基于十九届智能车百度完全模型组线上赛baseline修改

调整参数最高能到0.989吧

一、环境准备

1.安装PaddleDetection

In [1]

# 解压PaddleDetection压缩包
%cd /home/aistudio/data/data267567
!unzip -q PaddleDetection-release-2.6.zip -d /home/aistudio

/home/aistudio/data/data267567

2.安装依赖

In [2]

# 安装requirements中的依赖
%cd ~/PaddleDetection-release-2.6
!pip install -r requirements.txt
#安装过慢可以打开requirements.txt文件，注释掉opencv-python <= 4.6.0（在前面加“#”）
# 然后将此命令前的“#”删掉重新运行
!pip install opencv-python <= 4.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

# 编译安装paddledet
!python setup.py install
%cd ~

copying ppdet/modeling/reid/resnet.py -> build/lib/ppdet/modeling/reid
copying ppdet/modeling/reid/resnet_embedding.py -> build/lib/ppdet/modeling/reid
copying ppdet/modeling/reid/jde_embedding_head.py -> build/lib/ppdet/modeling/reid
creating build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/convnext.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/esnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/hardnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/cspresnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/vgg.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/dla.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/lcnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/ghostnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/csp_darknet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/transformer_utils.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/lite_hrnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/mobilenet_v1.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/senet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/hrnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/swin_transformer.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/vision_transformer.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/mobileone.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/name_adapter.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/shufflenet_v2.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/__init__.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/resnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/trans_encoder.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/darknet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/blazenet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/res2net.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/mobilenet_v3.py -> build/lib/ppdet/modeling/backbones
creating build/lib/ppdet/modeling/ssod
copying ppdet/modeling/ssod/utils.py -> build/lib/ppdet/modeling/ssod
copying ppdet/modeling/ssod/__init__.py -> build/lib/ppdet/modeling/ssod
copying ppdet/modeling/ssod/losses.py -> build/lib/ppdet/modeling/ssod
creating build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/solov2_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/bbox_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ttf_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/simota_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/pico_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ppyoloe_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/gfl_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/yolo_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/centertrack_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/face_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/roi_extractor.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/fcosr_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ppyoloe_r_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/sparse_roi_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/centernet_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/keypoint_hrhrnet_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/petr_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/retina_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/__init__.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/sparsercnn_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ssd_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/cascade_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/tood_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/yolof_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/fcos_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/detr_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ppyoloe_contrast_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/mask_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/s2anet_head.py -> build/lib/ppdet/modeling/heads
creating build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/proposal_generator.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/anchor_generator.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/embedding_rpn_head.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/target.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/rpn_head.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/__init__.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/target_layer.py -> build/lib/ppdet/modeling/proposal_generator
creating build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/ocsort_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/base_jde_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/center_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/jde_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/__init__.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/botsort_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/deepsort_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/base_sde_tracker.py -> build/lib/ppdet/modeling/mot/tracker
creating build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/deepsort_matching.py -> build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/ocsort_matching.py -> build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/__init__.py -> build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/jde_matching.py -> build/lib/ppdet/modeling/mot/matching
creating build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/gmc.py -> build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/kalman_filter.py -> build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/ocsort_kalman_filter.py -> build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/__init__.py -> build/lib/ppdet/modeling/mot/motion
creating build/lib/ppdet/core/config
copying ppdet/core/config/yaml_helpers.py -> build/lib/ppdet/core/config
copying ppdet/core/config/schema.py -> build/lib/ppdet/core/config
copying ppdet/core/config/__init__.py -> build/lib/ppdet/core/config
creating build/lib/ppdet/model_zoo/tests
copying ppdet/model_zoo/tests/test_get_model.py -> build/lib/ppdet/model_zoo/tests
copying ppdet/model_zoo/tests/test_list_model.py -> build/lib/ppdet/model_zoo/tests
copying ppdet/model_zoo/tests/__init__.py -> build/lib/ppdet/model_zoo/tests
creating build/lib/ppdet/data/crop_utils
copying ppdet/data/crop_utils/__init__.py -> build/lib/ppdet/data/crop_utils
copying ppdet/data/crop_utils/chip_box_utils.py -> build/lib/ppdet/data/crop_utils
copying ppdet/data/crop_utils/annotation_cropper.py -> build/lib/ppdet/data/crop_utils
creating build/lib/ppdet/data/source
copying ppdet/data/source/mot.py -> build/lib/ppdet/data/source
copying ppdet/data/source/category.py -> build/lib/ppdet/data/source
copying ppdet/data/source/sniper_coco.py -> build/lib/ppdet/data/source
copying ppdet/data/source/keypoint_coco.py -> build/lib/ppdet/data/source
copying ppdet/data/source/dataset.py -> build/lib/ppdet/data/source
copying ppdet/data/source/voc.py -> build/lib/ppdet/data/source
copying ppdet/data/source/__init__.py -> build/lib/ppdet/data/source
copying ppdet/data/source/pose3d_cmb.py -> build/lib/ppdet/data/source
copying ppdet/data/source/widerface.py -> build/lib/ppdet/data/source
copying ppdet/data/source/coco.py -> build/lib/ppdet/data/source
creating build/lib/ppdet/data/transform
copying ppdet/data/transform/autoaugment_utils.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/op_helper.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/atss_assigner.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/mot_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/batch_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/gridmask_utils.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/rotated_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/keypoint_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/keypoints_3d_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/__init__.py -> build/lib/ppdet/data/transform
copying ppdet/model_zoo/MODEL_ZOO -> build/lib/ppdet/model_zoo
creating build/bdist.linux-x86_64
creating build/bdist.linux-x86_64/egg
creating build/bdist.linux-x86_64/egg/ppdet
copying build/lib/ppdet/version.py -> build/bdist.linux-x86_64/egg/ppdet
creating build/bdist.linux-x86_64/egg/ppdet/modeling
creating build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/yolo.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/pose3d_metro.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/keypoint_hrhrnet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/detr.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/queryinst.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/picodet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/gfl.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/fcos.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/mask_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/bytetrack.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/jde.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/sparse_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/meta_arch.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/keypoint_hrnet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/deepsort.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/s2anet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/yolox.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/retinanet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/solov2.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/tood.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/centertrack.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/blazeface.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/yolof.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/fairmot.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/centernet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/faster_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/keypoint_petr.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/ppyoloe.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/cascade_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/ssd.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/ttfnet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/post_process.py -> build/bdist.linux-x86_64/egg/ppdet/modeling
copying build/lib/ppdet/modeling/ops.py -> build/bdist.linux-x86_64/egg/ppdet/modeling
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/ocsort_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/base_jde_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/center_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/jde_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/botsort_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/deepsort_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/base_sde_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/utils.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/deepsort_matching.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/ocsort_matching.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/jde_matching.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/gmc.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/kalman_filter.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/ocsort_kalman_filter.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/visualization.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot
copying build/lib/ppdet/modeling/shape_spec.py -> build/bdist.linux-x86_64/egg/ppdet/modeling
creating build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/varifocal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/smooth_l1_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/gfocal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/focal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/jde_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/probiou_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/iou_aware_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/cot_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/iou_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/yolo_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/ssd_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/pose3d_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/queryinst_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/supcontrast.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/fairmot_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/sparsercnn_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/detr_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/fcos_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/ctfocal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/keypoint_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/solov2_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
creating build/bdist.linux-x86_64/egg/ppdet/modeling/necks
copying build/lib/ppdet/modeling/necks/dilated_encoder.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/necks
copying build/lib/ppdet/modeling/necks/custom_pan.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/necks
copying build/lib/ppdet/modeling/necks/bifpn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/necks
copying build/lib/ppdet/modeling/necks/blazeface_fpn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/necks
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copying build/lib/ppdet/data/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data
creating build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/mot.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/category.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/sniper_coco.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/keypoint_coco.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/dataset.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/voc.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/pose3d_cmb.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/widerface.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/coco.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/shm_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data
creating build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/autoaugment_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/op_helper.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/atss_assigner.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/mot_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/batch_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/gridmask_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/rotated_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/keypoint_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/keypoints_3d_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
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byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/utils.py to utils.cpython-37.pyc
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creating build/bdist.linux-x86_64/egg/EGG-INFO
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ppdet.data.transform.__pycache__.autoaugment_utils.cpython-37: module MAY be using inspect.stack
ppdet.modeling.tests.__pycache__.test_mstest.cpython-37: module references __file__
ppdet.modeling.tests.__pycache__.test_ops.cpython-37: module references __file__
ppdet.modeling.tests.__pycache__.test_yolov3_loss.cpython-37: module references __file__
creating dist
creating 'dist/paddledet-2.6.0-py3.7.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing paddledet-2.6.0-py3.7.egg
creating /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddledet-2.6.0-py3.7.egg
Extracting paddledet-2.6.0-py3.7.egg to /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Installed /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddledet-2.6.0-py3.7.egg
Processing dependencies for paddledet==2.6.0
Searching for pyclipper==1.3.0.post5
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: motmetrics 1.4.0
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: setuptools 56.2.0
Adding setuptools 56.2.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: scipy 1.6.3
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: shapely 2.0.4
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Best match: PyYAML 5.1.2
Adding PyYAML 5.1.2 to easy-install.pth file

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Best match: opencv-python 4.1.1.26
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: scikit-learn 0.24.2
Adding scikit-learn 0.24.2 to easy-install.pth file

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Adding xmltodict 0.13.0 to easy-install.pth file

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Best match: pandas 1.1.5
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Adding matplotlib 2.2.3 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for protobuf==3.20.0
Best match: protobuf 3.20.0
Adding protobuf 3.20.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Flask==1.1.1
Best match: Flask 1.1.1
Adding Flask 1.1.1 to easy-install.pth file
Installing flask script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for six==1.16.0
Best match: six 1.16.0
Adding six 1.16.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Pillow==8.2.0
Best match: Pillow 8.2.0
Adding Pillow 8.2.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for bce-python-sdk==0.8.53
Best match: bce-python-sdk 0.8.53
Adding bce-python-sdk 0.8.53 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for requests==2.24.0
Best match: requests 2.24.0
Adding requests 2.24.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Flask-Babel==1.0.0
Best match: Flask-Babel 1.0.0
Adding Flask-Babel 1.0.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for importlib-metadata==4.2.0
Best match: importlib-metadata 4.2.0
Adding importlib-metadata 4.2.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for typing-extensions==4.7.1
Best match: typing-extensions 4.7.1
Adding typing-extensions 4.7.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for joblib==0.14.1
Best match: joblib 0.14.1
Adding joblib 0.14.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for threadpoolctl==2.1.0
Best match: threadpoolctl 2.1.0
Adding threadpoolctl 2.1.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for python-dateutil==2.8.2
Best match: python-dateutil 2.8.2
Adding python-dateutil 2.8.2 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pytz==2019.3
Best match: pytz 2019.3
Adding pytz 2019.3 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pyparsing==3.0.9
Best match: pyparsing 3.0.9
Adding pyparsing 3.0.9 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for cycler==0.10.0
Best match: cycler 0.10.0
Adding cycler 0.10.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for kiwisolver==1.1.0
Best match: kiwisolver 1.1.0
Adding kiwisolver 1.1.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Click==7.0
Best match: Click 7.0
Adding Click 7.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for itsdangerous==1.1.0
Best match: itsdangerous 1.1.0
Adding itsdangerous 1.1.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Werkzeug==0.16.0
Best match: Werkzeug 0.16.0
Adding Werkzeug 0.16.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Jinja2==3.0.0
Best match: Jinja2 3.0.0
Adding Jinja2 3.0.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for future==0.18.0
Best match: future 0.18.0
Adding future 0.18.0 to easy-install.pth file
Installing futurize script to /opt/conda/envs/python35-paddle120-env/bin
Installing pasteurize script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pycryptodome==3.9.9
Best match: pycryptodome 3.9.9
Adding pycryptodome 3.9.9 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for idna==2.8
Best match: idna 2.8
Adding idna 2.8 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for certifi==2019.9.11
Best match: certifi 2019.9.11
Adding certifi 2019.9.11 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for chardet==3.0.4
Best match: chardet 3.0.4
Adding chardet 3.0.4 to easy-install.pth file
Installing chardetect script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for urllib3==1.25.6
Best match: urllib3 1.25.6
Adding urllib3 1.25.6 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Babel==2.8.0
Best match: Babel 2.8.0
Adding Babel 2.8.0 to easy-install.pth file
Installing pybabel script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for zipp==3.8.1
Best match: zipp 3.8.1
Adding zipp 3.8.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for MarkupSafe==2.0.1
Best match: MarkupSafe 2.0.1
Adding MarkupSafe 2.0.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Finished processing dependencies for paddledet==2.6.0
/home/aistudio

二、数据准备

1.解压官方数据集压缩包

In [3]

# 移动到当前挂载的数据集目录
# 解压数据集到指定目录下
%cd /home/aistudio/data/data257994
!unzip -q Car2024.zip -d /home/aistudio

/home/aistudio/data/data257994

2.生成训练所需文件

这里训练集和验证集是按4：1划分的，可以将全部的数据集都用作训练

In [4]

import os

#生成train.txt、val.txt
xml_dir  = '/home/aistudio/Car2024/annotations'
img_dir = '/home/aistudio/Car2024/images'
path_list = list()
for img in os.listdir(img_dir):
    img_path = os.path.join(img_dir,img)
    xml_path = os.path.join(xml_dir,img.replace('jpg', 'xml'))
    path_list.append((img_path, xml_path))

train_f = open('/home/aistudio/Car2024/train.txt','w') 
val_f = open('/home/aistudio/Car2024/val.txt','w') 

for i ,content in enumerate(path_list):
    img, xml = content
    text = img + ' ' + xml + '\n'
    if i % 5 == 0:
        val_f.write(text)
    else:
        train_f.write(text)
train_f.close()
val_f.close()

In [5]

%cd ~/PaddleDetection-release-2.6
# 生成训练集
!python tools/x2coco.py \
        --dataset_type voc \
        --voc_anno_dir /home/aistudio/Car2024/annotations/ \
        --voc_anno_list /home/aistudio/Car2024/train.txt \
        --voc_label_list /home/aistudio/Car2024/label_list.txt \
        --voc_out_name /home/aistudio/Car2024/train.json

# 生成验证集
!python tools/x2coco.py \
        --dataset_type voc \
        --voc_anno_dir /home/aistudio/Car2024/annotations/ \
        --voc_anno_list /home/aistudio/Car2024/val.txt \
        --voc_label_list /home/aistudio/Car2024/label_list.txt \
        --voc_out_name /home/aistudio/Car2024/val.json

/home/aistudio/PaddleDetection-release-2.6
Start converting !
100%|████████████████████████████████████| 1655/1655 [00:00<00:00, 14389.39it/s]
Start converting !
100%|██████████████████████████████████████| 414/414 [00:00<00:00, 12318.42it/s]

三、数据分析

1.标签数量统计

In [6]

import os
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def count_num(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')

    dict = {} # 新建字典，用于存放各类标签名及其对应的数目
    for i, file in enumerate(annotations): # 遍历xml文件
       
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()

        # 遍历文件的所有标签
        for obj in root.iter('object'):
            name = obj.find('name').text
            if(name in dict.keys()): dict[name] += 1 # 如果标签不是第一次出现，则+1
            else: dict[name] = 1 # 如果标签是第一次出现，则将该标签名对应的value初始化为1

    # 打印结果
    print("各类标签的数量分别为：")
    for key in dict.keys(): 
        print(key + ': ' + str(dict[key]))            

indir='/home/aistudio/Car2024/annotations'   # xml文件所在的目录
count_num(indir) # 调用函数统计各类标签数目

各类标签的数量分别为：
block: 315
cone: 4942
patient: 176
bridge: 209
bomb: 212
safety: 198
evil: 94
prop: 405
spy: 23
danger: 176
crosswalk: 62
tumble: 129
thief: 37

2.标注框高宽比分析

第一次运行卡了，可以尝试重新运行一次，下面应该是要有图的

In [8]

import os
import matplotlib.pyplot as plt
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def ratio(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')
    # count_0, count_1, count_2, count_3 = 0, 0, 0, 0 # 举反例，不要这么写
    count = [0 for i in range(20)]

    for i, file in enumerate(annotations): # 遍历xml文件
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()

        # 遍历文件的所有检测框
        for obj in root.iter('object'):
            xmin = obj.find('bndbox').find('xmin').text
            ymin = obj.find('bndbox').find('ymin').text
            xmax = obj.find('bndbox').find('xmax').text
            ymax = obj.find('bndbox').find('ymax').text
            Aspect_ratio = (int(ymax)-int(ymin)) / (int(xmax)-int(xmin))
            if int(Aspect_ratio/0.25) < 19:
                count[int(Aspect_ratio/0.25)] += 1
            else:
                count[-1] += 1
    sign = [0.25*i for i in range(20)]
    plt.bar(x=sign, height=count)
    print(count)

indir='/home/aistudio/Car2024/annotations/'   # xml文件所在的目录
ratio(indir)

[52, 187, 877, 846, 1999, 2089, 548, 190, 99, 39, 30, 13, 5, 3, 0, 0, 1, 0, 0, 0]

<Figure size 640x480 with 1 Axes>

3.图像尺寸分析

In [9]

import os
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def Image_size(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')
    width_heights = []

    for i, file in enumerate(annotations): # 遍历xml文件
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()
        width = int(root.find('size').find('width').text)
        height = int(root.find('size').find('height').text)
        if [width, height] not in width_heights: width_heights.append([width, height])
    print("数据集中，有{}种不同的尺寸，分别是：".format(len(width_heights)))
    for item in width_heights:
        print(item)

indir='/home/aistudio/Car2024/annotations/'   # xml文件所在的目录
Image_size(indir)

数据集中，有1种不同的尺寸，分别是：
[320, 240]

4.检测框中心分布分析

In [10]

import os
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def distribution(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')
    data_x, data_y = [], []

    for i, file in enumerate(annotations): # 遍历xml文件
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()
        width = int(root.find('size').find('width').text)
        height = int(root.find('size').find('height').text)

        # 遍历文件的所有检测框
        for obj in root.iter('object'):
            xmin = int(obj.find('bndbox').find('xmin').text)
            ymin = int(obj.find('bndbox').find('ymin').text)
            xmax = int(obj.find('bndbox').find('xmax').text)
            ymax = int(obj.find('bndbox').find('ymax').text)
            x = (xmin + (xmax-xmin)) / width
            y = (ymin + (ymax-ymin)) / height
            data_x.append(x)
            data_y.append(y)
            
    plt.scatter(data_x, data_y, s=1, alpha=0.1)

indir='/home/aistudio/Car2024/annotations/'   # xml文件所在的目录
distribution(indir)

<Figure size 640x480 with 1 Axes>

四、模型训练

1.修改数据集配置文件

修改模型配置文件中的第一行，将ppyoloe_plus_crn_m_80e_coco.yml第一行的coco_detection.yml修改为coco_newcar.yml

In [11]

%cd /home/aistudio
!cp coco_newcar.yml /home/aistudio/PaddleDetection-release-2.6/configs/datasets/

/home/aistudio

2.修改模型配置文件参数

具体参数需自行调整，默认使用官方的，建议调整下base_lr，由于我们的单卡，可以将默认的数值改为原先的1/8

3.模型训练

这里采用的模型是ppyoloe+_m

In [12]

%cd /home/aistudio/PaddleDetection-release-2.6
# 模型训练 
# 如果要恢复训练，则加上 -r output/ppyoloe_plus_crn_m_80e_coco/best_model
# 如果要边训练边评估，则加上--eval
# 训练时的日志输出将保存在--vdl_log_dir所指的路径下
# 模型压缩则加上--slim_config configs/slim/xxx/{SLIM_CONFIG.yml}（{SLIM_CONFIG.yml}为指定压缩策略配置文件）
!python tools/train.py \
    -c configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml --eval \
    --use_vdl=true \
    --vdl_log_dir=VisualDL

/home/aistudio/PaddleDetection-release-2.6
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
[06/01 12:27:00] ppdet.data.source.coco INFO: Load [1655 samples valid, 0 samples invalid] in file /home/aistudio/Car2024/train.json.
W0601 12:27:00.946624  1254 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 12.0, Runtime API Version: 11.2
W0601 12:27:00.954115  1254 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
[06/01 12:27:05] ppdet.utils.download INFO: Downloading ppyoloe_crn_m_obj365_pretrained.pdparams from https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
100%|█████████████████████████████████| 104604/104604 [00:10<00:00, 9832.91KB/s]
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.0.bias is unmatched with the shape [13] in model yolo_head.pred_cls.0.bias. And the weight yolo_head.pred_cls.0.bias will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365, 576, 3, 3] in pretrained weight yolo_head.pred_cls.0.weight is unmatched with the shape [13, 576, 3, 3] in model yolo_head.pred_cls.0.weight. And the weight yolo_head.pred_cls.0.weight will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.1.bias is unmatched with the shape [13] in model yolo_head.pred_cls.1.bias. And the weight yolo_head.pred_cls.1.bias will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365, 288, 3, 3] in pretrained weight yolo_head.pred_cls.1.weight is unmatched with the shape [13, 288, 3, 3] in model yolo_head.pred_cls.1.weight. And the weight yolo_head.pred_cls.1.weight will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.2.bias is unmatched with the shape [13] in model yolo_head.pred_cls.2.bias. And the weight yolo_head.pred_cls.2.bias will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365, 144, 3, 3] in pretrained weight yolo_head.pred_cls.2.weight is unmatched with the shape [13, 144, 3, 3] in model yolo_head.pred_cls.2.weight. And the weight yolo_head.pred_cls.2.weight will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/ppyoloe_crn_m_obj365_pretrained.pdparams
[06/01 12:27:20] ppdet.engine INFO: Epoch: [0] [  0/206] learning_rate: 0.000000 loss: 4.613988 loss_cls: 3.654145 loss_iou: 0.210726 loss_dfl: 0.866056 loss_l1: 0.385787 eta: 17:34:44 batch_cost: 3.8401 data_cost: 0.0005 ips: 2.0833 images/s
[06/01 12:28:30] ppdet.engine INFO: Epoch: [0] [100/206] learning_rate: 0.000097 loss: 3.047830 loss_cls: 2.266012 loss_iou: 0.168912 loss_dfl: 0.721105 loss_l1: 0.231601 eta: 2:34:58 batch_cost: 0.5350 data_cost: 0.0003 ips: 14.9541 images/s
[06/01 12:29:41] ppdet.engine INFO: Epoch: [0] [200/206] learning_rate: 0.000194 loss: 2.323405 loss_cls: 1.533948 loss_iou: 0.158725 loss_dfl: 0.670796 loss_l1: 0.194519 eta: 2:30:20 batch_cost: 0.5404 data_cost: 0.0003 ips: 14.8045 images/s
[06/01 12:29:47] ppdet.engine INFO: Epoch: [1] [  0/206] learning_rate: 0.000200 loss: 2.257758 loss_cls: 1.521657 loss_iou: 0.158959 loss_dfl: 0.669803 loss_l1: 0.195281 eta: 2:32:04 batch_cost: 0.5521 data_cost: 0.0098 ips: 14.4891 images/s
[06/01 12:30:59] ppdet.engine INFO: Epoch: [1] [100/206] learning_rate: 0.000297 loss: 2.089173 loss_cls: 1.335514 loss_iou: 0.161354 loss_dfl: 0.688102 loss_l1: 0.199043 eta: 2:30:42 batch_cost: 0.5558 data_cost: 0.0003 ips: 14.3943 images/s
[06/01 12:32:10] ppdet.engine INFO: Epoch: [1] [200/206] learning_rate: 0.000394 loss: 1.935451 loss_cls: 1.187285 loss_iou: 0.156842 loss_dfl: 0.679105 loss_l1: 0.195983 eta: 2:29:01 batch_cost: 0.5476 data_cost: 0.0003 ips: 14.6086 images/s
[06/01 12:32:17] ppdet.engine INFO: Epoch: [2] [  0/206] learning_rate: 0.000400 loss: 1.921179 loss_cls: 1.183878 loss_iou: 0.156247 loss_dfl: 0.673825 loss_l1: 0.190812 eta: 2:29:47 batch_cost: 0.5597 data_cost: 0.0088 ips: 14.2935 images/s
[06/01 12:33:28] ppdet.engine INFO: Epoch: [2] [100/206] learning_rate: 0.000497 loss: 2.121108 loss_cls: 1.389727 loss_iou: 0.166188 loss_dfl: 0.685305 loss_l1: 0.205390 eta: 2:28:00 batch_cost: 0.5429 data_cost: 0.0003 ips: 14.7344 images/s
[06/01 12:34:40] ppdet.engine INFO: Epoch: [2] [200/206] learning_rate: 0.000594 loss: 2.134946 loss_cls: 1.317788 loss_iou: 0.178044 loss_dfl: 0.697273 loss_l1: 0.206171 eta: 2:27:04 batch_cost: 0.5560 data_cost: 0.0004 ips: 14.3877 images/s
[06/01 12:34:46] ppdet.engine INFO: Epoch: [3] [  0/206] learning_rate: 0.000600 loss: 2.152541 loss_cls: 1.326942 loss_iou: 0.179959 loss_dfl: 0.698840 loss_l1: 0.206171 eta: 2:27:36 batch_cost: 0.5719 data_cost: 0.0070 ips: 13.9893 images/s
[06/01 12:36:01] ppdet.engine INFO: Epoch: [3] [100/206] learning_rate: 0.000697 loss: 1.838928 loss_cls: 1.065280 loss_iou: 0.162629 loss_dfl: 0.671139 loss_l1: 0.188668 eta: 2:27:09 batch_cost: 0.5717 data_cost: 0.0003 ips: 13.9929 images/s
[06/01 12:37:15] ppdet.engine INFO: Epoch: [3] [200/206] learning_rate: 0.000794 loss: 1.853414 loss_cls: 1.093851 loss_iou: 0.167201 loss_dfl: 0.688709 loss_l1: 0.191645 eta: 2:26:16 batch_cost: 0.5616 data_cost: 0.0003 ips: 14.2438 images/s
[06/01 12:37:21] ppdet.engine INFO: Epoch: [4] [  0/206] learning_rate: 0.000800 loss: 1.853414 loss_cls: 1.082545 loss_iou: 0.167922 loss_dfl: 0.688709 loss_l1: 0.194466 eta: 2:26:43 batch_cost: 0.5756 data_cost: 0.0080 ips: 13.8986 images/s
[06/01 12:38:36] ppdet.engine INFO: Epoch: [4] [100/206] learning_rate: 0.000897 loss: 1.758096 loss_cls: 0.987893 loss_iou: 0.174324 loss_dfl: 0.682745 loss_l1: 0.194987 eta: 2:26:02 batch_cost: 0.5711 data_cost: 0.0003 ips: 14.0068 images/s
[06/01 12:39:50] ppdet.engine INFO: Epoch: [4] [200/206] learning_rate: 0.000994 loss: 1.795157 loss_cls: 0.999800 loss_iou: 0.169698 loss_dfl: 0.678204 loss_l1: 0.195827 eta: 2:25:03 batch_cost: 0.5615 data_cost: 0.0003 ips: 14.2487 images/s
[06/01 12:39:58] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyoloe_plus_crn_m_80e_coco
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
[06/01 12:39:58] ppdet.data.source.coco INFO: Load [414 samples valid, 0 samples invalid] in file /home/aistudio/Car2024/val.json.
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
[06/01 12:39:59] ppdet.engine INFO: Eval iter: 0
[06/01 12:40:19] ppdet.engine INFO: Eval iter: 100
[06/01 12:40:40] ppdet.engine INFO: Eval iter: 200
[06/01 12:40:44] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[06/01 12:40:44] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=1.66s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=4.85s).
Accumulating evaluation results...
DONE (t=1.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.572
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.715
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.669
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.642
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.620
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.683
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.699
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.737
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.757
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.812
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.729
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700
[06/01 12:40:52] ppdet.engine INFO: Total sample number: 414, average FPS: 9.557503568249556
[06/01 12:40:53] ppdet.engine INFO: Best test bbox ap is 0.572.
[06/01 12:40:54] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyoloe_plus_crn_m_80e_coco
[06/01 12:40:55] ppdet.engine INFO: Epoch: [5] [  0/206] learning_rate: 0.001000 loss: 1.764578 loss_cls: 0.977293 loss_iou: 0.169698 loss_dfl: 0.676421 loss_l1: 0.194450 eta: 2:25:02 batch_cost: 0.5640 data_cost: 0.0003 ips: 14.1832 images/s
[06/01 12:42:09] ppdet.engine INFO: Epoch: [5] [100/206] learning_rate: 0.001000 loss: 1.738458 loss_cls: 0.985305 loss_iou: 0.167200 loss_dfl: 0.675814 loss_l1: 0.190306 eta: 2:24:17 batch_cost: 0.5716 data_cost: 0.0003 ips: 13.9952 images/s
[06/01 12:43:24] ppdet.engine INFO: Epoch: [5] [200/206] learning_rate: 0.001000 loss: 1.733965 loss_cls: 0.969274 loss_iou: 0.160105 loss_dfl: 0.678193 loss_l1: 0.183968 eta: 2:23:34 batch_cost: 0.5750 data_cost: 0.0003 ips: 13.9119 images/s
[06/01 12:43:31] ppdet.engine INFO: Epoch: [6] [  0/206] learning_rate: 0.001000 loss: 1.729218 loss_cls: 0.963780 loss_iou: 0.158756 loss_dfl: 0.675050 loss_l1: 0.182651 eta: 2:23:51 batch_cost: 0.5912 data_cost: 0.0109 ips: 13.5321 images/s
[06/01 12:44:45] ppdet.engine INFO: Epoch: [6] [100/206] learning_rate: 0.000999 loss: 1.613363 loss_cls: 0.892368 loss_iou: 0.150079 loss_dfl: 0.648608 loss_l1: 0.161639 eta: 2:22:53 batch_cost: 0.5655 data_cost: 0.0003 ips: 14.1480 images/s
[06/01 12:45:59] ppdet.engine INFO: Epoch: [6] [200/206] learning_rate: 0.000999 loss: 1.648051 loss_cls: 0.876875 loss_iou: 0.154434 loss_dfl: 0.659376 loss_l1: 0.170266 eta: 2:21:52 batch_cost: 0.5619 data_cost: 0.0003 ips: 14.2379 images/s
[06/01 12:46:05] ppdet.engine INFO: Epoch: [7] [  0/206] learning_rate: 0.000999 loss: 1.662382 loss_cls: 0.876875 loss_iou: 0.154701 loss_dfl: 0.660583 loss_l1: 0.170266 eta: 2:22:01 batch_cost: 0.5708 data_cost: 0.0086 ips: 14.0163 images/s
[06/01 12:47:19] ppdet.engine INFO: Epoch: [7] [100/206] learning_rate: 0.000998 loss: 1.652610 loss_cls: 0.915990 loss_iou: 0.153202 loss_dfl: 0.654600 loss_l1: 0.164406 eta: 2:20:56 batch_cost: 0.5584 data_cost: 0.0003 ips: 14.3269 images/s
^C

In [ ]

# 当训练完成，如有需要，可以删除所有的checkpoint，只保留best_model
%cd ~/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco
!find . -type f -name '[0-9]*'
!find . -type f -name '[0-9]*' -exec rm -f {} \;
!echo "delete checkpoints complete!"

4.模型导出

出现full_graph问题就打开trainer.py把full_graph=True删掉即可

In [14]

%cd /home/aistudio/PaddleDetection-release-2.6
# 将"-o weights"里的模型路径换成你自己训好的模型
!python tools/export_model.py \
    -c configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml \
    -o weights=/home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams \
    TestReader.fuse_normalize=true

/home/aistudio/PaddleDetection-release-2.6
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
[06/01 12:50:19] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[06/01 12:50:19] ppdet.engine INFO: Export inference config file to output_inference/ppyoloe_plus_crn_m_80e_coco/infer_cfg.yml
[06/01 12:50:25] ppdet.engine INFO: Export model and saved in output_inference/ppyoloe_plus_crn_m_80e_coco

5.模型预测

可以运行了看一下对每张图片的识别,比对后可以针对性进行数据增强

In [16]

%cd /home/aistudio/PaddleDetection-release-2.6
# infer_img表示预测改路径的单张图片
# infer_dir表示对该路径下的所有图片进行预测
# draw_threshold表示置信度大于该值的框才画出来
# 生成的图片保存在/home/aistudio/work/PaddleDetection/infer_output
!python tools/infer.py \
    -c configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml \
    --infer_dir=/home/aistudio/Car2024/images \
    --output_dir=infer_output/ \
    --draw_threshold=0.5 \
    -o weights=/home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams

/home/aistudio/PaddleDetection-release-2.6
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
W0601 12:51:06.215662  5482 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 12.0, Runtime API Version: 11.2
W0601 12:51:06.221030  5482 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
[06/01 12:51:09] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams
[06/01 12:51:09] train INFO: Found 2069 inference images in total.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
  3%|█▏                                       | 58/2069 [00:13<06:17,  5.32it/s]^C
  3%|█▏                                       | 58/2069 [00:13<07:50,  4.27it/s]
Traceback (most recent call last):
  File "tools/infer.py", line 236, in <module>
    main()
  File "tools/infer.py", line 232, in main
    run(FLAGS, cfg)
  File "tools/infer.py", line 187, in run
    visualize=FLAGS.visualize)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/engine/trainer.py", line 983, in predict
    outs = self.model(data)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/architectures/meta_arch.py", line 76, in forward
    outs.append(self.get_pred())
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/architectures/yolo.py", line 150, in get_pred
    return self._forward()
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/architectures/yolo.py", line 81, in _forward
    body_feats = self.backbone(self.inputs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 309, in forward
    x = stage(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 221, in forward
    y2 = self.blocks(self.conv2(x))
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/container.py", line 98, in forward
    input = layer(input)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 157, in forward
    y = self.conv1(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 61, in forward
    x = self.conv(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/conv.py", line 678, in forward
    use_cudnn=self._use_cudnn)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/conv.py", line 144, in _conv_nd
    pre_bias = getattr(_C_ops, op_type)(x, weight, *attrs)
KeyboardInterrupt

五、打包代码

1.生成output.txt便于之后尝试跑通predict

In [17]

import os  
# 指定要搜索的文件夹路径  
folder_path = "/home/aistudio/Car2024/images"  
# 指定要写入的txt文件路径  
output_file = "/home/aistudio/work/submission/output.txt"
# 获取文件夹下的所有文件和子文件夹  
files = os.listdir(folder_path)
# 打开txt文件准备写入  
with open(output_file, "w") as file:  
    # 遍历所有文件和子文件夹  
    for item in files:  
        # 如果不是文件夹，则写入文件路径到txt文件中  
        if not os.path.isdir(os.path.join(folder_path, item)):  
            file.write(os.path.join(folder_path, item) + "\n")

2.将导出的模型复制到submission/model中

In [22]

# 在work目录下整理提交代码
# 创建model文件夹
if not os.path.exists('/home/aistudio/work/submission/model/'):
    os.makedirs('/home/aistudio/work/submission/model/') #创建路径

if not os.path.exists('/home/aistudio/work/submission/PaddleDetection/deploy/python'):
    os.makedirs('/home/aistudio/work/submission/PaddleDetection/deploy/python')


# 将检测模型拷贝到model文件夹中
!cp -r /home/aistudio/PaddleDetection-release-2.6/output_inference/ppyoloe_plus_crn_m_80e_coco/* /home/aistudio/work/submission/model/
#!cp -r /home/aistudio/PaddleDetection-release-2.6/deploy/python/* /home/aistudio/work/submission/PaddleDetection/deploy/python
#!cp -r /home/aistudio/PaddleDetection-release-2.6/tools/train.py /home/aistudio/work/submission
# !rm -rf /home/aistudio/work/submission/model/model.pdiparams.info

3.尝试跑通predict

predict能运行成功就能提交成功

In [20]

# 可能耗时较长，40s-100s不等
%cd ~/work/submission
!python predict.py output.txt result.json

/home/aistudio/work/submission
Model Arch: YOLO
--transform op: Resize
--transform op: Permute
total time: 202.27240180969238

4.打包下载

在submission目录下下载submission.zip

In [21]

# 打包代码
%cd /home/aistudio/work/submission/
!zip -r -q -o submission.zip model/ PaddleDetection/ train.py predict.py

/home/aistudio/work/submission