optimize nn MUD parse labels strip space

docs/doc/en/pro/customize_model.md

@@ -71,24 +71,65 @@ import numpy as np
 def parse_str_values(value: str) -> list[float]:
     return [float(v) for v in value.split(",")]
 
-def load_labels(model_path, path_or_labels: str):
-    path = os.path.join(os.path.dirname(model_path), path_or_labels)
-    labels0 = open(path, encoding="utf-8").readlines() if os.path.exists(path) else path_or_labels.split(",")
-    return [label.strip() for label in labels0]
+def load_labels(model_path, path_or_labels : str):
+    path = ""
+    if not ("," in path_or_labels or " " in path_or_labels or "\n" in path_or_labels):
+      path = os.path.join(os.path.dirname(model_path), path_or_labels)
+    if path and os.path.exists(path):
+      with open(path, encoding = "utf-8") as f:
+        labels0 = f.readlines()
+    else:
+      labels0 = path_or_labels.split(",")
+    labels = []
+    for label in labels0:
+        labels.append(label.strip())
+    return labels
 
 class My_Classifier:
-    def __init__(self, model: str):
-      self.model = nn.NN(model, dual_buff=False)
+    def __init__(self, model : str):
+      self.model = nn.NN(model, dual_buff = False)
+      self.extra_info = self.model.extra_info()
+      self.mean = parse_str_values(self.extra_info["mean"])
+      self.scale = parse_str_values(self.extra_info["scale"])
+      self.labels = self.model.extra_info_labels()
+      # self.labels = load_labels(model, self.extra_info["labels"]) # same as self.model.extra_info_labels()
+
+    def classify(self, img : image.Image):
+      outs = self.model.forward_image(img, self.mean, self.scale, copy_result = False)
+      # 后处理， 以分类模型为例
+      for k in outs.keys():
+        out = nn.F.softmax(outs[k], replace=True)
+        out = tensor.tensor_to_numpy_float32(out, copy = False).flatten()
+        max_idx = out.argmax()
+        return self.labels[max_idx], out[max_idx]def load_labels(model_path, path_or_labels : str):
+    path = ""
+    if not ("," in path_or_labels or " " in path_or_labels or "\n" in path_or_labels):
+      path = os.path.join(os.path.dirname(model_path), path_or_labels)
+    if path and os.path.exists(path):
+      with open(path, encoding = "utf-8") as f:
+        labels0 = f.readlines()
+    else:
+      labels0 = path_or_labels.split(",")
+    labels = []
+    for label in labels0:
+        labels.append(label.strip())
+    return labels
+
+class My_Classifier:
+    def __init__(self, model : str):
+      self.model = nn.NN(model, dual_buff = False)
       self.extra_info = self.model.extra_info()
       self.mean = parse_str_values(self.extra_info["mean"])
       self.scale = parse_str_values(self.extra_info["scale"])
-      self.labels = load_labels(model, self.extra_info["labels"])
+      self.labels = self.model.extra_info_labels()
+      # self.labels = load_labels(model, self.extra_info["labels"]) # same as self.model.extra_info_labels()
 
-    def classify(self, img: image.Image):
-      outs = self.model.forward_image(img, self.mean, self.scale, copy_result=False)
+    def classify(self, img : image.Image):
+      outs = self.model.forward_image(img, self.mean, self.scale, copy_result = False)
+      # 后处理， 以分类模型为例
       for k in outs.keys():
         out = nn.F.softmax(outs[k], replace=True)
-        out = tensor.tensor_to_numpy_float32(out, copy=False).flatten()
+        out = tensor.tensor_to_numpy_float32(out, copy = False).flatten()
         max_idx = out.argmax()
         return self.labels[max_idx], out[max_idx]
 

docs/doc/zh/pro/customize_model.md

@@ -80,8 +80,10 @@ def parse_str_values(value : str) -> list[float]:
       return [float(value)]
 
 def load_labels(model_path, path_or_labels : str):
-    path = os.path.join(os.path.dirname(model_path), path_or_labels)
-    if os.path.exists(path):
+    path = ""
+    if not ("," in path_or_labels or " " in path_or_labels or "\n" in path_or_labels):
+      path = os.path.join(os.path.dirname(model_path), path_or_labels)
+    if path and os.path.exists(path):
       with open(path, encoding = "utf-8") as f:
         labels0 = f.readlines()
     else:
@@ -97,7 +99,8 @@ class My_Classifier:
       self.extra_info = self.model.extra_info()
       self.mean = parse_str_values(self.extra_info["mean"])
       self.scale = parse_str_values(self.extra_info["scale"])
-      self.labels = load_labels(model, self.extra_info["labels"])
+      self.labels = self.model.extra_info_labels()
+      # self.labels = load_labels(model, self.extra_info["labels"]) # same as self.model.extra_info_labels()
 
     def classify(self, img : image.Image):
       outs = self.model.forward_image(img, self.mean, self.scale, copy_result = False)

examples/vision/ai_vision/nn_custom_classifier.py

@@ -13,8 +13,10 @@ def parse_str_values(value : str) -> list[float]:
       return [float(value)]
 
 def load_labels(model_path, path_or_labels : str):
-    path = os.path.join(os.path.dirname(model_path), path_or_labels)
-    if os.path.exists(path):
+    path = ""
+    if not ("," in path_or_labels or " " in path_or_labels or "\n" in path_or_labels):
+      path = os.path.join(os.path.dirname(model_path), path_or_labels)
+    if path and os.path.exists(path):
       with open(path, encoding = "utf-8") as f:
         labels0 = f.readlines()
     else:
@@ -30,7 +32,8 @@ def __init__(self, model : str):
       self.extra_info = self.model.extra_info()
       self.mean = parse_str_values(self.extra_info["mean"])
       self.scale = parse_str_values(self.extra_info["scale"])
-      self.labels = load_labels(model, self.extra_info["labels"])
+      self.labels = self.model.extra_info_labels()
+      # self.labels = load_labels(model, self.extra_info["labels"]) # same as self.model.extra_info_labels()
 
     def classify(self, img : image.Image):
       outs = self.model.forward_image(img, self.mean, self.scale, copy_result = False)

examples/vision/ai_vision/nn_yolo11_seg.py

@@ -1,6 +1,6 @@
 from maix import camera, display, image, nn, app, time
 
-detector = nn.YOLO11(model="/root/models/yolov11n_seg.mud", dual_buff = True)
+detector = nn.YOLO11(model="/root/models/yolo11n_seg.mud", dual_buff = True)
 
 cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
 disp = display.Display()