添加 .gitignore 文件，更新训练和预测配置，修改模型类别，删除冗余的 readme 文件，增加测试脚本，优化学习率调度器参数

.gitignore

@@ -0,0 +1,3 @@
+*.jpg
+*.png
+__pycache__

config/lr_scheduler.yaml

@@ -3,8 +3,8 @@ scheduler_type: LINEAR_WARMUP_THEN_POLY_SCHEDULER
 # total_iters=epochs * 训练的图像数量
 kwargs: |
   {
-    "warmup_iters": 62, 
+    "warmup_iters": 4356, 
-    "total_iters": 620, 
+    "total_iters": 290400, 
     "warmup_ratio": 0.000001, 
     "min_lr": 0., 
     "power": 1.

config/model.yaml

@@ -40,4 +40,6 @@ nmf2d_config:
 # 类别
 classes:
   - background # 必须要
-  - leaf
+  - 1
+  - 2
+  - 3

config/predict.yaml

@@ -3,7 +3,7 @@ device: "cuda"
 # -1表示从不加载任何权重就进行预测
 # 0表示使用官方提供的权重进行预测
 # 1表示使用自己的权重进行预测
-mode: 0
+mode: -1
 checkpoint:
   - pretrained # 目录
   - segnext_tiny_512x512_ade_160k.pth # 文件名

config/train.yaml

@@ -1,9 +1,9 @@
 device: cuda
-batch_size: 2
+batch_size: 16
-image_height: 512
+image_height: 200
-image_width: 512
+image_width: 200
 workers: 0
-epochs: 10
+epochs: 100
 
 # 每一类的占比权重，如果要让每一类的占比权重相同，为1.0即可
 weight:
@@ -30,7 +30,7 @@ save_path:
 # -1表示从零开始训练网络，即不加载任何权重
 # 0表示使用官方提供的权重
 # 1表示使用自己的权重
-mode: 0
+mode: -1
 checkpoint:
   - pretrained # 目录
   - segnext_tiny_512x512_ade_160k.pth # 文件名

dataset/train/images/readme

@@ -1 +0,0 @@
-训练图像

dataset/train/labels/readme

@@ -1 +0,0 @@
-标签

dataset/val/images/readme

@@ -1 +0,0 @@
-验证图像数据

dataset/val/labels/readme

@@ -1 +0,0 @@
-标签数据

learning_rate_scheduler.py

@@ -187,7 +187,7 @@ class LinearWarmupThenPolyScheduler:
         total_iters: 总训练步数
         min_lr: 最低学习率
     """
-    def __init__(self, optimizer, warmup_iters=1500, total_iters=2000, warmup_ratio=1e-6, min_lr=0., power=1.):
+    def __init__(self, optimizer, warmup_iters=1500, total_iters=20000, warmup_ratio=1e-6, min_lr=0., power=1.):
         self.optimizer = optimizer
         self.current_iters = 0
         self.warmup_iters = warmup_iters

pretrained/readme

@@ -1 +0,0 @@
-权重, 预训练权重文件请移步官方实现的仓库下载

test.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(200, 200, 3)\n",
+      "[[[226 226 226]\n",
+      "  [227 227 227]\n",
+      "  [225 225 225]\n",
+      "  ...\n",
+      "  [153 153 153]\n",
+      "  [153 153 153]\n",
+      "  [152 152 152]]\n",
+      "\n",
+      " [[227 227 227]\n",
+      "  [222 222 222]\n",
+      "  [221 221 221]\n",
+      "  ...\n",
+      "  [151 151 151]\n",
+      "  [150 150 150]\n",
+      "  [149 149 149]]\n",
+      "\n",
+      " [[227 227 227]\n",
+      "  [226 226 226]\n",
+      "  [221 221 221]\n",
+      "  ...\n",
+      "  [153 153 153]\n",
+      "  [152 152 152]\n",
+      "  [153 153 153]]\n",
+      "\n",
+      " ...\n",
+      "\n",
+      " [[234 234 234]\n",
+      "  [229 229 229]\n",
+      "  [229 229 229]\n",
+      "  ...\n",
+      "  [160 160 160]\n",
+      "  [159 159 159]\n",
+      "  [159 159 159]]\n",
+      "\n",
+      " [[231 231 231]\n",
+      "  [233 233 233]\n",
+      "  [230 230 230]\n",
+      "  ...\n",
+      "  [159 159 159]\n",
+      "  [160 160 160]\n",
+      "  [159 159 159]]\n",
+      "\n",
+      " [[230 230 230]\n",
+      "  [236 236 236]\n",
+      "  [234 234 234]\n",
+      "  ...\n",
+      "  [155 155 155]\n",
+      "  [156 156 156]\n",
+      "  [154 154 154]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 读取图片\n",
+    "image_path = 'dataset/train/images/000229.jpg'\n",
+    "image = Image.open(image_path)\n",
+    "\n",
+    "# 将图片转换为 NumPy 数组\n",
+    "image_array = np.array(image)\n",
+    "\n",
+    "# 显示图片的像素值\n",
+    "print(image_array.shape)\n",
+    "print(image_array)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

train.py

@@ -260,7 +260,8 @@ def train(
             f=f"{os.path.sep.join(train_config['save_path'])}_val_Iou{100 * best_val_model['avg_iou']:.3f}_{datetime.strftime(datetime.now(), '%Y%m%d%H%M%S')}.pth"
         )
 
-
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
 
 
 
@@ -273,6 +274,11 @@ if __name__ == "__main__":
         net=net,
         optimizer=optimizer
     )
+
+    # 计算并打印网络的参数量
+    # num_params = count_parameters(net)
+    # print(f"网络的参数量: {num_params}")
+
     train(
         net=net,
         optimizer=optimizer,