{"id":36338,"date":"2025-05-09T09:26:55","date_gmt":"2025-05-09T01:26:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/36338.html"},"modified":"2025-05-09T09:26:55","modified_gmt":"2025-05-09T01:26:55","slug":"querydet%e6%9c%8d%e5%8a%a1%e5%99%a8%e5%a4%8d%e7%8e%b0%e8%ae%b0%e5%bd%95","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/36338.html","title":{"rendered":"querydet\u670d\u52a1\u5668\u590d\u73b0\u8bb0\u5f55"},"content":{"rendered":"

conda create -n querydet python=3.7 -y
\nsource activate querydet
\npython\u7248\u672c3.7.16<\/p>\n

CUDA\u00a0release 11.3, V11.3.58\u00a0 \u6700\u9ad812.5<\/p>\n

\u4e0b\u8f7dpytorch1.8.1<\/p>\n

conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=11.3 -c pytorch -c conda-forge <\/p>\n

\"\"<\/p>\n

\u4e00\u76f4\u5361solve<\/p>\n

\u4e0b\u8f7dpytorch1.11\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 cuda11.3<\/p>\n

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 –extra-index-url https:\/\/download.pytorch.org\/whl\/cu113 <\/p>\n

\u4e0b\u8f7d\u597dcoco visdrone\u6570\u636e\u96c6<\/p>\n

\u9884\u5904\u7406visdrone：python visdrone\/data_prepare.py –visdrone-root data\/visdrone<\/p>\n

\u5b89\u88c5CV2<\/p>\n

pip install opencv-python<\/p>\n

\u8bad\u7ec3\u62a5\u9519<\/p>\n

python train_coco.py –config-file configs\/coco\/retinanet_train.yaml –num-gpu 8 OUTPUT_DIR work_dirs\/coco_retinanet<\/p>\n<\/p>\n

Traceback (most recent call last): \u00a0 File "train_coco.py", line 3, in <module> \u00a0 \u00a0 from detectron2.engine import launch \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/engine\/__init__.py", line 11, in <module> \u00a0 \u00a0 from .hooks import * \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/engine\/hooks.py", line 18, in <module> \u00a0 \u00a0 from detectron2.evaluation.testing import flatten_results_dict \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/evaluation\/__init__.py", line 2, in <module> \u00a0 \u00a0 from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/evaluation\/cityscapes_evaluation.py", line 11, in <module> \u00a0 \u00a0 from detectron2.data import MetadataCatalog \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/data\/__init__.py", line 4, in <module> \u00a0 \u00a0 from .build import ( \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/data\/build.py", line 12, in <module> \u00a0 \u00a0 from detectron2.structures import BoxMode \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/structures\/__init__.py", line 7, in <module> \u00a0 \u00a0 from .masks import BitMasks, PolygonMasks, polygons_to_bitmask \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/structures\/masks.py", line 9, in <module> \u00a0 \u00a0 from detectron2.layers.roi_align import ROIAlign \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/layers\/__init__.py", line 3, in <module> \u00a0 \u00a0 from .deform_conv import DeformConv, ModulatedDeformConv \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/site-packages\/detectron2\/layers\/deform_conv.py", line 11, in <module> \u00a0 \u00a0 from detectron2 import _C ImportError: libcudart.so.10.2: cannot open shared object file: No such file or directory \u5e94\u8be5\u662fdectron2\u7248\u672c\u4e0d\u5bf9，\u6211\u662fcuda11.3 \u5378\u8f7dpip uninstall detectron2<\/p>\n

\u91cd\u65b0\u4e0b\u8f7d\u3010Detectron2\u3011\u8e29\u5751\u5b9e\u8bb0 – \u77e5\u4e4e git clone https:\/\/github.com\/facebookresearch\/detectron2.git python -m pip install -e detectron2<\/p>\n

\u6210\u529f\u5b89\u88c52.0.6<\/p>\n

\"\"<\/p>\n

\u8fd0\u884c\u62a5\u9519<\/p>\n

Traceback (most recent call last): \u00a0 File "train_coco.py", line 3, in <module> \u00a0 \u00a0 from detectron2.engine import launch \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/detectron2\/detectron2\/engine\/__init__.py", line 4, in <module> \u00a0 \u00a0 from .train_loop import * \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/detectron2\/detectron2\/engine\/train_loop.py", line 13, in <module> \u00a0 \u00a0 from detectron2.utils.events import EventStorage, get_event_storage \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/detectron2\/detectron2\/utils\/events.py", line 9, in <module> \u00a0 \u00a0 from functools import cached_property ImportError: cannot import name 'cached_property' from 'functools' (\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.7\/functools.py)<\/p>\n<\/p>\n

\u5e94\u8be5\u662fdectron2\u7248\u672c\u548ccuda torch\u7248\u672c\u4e0d\u5bf9<\/p>\n\n\n\n
python -m pip install detectron2 -f \\\\
\n https:\/\/dl.fbaipublicfiles.com\/detectron2\/wheels\/cu113\/torch1.10\/index.html\n <\/td>\n		<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5378\u8f7dpip uninstall detectron2<\/p>\n

\u6309\u7167deepseek\u65b9\u6848<\/p>\n

\u8fd8\u662f\u62a5\u9519<\/p>\n<\/p>\n

\u91cd\u65b0\u521b\u73af\u5883<\/p>\n

\u5b89\u88c5torch1.10 cuda11.3 python3.8\u00a0<\/p>\n

conda create -n querydet python=3.7 -y <\/p>\n

\u5b89\u88c5torch1.10.1<\/p>\n<\/p>\n

conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge <\/p>\n

\u5361solve<\/p>\n

\u5b89\u88c5\u79bb\u7ebf\u53051.10.0<\/p>\n

\"\"<\/p>\n

\u53bb\u670d\u52a1\u5668\u5b89\u88c5<\/p>\n

pip install \/home\/liuyadong\/QueryDet-PyTorch\/torch-1.10.0+cu113-cp38-cp38-linux_x86_64.whl<\/p>\n

pip install \/home\/liuyadong\/QueryDet-PyTorch\/torchvision-0.11.0+cu113-cp38-cp38-linux_x86_64.whl<\/p>\n

\u5b89\u88c5dectron2<\/p>\n\n\n\n
python -m pip install detectron2 -f \\\\
\n https:\/\/dl.fbaipublicfiles.com\/detectron2\/wheels\/cu113\/torch1.10\/index.html\n <\/td>\n		<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u672c\u5730\u4e0b\u8f7d\u597d\u5b89\u88c5<\/p>\n

\u5b89\u88c5\u5b8c\u6210<\/p>\n

\u5b89\u88c5spconv<\/p>\n

pip install spconv-cu113==2.3.6<\/p>\n

\u62a5\u9519Traceback (most recent call last): \u00a0 File "train_coco.py", line 3, in <module> \u00a0 \u00a0 from detectron2.engine import launch \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/__init__.py", line 11, in <module> \u00a0 \u00a0 from .hooks import * \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/hooks.py", line 22, in <module> \u00a0 \u00a0 from detectron2.evaluation.testing import flatten_results_dict \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/evaluation\/__init__.py", line 2, in <module> \u00a0 \u00a0 from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/evaluation\/cityscapes_evaluation.py", line 11, in <module> \u00a0 \u00a0 from detectron2.data import MetadataCatalog \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/data\/__init__.py", line 2, in <module> \u00a0 \u00a0 from . import transforms \u00a0# isort:skip \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/data\/transforms\/__init__.py", line 4, in <module> \u00a0 \u00a0 from .transform import * \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/data\/transforms\/transform.py", line 36, in <module> \u00a0 \u00a0 class ExtentTransform(Transform): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/data\/transforms\/transform.py", line 46, in ExtentTransform \u00a0 \u00a0 def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0): AttributeError: module 'PIL.Image' has no attribute 'LINEAR'<\/p>\n<\/p>\n

deepseek\u65b9\u6848<\/p>\n

\u964d\u7ea7 Pillow \u5230\u517c\u5bb9\u7248\u672c\u00a0 \u00a0\u5f53\u524d10.4.0<\/p>\n

# \u5378\u8f7d\u5f53\u524d Pillow pip uninstall pillow -y<\/p>\n

# \u5b89\u88c5 Pillow 9.0.1（\u4ecd\u652f\u6301 Image.LINEAR） pip install pillow==9.0.1<\/p>\n

\u62a5\u9519\u6ca1CV2<\/p>\n

pip install\u00a0opencv-python<\/p>\n

\u62a5\u9519<\/p>\n

Command Line Args: Namespace(config_file='configs\/coco\/retinanet_train.yaml', dist_url='tcp:\/\/127.0.0.1:50158', eval_only=False, machine_rank=0, no_pretrain=False, num_gpus=2, num_machines=1, opts=['OUTPUT_DIR', 'work_dirs\/coco_retinanet'], resume=False) Traceback (most recent call last): \u00a0 File "train_coco.py", line 13, in <module> \u00a0 \u00a0 launch( \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/launch.py", line 67, in launch \u00a0 \u00a0 mp.spawn( \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/multiprocessing\/spawn.py", line 230, in spawn \u00a0 \u00a0 return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/multiprocessing\/spawn.py", line 188, in start_processes \u00a0 \u00a0 while not context.join(): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/multiprocessing\/spawn.py", line 150, in join \u00a0 \u00a0 raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException:\u00a0<\/p>\n

— Process 1 terminated with the following error: Traceback (most recent call last): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/multiprocessing\/spawn.py", line 59, in _wrap \u00a0 \u00a0 fn(i, *args) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/launch.py", line 126, in _distributed_worker \u00a0 \u00a0 main_func(*args) \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/train_tools\/coco_train.py", line 190, in start_train \u00a0 \u00a0 cfg = setup(args) \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/train_tools\/coco_train.py", line 182, in setup \u00a0 \u00a0 cfg.merge_from_file(args.config_file) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/config\/config.py", line 46, in merge_from_file \u00a0 \u00a0 loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/fvcore\/common\/config.py", line 61, in load_yaml_with_base \u00a0 \u00a0 cfg = yaml.safe_load(f) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/__init__.py", line 125, in safe_load \u00a0 \u00a0 return load(stream, SafeLoader) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/__init__.py", line 81, in load \u00a0 \u00a0 return loader.get_single_data() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/constructor.py", line 49, in get_single_data \u00a0 \u00a0 node = self.get_single_node() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/composer.py", line 36, in get_single_node \u00a0 \u00a0 document = self.compose_document() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/composer.py", line 55, in compose_document \u00a0 \u00a0 node = self.compose_node(None, None) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/composer.py", line 84, in compose_node \u00a0 \u00a0 node = self.compose_mapping_node(anchor) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/composer.py", line 127, in compose_mapping_node \u00a0 \u00a0 while not self.check_event(MappingEndEvent): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/parser.py", line 98, in check_event \u00a0 \u00a0 self.current_event = self.state() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/yaml\/parser.py", line 438, in parse_block_mapping_key \u00a0 \u00a0 raise ParserError("while parsing a block mapping", self.marks[-1], yaml.parser.ParserError: while parsing a block mapping \u00a0 in "configs\/coco\/retinanet_train.yaml", line 1, column 1 expected <block end>, but found '<scalar>' \u00a0 in "configs\/coco\/retinanet_train.yaml", line 2, column 15<\/p>\n

\u8bad\u7ec3\u524d\u9762\u6b63\u5e38，\u540e\u9762\u62a5\u9519<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u91c7\u7528deepseek\u65b9\u6848<\/p>\n

# \u964d\u7ea7 setuptools \u81f3\u517c\u5bb9\u7248\u672c pip install setuptools==58.0.4<\/p>\n

\u5c06GPU\u6570\u91cf\u8bbe\u7f6e\u4e3a1<\/p>\n

\u7136\u540e\u8fd0\u884c\u8bad\u7ec3\u6210\u529f<\/p>\n

20\u5c0f\u65f6……….<\/p>\n

\"\"<\/p>\n

\u5988\u7684\u62a5\u9519RuntimeError: DataLoader worker (pid 1014901) is killed by signal: Segmentation fault.\u00a0<\/p>\n

\"\"\u4fee\u6539visdrone-dataloader.py<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u6062\u590d<\/p>\n

python train_coco.py –config-file configs\/coco\/retinanet_train.yaml –num-gpu 2 –resume OUTPUT_DIR work_dirs\/coco_retinanet<\/p>\n

\u663e\u5b58\u51cf\u534a\u00a0 11\u5c0f\u65f6<\/p>\n

\"\"<\/p>\n

\u4ece\u68c0\u67e5\u70b9\u8bad\u7ec3<\/p>\n

python train_coco.py –config-file configs\/coco\/retinanet_train.yaml –num-gpu 2 MODEL.WEIGHTS work_dirs\/coco_retinanet\/model_0064999.pth OUTPUT_DIR work_dirs\/coco_retinanet<\/p>\n

\u62a5\u9519<\/p>\n

Traceback (most recent call last): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/utils\/data\/dataloader.py", line 990, in _try_get_data \u00a0 \u00a0 data = self._data_queue.get(timeout=timeout) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/multiprocessing\/queues.py", line 107, in get \u00a0 \u00a0 if not self._poll(timeout): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/multiprocessing\/connection.py", line 257, in poll \u00a0 \u00a0 return self._poll(timeout) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/multiprocessing\/connection.py", line 424, in _poll \u00a0 \u00a0 r = wait([self], timeout) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/multiprocessing\/connection.py", line 931, in wait \u00a0 \u00a0 ready = selector.select(timeout) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/selectors.py", line 415, in select \u00a0 \u00a0 fd_event_list = self._selector.poll(timeout) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/utils\/data\/_utils\/signal_handling.py", line 66, in handler \u00a0 \u00a0 _error_if_any_worker_fails() RuntimeError: DataLoader worker (pid 2297896) is killed by signal: Aborted.\u00a0<\/p>\n

The above exception was the direct cause of the following exception:<\/p>\n

Traceback (most recent call last): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/train_loop.py", line 149, in train \u00a0 \u00a0 self.run_step() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/defaults.py", line 494, in run_step \u00a0 \u00a0 self._trainer.run_step() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/train_loop.py", line 391, in run_step \u00a0 \u00a0 data = next(self._data_loader_iter) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/data\/common.py", line 234, in __iter__ \u00a0 \u00a0 for d in self.dataset: \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/utils\/data\/dataloader.py", line 521, in __next__ \u00a0 \u00a0 data = self._next_data() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/utils\/data\/dataloader.py", line 1186, in _next_data \u00a0 \u00a0 idx, data = self._get_data() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/utils\/data\/dataloader.py", line 1152, in _get_data \u00a0 \u00a0 success, data = self._try_get_data() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/utils\/data\/dataloader.py", line 1003, in _try_get_data \u00a0 \u00a0 raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e RuntimeError: DataLoader worker (pid(s) 2297896) exited unexpectedly<\/p>\n<\/p>\n

\u7981\u7528opencv\u7684\u591a\u7ebf\u7a0b\u518d\u8bd5\u8bd5<\/p>\n

\"\" \u00a0\u00a0 \u00a0…<\/p>\n

\"\"<\/p>\n

\u91cd\u65b0\u8bad\u7ec3<\/p>\n

python train_coco.py –config-file configs\/coco\/retinanet_train.yaml –num-gpu 1\u00a0–resume OUTPUT_DIR work_dirs\/coco_retinanet<\/p>\n

\u68c0\u67e5\u70b9\u5f00\u59cb\u00a0 2\u5f20\u5361<\/p>\n

python train_coco.py –config-file configs\/coco\/retinanet_train.yaml –num-gpu 2 MODEL.WEIGHTS work_dirs\/coco_retinanet\/model_0064999.pth OUTPUT_DIR work_dirs\/coco_retinanet<\/p>\n

\"\"10\u5c0f\u65f6<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

batch\u6539\u4e3a8\u8bd5\u8bd5<\/p>\n

\"\"<\/p>\n

\u53ea\u89815\u5c0f\u65f6<\/p>\n

\"\"<\/p>\n

FloatingPointError: Loss became infinite or NaN at iteration=982!<\/p>\n

\u8bad\u7ec3\u7ed3\u675f<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n<\/p>\n<\/p>\n

\u8bad\u7ec3\u7b2c\u4e8c\u6761<\/p>\n

python train_coco.py –config-file configs\/coco\/querydet_train.yaml –num-gpu 2 OUTPUT_DIR work_dirs\/coco_querydet<\/p>\n

\"\"<\/p>\n<\/p>\n

batch\u6539\u62108\u00a0 \u8fd8\u662f\u4e00\u6837<\/p>\n

\u8bbe\u7f6e\u5206\u914d32<\/p>\n

importosimport os os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:32'<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u540c\u65f6\u8bbe\u7f6e\u7ec8\u7aef<\/p>\n

export PYTORCH_CUDA_ALLOC_CONF='max_split_size_mb:32' \u00a0<\/p>\n

\u8bbe\u7f6eopencv\u00a0 train_coco.py<\/p>\n

\u8bad\u7ec3\u8fd8\u662f\u62a5\u9519<\/p>\n

export PYTORCH_CUDA_ALLOC_CONF='max_split_size_mb:128' \u00a0<\/p>\n

\u6e05\u7406<\/p>\n

\"\"<\/p>\n

batch\u65398\u00a0 \u53ef\u4ee5\u8dd1\u4e86<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u7ec8\u4e8e\u627e\u5230num_works\u4e86\u00a0 \u4fee\u65396\u5904<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u8bad\u7ec3\u8bd5\u8bd5<\/p>\n

python train_coco.py –config-file configs\/coco\/querydet_train.yaml –num-gpu 2 OUTPUT_DIR work_dirs\/coco_querydet <\/p>\n

\u5927\u529f\u544a\u6210<\/p>\n

\"\"<\/p>\n

\u62a5\u9519outofmem<\/p>\n

batch\u8bbe\u4e3a8\u00a0 17\u5c0f\u65f6<\/p>\n

\"\"<\/p>\n

\u5df2\u6740\u6b7b\u5988\u7684<\/p>\n

\u5c06num_works\u8bbe\u4e3a1\u8bd5\u8bd5\u00a0 \u6539detectron2\u7684\u9ed8\u8ba4default<\/p>\n

\"\"<\/p>\n

10\u901f\u5ea6\u6700\u5feb<\/p>\n

\u6ce8\u91ca\u68c0\u67e5\u70b9\u7f6e\u96f6<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u8dd1\u5b8c\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u8bad\u7ec3\u7b2c\u4e09\u6761\u00a0 batch\u653916 num_works\u65392<\/p>\n

python train_visdrone.py –config-file configs\/visdrone\/retinanet_train.yaml –num-gpu 2 OUTPUT_DIR work_dirs\/visdrone_retinanet <\/p>\n

\"\"<\/p>\n

\u628avisdrone dataloader num_workers\u6539\u4e86<\/p>\n

\"\"<\/p>\n

\"\"\u8fd8\u662f8<\/p>\n

\"\"2<\/p>\n

\"\"<\/p>\n

\u8bad\u7ec3\u5b8c\u4e86 \u8bc4\u4f30bash eval_visdrone.sh \/path\/to\/visdrone_infer.json<\/p>\n

\"\"<\/p>\n

bash eval_visdrone.sh\u00a0work_dirs\/visdrone_retinanet\/visdrone_infer.json<\/p>\n

\"\"<\/p>\n

\u5b89\u88c5\u8bc4\u4f30\u5de5\u5177pip install -e .<\/p>\n

\"\"<\/p>\n

\u5148\u8bad\u7ec3\u7b2c4\u6761<\/p>\n

python train_visdrone.py –config-file configs\/visdrone\/querydet_train.pyaml –num-gpu 2 OUTPUT_DIR work_dirs\/visdrone_querydet <\/p>\n

\"\"<\/p>\n

12\u534a\u5c0f\u65f6<\/p>\n

\"\"<\/p>\n

— Process 0 terminated with the following error: Traceback (most recent call last): \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/torch\/multiprocessing\/spawn.py", line 59, in _wrap \u00a0 \u00a0 fn(i, *args) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/launch.py", line 126, in _distributed_worker \u00a0 \u00a0 main_func(*args) \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/train_tools\/visdrone_train.py", line 250, in start_train \u00a0 \u00a0 return trainer.train() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/defaults.py", line 484, in train \u00a0 \u00a0 super().train(self.start_iter, self.max_iter) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/train_loop.py", line 149, in train \u00a0 \u00a0 self.run_step() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/defaults.py", line 494, in run_step \u00a0 \u00a0 self._trainer.run_step() \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/train_loop.py", line 405, in run_step \u00a0 \u00a0 self._write_metrics(loss_dict, data_time) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/train_loop.py", line 302, in _write_metrics \u00a0 \u00a0 SimpleTrainer.write_metrics(loss_dict, data_time, prefix) \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/detectron2\/engine\/train_loop.py", line 338, in write_metrics \u00a0 \u00a0 raise FloatingPointError( FloatingPointError: Loss became infinite or NaN at iteration=665! loss_dict = {'loss_cls': 1.8113356828689575, 'loss_box_reg': 6.629820704460144, 'loss_query': nan}<\/p>\n<\/p>\n

\u91cd\u65b0\u8bad\u7ec3\u52694\u5c0f\u65f6\u5df2\u6740\u6b7b\u5988\u7684<\/p>\n

num_workers\u6539\u4e3a1\u8bd5\u8bd5<\/p>\n

\u4f7f\u7528gemini\u65b9\u6848\u8bd5\u8bd5，\u770b\u770b\u53c2\u6570\u80fd\u4e0d\u80fd\u7ee7\u7eed\u4f7f\u7528<\/p>\n

\"\"<\/p>\n

\u539f\u4ee3\u7801<\/p>\n

def __init__(self, cfg, resume=False, reuse_ckpt=False):
\n """
\n Args:
\n cfg (CfgNode):
\n """
\n super(DefaultTrainer, self).__init__()<\/p>\n

logger = logging.getLogger("detectron2")
\n if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2
\n setup_logger()
\n cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())<\/p>\n

# Assume these objects must be constructed in this order.
\n model = self.build_model(cfg)<\/p>\n

ckpt = DetectionCheckpointer(model)
\n self.start_iter = 0
\n self.start_iter = ckpt.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
\n self.iter =self.start_iter<\/p>\n

optimizer = self.build_optimizer(cfg, model)
\n data_loader = self.build_train_loader(cfg)<\/p>\n

# For training, wrap with DDP. But don't need this for inference.
\n if comm.get_world_size() > 1:
\n model = DistributedDataParallel(
\n model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
\n )
\n self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
\n model, data_loader, optimizer
\n )<\/p>\n

self.scheduler = self.build_lr_scheduler(cfg, optimizer)
\n self.checkpointer = DetectionCheckpointer(
\n model,
\n cfg.OUTPUT_DIR,
\n optimizer=optimizer,
\n scheduler=self.scheduler,
\n )
\n self.start_iter = 0
\n self.max_iter = cfg.SOLVER.MAX_ITER
\n self.cfg = cfg<\/p>\n

self.register_hooks(self.build_hooks()) <\/p>\n

\u6539\u540e\u4ee3\u7801<\/p>\n

def __init__(self, cfg, resume=False, reuse_ckpt=False): # \u6ce8\u610f\u8fd9\u91cc\u4f20\u5165 resume \u6807\u5fd7
\n """
\n Args:
\n cfg (CfgNode):
\n resume (bool): Whether to resume from the checkpoint directory.
\n Passed from the command line args.resume.
\n """
\n # super(DefaultTrainer, self).__init__() # \u8c03\u7528\u7236\u7c7b\u7684 __init__ \u4e0d\u662f\u6807\u51c6\u505a\u6cd5，\u901a\u5e38\u76f4\u63a5\u5199\u903b\u8f91<\/p>\n

logger = logging.getLogger("detectron2")
\n # setup_logger is handled by default_setup, no need to call here usually
\n # if not logger.isEnabledFor(logging.INFO):
\n # setup_logger()
\n cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())<\/p>\n

# 1. \u6784\u5efa\u6a21\u578b
\n model = self.build_model(cfg)
\n # 2. \u6784\u5efa\u4f18\u5316\u5668
\n optimizer = self.build_optimizer(cfg, model)
\n # 3. \u6784\u5efa\u6570\u636e\u52a0\u8f7d\u5668
\n data_loader = self.build_train_loader(cfg)<\/p>\n

# — \u5904\u7406\u5206\u5e03\u5f0f\u8bad\u7ec3 —
\n if comm.get_world_size() > 1:
\n model = DistributedDataParallel(
\n model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
\n )
\n # ——————–<\/p>\n

# 4. \u521d\u59cb\u5316\u5e95\u5c42\u7684 Trainer (SimpleTrainer or AMPTrainer)
\n # \u6ce8\u610f：\u8fd9\u91cc\u5148\u4e0d\u4f20\u9012 optimizer，\u56e0\u4e3a resume_or_load \u53ef\u80fd\u4f1a\u52a0\u8f7d\u4f18\u5316\u5668\u72b6\u6001\u8986\u76d6\u5b83
\n self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
\n model, data_loader, optimizer # \u4f18\u5316\u5668\u5728\u8fd9\u91cc\u4f20\u9012\u662f Detectron2 \u7684\u6807\u51c6\u505a\u6cd5
\n )<\/p>\n

# 5. \u6784\u5efa\u5b66\u4e60\u7387\u8c03\u5ea6\u5668
\n self.scheduler = self.build_lr_scheduler(cfg, optimizer)<\/p>\n

# 6. \u6784\u5efa Checkpointer (\u5173\u8054\u6a21\u578b\u3001\u4f18\u5316\u5668\u3001\u8c03\u5ea6\u5668)
\n self.checkpointer = DetectionCheckpointer(
\n model,
\n cfg.OUTPUT_DIR,
\n optimizer=optimizer,
\n scheduler=self.scheduler,
\n )<\/p>\n

# 7. \u52a0\u8f7d\u68c0\u67e5\u70b9\u6216\u9884\u8bad\u7ec3\u6743\u91cd，\u5e76\u83b7\u53d6\u8d77\u59cb\u8fed\u4ee3\u6b21\u6570
\n # resume_or_load \u4f1a\u5904\u7406 resume=True \u7684\u60c5\u51b5 (\u52a0\u8f7d\u6700\u65b0\u68c0\u67e5\u70b9)
\n # \u548c resume=False \u7684\u60c5\u51b5 (\u52a0\u8f7d cfg.MODEL.WEIGHTS)
\n # \u5b83\u4f1a\u6b63\u786e\u5730\u52a0\u8f7d\u6a21\u578b\u3001\u4f18\u5316\u5668\u3001\u8c03\u5ea6\u5668\u72b6\u6001（\u5982\u679c resume=True \u4e14\u68c0\u67e5\u70b9\u5b58\u5728）
\n checkpoint_data = self.checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume)<\/p>\n

# — \u5904\u7406\u5206\u5e03\u5f0f resume \u65f6\u7684\u540c\u6b65 —
\n if resume and self.checkpointer.has_checkpoint():
\n # checkpoint.get("iteration", -1) \u8fd4\u56de\u7684\u662f\u521a\u5b8c\u6210\u7684\u8fed\u4ee3，\u6240\u4ee5+1
\n self.start_iter = checkpoint_data.get("iteration", -1) + 1
\n else:
\n # \u5982\u679c\u4e0d\u662f resume \u6216\u6ca1\u6709\u68c0\u67e5\u70b9，\u5219\u4ece 0 \u5f00\u59cb
\n self.start_iter = 0
\n # \u786e\u4fdd\u6240\u6709 rank \u7684 start_iter \u4e00\u81f4
\n if comm.get_world_size() > 1:
\n self.start_iter = comm.all_gather(self.start_iter)[0]
\n # ——————————-<\/p>\n

# 8. \u8bbe\u7f6e\u6700\u5927\u8fed\u4ee3\u6b21\u6570
\n self.max_iter = cfg.SOLVER.MAX_ITER
\n # 9. \u4fdd\u5b58\u914d\u7f6e
\n self.cfg = cfg<\/p>\n

# 10. \u6ce8\u518c\u8bad\u7ec3\u94a9\u5b50 (Hooks)
\n self.register_hooks(self.build_hooks())<\/p>\n

# — \u4e0d\u518d\u9700\u8981\u5355\u72ec\u7684 resume_or_load \u65b9\u6cd5，\u903b\u8f91\u5df2\u5408\u5e76\u5230 __init__ —
\n # def resume_or_load(self, resume=True):
\n # … (\u53ef\u4ee5\u5220\u9664\u8fd9\u4e2a\u65b9\u6cd5) …<\/p>\n

# — \u5176\u4ed6\u65b9\u6cd5\u4fdd\u6301\u4e0d\u53d8 — <\/p>\n

\"\"<\/p>\n

\u62a5\u9519\"\"<\/p>\n

\u91c7\u7eb3\u8bd5\u8bd5<\/p>\n

\"\"<\/p>\n

\u62a5\u9519<\/p>\n

\"\"<\/p>\n

\u91c7\u7eb3\u8bd5\u8bd5<\/p>\n

\"\"<\/p>\n

\u80fd\u6b63\u5e38\u8fd0\u884c，\u4f46\u662f\u901f\u5ea6\u5f88\u6162，\u800c\u4e14\u65f6\u95f4\u5f88\u957f，\u6bcf\u6b21\u8f93\u51fa\u4e862\u8f6e，\u4e4b\u524d\u90fd\u53ea\u8f93\u51fa\u4e00\u8f6e\u7684<\/p>\n

[04\/27 10:26:09 d2.utils.events]: \u00a0eta: 14:00:17 \u00a0iter: 30019 \u00a0total_loss: 0.8324 \u00a0loss_cls: 0.4716 \u00a0loss_box_reg: 0.3239 \u00a0loss_query: 0.02985 \u00a0time: 2.5858 \u00a0data_time: 0.0037 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:26:09 d2.utils.events]: \u00a0eta: 13:58:30 \u00a0iter: 30019 \u00a0total_loss: 0.8324 \u00a0loss_cls: 0.4716 \u00a0loss_box_reg: 0.3239 \u00a0loss_query: 0.02985 \u00a0time: 2.5831 \u00a0data_time: 0.0037 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:26:58 d2.utils.events]: \u00a0eta: 13:52:30 \u00a0iter: 30039 \u00a0total_loss: 0.4814 \u00a0loss_cls: 0.2563 \u00a0loss_box_reg: 0.1968 \u00a0loss_query: 0.02421 \u00a0time: 2.5382 \u00a0data_time: 0.0034 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:26:58 d2.utils.events]: \u00a0eta: 13:51:39 \u00a0iter: 30039 \u00a0total_loss: 0.4814 \u00a0loss_cls: 0.2563 \u00a0loss_box_reg: 0.1968 \u00a0loss_query: 0.02421 \u00a0time: 2.5372 \u00a0data_time: 0.0034 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:27:49 d2.utils.events]: \u00a0eta: 13:55:01 \u00a0iter: 30059 \u00a0total_loss: 0.5486 \u00a0loss_cls: 0.3166 \u00a0loss_box_reg: 0.2069 \u00a0loss_query: 0.02835 \u00a0time: 2.5330 \u00a0data_time: 0.0035 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:27:49 d2.utils.events]: \u00a0eta: 13:54:52 \u00a0iter: 30059 \u00a0total_loss: 0.5486 \u00a0loss_cls: 0.3166 \u00a0loss_box_reg: 0.2069 \u00a0loss_query: 0.02835 \u00a0time: 2.5324 \u00a0data_time: 0.0035 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:28:39 d2.utils.events]: \u00a0eta: 13:51:56 \u00a0iter: 30079 \u00a0total_loss: 0.4665 \u00a0loss_cls: 0.2634 \u00a0loss_box_reg: 0.1752 \u00a0loss_query: 0.02661 \u00a0time: 2.5200 \u00a0data_time: 0.0034 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:28:39 d2.utils.events]: \u00a0eta: 13:51:40 \u00a0iter: 30079 \u00a0total_loss: 0.4665 \u00a0loss_cls: 0.2634 \u00a0loss_box_reg: 0.1752 \u00a0loss_query: 0.02661 \u00a0time: 2.5194 \u00a0data_time: 0.0034 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:29:29 d2.utils.events]: \u00a0eta: 13:51:06 \u00a0iter: 30099 \u00a0total_loss: 0.4453 \u00a0loss_cls: 0.2529 \u00a0loss_box_reg: 0.1687 \u00a0loss_query: 0.02287 \u00a0time: 2.5201 \u00a0data_time: 0.0036 \u00a0lr: 0.001 \u00a0max_mem: 20413M [04\/27 10:29:29 d2.utils.events]: \u00a0eta: 13:50:50 \u00a0iter: 30099 \u00a0total_loss: 0.4453 \u00a0loss_cls: 0.2529 \u00a0loss_box_reg: 0.1687 \u00a0loss_query: 0.02287 \u00a0time: 2.5196 \u00a0data_time: 0.0036 \u00a0lr: 0.001 \u00a0max_mem: 20413M<\/p>\n<\/p>\n

\u628a\u95ee\u9898\u4e22\u7ed9gemini\u00a0 \u4ed6\u8bf4<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u628a\u4e4b\u524d\u91c7\u7eb3\u7684\u6ce8\u91ca\u6389，\u7136\u540e\u91c7\u53d6gemini\u65b9\u6848<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u624b\u52a8\u6ce8\u518c\u540e，\u786e\u5b9e\u4e0d\u4f1a\u8f93\u51fa\u4e24\u6b21\u91cd\u590d\u7684\u4e86，\u4f46\u662f\u901f\u5ea6\u8fd8\u662f\u5f88\u6162<\/p>\n

\u91c7\u7528\u4ed6\u4e4b\u524d\u7684\u89e3\u51b3\u65b9\u6848\u8bd5\u8bd5<\/p>\n

\u539f\u4ee3\u7801<\/p>\n

def __init__(self, cfg, resume=False, reuse_ckpt=False): # \u6ce8\u610f\u8fd9\u91cc\u4f20\u5165 resume \u6807\u5fd7
\n """
\n Args:
\n cfg (CfgNode):
\n resume (bool): Whether to resume from the checkpoint directory.
\n Passed from the command line args.resume.
\n """
\n # super().__init__(cfg) # \u6b63\u786e\u8c03\u7528\u7236\u7c7b\u521d\u59cb\u5316\u65b9\u6cd5<\/p>\n

logger = logging.getLogger("detectron2")
\n # setup_logger is handled by default_setup, no need to call here usually
\n # if not logger.isEnabledFor(logging.INFO):
\n # setup_logger()
\n cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())<\/p>\n

# 1. \u6784\u5efa\u6a21\u578b
\n model = self.build_model(cfg)
\n # 2. \u6784\u5efa\u4f18\u5316\u5668
\n optimizer = self.build_optimizer(cfg, model)
\n # 3. \u6784\u5efa\u6570\u636e\u52a0\u8f7d\u5668
\n data_loader = self.build_train_loader(cfg)<\/p>\n

# — \u5904\u7406\u5206\u5e03\u5f0f\u8bad\u7ec3 —
\n if comm.get_world_size() > 1:
\n model = DistributedDataParallel(
\n model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
\n )
\n # ——————–<\/p>\n

# 4. \u521d\u59cb\u5316\u5e95\u5c42\u7684 Trainer (SimpleTrainer or AMPTrainer)
\n # \u6ce8\u610f：\u8fd9\u91cc\u5148\u4e0d\u4f20\u9012 optimizer，\u56e0\u4e3a resume_or_load \u53ef\u80fd\u4f1a\u52a0\u8f7d\u4f18\u5316\u5668\u72b6\u6001\u8986\u76d6\u5b83
\n self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
\n model, data_loader, optimizer # \u4f18\u5316\u5668\u5728\u8fd9\u91cc\u4f20\u9012\u662f Detectron2 \u7684\u6807\u51c6\u505a\u6cd5
\n )<\/p>\n

# 5. \u6784\u5efa\u5b66\u4e60\u7387\u8c03\u5ea6\u5668
\n self.scheduler = self.build_lr_scheduler(cfg, optimizer)<\/p>\n

# 6. \u6784\u5efa Checkpointer (\u5173\u8054\u6a21\u578b\u3001\u4f18\u5316\u5668\u3001\u8c03\u5ea6\u5668)
\n self.checkpointer = DetectionCheckpointer(
\n model,
\n cfg.OUTPUT_DIR,
\n optimizer=optimizer,
\n scheduler=self.scheduler,
\n )<\/p>\n

# 7. \u52a0\u8f7d\u68c0\u67e5\u70b9\u6216\u9884\u8bad\u7ec3\u6743\u91cd，\u5e76\u83b7\u53d6\u8d77\u59cb\u8fed\u4ee3\u6b21\u6570
\n # resume_or_load \u4f1a\u5904\u7406 resume=True \u7684\u60c5\u51b5 (\u52a0\u8f7d\u6700\u65b0\u68c0\u67e5\u70b9)
\n # \u548c resume=False \u7684\u60c5\u51b5 (\u52a0\u8f7d cfg.MODEL.WEIGHTS)
\n # \u5b83\u4f1a\u6b63\u786e\u5730\u52a0\u8f7d\u6a21\u578b\u3001\u4f18\u5316\u5668\u3001\u8c03\u5ea6\u5668\u72b6\u6001（\u5982\u679c resume=True \u4e14\u68c0\u67e5\u70b9\u5b58\u5728）
\n checkpoint_data = self.checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume)<\/p>\n

# — \u5904\u7406\u5206\u5e03\u5f0f resume \u65f6\u7684\u540c\u6b65 —
\n if resume and self.checkpointer.has_checkpoint():
\n # checkpoint.get("iteration", -1) \u8fd4\u56de\u7684\u662f\u521a\u5b8c\u6210\u7684\u8fed\u4ee3，\u6240\u4ee5+1
\n self.start_iter = checkpoint_data.get("iteration", -1) + 1
\n else:
\n # \u5982\u679c\u4e0d\u662f resume \u6216\u6ca1\u6709\u68c0\u67e5\u70b9，\u5219\u4ece 0 \u5f00\u59cb
\n self.start_iter = 0
\n # \u786e\u4fdd\u6240\u6709 rank \u7684 start_iter \u4e00\u81f4
\n if comm.get_world_size() > 1:
\n self.start_iter = comm.all_gather(self.start_iter)[0]
\n # ——————————-<\/p>\n

# 8. \u8bbe\u7f6e\u6700\u5927\u8fed\u4ee3\u6b21\u6570
\n self.max_iter = cfg.SOLVER.MAX_ITER
\n # 9. \u4fdd\u5b58\u914d\u7f6e
\n self.cfg = cfg<\/p>\n

# — FIX: Manually initialize the _hooks list —
\n self._hooks = []
\n # ————————————————-<\/p>\n

# 10. \u6ce8\u518c\u8bad\u7ec3\u94a9\u5b50 (Hooks)
\n self.register_hooks(self.build_hooks())<\/p>\n

# — \u4e0d\u518d\u9700\u8981\u5355\u72ec\u7684 resume_or_load \u65b9\u6cd5，\u903b\u8f91\u5df2\u5408\u5e76\u5230 __init__ —
\n # def resume_or_load(self, resume=True):
\n # … (\u53ef\u4ee5\u5220\u9664\u8fd9\u4e2a\u65b9\u6cd5) …<\/p>\n

# — \u5176\u4ed6\u65b9\u6cd5\u4fdd\u6301\u4e0d\u53d8 — <\/p>\n

\u4fee\u6539\u540e<\/p>\n

\u8fd0\u884c\u62a5\u9519<\/p>\n

\"\"<\/p>\n

\u7f29\u8fdb\u4e0d\u5bf9<\/p>\n

\u624b\u52a8\u4fee\u6539\u8bd5\u8bd5<\/p>\n

\"\"<\/p>\n

\u5e76\u4e14\u628a\u6ce8\u91ca\u6389\u7684\u6062\u590d<\/p>\n

\"\"<\/p>\n

\u8fd8\u662f\u62a5\u9519<\/p>\n

\"\"<\/p>\n

\u91c7\u7528gemin\u8bd5\u8bd5<\/p>\n

\"\"<\/p>\n

\u6539\u4e4b\u524d<\/p>\n

trainer = Trainer(cfg, resume=args.resume, reuse_ckpt=args.no_pretrain)
\n return trainer.train() <\/p>\n

\u6539\u540e<\/p>\n

# 1. \u521b\u5efa Trainer \u5b9e\u4f8b，\u53ea\u4f20\u9012 cfg
\n trainer = Trainer(cfg)<\/p>\n

# 2. \u5728\u521b\u5efa\u5b9e\u4f8b\u540e，\u8c03\u7528 resume_or_load \u65b9\u6cd5\u5904\u7406\u6062\u590d\u903b\u8f91
\n # \u5c06\u547d\u4ee4\u884c\u4f20\u5165\u7684 resume \u6807\u5fd7\u7528\u5728\u8fd9\u91cc！
\n trainer.resume_or_load(resume=args.resume)<\/p>\n

# 3. \u5f00\u59cb\u8bad\u7ec3
\n return trainer.train() <\/p>\n

\"\"<\/p>\n

\u6539\u5b8c\u62a5\u9519<\/p>\n

\"\"<\/p>\n<\/p>\n

\u6062\u590d\u5230\u8bad\u7ec3\u5f88\u6162，\u4f46\u662f\u4e0d\u91cd\u590d\u8f93\u51fa\u90a3\u4e00\u6b65<\/p>\n

\u6539\u8bd5\u8bd5<\/p>\n

\"\"<\/p>\n

\u6062\u590dinit\u4ee3\u7801，\u53d6\u6d88\u6ce8\u91caresumeload<\/p>\n

\u4ece0\u5f00\u59cb\u8bad\u7ec3\u8fd8\u62a5\u9519oom<\/p>\n

\u8bd5\u4e00\u4e0b\"\"<\/p>\n

\u6539\u4e4b\u524d<\/p>\n

\"\"<\/p>\n

\u6539\u4e4b\u540e<\/p>\n

\"\"<\/p>\n

\u6539visdrone_train.py\u00a0 \u6539\u4e4b\u524d\u548c\u539f\u539f\u539f\u4ee3\u7801\u4e00\u6837<\/p>\n

\u6539\u4e4b\u540e<\/p>\n

class Trainer(DefaultTrainer):
\n """
\n \u7528\u4e8e VisDrone \u7684\u81ea\u5b9a\u4e49 Trainer \u7c7b，\u7ee7\u627f\u81ea DefaultTrainer\u3002
\n \u91cd\u5199\u4e86\u6570\u636e\u52a0\u8f7d\u548c\u8bc4\u4f30\u65b9\u6cd5\u3002
\n """<\/p>\n

# __init__ \u65b9\u6cd5\u7ee7\u627f\u81ea DefaultTrainer
\n # resume_or_load \u65b9\u6cd5\u7ee7\u627f\u81ea DefaultTrainer<\/p>\n

@classmethod
\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):
\n """
\n \u4e3a VisDrone \u6570\u636e\u96c6\u6784\u5efa\u8bc4\u4f30\u5668\u3002
\n \u521b\u5efa\u4e00\u4e2a\u7528\u4e8e\u4fdd\u5b58\u68c0\u6d4b\u7ed3\u679c\u7684 JSON \u6587\u4ef6\u3002
\n \u53ef\u9009\u5730\u6dfb\u52a0 GPU \u65f6\u95f4\u8bc4\u4f30\u5668\u3002
\n """
\n if output_folder is None:
\n # \u9ed8\u8ba4\u8f93\u51fa\u6587\u4ef6\u5939\u4f4d\u4e8e\u4e3b\u8f93\u51fa\u76ee\u5f55\u4e0b
\n output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
\n os.makedirs(output_folder, exist_ok=True) # \u786e\u4fdd\u6587\u4ef6\u5939\u5b58\u5728<\/p>\n

evaluator_list = []
\n # \u4f7f\u7528 JsonEvaluator \u4fdd\u5b58 COCO JSON \u683c\u5f0f\u7684\u68c0\u6d4b\u7ed3\u679c，\u9002\u7528\u4e8e VisDrone \u7684\u8bc4\u4f30\u811a\u672c
\n evaluator_list.append(JsonEvaluator(os.path.join(output_folder, 'visdrone_infer.json'))) #
\n if cfg.META_INFO.EVAL_GPU_TIME: #
\n # \u5982\u679c\u914d\u7f6e\u4e86，\u6dfb\u52a0\u4e00\u4e2a\u8bc4\u4f30\u5668\u6765\u6d4b\u91cf GPU \u63a8\u7406\u65f6\u95f4
\n evaluator_list.append(GPUTimeEvaluator(True, 'minisecond')) #
\n # \u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u6240\u6709\u6307\u5b9a\u8bc4\u4f30\u5668\u7684 DatasetEvaluators \u5bf9\u8c61
\n return DatasetEvaluators(evaluator_list)<\/p>\n

@classmethod
\n def build_train_loader(cls, cfg):
\n """
\n \u4f7f\u7528\u81ea\u5b9a\u4e49\u7684 VisDrone \u52a0\u8f7d\u5668\u6784\u5efa\u7528\u4e8e\u8bad\u7ec3\u7684\u6570\u636e\u52a0\u8f7d\u5668\u3002
\n """
\n # \u4f7f\u7528 VisDrone \u8bad\u7ec3\u6570\u636e\u7684\u7279\u5b9a\u6570\u636e\u52a0\u8f7d\u5668\u5b9e\u73b0
\n return build_train_loader(cfg) #<\/p>\n

@classmethod
\n def build_test_loader(cls, cfg, dataset_name):
\n """
\n \u4f7f\u7528\u81ea\u5b9a\u4e49\u7684 VisDrone \u52a0\u8f7d\u5668\u6784\u5efa\u7528\u4e8e\u6d4b\u8bd5\/\u9a8c\u8bc1\u7684\u6570\u636e\u52a0\u8f7d\u5668\u3002
\n \u6ce8\u610f：\u5982\u679c\u914d\u7f6e\u5df2\u7ecf\u6307\u5b9a\u4e86\u6d4b\u8bd5\u6570\u636e\u96c6\u7684\u8be6\u7ec6\u4fe1\u606f，`dataset_name` \u53ef\u80fd\u4e0d\u662f\u5fc5\u9700\u7684\u3002
\n """
\n # \u4f7f\u7528 VisDrone \u6d4b\u8bd5\/\u9a8c\u8bc1\u6570\u636e\u7684\u7279\u5b9a\u6570\u636e\u52a0\u8f7d\u5668\u5b9e\u73b0
\n return build_test_loader(cfg) #<\/p>\n

@classmethod
\n def test(cls, cfg, model, evaluators=None):
\n """
\n \u5728 VisDrone \u6d4b\u8bd5\u6570\u636e\u96c6\u4e0a\u8fd0\u884c\u63a8\u7406\u3002
\n Args:
\n cfg: Detectron2 \u914d\u7f6e\u5bf9\u8c61\u3002
\n model: \u7528\u4e8e\u63a8\u7406\u7684\u5df2\u8bad\u7ec3\u6a21\u578b\u3002
\n evaluators: \u53ef\u9009；\u5982\u679c\u4e3a None，\u5219\u4f7f\u7528 `build_evaluator` \u6784\u5efa\u7684\u8bc4\u4f30\u5668\u3002
\n Returns:
\n \u4e00\u4e2a\u7a7a\u5217\u8868，\u56e0\u4e3a\u7ed3\u679c\u7531 JsonEvaluator \u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002
\n （Detectron2 \u7684\u6807\u51c6 test \u8fd4\u56de\u4e00\u4e2a\u5b57\u5178，\u4f46\u8fd9\u91cc\u6211\u4eec\u4e13\u6ce8\u4e8e\u6587\u4ef6\u8f93\u51fa）\u3002
\n """
\n logger = logging.getLogger(__name__)
\n # \u5047\u8bbe 'VisDrone_Test' \u6216\u7c7b\u4f3c\u540d\u79f0\u662f\u6d4b\u8bd5\u6570\u636e\u96c6\u7684\u6ce8\u518c\u540d\u79f0
\n # \u5982\u679c build_test_loader \u5b8c\u5168\u4f9d\u8d56\u4e8e cfg，\u5219\u6b64\u540d\u79f0\u53ef\u80fd\u4e0d\u4f1a\u76f4\u63a5\u4f7f\u7528
\n dataset_name = 'VisDrone_Test' # \u5360\u4f4d\u7b26\u540d\u79f0，\u5b9e\u9645\u6570\u636e\u6765\u81ea cfg<\/p>\n

# \u4f7f\u7528\u81ea\u5b9a\u4e49\u65b9\u6cd5\u6784\u5efa\u6d4b\u8bd5\u6570\u636e\u52a0\u8f7d\u5668
\n data_loader = cls.build_test_loader(cfg, dataset_name)
\n # \u4f7f\u7528\u81ea\u5b9a\u4e49\u65b9\u6cd5\u6784\u5efa\u8bc4\u4f30\u5668
\n if evaluators is None:
\n evaluators = cls.build_evaluator(cfg, dataset_name)<\/p>\n

# \u4f7f\u7528 detectron2 \u7684\u5de5\u5177\u51fd\u6570\u8fd0\u884c\u63a8\u7406
\n results = inference_on_dataset(model, data_loader, evaluators)
\n # \u6807\u51c6\u7684 detectron2 \u7ed3\u679c\u9a8c\u8bc1（\u53ef\u9009，\u4f46\u662f\u597d\u7684\u5b9e\u8df5）
\n # verify_results(cfg, results) # \u5982\u679c\u9700\u8981\u53ef\u4ee5\u542f\u7528<\/p>\n

# \u8fd4\u56de\u7a7a\u5217\u8868，\u56e0\u4e3a\u7ed3\u679c\u88ab JsonEvaluator \u4fdd\u5b58\u5230\u6587\u4ef6
\n return []<\/p>\n

# \u6ce8\u610f：default_argument_parser, setup, \u548c launch \u903b\u8f91\u901a\u5e38\u4f4d\u4e8e
\n# \u4f60\u7684\u4e3b\u811a\u672c（\u4f8b\u5982 train_visdrone.py）\u4e2d，\u800c\u4e0d\u662f\u901a\u5e38\u5728 Trainer \u5b9a\u4e49\u6587\u4ef6\u5185\u90e8\u3002 <\/p>\n

\u62a5\u9519oom \u8bad\u7ec3\u4ece1\u5f00\u59cb\u00a0<\/p>\n

\u6062\u590d\u5230\u5f88\u6162\u90a3\u4e00\u6b65\u00a0 \u9006\u5929<\/p>\n

\"\"<\/p>\n

bc\u6539\u4e3a4 11\u5c0f\u65f6，\u8bad\u7ec3\u5b8c\u6210<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u6d4b\u8bd5<\/p>\n

python infer_coco.py –config-file configs\/coco\/retinanet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/coco_retinanet\/model_final.pth OUTPUT_DIR work_dirs\/model_test <\/p>\n

\u62a5\u9519Traceback (most recent call last): \u00a0 File "infer_coco.py", line 4, in <module> \u00a0 \u00a0 from train_tools.coco_infer import default_argument_parser, start_train \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/train_tools\/coco_infer.py", line 45, in <module> \u00a0 \u00a0 from detectron2_backbone.config import add_backbone_config ModuleNotFoundError: No module named 'detectron2_backbone'<\/p>\n

\u8fd0\u884c
\ngit clone https:\/\/github.com\/sxhxliang\/detectron2_backbone.git
\ncd detectron2_backbone
\npython setup.py build develop <\/p>\n

error: numpy 1.18.5 is installed but numpy<2,>=1.20 is required by {'matplotlib'}<\/p>\n

pip install –upgrade "numpy>=1.20,<2.0"<\/p>\n

\u63a8\u7406\u7ed3\u675f\u00a0 \u548c\u7b2c\u4e00\u6b21\u8bad\u7ec3\u4e00\u6837<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u8fd0\u884c<\/p>\n

python infer_coco.py –config-file configs\/coco\/querydet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/coco_querydet\/model_final.pth OUTPUT_DIR work_dirs\/model_test <\/p>\n

\u62a5\u9519\"\"<\/p>\n

\u6dfb\u52a0<\/p>\n

\"\"<\/p>\n

\u63a8\u7406\u7ed3\u675f<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

666<\/p>\n

\"\"<\/p>\n

\u9700\u8981\u91cd\u65b0\u8bad\u7ec3<\/p>\n

\"\"<\/p>\n

\u8df3\u8fc7CSQ\"\"<\/p>\n

\u7b2c\u4e09\u6761<\/p>\n

python infer_coco.py –config-file configs\/visdrone\/retinanet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/visdrone_retinanet\/model_final.pth OUTPUT_DIR work_dirs\/model_test <\/p>\n

\u9006\u5929<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u4fee\u6539\u6d4b\u8bd5\u547d\u4ee4<\/p>\n

—————————————-VISdrone————————————————————-<\/p>\n

\u8bc4\u4f30\u57fa\u7ebf\u6a21\u578bretinanet<\/p>\n

python infer_visdrone.py –config-file configs\/visdrone\/retinanet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS \u00a0work_dirs\/visdrone_retinanet\/model_final.pth OUTPUT_DIR work_dirs\/model_test<\/p>\n

\u62a5\u9519\u627e\u4e0d\u5230<\/p>\n

\"\"<\/p>\n

\u6539\u7edd\u5bf9\u8def\u5f84<\/p>\n

python infer_visdrone.py –config-file configs\/visdrone\/retinanet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS \/home\/liuyadong\/QueryDet-PyTorch\/work_dirs\/visdrone_retinanet\/model_final.pth OUTPUT_DIR work_dirs\/model_test<\/p>\n

\u53ef\u4ee5\u4e86<\/p>\n

\"\"<\/p>\n

\u8fd0\u884cbash eval_visdrone.sh work_dirs\/model_test\/visdrone_infer.json\u8bc4\u4f30<\/p>\n

\u62a5\u9519<\/p>\n

\"\"<\/p>\n

\u4fee\u6539\u8fd9\u91cc<\/p>\n

\"\"<\/p>\n

\u8bc4\u4f30\u7ed3\u675f<\/p>\n

\"\"<\/p>\n

\u8bc4\u4f30querydet visdrone<\/p>\n

python infer_visdrone.py –config-file configs\/visdrone\/querydet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/visdrone_querydet\/model_final.pth OUTPUT_DIR work_dirs\/model_test <\/p>\n

\"\"<\/p>\n

\u8bc4\u4f30bash eval_visdrone.sh work_dirs\/model_test\/visdrone_infer.json<\/p>\n

\u57fa\u7ebf\u6a21\u578b\u7ed3\u679c\u548cquerydet\u7ed3\u679c\u90fd\u6bd4\u8bba\u6587\u597d<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u8bc4\u4f30\u4e00\u4e0b\u52a0CSQ\u7684vis<\/p>\n

export SPCONV_FILTER_HWIO="1"; python infer_visdrone.py –config-file configs\/visdrone\/querydet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/visdrone_querydet\/model_final.pth OUTPUT_DIR work_dirs\/model_test MODEL.QUERY.QUERY_INFER True <\/p>\n

\u62a5\u9519Traceback (most recent call last): \u00a0 File "infer_visdrone.py", line 4, in <module> \u00a0 \u00a0 from train_tools.visdrone_infer import default_argument_parser, start_train \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/train_tools\/visdrone_infer.py", line 47, in <module> \u00a0 \u00a0 from models.querydet.detector import RetinaNetQueryDet \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/models\/querydet\/detector.py", line 38, in <module> \u00a0 \u00a0 import models.querydet.qinfer as qf \u00a0 File "\/home\/liuyadong\/QueryDet-PyTorch\/models\/querydet\/qinfer.py", line 4, in <module> \u00a0 \u00a0 import spconv.pytorch as spconv \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/spconv\/__init__.py", line 15, in <module> \u00a0 \u00a0 from . import build as _build \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/spconv\/build.py", line 21, in <module> \u00a0 \u00a0 from .constants import PACKAGE_NAME, PACKAGE_ROOT, DISABLE_JIT, SPCONV_INT8_DEBUG \u00a0 File "\/home\/liuyadong\/.conda\/envs\/querydet\/lib\/python3.8\/site-packages\/spconv\/constants.py", line 30, in <module> \u00a0 \u00a0 raise NotImplementedError("SPCONV_FILTER_HWIO is deprecated. use SPCONV_SAVED_WEIGHT_LAYOUT instead.") NotImplementedError: SPCONV_FILTER_HWIO is deprecated. use SPCONV_SAVED_WEIGHT_LAYOUT instead.<\/p>\n

\u5148\u53d6\u6d88<\/p>\n

unset SPCONV_FILTER_HWIO<\/p>\n

\u5728\u8fd0\u884c<\/p>\n

export SPCONV_SAVED_WEIGHT_LAYOUT="RSCK"<\/p>\n

\"\"<\/p>\n

python infer_visdrone.py –config-file configs\/visdrone\/querydet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/visdrone_querydet\/model_final.pth OUTPUT_DIR work_dirs\/model_test_csq MODEL.QUERY.QUERY_INFER True<\/p>\n

\u62a5\u9519<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u8bc4\u4f30FPS<\/p>\n

\"\"<\/p>\n

AP \u8fd0\u884cbash eval_visdrone.sh work_dirs\/model_test_csq\/visdrone_infer.json<\/p>\n

\"\"<\/p>\n

\u7ed3\u679c\u5206\u6790<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n<\/p>\n

————————————————————————————————————–<\/p>\n

\u63a5\u4e0b\u6765，\u91cd\u65b0\u8bad\u7ec3COCO\u00a0 \u5e26query\u7684\u00a0 \u4e4b\u524d\u8bad\u7ec3\u7ed3\u679c\u6709\u95ee\u9898<\/p>\n

batch=4\u00a0 num_workers=2\u00a0\u00a0<\/p>\n

python train_coco.py –config-file configs\/coco\/querydet_train.yaml –num-gpu 2 OUTPUT_DIR work_dirs\/coco_querydet <\/p>\n

8\u5c0f\u65f6 batch\u65398\u8bd5\u8bd5\u00a0 16\u5c0f\u65f6….<\/p>\n

\u65396\u8bd5\u8bd5\u00a0 12\u5c0f\u65f6….<\/p>\n

\u8fd8\u662f\u65394<\/p>\n

\u8dd1\u5b8c\u4e86<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u770b\u770b\u5e26CSQ\u7684<\/p>\n

python infer_coco.py –config-file configs\/coco\/querydet_test.yaml –num-gpu 2 –eval-only MODEL.WEIGHTS work_dirs\/coco_querydet\/model_final.pth OUTPUT_DIR work_dirs\/model_test MODEL.QUERY.QUERY_INFER True <\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

\u5bc4<\/p>\n

\"\"<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

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