docs: add code example and introduction about image_classification. (#59

)

* chore: add tools in docker image

* docs: add code example about image_classification.

* docs: update README.md

.gitignore

@@ -5,4 +5,6 @@ main.ipynb
 main.py
 __pycache__
 .DS_Store
-data
+data
+model
+dataset

.vscode/settings.json

@@ -2,5 +2,7 @@
     "markdownlint.config": {
         "MD010": false,
         "MD033": false
-    }
+    },
+    "debugpy.debugJustMyCode": false,
+    // "debugpy.debugJustMyCode": true,
 }

README.md

@@ -43,12 +43,6 @@
 | 姓名   | 职责                   | 简介               |
 | :----- | :--------------------- | :----------------- |
 | 田健翔 | 项目负责人             | 内容创作者 |
-| 于小敏 | 项目指导人             | DataWhale正式成员  |
-| 卢鑫斌 | 第1章(Datasets)贡献者  | 内容创作者         |
-| 胥佳程 | 第3章(PEFT)贡献者      | 内容创作者         |
-| 秦子涵 | 第5章(Diffusers)贡献者 | 内容创作者         |
-| 陈凯歌 | 第7章(Gradio)贡献者    | 内容创作者         |
-| 刘硕   | 第7章(Gradio)贡献者    | 内容创作者         |
 
 - PEFT
     - LoRa：@[鑫民](https://github.com/fancyboi999)
@@ -57,6 +51,8 @@
     - Prefix-Tuning：@[鑫民](https://github.com/fancyboi999)
     - prompt-Tuning：@[鑫民](https://github.com/fancyboi999)
     - P-Tuning：@[鑫民](https://github.com/fancyboi999)
+- 代码案例
+    - 图像分类: @[陈相斌](https://github.com/chenxinxi)
 
 项目保姆(o^^o)：高增玉
 

docker-compose/Dockerfile

@@ -13,6 +13,7 @@ RUN apt-get update && apt-get install -y \
     unzip \
     inetutils-ping \
     tmux \
+    watch \
     && apt-get clean
 
 COPY --from=miniconda-stage /opt/conda /opt/conda

docs/chapter6/code_index.md

@@ -14,3 +14,4 @@ title: 索引
 | 文本翻译           | [中英文本翻译](./translation/translation.md)                                                    |
 | 扩散去噪           | [ddpm-unet简单去噪](./ddpm-unet-mnist/ddpm-unet-mnist.md)                                       |
 | 文本分类           | [基金年报问答意图识别](./financial_report/financial_report.md)                                  |
+| 图像分类           | [菜肴图像分类](./image_classification/image_classification.md)                                  |

docs/chapter6/image_classification/image_classification.md

@@ -0,0 +1,221 @@
+---
+comments: true
+title: 菜肴图像分类
+---
+
+![image_classification](./imgs/image_classification.png)
+
+## 前言
+
+## 代码
+
+```python
+model_checkpoint = "google/vit-base-patch16-224-in21k"
+```
+
+### 导入函数库
+
+```python
+import evaluate
+import numpy as np
+import torch
+from datasets import load_dataset
+from peft import LoraConfig, get_peft_model
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    Normalize,
+    RandomHorizontalFlip,
+    RandomResizedCrop,
+    Resize,
+    ToTensor,
+)
+from transformers import (
+    AutoImageProcessor,
+    AutoModelForImageClassification,
+    Trainer,
+    TrainingArguments,
+)
+```
+
+### 读取数据集
+
+```python
+dataset = load_dataset("food101", split="train[:5000]")
+
+labels = dataset.features["label"].names
+
+label2id, id2label = dict(), dict()
+for i, label in enumerate(labels):
+    label2id[label] = i
+    id2label[i] = label
+```
+
+下面是数据集`food101`的数据集主页。
+
+<iframe
+  src="https://huggingface.co/datasets/ethz/food101/embed/viewer/default/train"
+  frameborder="0"
+  width="100%"
+  height="560px"
+></iframe>
+
+### 加载模型
+
+```python
+model = AutoModelForImageClassification.from_pretrained(
+    model_checkpoint,
+    label2id=label2id,
+    id2label=id2label,
+    # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
+    ignore_mismatched_sizes=True,
+)
+
+config = LoraConfig(
+    r=16,
+    lora_alpha=16,
+    target_modules=["query", "value"],
+    lora_dropout=0.1,
+    bias="none",
+    modules_to_save=["classifier"],
+)
+lora_model = get_peft_model(model, config)
+```
+
+使用参数高效微调后打印可训练参数如下：
+
+```python title="model.print_trainable_parameters()"
+trainable params: 667,493 || all params: 86,543,818 || trainable%: 0.7713
+```
+
+### 加载预处理器
+
+```python
+image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)
+```
+
+### 定义数据转换
+
+```python
+normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+train_transforms = Compose(
+    [
+        RandomResizedCrop(image_processor.size["height"]),
+        RandomHorizontalFlip(),
+        ToTensor(),
+        normalize,
+    ]
+)
+
+val_transforms = Compose(
+    [
+        Resize(image_processor.size["height"]),
+        CenterCrop(image_processor.size["height"]),
+        ToTensor(),
+        normalize,
+    ]
+)
+
+def preprocess_train(example_batch):
+    """Apply train_transforms across a batch."""
+    example_batch["pixel_values"] = [
+        train_transforms(image.convert("RGB")) for image in example_batch["image"]
+    ]
+    return example_batch
+
+
+def preprocess_val(example_batch):
+    """Apply val_transforms across a batch."""
+    example_batch["pixel_values"] = [
+        val_transforms(image.convert("RGB")) for image in example_batch["image"]
+    ]
+    return example_batch
+```
+
+### 数据预处理
+
+```python
+splits = dataset.train_test_split(test_size=0.1)
+train_ds = splits["train"]
+val_ds = splits["test"]
+
+train_ds.set_transform(preprocess_train)
+val_ds.set_transform(preprocess_val)
+```
+
+### 定义评价指标
+
+```python
+metric = evaluate.load("accuracy")
+
+def compute_metrics(eval_pred):
+    """Computes accuracy on a batch of predictions"""
+    predictions = np.argmax(eval_pred.predictions, axis=1)
+    return metric.compute(predictions=predictions, references=eval_pred.label_ids)
+```
+
+### 定义动态数据整理
+
+```python
+def collate_fn(examples):
+    pixel_values = torch.stack([example["pixel_values"] for example in examples])
+    labels = torch.tensor([example["label"] for example in examples])
+    return {"pixel_values": pixel_values, "labels": labels}
+```
+
+### 定义训练参数
+
+```python
+args = TrainingArguments(
+    "vit-finetuned-lora-food101",
+    remove_unused_columns=False,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    save_total_limit=2,
+    learning_rate=5e-3,
+    per_device_train_batch_size=128,
+    gradient_accumulation_steps=4,
+    per_device_eval_batch_size=128,
+    fp16=True,
+    num_train_epochs=5,
+    logging_steps=10,
+    load_best_model_at_end=True,
+    metric_for_best_model="accuracy",
+    label_names=["labels"],
+    use_cpu=False,
+)
+```
+
+### 定义训练器
+
+```python
+trainer = Trainer(
+    lora_model,
+    args,
+    train_dataset=train_ds,
+    eval_dataset=val_ds,
+    tokenizer=image_processor,
+    compute_metrics=compute_metrics,
+    data_collator=collate_fn,
+)
+```
+
+### 训练与评估
+
+```python
+trainer.train()
+trainer.evaluate(val_ds)
+```
+
+下面是训练时的过程结果。
+
+| 轮次 | 评估损失 | 评估准确率 |
+| ---- | -------- | ---------- |
+| 0.8  | 4.0372   | 0.80       |
+| 1.6  | 3.5086   | 0.876      |
+| 2.4  | 3.0289   | 0.896      |
+| 4.0  | 2.4545   | 0.908      |
+
+## 参考资料
+
+待补充