[Example] add Predict_Composite_Strength

examples/CNN_UTS/MyProjectDocs/docs/index.md

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+# Predicting the Strength of Composites
+
+## 参考
+Po-Hao Lai, et al. "Predicting the Strength of Composites with Computer Vision Using Small Experimental Datasets"
+<https://doi.org/10.5281/zenodo.14803929>
+
+## 目录结构
+```
+CNN_UTS/
+│
+├─ conf/  
+│    └─ resnet.yaml
+├─ data_utils.py  
+├─ model_utils.py  
+├─ main.py  
+├─ requirements.txt  
+├─ readme.md  
+├─ resnet18-v5-finetune/  
+├─ outputs/  
+├─ Saved_Output/  
+└─ Dataset/  
+     ├─ Train_val/  
+     └─ Test/  
+```
+
+## 环境依赖
+
+见 requirements.txt
+
+## 数据格式说明
+数据集下载链接:https://zenodo.org/records/14803929
+
+- `Dataset/Train_val/` 和 `Dataset/Test/` 下为若干子文件夹，每个子文件夹代表一个样本组。
+- 每个子文件夹内包含若干 `.jpg` 图像和一个 `.csv` 文件。
+- `.csv` 文件示例（每行对应一张图片，需包含 `Image Name`、若干特征列、`UTS (MPa)` 等标签）：
+
+| Image Name         | ...特征列... | UTS (MPa) | ... |
+|--------------------|--------------|-----------|-----|
+| IPP_10__40060.jpg  | ...          | 0.56      | ... |
+| ...                | ...          | ...       | ... |
+
+## 快速开始
+
+### 1. 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. 配置参数
+
+编辑 `conf/resnet.yaml`，可自定义训练/评估参数：
+
+```yaml
+mode: "eval"
+seed: 42
+device: "cuda:0"
+data:
+  train_path: "./Dataset/Train_val"
+  test_path: "./Dataset/Test"
+  N: 1
+train:
+  epochs: 32
+  n_splits: 5
+  batch_size: 32
+  lr: 0.0009761248347350309
+output_dir: "./Saved_Output"
+```
+
+### 3. 训练模型
+
+```bash
+python main.py mode=train
+```
+
+### 4. 评估模型
+
+```bash
+python main.py mode=eval
+```
+
+### 5. 可视化与结果
+
+- 训练和评估后，预测结果、统计指标、可视化图片会自动保存在 `Saved_Output/` 目录下。
+。

examples/CNN_UTS/MyProjectDocs/mkdocs.yml

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+site_name: My Docs

examples/CNN_UTS/conf/resnet.yaml

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+mode: "train"
+seed: 42
+device: "cpu"
+data:
+  train_path: "./Dataset/Train_val"
+  test_path: "./Dataset/Test"
+  N: 1
+train:
+  epochs: 32
+  n_splits: 5
+  batch_size: 32
+  lr: 0.0009761248347350309
+output_dir: "./Saved_Output"

examples/CNN_UTS/data_utils.py

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+# data_utils.py
+import os
+import random
+
+import paddle
+import pandas as pd
+from PIL import Image
+
+
+def device2str(type=None, index=None, *, device=None):
+    type = device if device else type
+    if isinstance(type, int):
+        type = f"gpu:{type}"
+    elif isinstance(type, str):
+        if "cuda" in type:
+            type = type.replace("cuda", "gpu")
+        if "cpu" in type:
+            type = "cpu"
+        elif index is not None:
+            type = f"{type}:{index}"
+    elif isinstance(type, paddle.CPUPlace) or (type is None):
+        type = "cpu"
+    elif isinstance(type, paddle.CUDAPlace):
+        type = f"gpu:{type.get_device_id()}"
+    return type
+
+
+class CustomDataset(paddle.io.Dataset):
+    def __init__(self, data, device="cpu"):
+        self.data = data
+        self.device = device
+        self.preload_to_device()
+
+    def preload_to_device(self):
+        self.data = [
+            (
+                image.to(self.device),
+                group,
+                paddle.to_tensor(data=features).astype(dtype="float32").to(self.device),
+            )
+            for image, group, features in self.data
+        ]
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        image, group, features = self.data[index]
+        return image, group, features
+
+
+image_transforms = paddle.vision.transforms.Compose(
+    transforms=[
+        paddle.vision.transforms.CenterCrop(size=224),
+        paddle.vision.transforms.ToTensor(),
+        paddle.vision.transforms.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        ),
+    ]
+)
+
+
+def make_dataset(data_folder, N=1, verbose=False, device="cpu"):
+    random.seed(16)
+    this_data = []
+    all_subfolders = [
+        f
+        for f in os.listdir(data_folder)
+        if os.path.isdir(os.path.join(data_folder, f)) and len(f.split("_")) >= 3
+    ]
+
+    def safe_folder_sort_key(x):
+        parts = x.split("_")
+        try:
+            return float(parts[-3])
+        except Exception:
+            return float("inf")
+
+    subfolders = sorted(all_subfolders, key=safe_folder_sort_key)
+    grouped_subfolders = [[] for _ in range(5)]
+    for i, subfolder in enumerate(subfolders):
+        index = i // (len(subfolders) // 5)
+        if index >= 5:
+            index = 4
+        grouped_subfolders[index].append(subfolder)
+    if verbose:
+        print("分组结果：", grouped_subfolders)
+    chunk_keys = {}
+    for i, gs in enumerate(grouped_subfolders):
+        for sf in gs:
+            chunk_keys[sf] = i
+    sample_keys = {k: i for i, k in enumerate(subfolders)}
+    for _ in range(len(subfolders) // 5 + 1):
+        for k, group in enumerate(grouped_subfolders):
+            if not group:
+                continue
+            selected_subfolder = random.choice(group)
+            group.remove(selected_subfolder)
+            folder_path = os.path.join(data_folder, selected_subfolder)
+            if not os.path.isdir(folder_path):
+                print(f"Warning: {folder_path} is not a valid directory")
+                continue
+            csv_data = None
+            try:
+                for file_name in os.listdir(folder_path):
+                    if file_name.endswith(".csv"):
+                        csv_path = os.path.join(folder_path, file_name)
+                        try:
+                            csv_data = pd.read_csv(csv_path)
+                            break
+                        except Exception as e:
+                            print(f"Error reading CSV file {csv_path}: {str(e)}")
+                            continue
+            except Exception as e:
+                print(f"Error accessing directory {folder_path}: {str(e)}")
+                continue
+            num = 0
+            try:
+                image_names = [
+                    image_name
+                    for image_name in os.listdir(folder_path)
+                    if image_name.endswith(".jpg")
+                ]
+                image_names.sort()
+            except Exception as e:
+                print(f"Error reading images from {folder_path}: {str(e)}")
+                continue
+            for i, image_name in enumerate(image_names):
+                if i % N != 0:
+                    continue
+                num += 1
+                image_path = os.path.join(folder_path, image_name)
+                image_data = Image.open(image_path).convert("RGB")
+                image_data = image_transforms(image_data)
+                if csv_data is not None:
+                    image_features = (
+                        csv_data.loc[csv_data["Image Name"] == image_name, "UTS (MPa)"]
+                        .values[0]
+                        .astype(float)
+                    )
+                else:
+                    image_features = None
+                this_data.append(
+                    (
+                        image_data,
+                        (
+                            chunk_keys[selected_subfolder],
+                            sample_keys[selected_subfolder],
+                        ),
+                        image_features,
+                    )
+                )
+            if verbose:
+                print(f"文件夹 {selected_subfolder} 采样图片数: {num}")
+    return CustomDataset(this_data, device=device)