Fix som issues:

1. Remove unused  arguments.
2. Refine doc.
3. Change 'device' to 'use_gpu'.

fluid/ocr_recognition/README.md

 
 [toc]
 
-运行本目录下的程序示例需要使用PaddlePaddle v0.11.0 版本。如果您的PaddlePaddle安装版本低于此要求，请按照安装文档中的说明更新PaddlePaddle安装版本。
+运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。如果您的PaddlePaddle安装版本低于此要求，请按照安装文档中的说明更新PaddlePaddle安装版本。
 
 # Optical Character Recognition
 
@@ -108,22 +108,16 @@ data/test_images/00003.jpg
 
 #### 1.2 训练
 
-Train on one GPU with defalut trainning data:
+使用默认数据在GPU单卡上训练:
 
 ```
-env CUDA_VISIABLE_DEVICES=0 python ctc_train.py \
-    --device=0 \
-    --parallel=False \
-    --batch_size=32
+env CUDA_VISIABLE_DEVICES=0 python ctc_train.py
 ```
 
-Train on multi-GPU with defalut trainning data:
+使用默认数据在GPU多卡上训练:
 
 ```
-env CUDA_VISIABLE_DEVICES=0,1,2,3 python ctc_train.py \
-    --device=0 \
-    --parallel=True \
-    --batch_size=128
+env CUDA_VISIABLE_DEVICES=0,1,2,3 python ctc_train.py --parallel=True
 ```
 
 执行`python ctc_train.py --help`可查看更多使用方式和参数详细说明。
@@ -137,7 +131,7 @@ env CUDA_VISIABLE_DEVICES=0,1,2,3 python ctc_train.py \
 
 
 
-### 1.3 Evaluate
+### 1.3 评估
 
 通过以下命令调用评估脚本用指定数据集对模型进行评估：
 
@@ -146,25 +140,40 @@ env CUDA_VISIBLE_DEVICE=0 python eval.py \
     --model_path="./models/model_0" \
     --input_images_dir="./eval_data/images/" \
     --input_images_list="./eval_data/eval_list\" \
-    --device 0
 ```
 
 执行`python ctc_train.py --help`可查看参数详细说明。
 
 
-### 1.4 Inference
+### 1.4 预测
 
-Read image path from stdin and inference：
+从标准输入读取一张图片的路径，并对齐进行预测：
 
 ```
 env CUDA_VISIBLE_DEVICE=0 python inference.py \
-    --model_path models/model_00044_15000
+    --model_path="models/model_00044_15000"
 ```
 
-Read image path from list file and inference：
+执行上述命令进行预测的效果如下：
+
+```
+-----------  Configuration Arguments -----------
+use_gpu: True
+input_images_dir: None
+input_images_list: None
+model_path: /home/work/models/fluid/ocr_recognition/models/model_00052_15000
+------------------------------------------------
+Init model from: /home/work/models/fluid/ocr_recognition/models/model_00052_15000.
+Please input the path of image: /home/work/models/fluid/ocr_recognition/data/test_images/00001_0060.jpg
+result: [3298 2371 4233 6514 2378 3298 2363]
+Please input the path of image: /home/work/models/fluid/ocr_recognition/data/test_images/00001_0429.jpg
+result: [2067 2067 8187 8477 5027 7191 2431 1462]
+```
+
+从文件中批量读取图片路径，并对其进行预测：
 
 ```
 env CUDA_VISIBLE_DEVICE=0 python inference.py \
-    --model_path=models/model_00044_15000 \
+    --model_path="models/model_00044_15000" \
     --input_images_list="data/test.list"
 ```

fluid/ocr_recognition/ctc_train.py

@@ -21,12 +21,9 @@ add_arg('save_model_dir',    str,   "./models", "The directory the model to be s
 add_arg('init_model',        str,   None,       "The init model file of directory.")
 add_arg('learning_rate',     float, 1.0e-3,    "Learning rate.")
 add_arg('l2',                float, 0.0004,    "L2 regularizer.")
-add_arg('max_clip',          float, 10.0,      "Max clip threshold.")
-add_arg('min_clip',          float, -10.0,     "Min clip threshold.")
 add_arg('momentum',          float, 0.9,       "Momentum.")
 add_arg('rnn_hidden_size',   int,   200,       "Hidden size of rnn layers.")
-add_arg('device',            int,   0,         "Device id.'-1' means running on CPU"
-                                               "while '0' means GPU-0.")
+add_arg('use_gpu',           bool,  True,      "Whether use GPU to train.")
 add_arg('min_average_window',int,   10000,     "Min average window.")
 add_arg('max_average_window',int,   15625,     "Max average window. It is proposed to be set as the number of minibatch in a pass.")
 add_arg('average_window',    float, 0.15,      "Average window.")
@@ -41,31 +38,33 @@ add_arg('test_list',        str,    None,   "The list file of training images."
         "None means using the default test_list file of reader.")
 add_arg('num_classes',      int,    None,      "The number of classes."
         "None means using the default num_classes from reader.")
-# yapf: disable
+# yapf: enable
 
 
 def train(args, data_reader=ctc_reader):
     """OCR CTC training"""
-    num_classes = data_reader.num_classes() if args.num_classes is None else args.num_classes
+    num_classes = data_reader.num_classes(
+    ) if args.num_classes is None else args.num_classes
     data_shape = data_reader.data_shape()
     # define network
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
-    label = fluid.layers.data(name='label', shape=[1], dtype='int32', lod_level=1)
-    sum_cost, error_evaluator, inference_program, model_average = ctc_train_net(images, label, args, num_classes)
+    label = fluid.layers.data(
+        name='label', shape=[1], dtype='int32', lod_level=1)
+    sum_cost, error_evaluator, inference_program, model_average = ctc_train_net(
+        images, label, args, num_classes)
 
     # data reader
     train_reader = data_reader.train(
-            args.batch_size,
-            train_images_dir=args.train_images,
-            train_list_file=args.train_list)
+        args.batch_size,
+        train_images_dir=args.train_images,
+        train_list_file=args.train_list)
     test_reader = data_reader.test(
-            test_images_dir=args.test_images,
-            test_list_file=args.test_list)
+        test_images_dir=args.test_images, test_list_file=args.test_list)
 
     # prepare environment
     place = fluid.CPUPlace()
-    if args.device >= 0:
-        place = fluid.CUDAPlace(args.device)
+    if args.use_gpu:
+        place = fluid.CUDAPlace(0)
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
@@ -74,8 +73,8 @@ def train(args, data_reader=ctc_reader):
         model_dir = args.init_model
         model_file_name = None
         if not os.path.isdir(args.init_model):
-            model_dir=os.path.dirname(args.init_model)
-            model_file_name=os.path.basename(args.init_model)
+            model_dir = os.path.dirname(args.init_model)
+            model_file_name = os.path.basename(args.init_model)
         fluid.io.load_params(exe, dirname=model_dir, filename=model_file_name)
         print "Init model from: %s." % args.init_model
 
@@ -95,15 +94,17 @@ def train(args, data_reader=ctc_reader):
             # training log
             if batch_id % args.log_period == 0:
                 print "\nTime: %s; Pass[%d]-batch[%d]; Avg Warp-CTC loss: %s; Avg seq error: %s." % (
-                    time.time(),
-                    pass_id, batch_id, total_loss / (batch_id * args.batch_size), total_seq_error / (batch_id * args.batch_size))
+                    time.time(), pass_id, batch_id,
+                    total_loss / (batch_id * args.batch_size),
+                    total_seq_error / (batch_id * args.batch_size))
                 sys.stdout.flush()
             # evaluate
             if batch_id % args.eval_period == 0:
                 with model_average.apply(exe):
                     error_evaluator.reset(exe)
                     for data in test_reader():
-                        exe.run(inference_program, feed=get_feeder_data(data, place))
+                        exe.run(inference_program,
+                                feed=get_feeder_data(data, place))
                     _, test_seq_error = error_evaluator.eval(exe)
 
                     print "\nTime: %s; Pass[%d]-batch[%d]; Test seq error: %s.\n" % (
@@ -112,8 +113,10 @@ def train(args, data_reader=ctc_reader):
             if batch_id % args.save_model_period == 0:
                 with model_average.apply(exe):
                     filename = "model_%05d_%d" % (pass_id, batch_id)
-                    fluid.io.save_params(exe, dirname=args.save_model_dir, filename=filename)
-                    print "Saved model to: %s/%s." %(args.save_model_dir, filename)
+                    fluid.io.save_params(
+                        exe, dirname=args.save_model_dir, filename=filename)
+                    print "Saved model to: %s/%s." % (args.save_model_dir,
+                                                      filename)
 
             batch_id += 1
 
@@ -123,5 +126,6 @@ def main():
     print_arguments(args)
     train(args, data_reader=ctc_reader)
 
+
 if __name__ == "__main__":
     main()

fluid/ocr_recognition/eval.py

@@ -14,8 +14,8 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('model_path',         str,  None,   "The model path to be used for inference.")
 add_arg('input_images_dir',   str,  None,   "The directory of images.")
 add_arg('input_images_list',  str,  None,   "The list file of images.")
-add_arg('device',             int,  0,      "Device id.'-1' means running on CPU")
-# yapf: disable
+add_arg('use_gpu',            bool,  True,      "Whether use GPU to eval.")
+# yapf: enable
 
 
 def evaluate(args, eval=ctc_eval, data_reader=ctc_reader):
@@ -29,12 +29,14 @@ def evaluate(args, eval=ctc_eval, data_reader=ctc_reader):
     evaluator, cost = eval(images, label, num_classes)
 
     # data reader
-    test_reader = data_reader.test(test_images_dir=args.input_images_dir, test_list_file=args.input_images_list)
+    test_reader = data_reader.test(
+        test_images_dir=args.input_images_dir,
+        test_list_file=args.input_images_list)
 
     # prepare environment
     place = fluid.CPUPlace()
-    if args.device >= 0:
-        place = fluid.CUDAPlace(args.device)
+    if use_gpu:
+        place = fluid.CUDAPlace(0)
 
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
@@ -43,8 +45,8 @@ def evaluate(args, eval=ctc_eval, data_reader=ctc_reader):
     model_dir = args.model_path
     model_file_name = None
     if not os.path.isdir(args.model_path):
-        model_dir=os.path.dirname(args.model_path)
-        model_file_name=os.path.basename(args.model_path)
+        model_dir = os.path.dirname(args.model_path)
+        model_file_name = os.path.basename(args.model_path)
     fluid.io.load_params(exe, dirname=model_dir, filename=model_file_name)
     print "Init model from: %s." % args.model_path
 
@@ -52,11 +54,11 @@ def evaluate(args, eval=ctc_eval, data_reader=ctc_reader):
     count = 0
     for data in test_reader():
         count += 1
-        exe.run(
-            fluid.default_main_program(),
-            feed=get_feeder_data(data, place))
+        exe.run(fluid.default_main_program(), feed=get_feeder_data(data, place))
     avg_distance, avg_seq_error = evaluator.eval(exe)
-    print "Read %d samples; avg_distance: %s; avg_seq_error: %s" % (count, avg_distance, avg_seq_error)
+    print "Read %d samples; avg_distance: %s; avg_seq_error: %s" % (
+        count, avg_distance, avg_seq_error)
+
 
 def main():
     args = parser.parse_args()

fluid/ocr_recognition/inference.py

@@ -14,8 +14,9 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 add_arg('model_path',         str,  None,   "The model path to be used for inference.")
 add_arg('input_images_dir',   str,  None,   "The directory of images.")
 add_arg('input_images_list',  str,  None,   "The list file of images.")
-add_arg('device',             int,  0,      "Device id.'-1' means running on CPU")
-# yapf: disable
+add_arg('use_gpu',            bool,  True,      "Whether use GPU to infer.")
+# yapf: enable
+
 
 def inference(args, infer=ctc_infer, data_reader=ctc_reader):
     """OCR inference"""
@@ -25,11 +26,13 @@ def inference(args, infer=ctc_infer, data_reader=ctc_reader):
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
     sequence = infer(images, num_classes)
     # data reader
-    infer_reader = data_reader.inference(infer_images_dir=args.input_images_dir, infer_list_file=args.input_images_list)
+    infer_reader = data_reader.inference(
+        infer_images_dir=args.input_images_dir,
+        infer_list_file=args.input_images_list)
     # prepare environment
     place = fluid.CPUPlace()
-    if args.device >= 0:
-        place = fluid.CUDAPlace(args.device)
+    if use_gpu:
+        place = fluid.CUDAPlace(0)
 
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
@@ -38,12 +41,11 @@ def inference(args, infer=ctc_infer, data_reader=ctc_reader):
     model_dir = args.model_path
     model_file_name = None
     if not os.path.isdir(args.model_path):
-        model_dir=os.path.dirname(args.model_path)
-        model_file_name=os.path.basename(args.model_path)
+        model_dir = os.path.dirname(args.model_path)
+        model_file_name = os.path.basename(args.model_path)
     fluid.io.load_params(exe, dirname=model_dir, filename=model_file_name)
     print "Init model from: %s." % args.model_path
 
-
     for data in infer_reader():
         result = exe.run(fluid.default_main_program(),
                          feed=get_feeder_data(
@@ -58,5 +60,6 @@ def main():
     print_arguments(args)
     inference(args, data_reader=ctc_reader)
 
+
 if __name__ == "__main__":
     main()