add a demo to show how to use user-defined data for training (#30)

* add custome_reader.py

* update README.md

README.md

@@ -31,6 +31,7 @@
     * [Base64格式图像数据预处理](#Base64格式图像数据预处理)
     * [混合精度训练](#混合精度训练)
     * [自定义模型](#自定义模型)
+    * [自定义训练数据](#自定义训练数据)
 * [预训练模型和性能](#预训练模型和性能)
     * [预训练模型](#预训练模型)
     * [训练性能](#训练性能)
@@ -625,6 +626,98 @@ build_network方法的输入如下：
 
 build_network方法返回用户自定义组网的输出变量。
 
+### 自定义训练数据
+
+默认地，我们假设用户的训练数据目录组织如下：
+
+```shell script
+train_data/
+|-- images
+`-- label.txt
+```
+
+其中，images目录中存放用户训练数据，label.txt文件记录用户训练数据中每幅图像的地址和对应的类别标签。
+
+当用户的训练数据按照其它自定义格式组织时，可以按照下面的步骤使用自定义训练数据：
+
+1. 定义reader函数(生成器），该函数对用户数据进行预处理（如裁剪），并使用yield生成数据样本；
+    * 数据样本的格式为形如(data, label)的元组，其中data为解码和预处理后的图像数据，label为该图像的类别标签。
+2. 使用paddle.batch封装reader生成器，得到新的生成器batched_reader；
+3. 将batched_reader赋值给plsc.Entry类示例的train_reader成员。
+
+为了便于描述，我们仍然假设用户训练数据组织结构如下：
+
+```shell script
+train_data/
+|-- images
+`-- label.txt
+```
+
+定义样本生成器的代码如下所示(reader.py)：
+
+```python
+import random
+import os
+from PIL import Image
+
+def arc_train(data_dir):
+    label_file = os.path.join(data_dir, 'label.txt')
+    train_image_list = None
+    with open(label_file, 'r') as f:
+        train_image_list = f.readlines()
+    train_image_list = get_train_image_list(data_dir)
+
+    def reader():
+        for j in range(len(train_image_list)):
+            path, label = train_image_list[j]
+            path = os.path.join(data_dir, path)
+            img = Image.open(path)
+            if random.randint(0, 1) == 1:
+                img = img.transpose(Image.FLIP_LEFT_RIGHT)
+            if img.mode != 'RGB':
+                img = img.convert('RGB')
+            img = np.array(img).astype('float32').transpose((2, 0, 1))
+            yield img, label
+
+    return reader
+```
+
+使用用户自定义训练数据的训练代码如下：
+
+```python
+import argparse
+import paddle
+from plsc import Entry
+import reader
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_dir",
+                    type=str,
+                    default="./data",
+                    help="Directory for datasets.")
+args = parser.parse_args()
+
+
+def main():
+    global args
+    ins = Entry()
+    ins.set_dataset_dir(args.data_dir)
+    train_reader = reader.arc_train(args.data_dir)
+    # Batch the above samples;
+    batched_train_reader = paddle.batch(train_reader,
+                                        ins.train_batch_size)
+    # Set the reader to use during training to the above batch reader.
+    ins.train_reader = batched_train_reader
+
+    ins.train()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+更多详情请参考[示例代码](./demo/custom_reader.py)
+
 ## 预训练模型和性能
 
 ### 预训练模型

demo/custom_reader.py

+# This demo shows how to use user-defined training dataset.
+# The following steps are needed to use user-defined training datasets:
+# 1. Build a reader, which preprocess images and yield a sample in the
+#    format (data, label) each time, where data is the decoded image data;
+# 2. Batch the above samples;
+# 3. Set the reader to use during training to the above batch reader.
+
+import argparse
+
+import paddle
+from plsc import Entry
+from plsc.utils import jpeg_reader as reader
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_save_dir",
+                    type=str,
+                    default="./saved_model",
+                    help="Directory to save models.")
+parser.add_argument("--data_dir",
+                    type=str,
+                    default="./data",
+                    help="Directory for datasets.")
+parser.add_argument("--num_epochs",
+                    type=int,
+                    default=2,
+                    help="Number of epochs to run.")
+parser.add_argument("--loss_type",
+                    type=str,
+                    default='arcface',
+                    help="Loss type to use.")
+args = parser.parse_args()
+
+
+def main():
+    global args
+    ins = Entry()
+    ins.set_model_save_dir(args.model_save_dir)
+    ins.set_dataset_dir(args.data_dir)
+    ins.set_train_epochs(args.num_epochs)
+    ins.set_loss_type(args.loss_type)
+    # 1. Build a reader, which yield a sample in the format (data, label)
+    #    each time, where data is the decoded image data;
+    train_reader = reader.arc_train(args.data_dir,
+                                    ins.num_classes)
+    # 2. Batch the above samples;
+    batched_train_reader = paddle.batch(train_reader,
+                                        ins.train_batch_size)
+    # 3. Set the reader to use during training to the above batch reader.
+    ins.train_reader = batched_train_reader
+
+    ins.train()
+
+
+if __name__ == "__main__":
+    main()