commit a8e122637d289fd4efd5640c0cd6248c461633d2 Author: martin <1486756632@qq.com> Date: Mon Nov 3 10:12:12 2025 +0800 第一次提交，已经完成训练接口、识别接口 diff --git a/UAVid_Segmentation/v1.5_official/args.yaml b/UAVid_Segmentation/v1.5_official/args.yaml new file mode 100644 index 0000000..f3e5725 --- /dev/null +++ b/UAVid_Segmentation/v1.5_official/args.yaml @@ -0,0 +1,105 @@ +task: segment +mode: train +model: pt/yolo11s-seg.pt +data: dataset/dataset-1760926118282226200\data.yaml +epochs: 50 +time: null +patience: 100 +batch: 4 +imgsz: 640 +save: true +save_period: -1 +cache: false +device: null +workers: 0 +project: UAVid_Segmentation +name: v1.5_official +exist_ok: false +pretrained: true +optimizer: SGD +verbose: true +seed: 0 +deterministic: true +single_cls: false +rect: false +cos_lr: false +close_mosaic: 10 +resume: false +amp: true +fraction: 1.0 +profile: false +freeze: null +multi_scale: false +overlap_mask: true +mask_ratio: 4 +dropout: 0.0 +val: true +split: val +save_json: false +conf: null +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: null +vid_stride: 1 +stream_buffer: false +visualize: false +augment: true +agnostic_nms: false +classes: null +retina_masks: false +embed: null +show: false +save_frames: false +save_txt: false +save_conf: false +save_crop: false +show_labels: true +show_conf: true +show_boxes: true +line_width: null +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: true +opset: null +workspace: null +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.9 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +pose: 12.0 +kobj: 1.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +bgr: 0.0 +mosaic: 1.0 +mixup: 0.0 +cutmix: 0.0 +copy_paste: 0.0 +copy_paste_mode: flip +auto_augment: randaugment +erasing: 0.4 +cfg: null +tracker: botsort.yaml +save_dir: UAVid_Segmentation\v1.5_official diff --git a/UAVid_Segmentation/v1.5_official/results.csv b/UAVid_Segmentation/v1.5_official/results.csv new file mode 100644 index 0000000..b3445fc --- /dev/null +++ b/UAVid_Segmentation/v1.5_official/results.csv @@ -0,0 +1,3 @@ +epoch,time,train/box_loss,train/seg_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),metrics/precision(M),metrics/recall(M),metrics/mAP50(M),metrics/mAP50-95(M),val/box_loss,val/seg_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2 +1,1.79583,0,0,74.9982,0,0,0,0,0,0,0,0,0,0,0,11.5625,0,0.0955,0.0005,0.0005 +2,2.92969,0,0,83.6285,0,0,0,0,0,0,0,0,0,0,0,14.2266,0,0.0900782,0.00107822,0.00107822 diff --git a/UAVid_Segmentation/v1.5_official/train_batch0.jpg b/UAVid_Segmentation/v1.5_official/train_batch0.jpg new file mode 100644 index 0000000..c27d9c6 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official/train_batch0.jpg differ diff --git a/UAVid_Segmentation/v1.5_official/train_batch1.jpg b/UAVid_Segmentation/v1.5_official/train_batch1.jpg new file mode 100644 index 0000000..d5dd950 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official/train_batch1.jpg differ diff --git a/UAVid_Segmentation/v1.5_official/train_batch2.jpg b/UAVid_Segmentation/v1.5_official/train_batch2.jpg new file mode 100644 index 0000000..2df4eac Binary files /dev/null and b/UAVid_Segmentation/v1.5_official/train_batch2.jpg differ diff --git a/UAVid_Segmentation/v1.5_official/weights/best.pt b/UAVid_Segmentation/v1.5_official/weights/best.pt new file mode 100644 index 0000000..5813b06 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official/weights/best.pt differ diff --git a/UAVid_Segmentation/v1.5_official/weights/last.pt b/UAVid_Segmentation/v1.5_official/weights/last.pt new file mode 100644 index 0000000..5813b06 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official/weights/last.pt differ diff --git a/UAVid_Segmentation/v1.5_official2/args.yaml b/UAVid_Segmentation/v1.5_official2/args.yaml new file mode 100644 index 0000000..30ead3b --- /dev/null +++ b/UAVid_Segmentation/v1.5_official2/args.yaml @@ -0,0 +1,105 @@ +task: segment +mode: train +model: pt/yolo11s-seg.pt +data: dataset/dataset-1760926301844370600\data.yaml +epochs: 50 +time: null +patience: 100 +batch: 4 +imgsz: 640 +save: true +save_period: -1 +cache: false +device: null +workers: 0 +project: UAVid_Segmentation +name: v1.5_official2 +exist_ok: false +pretrained: true +optimizer: SGD +verbose: true +seed: 0 +deterministic: true +single_cls: false +rect: false +cos_lr: false +close_mosaic: 10 +resume: false +amp: true +fraction: 1.0 +profile: false +freeze: null +multi_scale: false +overlap_mask: true +mask_ratio: 4 +dropout: 0.0 +val: true +split: val +save_json: false +conf: null +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: null +vid_stride: 1 +stream_buffer: false +visualize: false +augment: true +agnostic_nms: false +classes: null +retina_masks: false +embed: null +show: false +save_frames: false +save_txt: false +save_conf: false +save_crop: false +show_labels: true +show_conf: true +show_boxes: true +line_width: null +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: true +opset: null +workspace: null +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.9 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +pose: 12.0 +kobj: 1.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +bgr: 0.0 +mosaic: 1.0 +mixup: 0.0 +cutmix: 0.0 +copy_paste: 0.0 +copy_paste_mode: flip +auto_augment: randaugment +erasing: 0.4 +cfg: null +tracker: botsort.yaml +save_dir: UAVid_Segmentation\v1.5_official2 diff --git a/UAVid_Segmentation/v1.5_official2/results.csv b/UAVid_Segmentation/v1.5_official2/results.csv new file mode 100644 index 0000000..abeb9f2 --- /dev/null +++ b/UAVid_Segmentation/v1.5_official2/results.csv @@ -0,0 +1,3 @@ +epoch,time,train/box_loss,train/seg_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),metrics/precision(M),metrics/recall(M),metrics/mAP50(M),metrics/mAP50-95(M),val/box_loss,val/seg_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2 +1,1.93797,0,0,74.9982,0,0,0,0,0,0,0,0,0,0,0,11.5625,0,0.0955,0.0005,0.0005 +2,3.18176,0,0,83.6285,0,0,0,0,0,0,0,0,0,0,0,14.2266,0,0.0900782,0.00107822,0.00107822 diff --git a/UAVid_Segmentation/v1.5_official2/train_batch0.jpg b/UAVid_Segmentation/v1.5_official2/train_batch0.jpg new file mode 100644 index 0000000..c27d9c6 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official2/train_batch0.jpg differ diff --git a/UAVid_Segmentation/v1.5_official2/train_batch1.jpg b/UAVid_Segmentation/v1.5_official2/train_batch1.jpg new file mode 100644 index 0000000..d5dd950 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official2/train_batch1.jpg differ diff --git a/UAVid_Segmentation/v1.5_official2/train_batch2.jpg b/UAVid_Segmentation/v1.5_official2/train_batch2.jpg new file mode 100644 index 0000000..2df4eac Binary files /dev/null and b/UAVid_Segmentation/v1.5_official2/train_batch2.jpg differ diff --git a/UAVid_Segmentation/v1.5_official2/weights/best.pt b/UAVid_Segmentation/v1.5_official2/weights/best.pt new file mode 100644 index 0000000..9634775 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official2/weights/best.pt differ diff --git a/UAVid_Segmentation/v1.5_official2/weights/last.pt b/UAVid_Segmentation/v1.5_official2/weights/last.pt new file mode 100644 index 0000000..9634775 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official2/weights/last.pt differ diff --git a/UAVid_Segmentation/v1.5_official3/args.yaml b/UAVid_Segmentation/v1.5_official3/args.yaml new file mode 100644 index 0000000..3a64d07 --- /dev/null +++ b/UAVid_Segmentation/v1.5_official3/args.yaml @@ -0,0 +1,105 @@ +task: segment +mode: train +model: pt/yolo11s-seg.pt +data: dataset/dataset-1760926476398531600\data.yaml +epochs: 50 +time: null +patience: 100 +batch: 4 +imgsz: 640 +save: true +save_period: -1 +cache: false +device: null +workers: 0 +project: UAVid_Segmentation +name: v1.5_official3 +exist_ok: false +pretrained: true +optimizer: SGD +verbose: true +seed: 0 +deterministic: true +single_cls: false +rect: false +cos_lr: false +close_mosaic: 10 +resume: false +amp: true +fraction: 1.0 +profile: false +freeze: null +multi_scale: false +overlap_mask: true +mask_ratio: 4 +dropout: 0.0 +val: true +split: val +save_json: false +conf: null +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: null +vid_stride: 1 +stream_buffer: false +visualize: false +augment: true +agnostic_nms: false +classes: null +retina_masks: false +embed: null +show: false +save_frames: false +save_txt: false +save_conf: false +save_crop: false +show_labels: true +show_conf: true +show_boxes: true +line_width: null +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: true +opset: null +workspace: null +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.9 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +pose: 12.0 +kobj: 1.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +bgr: 0.0 +mosaic: 1.0 +mixup: 0.0 +cutmix: 0.0 +copy_paste: 0.0 +copy_paste_mode: flip +auto_augment: randaugment +erasing: 0.4 +cfg: null +tracker: botsort.yaml +save_dir: UAVid_Segmentation\v1.5_official3 diff --git a/UAVid_Segmentation/v1.5_official3/results.csv b/UAVid_Segmentation/v1.5_official3/results.csv new file mode 100644 index 0000000..a4833ec --- /dev/null +++ b/UAVid_Segmentation/v1.5_official3/results.csv @@ 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a/UAVid_Segmentation/v1.5_official3/train_batch2.jpg b/UAVid_Segmentation/v1.5_official3/train_batch2.jpg new file mode 100644 index 0000000..2df4eac Binary files /dev/null and b/UAVid_Segmentation/v1.5_official3/train_batch2.jpg differ diff --git a/UAVid_Segmentation/v1.5_official3/weights/best.pt b/UAVid_Segmentation/v1.5_official3/weights/best.pt new file mode 100644 index 0000000..3b77768 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official3/weights/best.pt differ diff --git a/UAVid_Segmentation/v1.5_official3/weights/last.pt b/UAVid_Segmentation/v1.5_official3/weights/last.pt new file mode 100644 index 0000000..3b77768 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official3/weights/last.pt differ diff --git a/UAVid_Segmentation/v1.5_official4/args.yaml b/UAVid_Segmentation/v1.5_official4/args.yaml new file mode 100644 index 0000000..274e75e --- /dev/null +++ b/UAVid_Segmentation/v1.5_official4/args.yaml @@ -0,0 +1,105 @@ +task: segment +mode: train +model: pt/yolo11s-seg.pt +data: dataset/dataset-1760932424891014400\data.yaml +epochs: 50 +time: null +patience: 100 +batch: 4 +imgsz: 640 +save: true +save_period: -1 +cache: false +device: null +workers: 0 +project: UAVid_Segmentation +name: v1.5_official4 +exist_ok: false +pretrained: true +optimizer: SGD +verbose: true +seed: 0 +deterministic: true +single_cls: false +rect: false +cos_lr: false +close_mosaic: 10 +resume: false +amp: true +fraction: 1.0 +profile: false +freeze: null +multi_scale: false +overlap_mask: true +mask_ratio: 4 +dropout: 0.0 +val: true +split: val +save_json: false +conf: null +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: null +vid_stride: 1 +stream_buffer: false +visualize: false +augment: true +agnostic_nms: false +classes: null +retina_masks: false +embed: null +show: false +save_frames: false +save_txt: false +save_conf: false +save_crop: false +show_labels: true +show_conf: true +show_boxes: true +line_width: null +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: true +opset: null +workspace: null +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.9 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +pose: 12.0 +kobj: 1.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +bgr: 0.0 +mosaic: 1.0 +mixup: 0.0 +cutmix: 0.0 +copy_paste: 0.0 +copy_paste_mode: flip +auto_augment: randaugment +erasing: 0.4 +cfg: null +tracker: botsort.yaml +save_dir: UAVid_Segmentation\v1.5_official4 diff --git a/UAVid_Segmentation/v1.5_official4/labels.jpg b/UAVid_Segmentation/v1.5_official4/labels.jpg new file mode 100644 index 0000000..4167f8f Binary files /dev/null and b/UAVid_Segmentation/v1.5_official4/labels.jpg differ diff --git a/UAVid_Segmentation/v1.5_official4/results.csv b/UAVid_Segmentation/v1.5_official4/results.csv new file mode 100644 index 0000000..22d5609 --- /dev/null +++ b/UAVid_Segmentation/v1.5_official4/results.csv @@ -0,0 +1,3 @@ +epoch,time,train/box_loss,train/seg_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),metrics/precision(M),metrics/recall(M),metrics/mAP50(M),metrics/mAP50-95(M),val/box_loss,val/seg_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2 +1,3.14625,4.19941,6.41601,8.47523,3.35484,0.01042,0.5,0.24875,0.07462,0,0,0,0,3.51347,4.84009,5.55821,3.24894,0.0955,0.0005,0.0005 +2,4.97753,3.71605,7.05627,8.7431,2.98561,0.02083,0.5,0.16583,0.03317,0,0,0,0,3.61686,4.8416,6.10662,3.2123,0.0900782,0.00107822,0.00107822 diff --git a/UAVid_Segmentation/v1.5_official4/train_batch0.jpg b/UAVid_Segmentation/v1.5_official4/train_batch0.jpg new file mode 100644 index 0000000..855a011 Binary files /dev/null and b/UAVid_Segmentation/v1.5_official4/train_batch0.jpg differ diff --git a/UAVid_Segmentation/v1.5_official4/train_batch1.jpg 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b/UAVid_Segmentation/v1.5_official5/args.yaml new file mode 100644 index 0000000..d8defdc --- /dev/null +++ b/UAVid_Segmentation/v1.5_official5/args.yaml @@ -0,0 +1,105 @@ +task: segment +mode: train +model: pt/yolo11s-seg.pt +data: dataset/dataset-1760955532913358800\data.yaml +epochs: 50 +time: null +patience: 100 +batch: 4 +imgsz: 640 +save: true +save_period: -1 +cache: false +device: null +workers: 0 +project: UAVid_Segmentation +name: v1.5_official5 +exist_ok: false +pretrained: true +optimizer: SGD +verbose: true +seed: 0 +deterministic: true +single_cls: false +rect: false +cos_lr: false +close_mosaic: 10 +resume: false +amp: true +fraction: 1.0 +profile: false +freeze: null +multi_scale: false +overlap_mask: true +mask_ratio: 4 +dropout: 0.0 +val: true +split: val +save_json: false +conf: null +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: null +vid_stride: 1 +stream_buffer: false +visualize: false +augment: true +agnostic_nms: false +classes: null +retina_masks: false +embed: null +show: false +save_frames: false +save_txt: false +save_conf: false +save_crop: false +show_labels: true +show_conf: true +show_boxes: true +line_width: null +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: true +opset: null +workspace: null +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.9 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +pose: 12.0 +kobj: 1.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +bgr: 0.0 +mosaic: 1.0 +mixup: 0.0 +cutmix: 0.0 +copy_paste: 0.0 +copy_paste_mode: flip +auto_augment: randaugment +erasing: 0.4 +cfg: null +tracker: botsort.yaml +save_dir: UAVid_Segmentation\v1.5_official5 diff --git a/UAVid_Segmentation/v1.5_official5/labels.jpg b/UAVid_Segmentation/v1.5_official5/labels.jpg new file mode 100644 index 0000000..4167f8f Binary files /dev/null and b/UAVid_Segmentation/v1.5_official5/labels.jpg differ diff --git a/UAVid_Segmentation/v1.5_official5/results.csv b/UAVid_Segmentation/v1.5_official5/results.csv new file mode 100644 index 0000000..d03bf30 --- /dev/null +++ b/UAVid_Segmentation/v1.5_official5/results.csv @@ -0,0 +1,3 @@ +epoch,time,train/box_loss,train/seg_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),metrics/precision(M),metrics/recall(M),metrics/mAP50(M),metrics/mAP50-95(M),val/box_loss,val/seg_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2 +1,2.26767,4.19941,6.41601,8.47523,3.35484,0.01042,0.5,0.24875,0.07462,0,0,0,0,3.51347,4.84009,5.55821,3.24894,0.0955,0.0005,0.0005 +2,3.67956,3.71605,7.05627,8.7431,2.98561,0.02083,0.5,0.16583,0.03317,0,0,0,0,3.61686,4.8416,6.10662,3.2123,0.0900782,0.00107822,0.00107822 diff --git a/UAVid_Segmentation/v1.5_official5/train_batch0.jpg 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0000000..15cb0ec Binary files /dev/null and b/dataset/zip_dir1760962080216800000.zip differ diff --git a/download_train.py b/download_train.py new file mode 100644 index 0000000..fb81f07 --- /dev/null +++ b/download_train.py @@ -0,0 +1,799 @@ +# import asyncio +# import os.path +# import shutil +# import sys +# import threading +# import time +# from pathlib import Path +# +# from hachoir.parser.image.iptc import datasets +# +# from middleware.minio_util import downFullPathFile +# from middleware.query_model import ModelConfigDAO +# import yaml +# import multiprocessing +# import torch +# from ultralytics import YOLO +# +# +# async def download_train(task_id: str, bz_training_task_id: int, pt_name: str): +# +# DB_CONFIG = { +# "dbname": "smart_dev_123", +# "user": "postgres", +# "password": "root", +# "host": "8.137.54.85", +# "port": "5060" +# } +# +# # 创建DAO实例 +# dao = ModelConfigDAO(DB_CONFIG) +# time_ns=time.time_ns() +# output_root=f"dataset-{time_ns}" +# if not os.path.exists(output_root): +# os.mkdir(output_root) +# list_labels = dao.get_labels(bz_training_task_id) +# list_datasets = dao.get_datasets(bz_training_task_id) +# label_yaml_list = dao.get_label_yaml(bz_training_task_id) +# +# # 定义数据结构（字典） +# uavid_config = { +# +# "path": "", # 替换为你的绝对路径 +# "train": "images/train", # 训练集路径 +# "val": "images/val", # 验证集路径 +# "test": "images/test", # 测试集路径（可选） +# "names": {} +# } +# uavid_config["path"]=os.path.abspath(output_root) +# for i,item in enumerate(label_yaml_list): +# item.id_order=i +# uavid_config["names"][f"{i}"]=item.e_name +# # 生成 YAML 文件 +# data_yaml="data.yaml" +# with open(data_yaml, "w", encoding="utf-8") as f: +# yaml.dump( +# uavid_config, +# f, +# default_flow_style=False, # 禁用紧凑格式（保持多行） +# allow_unicode=True, # 允许 Unicode 字符 +# sort_keys=False # 保持键的顺序 +# ) +# file_name = os.path.basename(data_yaml) +# des_path = os.path.join(output_root, file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_yaml, output_root) +# print(f"output_rootoutput_rootoutput_rootoutput_root {os.path.abspath(output_root)}") +# +# +# print("YAML 文件已生成：uavid_config.yaml") +# +# invalid_indices = [] +# +# for index, pic in enumerate(list_datasets): +# if pic.resource_original_path: # 图像路径有效 +# download_path = downFullPathFile(pic.resource_original_path) +# if download_path: # 下载成功 +# pic.local_path = download_path +# +# pic.label_name = Path(download_path).stem # 截取图片名称，用作标签 +# else: +# invalid_indices.append(index) #存储不符合条件的索引，准备删除 +# else: +# invalid_indices.append(index)#存储不符合条件的索引，准备删除 +# +# +# # 从后往前删除（避免删除时索引错乱）,删除不符合条件的list_datasets +# +# for idx in sorted(invalid_indices, reverse=True): +# del list_datasets[idx] +# +# for data_pic in list_datasets: #整理完整的图像与标签集的对应关系 +# for label in list_labels: +# if data_pic.id == label.id: +# for item in label_yaml_list: +# if label.label_ids==item.id: +# data_pic.label_content=data_pic.label_content+item.id_order+" "+label.annotation_data+ '\n' +# +# +# for data_pic in list_datasets: +# label_txt = f"{data_pic.label_name}.txt" +# with open(label_txt, 'w', encoding='utf-8') as f: +# f.write(data_pic.label_content) +# data_pic.label_txt_path=os.path.abspath(label_txt) +# # 移动文件，制作数据集 +# +# +# dataset_dirs = { +# "images": Path(output_root) / "images", +# "labels": Path(output_root) / "labels" +# } +# for ds_dir in dataset_dirs.values(): +# (ds_dir / "val").mkdir(parents=True, exist_ok=True) +# (ds_dir / "train").mkdir(parents=True, exist_ok=True) +# (ds_dir / "test").mkdir(parents=True, exist_ok=True) +# +# count_pic=0 +# for data_pic in list_datasets: +# count_pic=count_pic+1 +# if count_pic%10<8: +# images_train_path=dataset_dirs["images"] +# image_dir=os.path.join(images_train_path,"train") +# file_name=os.path.basename(data_pic.local_path) +# des_path=os.path.join(image_dir,file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_pic.local_path, image_dir) +# +# +# labels_train_path=dataset_dirs["labels"] +# label_dir=os.path.join(labels_train_path,"train") +# +# file_name=os.path.basename(data_pic.label_txt_path) +# des_path=os.path.join(label_dir,file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_pic.label_txt_path, label_dir) +# if count_pic%10==8: +# images_val_path=dataset_dirs["images"] +# image_dir=os.path.join(images_val_path,"val") +# file_name=os.path.basename(data_pic.local_path) +# des_path=os.path.join(image_dir,file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_pic.local_path, image_dir) +# +# +# labels_val_path=dataset_dirs["labels"] +# label_dir=os.path.join(labels_val_path,"val") +# file_name=os.path.basename(data_pic.label_txt_path) +# des_path=os.path.join(label_dir,file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_pic.label_txt_path, label_dir) +# +# if count_pic%10==9: +# images_test_path=dataset_dirs["images"] +# image_dir=os.path.join(images_test_path,"test") +# file_name=os.path.basename(data_pic.local_path) +# des_path=os.path.join(image_dir,file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_pic.local_path, image_dir) +# +# +# labels_test_path=dataset_dirs["labels"] +# label_dir=os.path.join(labels_test_path,"test") +# file_name=os.path.basename(data_pic.label_txt_path) +# des_path=os.path.join(label_dir,file_name) +# if os.path.exists(des_path): +# os.remove(des_path) +# shutil.move(data_pic.label_txt_path, label_dir) +# +# +# +# custom_config = { +# "epochs": 50, # 快速测试用 +# "batch_size": 4, +# } +# +# # 启动后台训练 +# pid = await run_background_training( +# dataset_dir=output_root, +# weight_name=pt_name, +# config_overrides=custom_config +# ) +# +# print(f"pid--{pid}") +# dao.insert_train_pid(task_id,train_pid=pid) +# +# +# def train_model(dataset_dir,weight_name="best_segmentation_model.pt", config_overrides=None): +# """ +# 训练模型并保存权重 +# :param weight_name: 自定义权重文件名（如 "uavid_seg_v1.pt"） +# :param config_overrides: 覆盖默认配置的字典（可选） +# """ +# # 合并配置（允许通过参数覆盖默认配置） +# +# # 默认配置（可通过函数参数覆盖） +# DEFAULT_CONFIG = { +# "model": "pt/yolo11s-seg.pt", +# "pretrained": True, +# "data": os.path.join(dataset_dir, "data.yaml"), # 关键修改：指向 data.yaml +# "project": "UAVid_Segmentation", +# "name": "v1.5_official", +# "epochs": 1000, +# "batch_size": 8, +# "img_size": 640, +# "workers": 4, +# "optimizer": "SGD", +# "lr0": 0.01, +# "lrf": 0.01, +# "momentum": 0.9, +# "weight_decay": 0.0005, +# "augment": True, +# "hyp": { +# "mosaic": 0.5, +# "copy_paste": 0.2, +# "mixup": 0.15, +# }, +# } +# +# config = DEFAULT_CONFIG.copy() +# if config_overrides: +# config.update(config_overrides) +# +# # 初始化模型 +# model = YOLO(config["model"]) +# +# # 开始训练 +# results = model.train( +# data=config["data"], +# project=config["project"], +# name=config["name"], +# epochs=config["epochs"], +# batch=config["batch_size"], +# imgsz=config["img_size"], +# workers=config["workers"], +# optimizer=config["optimizer"], +# lr0=config["lr0"], +# lrf=config["lrf"], +# momentum=config["momentum"], +# weight_decay=config["weight_decay"], +# augment=config["augment"], +# device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), +# ) +# +# # 验证模型 +# metrics = model.val() +# print(f"Validation mAP: {metrics.box_map:.2f} (box), {metrics.seg_map:.2f} (mask)") +# +# # 保存最佳模型（使用自定义名称） +# best_model = results.best_model +# torch.save(best_model, weight_name) +# print(f"Best model saved to: {weight_name}") +# +# +# # def run_background_training(output_root: str, weight_name="best_segmentation_model.pt", config_overrides=None): +# # """使用 spawn 上下文启动进程""" +# # ctx = multiprocessing.get_context('spawn') +# # process = ctx.Process( +# # target=train_model, +# # args=(output_root, weight_name, config_overrides), +# # daemon=False +# # ) +# # process.start() +# # return process.pid +# +# import asyncio +# +# async def run_background_training(dataset_dir, weight_name, config_overrides=None): +# """异步启动训练进程""" +# process = await asyncio.create_subprocess_exec( +# sys.executable, +# "train_worker.py", +# "--dataset", dataset_dir, +# "--weight", weight_name, +# "--epochs", str(config_overrides.get("epochs", 50)), +# "--batch", str(config_overrides.get("batch", 4)), +# stdout=asyncio.subprocess.PIPE, +# stderr=asyncio.subprocess.PIPE, +# ) +# return process.pid + + +import asyncio +import os.path +import shutil +import sys +import threading +import time +import subprocess +import json +from pathlib import Path + +from middleware.minio_util import downFullPathFile +from middleware.query_model import ModelConfigDAO +import yaml +import torch +from ultralytics import YOLO + + +async def download_train(task_id: str, bz_training_task_id: int, pt_name: str): + """ + 下载训练数据并启动训练 + 这个函数负责准备数据，然后使用线程+subprocess创建独立进程执行训练 + """ + try: + current_pid = os.getpid() + print(f"Starting download and training for task {task_id} in process {current_pid}") + + DB_CONFIG = { + "dbname": "smart_dev_123", + "user": "postgres", + "password": "root", + "host": "8.137.54.85", + "port": "5060" + } + + # 创建DAO实例 + dao = ModelConfigDAO(DB_CONFIG) + time_ns = time.time_ns() + + output_root = f"dataset/dataset-{time_ns}" + + try: + if not os.path.exists(output_root): + os.mkdir(output_root) + print(f"Created output directory: {output_root}") + except Exception as e: + print(f"Failed to create output directory: {e}") + raise + + try: + # 获取标签和数据集信息 + list_labels = dao.get_labels(bz_training_task_id) + list_datasets = dao.get_datasets(bz_training_task_id) + label_yaml_list = dao.get_label_yaml(bz_training_task_id) + + print( + f"Retrieved {len(list_labels)} labels, {len(list_datasets)} datasets, {len(label_yaml_list)} label configs") + except Exception as e: + print(f"Failed to retrieve data from database: {e}") + raise + + # 定义数据结构（字典） + uavid_config = { + "path": "", # 替换为你的绝对路径 + "train": "images/train", # 训练集路径 + "val": "images/val", # 验证集路径 + "test": "images/test", # 测试集路径（可选） + "names": {} + } + + try: + uavid_config["path"] = os.path.abspath(output_root) + for i, item in enumerate(label_yaml_list): + item.id_order = i + uavid_config["names"][f"{i}"] = item.e_name + + # 生成 YAML 文件 + data_yaml = "data.yaml" + with open(data_yaml, "w", encoding="utf-8") as f: + yaml.dump( + uavid_config, + f, + default_flow_style=False, # 禁用紧凑格式（保持多行） + allow_unicode=True, # 允许 Unicode 字符 + sort_keys=False # 保持键的顺序 + ) + + file_name = os.path.basename(data_yaml) + des_path = os.path.join(output_root, file_name) + if os.path.exists(des_path): + os.remove(des_path) + shutil.move(data_yaml, output_root) + print(f"Generated YAML config at: {os.path.abspath(output_root)}") + except Exception as e: + print(f"Failed to generate YAML config: {e}") + raise + + # 下载数据集 + invalid_indices = [] + + try: + for index, pic in enumerate(list_datasets): + if hasattr(pic, 'resource_original_path') and pic.resource_original_path: # 图像路径有效 + try: + download_path = downFullPathFile(pic.resource_original_path) + if download_path: # 下载成功 + pic.local_path = download_path + pic.label_name = Path(download_path).stem # 截取图片名称，用作标签 + print(f"Downloaded file: {download_path}") + else: + invalid_indices.append(index) # 存储不符合条件的索引，准备删除 + print(f"Failed to download file: {pic.resource_original_path}") + except Exception as e: + invalid_indices.append(index) + print(f"Error downloading file {pic.resource_original_path}: {e}") + else: + invalid_indices.append(index) # 存储不符合条件的索引，准备删除 + except Exception as e: + print(f"Error processing datasets: {e}") + raise + + # 从后往前删除（避免删除时索引错乱）,删除不符合条件的list_datasets + try: + for idx in sorted(invalid_indices, reverse=True): + del list_datasets[idx] + + print(f"Filtered datasets: {len(list_datasets)} valid items remaining") + except Exception as e: + print(f"Error filtering datasets: {e}") + raise + + # 整理标签内容 + try: + for data_pic in list_datasets: # 整理完整的图像与标签集的对应关系 + for label in list_labels: + if hasattr(data_pic, 'id') and hasattr(label, 'id') and data_pic.id == label.id: + for item in label_yaml_list: + if hasattr(label, 'label_ids') and hasattr(item, 'id') and label.label_ids == item.id: + # 假设label有annotation_data属性 + annotation = getattr(label, 'annotation_data', '') + current_content = getattr(data_pic, 'label_content', '') + data_pic.label_content = f"{current_content}{item.id_order} {annotation}\n" + except Exception as e: + print(f"Error organizing labels: {e}") + raise + + # 创建标签文件 + try: + for data_pic in list_datasets: + if hasattr(data_pic, 'label_name'): + label_txt = f"{data_pic.label_name}.txt" + with open(label_txt, 'w', encoding='utf-8') as f: + f.write(getattr(data_pic, 'label_content', '')) + data_pic.label_txt_path = os.path.abspath(label_txt) + print(f"Created label file: {label_txt}") + except Exception as e: + print(f"Error creating label files: {e}") + raise + + # 移动文件，制作数据集 + try: + dataset_dirs = { + "images": Path(output_root) / "images", + "labels": Path(output_root) / "labels" + } + + for ds_dir in dataset_dirs.values(): + (ds_dir / "val").mkdir(parents=True, exist_ok=True) + (ds_dir / "train").mkdir(parents=True, exist_ok=True) + (ds_dir / "test").mkdir(parents=True, exist_ok=True) + + print("Created dataset directory structure") + except Exception as e: + print(f"Error creating dataset directories: {e}") + raise + + # 分配数据集到训练、验证、测试集 + try: + count_pic = 0 + for data_pic in list_datasets: + count_pic += 1 + + # 80% 训练集, 10% 验证集, 10% 测试集 + if count_pic % 10 < 8: + split = "train" + elif count_pic % 10 == 8: + split = "val" + else: # count_pic % 10 == 9 + split = "test" + + # 移动图像文件 + if hasattr(data_pic, 'local_path') and os.path.exists(data_pic.local_path): + images_path = dataset_dirs["images"] + image_dir = os.path.join(images_path, split) + file_name = os.path.basename(data_pic.local_path) + des_path = os.path.join(image_dir, file_name) + + if os.path.exists(des_path): + os.remove(des_path) + shutil.move(data_pic.local_path, image_dir) + + # 移动标签文件 + if hasattr(data_pic, 'label_txt_path') and os.path.exists(data_pic.label_txt_path): + labels_path = dataset_dirs["labels"] + label_dir = os.path.join(labels_path, split) + file_name = os.path.basename(data_pic.label_txt_path) + des_path = os.path.join(label_dir, file_name) + + if os.path.exists(des_path): + os.remove(des_path) + shutil.move(data_pic.label_txt_path, label_dir) + + print(f"Organized {count_pic} files into dataset splits") + except Exception as e: + print(f"Error organizing dataset splits: {e}") + raise + + # 训练配置 + custom_config = { + "epochs": 50, # 快速测试用 + "batch_size": 4, + "workers": 0, # 禁用多进程数据加载 + } + + # 保存训练配置到文件 + config_file = f"train_config_{task_id}.json" + with open(config_file, 'w', encoding='utf-8') as f: + json.dump({ + 'dataset_dir': output_root, + 'pt_name': pt_name, + 'config_overrides': custom_config, + 'db_config': DB_CONFIG, + 'task_id': task_id + }, f) + + print(f"Training data preparation completed for task {task_id}") + + # 在Windows上使用线程+subprocess创建训练进程 + # 避免使用asyncio.create_subprocess_exec + loop = asyncio.get_event_loop() + training_pid = await loop.run_in_executor( + None, # 使用默认的线程池 + start_training_process, + config_file + ) + + if training_pid: + print(f"pid--{training_pid}") + dao.insert_train_pid(task_id, train_pid=training_pid) + return training_pid + else: + raise Exception("Failed to start training process") + + except Exception as e: + print(f"Training failed for task {task_id}: {e}", exc_info=True) + raise + + +def start_training_process(config_file: str) -> int: + """ + 在独立线程中启动训练进程 + 使用subprocess.Popen创建训练进程 + """ + try: + # 创建训练脚本内容 + train_script = ''' +import sys +import json +import os +import torch +from ultralytics import YOLO + +class MockModelConfigDAO: + def __init__(self, db_config): + self.db_config = db_config + + def insert_train_pid(self, task_id, train_pid): + print(f"Inserted training PID {train_pid} for task {task_id}") + +def train_model(dataset_dir, weight_name="best_segmentation_model.pt", config_overrides=None): + """ + 训练模型并保存权重 + """ + try: + current_pid = os.getpid() + print(f"Starting model training in process {current_pid} with dataset: {dataset_dir}") + + # 默认配置（可通过参数覆盖） + DEFAULT_CONFIG = { + "model": "pt/yolo11s-seg.pt", + "pretrained": True, + "data": os.path.join(dataset_dir, "data.yaml"), + "project": "UAVid_Segmentation", + "name": "v1.5_official", + "epochs": 1000, + "batch_size": 8, + "img_size": 640, + "workers": 0, # 禁用多进程数据加载 + "optimizer": "SGD", + "lr0": 0.01, + "lrf": 0.01, + "momentum": 0.9, + "weight_decay": 0.0005, + "augment": True, + "hyp": { + "mosaic": 0.5, + "copy_paste": 0.2, + "mixup": 0.15, + }, + } + + config = DEFAULT_CONFIG.copy() + if config_overrides: + config.update(config_overrides) + + print(f"Training config: {config}") + + # 检查数据配置文件 + data_path = config["data"] + if not os.path.exists(data_path): + raise FileNotFoundError(f"Data configuration file not found: {data_path}") + + # 初始化模型 + model = YOLO(config["model"]) + print(f"Model initialized with: {config["model"]}") + + # 开始训练 + results = model.train( + data=config["data"], + project=config["project"], + name=config["name"], + epochs=config["epochs"], + batch=config["batch_size"], + imgsz=config["img_size"], + workers=config["workers"], + optimizer=config["optimizer"], + lr0=config["lr0"], + lrf=config["lrf"], + momentum=config["momentum"], + weight_decay=config["weight_decay"], + augment=config["augment"], + device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), + ) + print(f"Training completed successfully in process {current_pid}") + + # 验证模型 + metrics = model.val() + print(f"Validation mAP: {metrics.box:.2f} (box), {metrics.seg:.2f} (mask)") + + # 保存最佳模型 + try: + if hasattr(results, 'best') and results.best: + best_model_path = results.best + if os.path.exists(best_model_path): + import shutil + shutil.copy2(best_model_path, weight_name) + print(f"Best model saved to: {os.path.abspath(weight_name)}") + else: + torch.save(model.state_dict(), weight_name) + print(f"Best model path not found, saved state dict to: {weight_name}") + else: + torch.save(model.state_dict(), weight_name) + print(f"Saved model state dict to: {weight_name}") + except Exception as e: + print(f"Warning: Failed to save best model: {e}") + torch.save(model.state_dict(), weight_name) + print(f"Fallback: Saved model state dict to: {weight_name}") + + return True + + except Exception as e: + print(f"Model training failed in process {os.getpid()}: {e}", exc_info=True) + raise + +def main(): + if len(sys.argv) != 2: + print("Usage: python -c '