feat: auto check & create dataset folder

mikazuki/app.py

@@ -89,13 +89,8 @@ async def create_toml_file(request: Request, background_tasks: BackgroundTasks):
     toml_data = await request.body()
     j = json.loads(toml_data.decode("utf-8"))
 
-    # ok = utils.check_training_params(j)
+    utils.validate_data_dir(j["train_data_dir"])
-    # if not ok:
+    suggest_cpu_threads = 8 if len(utils.get_total_images(j["train_data_dir"])) > 100 else 2
-    #     lock.release()
-    #     print("训练目录校验失败，请确保填写的目录存在")
-    #     return {"status": "fail", "detail": "训练目录校验失败，请确保填写的目录存在"}
-
-    suggest_cpu_threads = 8 if utils.get_total_images(j["train_data_dir"]) > 100 else 2
     trainer_file = "./sd-scripts/train_network.py"
 
     if j.pop("model_train_type", "sd-lora") == "sdxl-lora":
@@ -117,6 +112,8 @@ async def create_toml_file(request: Request, background_tasks: BackgroundTasks):
         j["sample_prompts"] = sample_prompts_file
         log.info(f"Writted promopts to file {sample_prompts_file}")
 
+
+
     with open(toml_file, "w") as f:
         f.write(toml.dumps(j))
 

mikazuki/utils.py

@@ -3,12 +3,48 @@ import importlib.util
 import os
 import subprocess
 import sys
+import re
+import shutil
 from typing import Optional
 from mikazuki.log import log
 
 python_bin = sys.executable
 
 
+def validate_data_dir(path):
+    if not os.path.exists(path):
+        log.error(f"Data dir {path} not exists, check your params")
+        return False
+
+    dir_content = os.listdir(path)
+
+    if len(dir_content) == 0:
+        log.error(f"Data dir {path} is empty, check your params")
+
+    subdirs = [f for f in dir_content if os.path.isdir(os.path.join(path, f))]
+
+    if len(subdirs) == 0:
+        log.warn(f"No subdir found in data dir")
+
+    ok_dir = [d for d in subdirs if re.findall(r"^\d+_.+", d)]
+
+    if len(ok_dir) == 0:
+        log.warning(f"No leagal dataset found. Try find avaliable images")
+        imgs = get_total_images(path, False)
+        log.info(f"{len(imgs)} images found")
+        if len(imgs) > 0:
+            dataset_path = os.path.join(path, "1_zkz")
+            os.makedirs(dataset_path)
+            for i in imgs:
+                shutil.move(i, dataset_path)
+            log.info(f"Auto dataset created {dataset_path}")
+        else:
+            log.error("No image found in data dir")
+            return False
+
+    return True
+
+
 def check_training_params(data):
     potential_path = [
         "train_data_dir", "reg_data_dir", "output_dir"
@@ -26,11 +62,16 @@ def check_training_params(data):
     return True
 
 
-def get_total_images(path):
+def get_total_images(path, recursive=True):
+    if recursive:
         image_files = glob.glob(path + '/**/*.jpg', recursive=True)
         image_files += glob.glob(path + '/**/*.jpeg', recursive=True)
         image_files += glob.glob(path + '/**/*.png', recursive=True)
-    return len(image_files)
+    else:
+        image_files = glob.glob(path + '/*.jpg')
+        image_files += glob.glob(path + '/*.jpeg')
+        image_files += glob.glob(path + '/*.png')
+    return image_files
 
 
 def is_installed(package):
@@ -82,6 +123,7 @@ stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.st
 def run_pip(command, desc=None, live=False):
     return run(f'"{python_bin}" -m pip {command}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
 
+
 def check_run(file: str) -> bool:
     result = subprocess.run([python_bin, file], capture_output=True, shell=False)
     log.info(result.stdout.decode("utf-8").strip())