update scripts in web_p (dataset, train)

web_p/rbs_train2.py

@@ -0,0 +1,64 @@
+"""
+  rbs_train2
+  Общая задача: web-service pipeline
+  Реализуемая функция: обучение нейросетевой модели по заданному BOP-датасету
+
+    python3 $PYTHON_EDUCATION --path /home/user/webservice/server/build/public/process/proc/inst_proc \
+        --form /home/user/webservice/server/build/public/process/proc/inst_proc/form.json
+
+  28.01.2025 @shalenikol release 0.1
+  17.02.2025 @shalenikol release 0.2 addon_dir
+"""
+import argparse
+import os
+import json
+from train_Yolo import train_YoloV8
+from train_Dope import train_Dope_i
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--path", required=True, help="Output path for weights")
+    parser.add_argument("--form", required=True, help="Json-file with training parameters")
+    args = parser.parse_args()
+
+    if not os.path.isdir(args.path):
+        print(f"Invalid output path '{args.path}'")
+        exit(-1)
+    wname = os.path.basename(args.path)
+    outpath = os.path.dirname(args.path)
+
+    if not os.path.isfile(args.form):
+        print(f"Error: no such file '{args.form}'")
+        exit(-2)
+    with open(args.form, "r") as f:
+        j_data = f.read()
+    try:
+        cfg = json.loads(j_data)
+    except json.JSONDecodeError as e:
+        print(f"JSon error: {e}")
+        exit(-3)
+
+    cfg = cfg["output"] # edited params
+    dataset_params = cfg["process"]["selectProcess"]["value"]
+    dataset_type = dataset_params["type"]
+    if dataset_type != "BOP_DATASET":
+        print(f"Error: Invalid dataset type '{dataset_type}'")
+        exit(-4)
+    dataset_name = dataset_params["instanceName"]
+    dataset_path = dataset_params["path"]
+    dataset_path = dataset_path.replace("//", "/") # !!! TODO !!! Nikita
+
+    epoch = cfg["n_epoch"]
+    pretrain = (cfg["pretrain"] == "True") #False
+    ttype = cfg["typeWeight"] #"ObjectDetection"
+
+    addon_dir = ""
+    if "addon" in cfg:
+        addon = cfg["addon"].strip()
+        if addon and os.path.isdir(addon):
+            addon_dir = addon
+
+    if ttype == "ObjectDetection":
+        train_YoloV8(dataset_path, wname, dataset_name, outpath, epoch, pretrain, addon_dir)
+    else:
+        train_Dope_i(dataset_path, wname, dataset_name, outpath, epoch, pretrain)