DefconBird
commented
2 years ago mmdetection-master/mmdet/datasets/voc.py
from collections import OrderedDict
from mmcv.utils import print_log
from mmdet.core import eval_map, eval_recalls from .builder import DATASETS from .xml_style import XMLDataset
@DATASETS.register_module() class VOCDataset(XMLDataset):
# 原本CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
# 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
# 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
# 'tvmonitor')
CLASSES = ('person', )
PALETTE = [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
(197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),
(153, 69, 1), (120, 166, 157), (0, 182, 199), (0, 226, 252),
(182, 182, 255), (0, 0, 230), (220, 20, 60), (163, 255, 0),
(0, 82, 0), (3, 95, 161), (0, 80, 100), (183, 130, 88)]
def __init__(self, **kwargs):
super(VOCDataset, self).__init__(**kwargs)
if 'VOC2007' in self.img_prefix:
self.year = 2007
elif 'VOC2012' in self.img_prefix:
self.year = 2012
else:
raise ValueError('Cannot infer dataset year from img_prefix')
def evaluate(self,
results,
metric='mAP',
logger=None,
proposal_nums=(100, 300, 1000),
iou_thr=0.5,
scale_ranges=None):
"""Evaluate in VOC protocol.
Args:
results (list[list | tuple]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated. Options are
'mAP', 'recall'.
logger (logging.Logger | str, optional): Logger used for printing
related information during evaluation. Default: None.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thr (float | list[float]): IoU threshold. Default: 0.5.
scale_ranges (list[tuple], optional): Scale ranges for evaluating
mAP. If not specified, all bounding boxes would be included in
evaluation. Default: None.
Returns:
dict[str, float]: AP/recall metrics.
"""
if not isinstance(metric, str):
assert len(metric) == 1
metric = metric[0]
allowed_metrics = ['mAP', 'recall']
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
annotations = [self.get_ann_info(i) for i in range(len(self))]
eval_results = OrderedDict()
iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
if metric == 'mAP':
assert isinstance(iou_thrs, list)
if self.year == 2007:
ds_name = 'voc07'
else:
ds_name = self.CLASSES
mean_aps = []
for iou_thr in iou_thrs:
print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
# Follow the official implementation,
# http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar
# we should use the legacy coordinate system in mmdet 1.x,
# which means w, h should be computed as 'x2 - x1 + 1` and
# `y2 - y1 + 1`
mean_ap, _ = eval_map(
results,
annotations,
scale_ranges=None,
iou_thr=iou_thr,
dataset=ds_name,
logger=logger,
use_legacy_coordinate=True)
mean_aps.append(mean_ap)
eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
eval_results.move_to_end('mAP', last=False)
elif metric == 'recall':
gt_bboxes = [ann['bboxes'] for ann in annotations]
recalls = eval_recalls(
gt_bboxes,
results,
proposal_nums,
iou_thrs,
logger=logger,
use_legacy_coordinate=True)
for i, num in enumerate(proposal_nums):
for j, iou_thr in enumerate(iou_thrs):
eval_results[f'recall@{num}@{iou_thr}'] = recalls[i, j]
if recalls.shape[1] > 1:
ar = recalls.mean(axis=1)
for i, num in enumerate(proposal_nums):
eval_results[f'AR@{num}'] = ar[i]
return eval_results
HeHuasu
Describe the bug
Reproduction
the command I use was: python tools/train.py configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
I meet this bug when the first epoch training over,in eval,the terminal shows File "/home/ayanami/mmdetection-master/mmdet/core/evaluation/mean_ap.py", line 584, in eval_map num_classes = len(det_results[0]) # positive class num IndexError: list index out of range
I want to train a model to detect the person,so, that was only one class 'person' in my dataset ,and my dataset is voc format. I changed four file under: ` mmdetection-master/mmdet/datasets/voc.py
mmdetection-master/mmdet/core/evaluation/class_names.py
mmdetection-master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
mmdetection-master/configs/base/models/faster_rcnn_r50_fpn.py `
Environment
sys.platform: linux Python: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] CUDA available: True GPU 0: NVIDIA GeForce MX250 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.6, V11.6.124 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.11.0+cu102 PyTorch compiling details: PyTorch built with:
TorchVision: 0.12.0+cu102 OpenCV: 4.5.5 MMCV: 1.5.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.2 MMDetection: 2.22.0+HEAD
Error traceback
2022-05-12 10:44:52,082 - mmdet - INFO - workflow: [('train', 1)], max: 4 epochs 2022-05-12 10:44:52,082 - mmdet - INFO - Checkpoints will be saved to /home/ayanami/mmdetection-master/WORK_DIR by HardDiskBackend. 2022-05-12 10:44:57,328 - mmdet - INFO - Saving checkpoint at 1 epochs completed: 0, elapsed: 0s ---------------iou_thr: 0.5--------------- Traceback (most recent call last): File "tools/train.py", line 209, in
main()
File "tools/train.py", line 198, in main
train_detector(
File "/home/ayanami/mmdetection-master/mmdet/apis/train.py", line 208, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/home/ayanami/miniconda3/envs/mmdet/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/ayanami/miniconda3/envs/mmdet/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 54, in train
self.call_hook('after_train_epoch')
File "/home/ayanami/miniconda3/envs/mmdet/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 309, in call_hook
getattr(hook, fn_name)(self)
File "/home/ayanami/miniconda3/envs/mmdet/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 267, in after_train_epoch
self._do_evaluate(runner)
File "/home/ayanami/mmdetection-master/mmdet/core/evaluation/eval_hooks.py", line 58, in _do_evaluate
key_score = self.evaluate(runner, results)
File "/home/ayanami/miniconda3/envs/mmdet/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 363, in evaluate
eval_res = self.dataloader.dataset.evaluate(
File "/home/ayanami/mmdetection-master/mmdet/datasets/voc.py", line 85, in evaluate
meanap, = eval_map(
File "/home/ayanami/mmdetection-master/mmdet/core/evaluation/mean_ap.py", line 584, in eval_map
num_classes = len(det_results[0]) # positive class num
IndexError: list index out of range