def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_context_preserving_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_panoptic_deeplab_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def main(_A: argparse.Namespace):
# Get the current device as set for current distributed process.
# Check `launch` function in `virtex.utils.distributed` module.
device = torch.cuda.current_device()
# Local process group is needed for detectron2.
pg = list(range(dist.get_world_size()))
d2.utils.comm._LOCAL_PROCESS_GROUP = torch.distributed.new_group(pg)
# Create a config object (this will be immutable) and perform common setup
# such as logging and setting up serialization directory.
if _A.weight_init == "imagenet":
_A.config_override.extend(["MODEL.VISUAL.PRETRAINED", True])
_C = Config(_A.config, _A.config_override)
# We use `default_setup` from detectron2 to do some common setup, such as
# logging, setting up serialization etc. For more info, look into source.
_D2C = build_detectron2_config(_C, _A)
default_setup(_D2C, _A)
# Prepare weights to pass in instantiation call of trainer.
if _A.weight_init in {"virtex", "torchvision"}:
if _A.resume:
# If resuming training, let detectron2 load weights by providing path.
model = None
weights = _A.checkpoint_path
else:
# Load backbone weights from VirTex pretrained checkpoint.
model = PretrainingModelFactory.from_config(_C)
if _A.weight_init == "virtex":
CheckpointManager(model=model).load(_A.checkpoint_path)
else:
model.visual.cnn.load_state_dict(
torch.load(_A.checkpoint_path, map_location="cpu")["state_dict"],
strict=False,
)
weights = model.visual.detectron2_backbone_state_dict()
else:
# If random or imagenet init, just load weights after initializing model.
model = PretrainingModelFactory.from_config(_C)
weights = model.visual.detectron2_backbone_state_dict()
# Back up pretrain config and model checkpoint (if provided).
_C.dump(os.path.join(_A.serialization_dir, "pretrain_config.yaml"))
if _A.weight_init == "virtex" and not _A.resume:
torch.save(
model.state_dict(),
os.path.join(_A.serialization_dir, "pretrain_model.pth"),
)
del model
trainer = DownstreamTrainer(_D2C, weights)
trainer.test() if _A.eval_only else trainer.train()
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
set_global_cfg(cfg)
default_setup(cfg, args)
return cfg
def setup(args):
cfg = get_cfg()
add_dataset_category_config(cfg)
add_densepose_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "densepose" module
setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="densepose")
return cfg
def setup(args):
"""
V.Bernasconi create COCOart
"""
keypoint_names = [
"nose", "left_eye", "right_eye", "left_ear", "right_ear",
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
"left_wrist", "right_wrist", "left_hip", "right_hip", "left_knee",
"right_knee", "left_ankle", "right_ankle"
]
keypoint_flip_map = {
'left_eye': 'right_eye',
'left_ear': 'right_ear',
'left_shoulder': 'right_shoulder',
'left_elbow': 'right_elbow',
'left_wrist': 'right_wrist',
'left_hip': 'right_hip',
'left_knee': 'right_knee',
'left_ankle': 'right_ankle'
}
register_coco_instances("cocoArt", {},
json_path + "person_keypoints_train2017.json",
img_path + "train2017/")
cocoArt_metadata = MetadataCatalog.get("cocoArt")
cocoArt_dicts = DatasetCatalog.get("cocoArt")
register_coco_instances("cocoArtTest", {},
json_path + "person_keypoints_val2017.json",
img_path + "val2017/")
cocoArtTest_metadata = MetadataCatalog.get("cocoArtTest")
cocoArtTest_dicts = DatasetCatalog.get("cocoArtTest")
cocoArt_metadata.keypoint_names = keypoint_names
cocoArt_metadata.keypoint_flip_map = keypoint_flip_map
cocoArtTest_metadata.keypoint_names = keypoint_names
cocoArtTest_metadata.keypoint_flip_map = keypoint_flip_map
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.DATASETS.TRAIN = ("cocoArt", )
cfg.DATASETS.TEST = ("cocoArtTest", )
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x")
cfg.TEST.EVAL_PERIOD = 100
#cfg.OUTPUT_DIR = "./output_RCNN_R_50_FPN_1x"
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup():
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.MODEL.DEVICE = 'cpu'
add_vovnet_config(cfg)
cfg.merge_from_file('projects/sku110/configs/faster_rcnn_ShuffleNetv2_05_FPNLite_1x.yaml')
cfg.SOLVER.IMS_PER_BATCH = 1
cfg.freeze()
default_setup(cfg, {})
return cfg
def setup(args):
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
setup_logger(output=cfg.OUTPUT_DIR,
distributed_rank=comm.get_rank(),
name="openlogo")
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
# To turn off defgrid, comment the line underneath
add_defgrid_maskhead_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def main(args):
cfg = LazyConfig.load(args.config_file)
cfg = LazyConfig.apply_overrides(cfg, args.opts)
default_setup(cfg, args)
if args.eval_only:
model = instantiate(cfg.model)
model = create_ddp_model(model)
DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
print(do_test(cfg, model))
else:
do_train(args, cfg)
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file) # 从config file 覆盖配置
cfg.merge_from_list(args.opts) # 从CLI参数 覆盖配置
plain_register_dataset()
# 更改配置参数
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_config(args, cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(switch_extract_mode(args.extract_mode))
cfg.merge_from_list(set_min_max_boxes(args.min_max_boxes))
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup():
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.MODEL.DEVICE = 'cpu'
add_vovnet_config(cfg)
cfg.merge_from_file(
'projects/vovnet_sku110/configs/faster_rcnn_V_19_FPN_1x.yaml')
cfg.freeze()
default_setup(cfg, {})
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_attribute_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.merge_from_list(switch_extract_mode(args.extract_mode))
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.OUTPUT_DIR = args.config_file.replace("configs/", "output/")[:-5]
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
cfg = get_cfg() # 默认配置
add_multitask_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "multitask" module
setup_logger(output=cfg.OUTPUT_DIR,
distributed_rank=comm.get_rank(),
name="multitask")
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(
cfg, args
) # if you don't like any of the default setup, write your own setup code
return cfg
def setup(args):
cfg = get_cfg()
add_theis_config(cfg)
cfg.merge_from_file('./configs/Miro-CAE-reconstruct.yaml')
download_kodak()
register_kodak()
cfg.freeze()
default_setup(cfg, args)
setup_logger(output=cfg.OUTPUT_DIR,
distributed_rank=comm.get_rank(),
name="cae")
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_config(args, cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.MODE = args.mode
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
out_dir = args.config_file.split(os.sep)[-1].rsplit('.', 1)[0]
cfg = get_cfg()
cfg.OUTPUT_DIR = os.path.join('./output', out_dir)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def main(args):
"""Main program entry point."""
# Generate detectron2 config from command line arguments.
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# The configuration file should not contain any datasets. They are configured
# from command line arguments instead.
if len(cfg.DATASETS.TRAIN) > 0 or len(cfg.DATASETS.TEST) > 0:
logging.error("Please set DATASETS.TRAIN = () and DATASETS.TEST = ().")
sys.exit(1)
cfg.DATASETS.TRAIN = (TRAIN_SPLIT_NAME, )
cfg.DATASETS.TEST = (VALID_SPLIT_NAME, )
cfg.freeze()
default_setup(cfg, args)
# Register synthetic sign datasets.
if args.image_width is not None or args.image_height is not None:
if args.image_width is None or args.image_height is None:
logging.error(
"Please specify both, image-width and image-height (or none).")
sys.exit(1)
image_shape = args.image_height, args.image_width
else:
image_shape = None
register_synthetic_signs(args.train_csv,
args.label_map,
cfg,
name=TRAIN_SPLIT_NAME,
image_shape=image_shape)
if args.valid_csv is not None:
register_synthetic_signs(args.valid_csv,
args.label_map,
cfg,
name=VALID_SPLIT_NAME,
image_shape=image_shape)
# Run training or evaluation.
if args.eval_only:
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
return res
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
def setup(args):
cfg = get_cfg()
get_sparseplane_cfg_defaults(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "meshrcnn" module
setup_logger(output=cfg.OUTPUT_DIR,
distributed_rank=comm.get_rank(),
name="planercnn")
return cfg
def set_ddp_seed(seed=0, outdir=None, CUDNN_BENCHMARK=False):
from template_lib.v2.config_cfgnode import TLCfgNode
import argparse
d2_cfg = TLCfgNode()
d2_cfg.SEED = seed
d2_cfg.OUTPUT_DIR = outdir
d2_cfg.CUDNN_BENCHMARK = CUDNN_BENCHMARK
args = argparse.Namespace()
default_setup(d2_cfg, args)
pass
def setup_pascal_voc(args):
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file('PascalVOC-Detection/faster_rcnn_R_50_C4.yaml'))
cfg.DATASETS.TEST = ('voc_2007_test_2012_eval',)
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
"PascalVOC-Detection/faster_rcnn_R_50_C4.yaml") # Let training initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.freeze()
default_setup(cfg, args)
setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="PascalVOCModel")
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
# 添加一些参数cfg.MODEL.ONENET.xxx 用于onenet 的配置
add_onenet_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_retinaface_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.MODEL.META_ARCHITECTURE = 'RetinaFaceDeploy'
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# customize reszied parameters
# cfg['INPUT']['MIN_SIZE_TRAIN'] = (20,)
# cfg['INPUT']['MAX_SIZE_TRAIN'] = 50
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "densepose" module
setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="pathway_parser")
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_attribute_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# force the final residual block to have dilations 1
cfg.MODEL.RESNETS.RES5_DILATION = 1
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_detr_config(cfg)
cfg.merge_from_file(
'../detr/d2/configs/detr_segm_256_6_6_torchvision.yaml')
cfg.merge_from_list(args.opts)
cfg = setup_dataset(cfg, args.split)
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# self define
# cfg.OUTPUT_DIR = os.path.join(cfg.OUTPUT_DIR, cfg.OUTPUT.TRAIN_VERSION + str(datetime.now()))
cfg.OUTPUT_DIR = os.path.join(cfg.OUTPUT_DIR, cfg.OUTPUT.TRAIN_VERSION)
cfg.freeze()
default_setup(cfg, args)
return cfg
