192 lines
11 KiB
Python
192 lines
11 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from typing import Any
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from .catalog import TASK_DEFAULTS, TASK_TYPES
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from .config import settings
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from .modules import build_module_task
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from .paths import rel
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SCRIPT_TASK_MAP: dict[str, str] = {
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"Back_Up.sh": "system.backup",
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"Check_Graph_Card.sh": "system.check_graph_card",
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"DataSet_Own/1. 图片预处理（内含使用手册）/1_rename_pics.sh": "dataset.rename",
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"DataSet_Own/1. 图片预处理（内含使用手册）/2_1_Trans_to_png.py": "dataset.to_png",
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"DataSet_Own/1. 图片预处理（内含使用手册）/2_2_Resize.py": "dataset.resize_single",
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"DataSet_Own/1. 图片预处理（内含使用手册）/2_reformate_pics.sh": "dataset.resize",
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"DataSet_Own/1. 图片预处理（内含使用手册）/3_pair_ori_label.sh": "dataset.pair",
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"DataSet_Own/1. 图片预处理（内含使用手册）/4_deal_labels.py": "dataset.deal_labels",
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"DataSet_Own/1. 图片预处理（内含使用手册）/4_deal_labels_old(老版程序).py": "dataset.deal_labels_old",
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"DataSet_Own/1. 图片预处理（内含使用手册）/4_rebuild_labels.sh": "dataset.rebuild_labels",
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"DataSet_Own/1. 图片预处理（内含使用手册）/5_TOOL_stack_pics.sh": "dataset.stack",
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"DataSet_Own/1. 图片预处理（内含使用手册）/5_stack_picture.py": "dataset.stack_single",
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"DataSet_Own/1. 图片预处理（内含使用手册）/6_TOOL_stitch_pics.sh": "dataset.stitch",
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"DataSet_Own/1. 图片预处理（内含使用手册）/6_stitch_picture.py": "dataset.stitch_single",
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"DataSet_Own/1. 图片预处理（内含使用手册）/Seg_data_run.sh": "dataset.run_wizard",
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"Seg_All_In_One_Analysis/1_Analysis_All.py": "analysis.all",
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"Seg_All_In_One_MMSeg/My_All_In_One/0_Initial_Save_All_Model_locally.py": "mmseg.init_weights",
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"Seg_All_In_One_MMSeg/My_All_In_One/1_Initial_Data_All-ori.py": "mmseg.generate_data_legacy",
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"Seg_All_In_One_MMSeg/My_All_In_One/1_Initial_Data_All_data_from_1_Data_Parameter-V1.py": "mmseg.generate_data_v1",
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"Seg_All_In_One_MMSeg/My_All_In_One/1_Initial_Data_All_data_from_1_Data_Parameter-V2.py": "mmseg.generate_data",
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"Seg_All_In_One_MMSeg/My_All_In_One/2_Initial_Alg_All-ori-old.py": "mmseg.generate_alg_legacy",
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"Seg_All_In_One_MMSeg/My_All_In_One/2_Initial_Alg_All_data_from_2_Alg_Program-V1.py": "mmseg.generate_alg_v1",
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"Seg_All_In_One_MMSeg/My_All_In_One/2_Initial_Alg_All_data_from_2_Alg_Program-V2.py": "mmseg.generate_alg",
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"Seg_All_In_One_MMSeg/My_All_In_One/3_Find_And_Delete_Special_Epoch.py": "mmseg.delete_epoch",
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"Seg_All_In_One_MMSeg/My_All_In_One/3_Tool_Copy_Result_To_Hardisk.sh": "mmseg.copy_result",
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"Seg_All_In_One_MMSeg/My_All_In_One/4_1_predict_params_FLOPs_FPS_V2.py": "mmseg.flops_fps",
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"Seg_All_In_One_MMSeg/My_All_In_One/4_1_predict_params_and_FLOPs_V1.py": "mmseg.flops_fps_v1",
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"Seg_All_In_One_MMSeg/My_All_In_One/4_2_predict_matrics_from_log_V1.py": "mmseg.metrics_v1",
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"Seg_All_In_One_MMSeg/My_All_In_One/4_2_predict_matrics_from_log_V2.py": "mmseg.metrics",
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"Seg_All_In_One_MMSeg/My_All_In_One/4_3_predict_draw_pictures_and_tabels.py": "mmseg.draw",
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"Seg_All_In_One_MMSeg/My_All_In_One/4_4_extract_loss_and_best_miou.py": "mmseg.extract_loss_miou",
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"Seg_All_In_One_MMSeg/My_All_In_One/x4_Predict_V1-.py": "mmseg.predict_v1",
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"Seg_All_In_One_MMSeg/My_All_In_One/x4_Predict_V2-.py": "mmseg.predict_v2",
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"Seg_All_In_One_MMSeg/tools/train.py": "mmseg.train",
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"Seg_All_In_One_SegModel/1_predict.py": "segmodel.predict",
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"Seg_All_In_One_SegModel/1_predict_raw_masks_check.py": "segmodel.raw_mask_check",
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"Seg_All_In_One_SegModel/2_predict_params_and_FLOPs_V1.py": "segmodel.flops",
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"Seg_All_In_One_SegModel/2_predict_params_and_FLOPs_V2.py": "segmodel.flops",
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"Seg_All_In_One_SegModel/3_predict_matrics_from_log.py": "segmodel.metrics",
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"Seg_All_In_One_SegModel/Tool_Copy_Best_Model.sh": "segmodel.copy_best",
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"Seg_All_In_One_SegModel/Tool_benchmark_smp.py": "segmodel.benchmark",
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"Seg_All_In_One_SegModel/Tool_get_params_and_FLOPs.py": "segmodel.params_flops",
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"Seg_All_In_One_SegModel/predict.sh": "segmodel.batch_predict",
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"Seg_All_In_One_SegModel/train.py": "segmodel.train",
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"Seg_All_In_One_SegModel/train.sh": "segmodel.batch_train",
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"Seg_All_In_One_YoloModel/Tool_Yolo_Copy_Best_Model.sh": "yolo.copy_best",
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"Seg_All_In_One_YoloModel/Yolo可视化测试/yolo_layer_tester.py": "yolo.layer_tester",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/0_1_check_picture_pair.py": "dataset.yolo_check_pairs",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/0_2_TOOL_stack_pics.sh": "dataset.yolo_stack",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/0_2_stack_picture.py": "dataset.yolo_stack_single",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/1_deal_labels.py": "dataset.yolo_rebuild_labels",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/2_Check_and_Gen_Txt_Label_ori_label.py": "dataset.yolo_txt_ori",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/2_Check_and_Gen_Txt_Label_sort_label.py": "dataset.yolo_txt_sort",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/Tool_convert_bmp_jpg_to_png.py": "dataset.yolo_convert_png",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/Tool_resize_pics.py": "dataset.yolo_resize",
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"Seg_All_In_One_YoloModel/yolo_predict.sh": "yolo.batch_predict",
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"Seg_All_In_One_YoloModel/yolo_predict_V1.py": "yolo.predict_v1",
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"Seg_All_In_One_YoloModel/yolo_predict_V2.py": "yolo.predict",
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"Seg_All_In_One_YoloModel/yolo_predict_V2_compare_all.py": "yolo.compare",
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"Seg_All_In_One_YoloModel/yolo_predict_raw_masks_check.py": "yolo.raw_mask_check",
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"Seg_All_In_One_YoloModel/yolo_predict_visualize_nn.py": "yolo.heatmap",
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"Seg_All_In_One_YoloModel/yolo_train.py": "yolo.train",
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"Seg_All_In_One_YoloModel/yolo_train.sh": "yolo.batch_train",
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"Seg_Predict_Own_Video_V2/1_Save_Frame_V1.py": "dataset.video_frames",
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"Seg_Predict_Own_Video_V2/1_Save_Frame_V2.py": "dataset.video_frames",
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"Seg_Predict_YoloModel/yolo_Seg_Video-V1-Visible.py": "yolo.video_visible",
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"Seg_Predict_YoloModel/yolo_Seg_Video-V2-UnVisible.py": "yolo.video_unvisible",
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"Tool-可视化/0_图片Labels生成/4_deal_labels.py": "visual.deal_labels",
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"Tool-可视化/0_图片Labels生成/Tool_deal_labels.py": "visual.tool_deal_labels_demo",
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"Tool-可视化/Tool_Check_and_Gen_Txt_Label_ori_label.py": "visual.label_ori",
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"Tool-可视化/Tool_Check_and_Gen_Txt_Label_sort_label.py": "visual.label_sort",
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"Tool-可视化/Tool_Gen_8_Bit_PNG[没用，不认].py": "visual.gen_8bit_png",
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"Tool-可视化/get_FPS.py": "visual.fps",
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"Tool-可视化/inference.py": "visual.inference",
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"Tool-可视化/train.py": "visual.train",
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"Tool-可视化/yolov11_heatmap_V1.py": "visual.yolo11_heatmap_v1",
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"Tool-可视化/yolov11_heatmap_V2.py": "visual.yolo11_heatmap_v2",
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"Tool-图片堆叠/1_check_picture_pair.py": "dataset.stack_pair_check",
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"Tool-图片堆叠/2_TOOL_stack_pics.sh": "dataset.stack_tool_batch",
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"Tool-图片堆叠/2_stack_picture.py": "dataset.stack_tool_single",
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}
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SUPPORTING_SCRIPT_PATTERNS = (
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"Seg_All_In_One_MMSeg/demo/",
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"Seg_All_In_One_MMSeg/build/",
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"Seg_All_In_One_MMSeg/configs/",
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"Seg_All_In_One_MMSeg/docker/",
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"Seg_All_In_One_MMSeg/docs/",
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"Seg_All_In_One_MMSeg/projects/",
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"Seg_All_In_One_MMSeg/mmseg/",
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"Seg_All_In_One_MMSeg/tests/",
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"Seg_All_In_One_MMSeg/setup.py",
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"Seg_All_In_One_MMSeg/My_All_In_One/2_Alg_Program/",
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"Seg_All_In_One_MMSeg/My_All_In_One/Initial_Alg_Program/",
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"Seg_All_In_One_MMSeg/My_All_In_One/Initial_Data_Program/",
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"Seg_All_In_One_MMSeg/My_All_In_One/Initial_Schedule_Program/",
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"Seg_All_In_One_MMSeg/tools/dist_",
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"Seg_All_In_One_MMSeg/tools/slurm_",
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"Seg_All_In_One_MMSeg/tools/test.py",
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"Seg_All_In_One_MMSeg/tools/analysis_tools/",
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"Seg_All_In_One_MMSeg/tools/dataset_converters/",
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"Seg_All_In_One_MMSeg/tools/deployment/",
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"Seg_All_In_One_MMSeg/tools/misc/",
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"Seg_All_In_One_MMSeg/tools/model_converters/",
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"Seg_All_In_One_MMSeg/tools/torchserve/",
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"Seg_All_In_One_SegModel/config.py",
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"Seg_All_In_One_SegModel/dataset.py",
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"Seg_All_In_One_SegModel/loss.py",
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"Seg_All_In_One_SegModel/utils.py",
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"Seg_All_In_One_YoloModel/yolo_config.py",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/Tool_Classes_And_Palette.py",
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"Seg_All_In_One_YoloModel/Yolo数据集构建/Tool_deal_labels.py",
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"Seg_Predict_YoloModel/yolo_config.py",
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"Seg_Predict_YoloModel/yolo_train.py",
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"DataSet_Own/2. 图片分割程序（mmseg）/",
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)
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def _script_inventory() -> list[str]:
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scripts = []
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for path in settings.source_root.rglob("*"):
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if path.is_file() and path.suffix in {".py", ".sh"}:
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scripts.append(rel(path, settings.source_root))
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return sorted(scripts)
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def _is_supporting_script(relative_path: str) -> bool:
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return any(relative_path.startswith(pattern) or relative_path == pattern for pattern in SUPPORTING_SCRIPT_PATTERNS)
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def _command_script_path(command: list[str]) -> Path | None:
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if not command:
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return None
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if command[0] == "bash" and len(command) > 1:
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return Path(command[1])
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if command[0] == "python" and len(command) > 1 and command[1] != "-c":
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return Path(command[1])
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if len(command) > 5 and command[:3] == ["conda", "run", "-n"] and command[4] == "python":
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return Path(command[5])
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return None
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def build_task_checks() -> list[dict[str, Any]]:
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checks = []
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for task in TASK_TYPES:
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params = TASK_DEFAULTS.get(task, {})
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try:
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spec = build_module_task(task, dict(params), settings.task_conda_env)
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script_path = _command_script_path(spec.command) if spec else None
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checks.append(
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{
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"task": task,
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"passed": spec is not None and (script_path is None or script_path.exists()),
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"command": spec.command if spec else None,
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"cwd": str(spec.cwd) if spec else None,
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"script_exists": None if script_path is None else script_path.exists(),
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"script": None if script_path is None else str(script_path),
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}
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)
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except Exception as exc:
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checks.append({"task": task, "passed": False, "error": str(exc)})
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return checks
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def get_coverage_report() -> dict[str, Any]:
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scripts = _script_inventory()
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mapped_scripts = [script for script in scripts if script in SCRIPT_TASK_MAP]
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user_scripts = [script for script in scripts if not _is_supporting_script(script)]
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unmapped_user_scripts = [script for script in user_scripts if script not in SCRIPT_TASK_MAP]
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task_checks = build_task_checks()
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return {
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"scripts_total": len(scripts),
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"user_scripts_total": len(user_scripts),
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"mapped_user_scripts": len(mapped_scripts),
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"task_build_passed": all(item["passed"] for item in task_checks),
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"unmapped_user_scripts": unmapped_user_scripts,
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"script_task_map": SCRIPT_TASK_MAP,
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"task_build_checks": task_checks,
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}
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