./set6.pkl')
# set7 = mmcv.load('./set7.pkl')
# set_combine = set0+set1+set2+set3+set4+set5+set6+set7
# prefix = 'mrcnnr50_14.3_clshead'
# set0 = mmcv.load('./{}_set0.pkl'.format(prefix))
# set1 = mmcv.load('./{}_set1.pkl'.format(prefix))
# set2 = mmcv.load('./{}_set2.pkl'.format(prefix))
# set3 = mmcv.load('./{}_set3.pkl'.format(prefix))
# set_combine = set0+set1+set2+set3
# prefix = '/mrcnnr50_ag_coco_clshead'
prefix = 'mrcnnr50_ag_3fc_ft_cocolongtail_cat400_epoch_2'
prefix = 'mrcnn_r50_ag_cocolt'
print(prefix)
set0 = mmcv.load('./{}_set0.pkl'.format(prefix))
set1 = mmcv.load('./{}_set1.pkl'.format(prefix))
set2 = mmcv.load('./{}_set2.pkl'.format(prefix))
set3 = mmcv.load('./{}_set3.pkl'.format(prefix))
set4 = mmcv.load('./{}_set4.pkl'.format(prefix))
set5 = mmcv.load('./{}_set5.pkl'.format(prefix))
set6 = mmcv.load('./{}_set6.pkl'.format(prefix))
set7 = mmcv.load('./{}_set7.pkl'.format(prefix))
# set0 = mmcv.load('./set0.pkl')
# set1 = mmcv.load('./set1.pkl')
# set2 = mmcv.load('./set2.pkl')
# set3 = mmcv.load('./set3.pkl')
# set4 = mmcv.load('./set4.pkl')
# set5 = mmcv.load('./set5.pkl')
# set6 = mmcv.load('./set6.pkl')
# set7 = mmcv.load('./set7.pkl')
set_combine = set0 + set1 + set2 + set3 + set4 + set5 + set6 + set7
# set_liyu = mmcv.load('../mmdet_ensemble/results319.pkl')
# mmcv.dump(set_combine, args.out)
# result_files = results2json(dataset, set_combine,
# args.out)
print('pkl result dumped, start eval')
# result_files = results2json(dataset, set_combine,
# args.out, dump=False)
#
# lvis_eval(result_files, args.eval, dataset.lvis)
result_files = results2json(dataset, set_combine, args.out, dump=False)
coco_eval(result_files, args.eval, dataset.coco)
def load_model():
model = build_detector(cfg.model, test_cfg=cfg.test_cfg)
_ = load_checkpoint(model, model_cfgs[0][1]) # 7 it/s
return model
def main():
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpuid
img_dir = args.img_dir
out_dir = args.out_dir
batch_size = args.batch_size
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
if args.img_dir != '':
file_list = common.load_filepaths(args.img_dir,
suffix=('.jpg', '.png', '.jpeg'),
recursive=True)
elif args.img_list != '':
file_list = parse_testfile(args.img_list)
else:
raise "Both img_dir and img_list is empty."
dataset = FilesDataset(file_list, cfg.test_pipeline)
data_loader = build_dataloader(dataset,
imgs_per_gpu=batch_size,
workers_per_gpu=batch_size,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
model = reweight_cls(model, args.tau).cuda()
model = MMDataParallel(model, device_ids=[0])
model.eval()
count = 0
for i, data in enumerate(data_loader):
with torch.no_grad():
# bbox_results, segm_results
results = model(return_loss=False, rescale=True, **data)
# batch
#for result in results:
# file_path = file_list[count]
# save_name = file_path.replace('/home/songbai.xb/workspace/projects/TAO/data/TAO/frames/val/', '')
# save_path = os.path.join(out_dir, save_name)
# common.makedirs(os.path.dirname(save_path))
# save_in_tao_format(result, save_path)
# count += 1
file_path = file_list[i]
save_name = file_path.replace(
'/home/songbai.xb/workspace/projects/TAO/data/TAO/frames/val/', '')
save_name = save_name.replace('.jpg', '.pkl').replace('.jpeg', '')
save_path = os.path.join(out_dir, save_name)
common.makedirs(os.path.dirname(save_path))
save_in_tao_format(results[0], save_path)
def main():
args = parse_args()
assert args.out or args.show, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, 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
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json(dataset, outputs, args.out)
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file)
coco_eval(result_files, eval_types, dataset.coco)
def main():
args = parse_args()
# assert args.show or args.json_out, \
# ('Please specify at least one operation (save or show the results) '
# 'with the argument "--out" or "--show" or "--json_out"')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
checkpoint_file = args.checkpoint
if not checkpoint_file:
def _epoch_num(name):
return int(
re.findall('epoch_[0-9]*.pth',
name)[0].replace('epoch_', '').replace('.pth', ''))
pths = sorted(glob.glob(os.path.join(cfg.work_dir, 'epoch_*.pth')),
key=_epoch_num)
if len(pths) > 0:
print("Found {}, use it as checkpoint by default.".format(
pths[-1]))
checkpoint_file = pths[-1]
if not checkpoint_file:
raise ValueError("Checkpoints not found, check work_dir non empty.")
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=args.shuffle) # TODO: hack shuffle True
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, checkpoint_file, 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
num_evals = args.num_evals
if num_evals < 0:
num_evals = len(data_loader)
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, num_evals, args.show)
rank, _ = get_dist_info()
if rank == 0:
gt_bboxes, gt_labels, gt_ignore, dataset_name = get_pascal_gts(
dataset, num_evals)
print('\nStarting evaluate {}'.format(dataset_name))
eval_map(outputs,
gt_bboxes,
gt_labels,
gt_ignore,
scale_ranges=None,
iou_thr=0.5,
dataset=dataset_name,
print_summary=True)
# Always output to pkl for analysing.
if args.out is None:
args.out = osp.join(
cfg.work_dir,
args.config.split('/')[-1].replace('.py', '_results.pkl'))
with open(args.out, 'wb') as f:
pickle.dump(outputs, f, pickle.HIGHEST_PROTOCOL)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
def main():
args = parse_args()
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))
if args.gpus == 1:
model = build_detector(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint)
model = MMDataParallel(model, device_ids=[0])
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
num_gpus=1,
dist=False,
shuffle=False)
outputs = single_test(model, data_loader, args.show)
else:
model_args = cfg.model.copy()
model_args.update(train_cfg=None, test_cfg=cfg.test_cfg)
model_type = getattr(detectors, model_args.pop('type'))
outputs = parallel_test(model_type,
model_args,
args.checkpoint,
dataset,
_data_func,
range(args.gpus),
workers_per_gpu=args.proc_per_gpu)
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_file = args.out + '.json'
results2json(dataset, outputs, result_file)
coco_eval(result_file, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}.json'.format(name)
results2json(dataset, outputs_, result_file)
coco_eval(result_file, eval_types, dataset.coco)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# import modules from string list.
if cfg.get('custom_imports', None):
from mmcv.utils import import_modules_from_strings
import_modules_from_strings(**cfg['custom_imports'])
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# build the dataloader
samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
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=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.fuse_conv_bn:
model = fuse_conv_bn(model)
model = MMDataParallel(model, device_ids=[0])
model.eval()
# the first several iterations may be very slow so skip them
num_warmup = 5
pure_inf_time = 0
# benchmark with 2000 image and take the average
for i, data in enumerate(data_loader):
torch.cuda.synchronize()
start_time = time.perf_counter()
with torch.no_grad():
model(return_loss=False, rescale=True, **data)
torch.cuda.synchronize()
elapsed = time.perf_counter() - start_time
if i >= num_warmup:
pure_inf_time += elapsed
if (i + 1) % args.log_interval == 0:
fps = (i + 1 - num_warmup) / pure_inf_time
print(f'Done image [{i + 1:<3}/ 2000], fps: {fps:.1f} img / s')
if (i + 1) == 2000:
pure_inf_time += elapsed
fps = (i + 1 - num_warmup) / pure_inf_time
spf = 1 / fps
print(f'Overall fps: {fps:.1f} img / s')
print(f'Overall spf: {spf:.4f} s / img')
break
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
from mmcv.runner import get_dist_info
rank, world_size = get_dist_info()
if rank == 0:
print(model)
print("Model have {} paramerters.".format(
sum(x.numel() for x in model.parameters()) / 1e6))
if hasattr(model, 'backbone'):
print("Model has {} backbone.".format(
sum(x.numel() for x in model.backbone.parameters()) / 1e6))
if hasattr(model, 'neck'):
print("Model has {} neck.".format(
sum(x.numel() for x in model.neck.parameters()) / 1e6))
if hasattr(model, 'roi_head'):
print("Model has {} bbox head.".format(
sum(x.numel()
for x in model.roi_head.bbox_head.parameters()) / 1e6))
if hasattr(model, 'bbox_head'):
print("Model has {} bbox head.".format(
sum(x.numel() for x in model.bbox_head.parameters()) / 1e6))
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
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.pretty_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,
timestamp=timestamp,
meta=meta)
from mmdet.datasets import build_dataloader, build_dataset
from mmcv.runner import load_checkpoint
from mmdet.models import build_detector
from mmdet.core.evaluation import reval_map
cfg = Config.fromfile('../configs/r3det/r3det_r50_fpn_2x.py')
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(dataset,
samples_per_gpu=1,
workers_per_gpu=0,
dist=False,
shuffle=False)
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(
model,
"../work_dirs/r3det_r50_fpn_2x_20200616/epoch_24.pth",
map_location='cpu')
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader)
dataset.evaluate(outputs)
def main():
args = parse_args()
assert args.out or args.eval or args.format_only or args.show, \
('Please specify at least one operation (save/eval/format/show the '
'results) with the argument "--out", "--eval", "--format_only" '
'or "--show"')
if args.eval and args.format_only:
raise ValueError('--eval and --format_only cannot be both specified')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# load anchors
if isinstance(cfg.model, dict) and cfg.model.get('type', 'FasterRCNN') == 'MyFasterRCNN':
anchors = dict()
with open(os.path.join(cfg.work_dir, 'anchors.json'), 'r') as f:
anchors = json.load(f)
print("loaded anchors: {}\n".format(anchors))
cfg.model['anchors'] = anchors
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.fuse_conv_bn:
model = fuse_module(model)
# 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
my_config.set('classes', model.CLASSES)
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
if args.out:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
kwargs = {} if args.options is None else args.options
if args.format_only:
dataset.format_results(outputs, **kwargs)
if args.eval:
dataset.evaluate(outputs, args.eval, **kwargs)
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)
# update gpu num
if dist.is_initialized():
cfg.gpus = dist.get_world_size()
cfg.data.imgs_per_gpu = int(cfg.data.imgs_per_gpu * args.scale_bs)
cfg.data.workers_per_gpu = int(cfg.data.workers_per_gpu * args.scale_bs)
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer[
'lr'] * cfg.gpus / 8 * cfg.data.imgs_per_gpu / 2
# 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)
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,
git_hash=get_git_hash())
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
try:
logger.info(summary(model, cfg))
except RuntimeError:
logger.info('RuntimeError during summary')
logger.info(str(model))
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
logger.info('git hash: {}'.format(get_git_hash()))
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
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([('{}: {}'.format(k, v))
for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info('Distributed training: {}'.format(distributed))
logger.info('Config:\n{}'.format(cfg.text))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}, deterministic: {}'.format(
args.seed, args.deterministic))
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['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:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
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,
timestamp=timestamp,
meta=meta)
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 )
