Merge pull request PaddlePaddle#49 from jiweibo/2.0_python_api

update demo with 2.0 api.

README.md

@@ -18,3 +18,5 @@ Paddle Inference为飞桨核心框架推理引擎。Paddle Inference功能特性
 1） 在python目录中，我们通过真实输入的方式罗列了一系列的测试样例，其中包括图像的分类，分割，检测，以及NLP的Ernie/Bert等Python使用样例，同时也包含Paddle-TRT， 多线程的使用样例。
 
 2） 在c++目录中，我们通过单测方式展现了一系列的测试样例，其中包括图像的分类，分割，检测，以及NLP的Ernie/Bert等C++使用样例，同时也包含Paddle-TRT， 多线程的使用样例。
+
+注意：如果您使用2.0以前的Paddle，请参考[release/1.8分支](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/release/1.8)

python/ELMo/README.md

@@ -2,7 +2,7 @@
 
 ### 一：准备环境
 
-请您在环境中安装1.7或以上版本的Paddle，具体的安装方式请参照[飞桨官方页面](https://www.paddlepaddle.org.cn/)的指示方式。
+请您在环境中安装2.0或以上版本的Paddle，具体的安装方式请参照[飞桨官方页面](https://www.paddlepaddle.org.cn/)的指示方式。
 
 ### 二：下载模型以及测试数据
 

python/ELMo/infer.py

@@ -6,45 +6,39 @@
 import reader
 import sys
 
-from paddle.fluid.core import AnalysisConfig
-from paddle.fluid.core import create_paddle_predictor
+from paddle.inference import Config
+from paddle.inference import create_predictor
 
 
 def parse_args():
     """
     Parsing the input parameters.
     """
     parser = argparse.ArgumentParser("Inference for lexical analyzer.")
-    parser.add_argument(
-        "--model_dir",
-        type=str,
-        default="elmo",
-        help="The folder where the test data is located.")
-    parser.add_argument(
-        "--testdata_dir",
-        type=str,
-        default="elmo_data/dev",
-        help="The folder where the test data is located.")
-    parser.add_argument(
-        "--use_gpu",
-        type=int,
-        default=False,
-        help="Whether or not to use GPU. 0-->CPU 1-->GPU")
-    parser.add_argument(
-        "--word_dict_path",
-        type=str,
-        default="elmo_data/vocabulary_min5k.txt",
-        help="The path of the word dictionary.")
-    parser.add_argument(
-        "--label_dict_path",
-        type=str,
-        default="elmo_data/tag.dic",
-        help="The path of the label dictionary.")
-    parser.add_argument(
-        "--word_rep_dict_path",
-        type=str,
-        default="elmo_data/q2b.dic",
-        help="The path of the word replacement Dictionary.")
+    parser.add_argument("--model_dir",
+                        type=str,
+                        default="elmo",
+                        help="The folder where the test data is located.")
+    parser.add_argument("--testdata_dir",
+                        type=str,
+                        default="elmo_data/dev",
+                        help="The folder where the test data is located.")
+    parser.add_argument("--use_gpu",
+                        type=int,
+                        default=False,
+                        help="Whether or not to use GPU. 0-->CPU 1-->GPU")
+    parser.add_argument("--word_dict_path",
+                        type=str,
+                        default="elmo_data/vocabulary_min5k.txt",
+                        help="The path of the word dictionary.")
+    parser.add_argument("--label_dict_path",
+                        type=str,
+                        default="elmo_data/tag.dic",
+                        help="The path of the label dictionary.")
+    parser.add_argument("--word_rep_dict_path",
+                        type=str,
+                        default="elmo_data/q2b.dic",
+                        help="The path of the word replacement Dictionary.")
 
     args = parser.parse_args()
     return args
@@ -65,13 +59,12 @@ def to_lodtensor(data):
     return flattened_data, [lod]
 
 
-def create_predictor(args):
+def init_predictor(args):
     if args.model_dir is not "":
-        config = AnalysisConfig(args.model_dir)
+        config = Config(args.model_dir)
     else:
-        config = AnalysisConfig(args.model_file, args.params_file)
+        config = Config(args.model_file, args.params_file)
 
-    config.switch_use_feed_fetch_ops(False)
     config.enable_memory_optim()
     if args.use_gpu:
         config.enable_use_gpu(1000, 0)
@@ -80,26 +73,26 @@ def create_predictor(args):
         # The thread num should not be greater than the number of cores in the CPU.
         config.set_cpu_math_library_num_threads(4)
 
-    predictor = create_paddle_predictor(config)
+    predictor = create_predictor(config)
     return predictor
 
 
 def run(predictor, datas, lods):
     input_names = predictor.get_input_names()
     for i, name in enumerate(input_names):
-        input_tensor = predictor.get_input_tensor(name)
+        input_tensor = predictor.get_input_handle(name)
         input_tensor.reshape(datas[i].shape)
         input_tensor.copy_from_cpu(datas[i].copy())
         input_tensor.set_lod(lods[i])
 
     # do the inference
-    predictor.zero_copy_run()
+    predictor.run()
 
     results = []
     # get out data from output tensor
     output_names = predictor.get_output_names()
     for i, name in enumerate(output_names):
-        output_tensor = predictor.get_output_tensor(name)
+        output_tensor = predictor.get_output_handle(name)
         output_data = output_tensor.copy_to_cpu()
         results.append(output_data)
     return results
@@ -114,12 +107,12 @@ def run(predictor, datas, lods):
     word_dict_len = max(map(int, word2id_dict.values())) + 1
     label_dict_len = max(map(int, label2id_dict.values())) + 1
 
-    pred = create_predictor(args)
+    pred = init_predictor(args)
 
-    test_data = paddle.batch(
-        reader.file_reader(args.testdata_dir, word2id_dict, label2id_dict,
-                           word_rep_dict),
-        batch_size=1)
+    test_data = paddle.batch(reader.file_reader(args.testdata_dir,
+                                                word2id_dict, label2id_dict,
+                                                word_rep_dict),
+                             batch_size=1)
     batch_id = 0
     id2word = {v: k for k, v in word2id_dict.items()}
     id2label = {v: k for k, v in label2id_dict.items()}

python/resnet50/README.md

@@ -3,7 +3,7 @@
 
 ### 一：准备环境
 
-请您在环境中安装1.7或以上版本的Paddle，具体的安装方式请参照[飞桨官方页面](https://www.paddlepaddle.org.cn/)的指示方式。
+请您在环境中安装2.0或以上版本的Paddle，具体的安装方式请参照[飞桨官方页面](https://www.paddlepaddle.org.cn/)的指示方式。
 
 
 ### 二：下载模型以及测试数据

python/resnet50/img_preprocess.py

@@ -1,6 +1,7 @@
 import cv2
 import numpy as np
 
+
 def resize_short(img, target_size):
     """ resize_short """
     percent = float(target_size) / min(img.shape[0], img.shape[1])
@@ -9,6 +10,7 @@ def resize_short(img, target_size):
     resized = cv2.resize(img, (resized_width, resized_height))
     return resized
 
+
 def crop_image(img, target_size, center):
     """ crop_image """
     height, width = img.shape[:2]
@@ -21,18 +23,19 @@ def crop_image(img, target_size, center):
         h_start = np.random.randint(0, height - size + 1)
     w_end = w_start + size
     h_end = h_start + size
-    img = img[int(h_start):int(h_end), int(w_start):int(w_end), :]w_start:w_end, :]
+    img = img[int(h_start):int(h_end), int(w_start):int(w_end), :]
     return img
 
+
 def preprocess(img):
-   mean = [0.485, 0.456, 0.406] 
-   std = [0.229, 0.224, 0.225]
-   img = resize_short(img, 224)
-   img = crop_image(img, 224, True)
-   # bgr-> rgb && hwc->chw
-   img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
-   img_mean = np.array(mean).reshape((3, 1, 1))
-   img_std = np.array(std).reshape((3, 1, 1))
-   img -= img_mean
-   img /= img_std
-   return img[np.newaxis, :]
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+    img = resize_short(img, 224)
+    img = crop_image(img, 224, True)
+    # bgr-> rgb && hwc->chw
+    img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
+    img_mean = np.array(mean).reshape((3, 1, 1))
+    img_std = np.array(std).reshape((3, 1, 1))
+    img -= img_mean
+    img /= img_std
+    return img[np.newaxis, :]

python/resnet50/infer_resnet.py

@@ -2,64 +2,87 @@
 import argparse
 import cv2
 
-from paddle.fluid.core import AnalysisConfig
-from paddle.fluid.core import create_paddle_predictor
+from paddle.inference import Config
+from paddle.inference import create_predictor
 
 from img_preprocess import preprocess
 
-def create_predictor(args):
-   if args.model_dir is not "":
-     config = AnalysisConfig(args.model_dir)
-   else:
-     config = AnalysisConfig(args.model_file, args.params_file)
 
-   config.switch_use_feed_fetch_ops(False)
-   config.enable_memory_optim()
-   if args.use_gpu:
-     config.enable_use_gpu(1000, 0)
-   else:
-	 # If not specific mkldnn, you can set the blas thread.
-	 # The thread num should not be greater than the number of cores in the CPU.
-	 config.set_cpu_math_library_num_threads(4)
-     #config.enable_mkldnn()
+def init_predictor(args):
+    if args.model_dir is not "":
+        config = Config(args.model_dir)
+    else:
+        config = Config(args.model_file, args.params_file)
+
+    config.enable_memory_optim()
+    if args.use_gpu:
+        config.enable_use_gpu(1000, 0)
+    else:
+        # If not specific mkldnn, you can set the blas thread.
+        # The thread num should not be greater than the number of cores in the CPU.
+        config.set_cpu_math_library_num_threads(4)
+        config.enable_mkldnn()
+
+    predictor = create_predictor(config)
+    return predictor
+
 
-   predictor = create_paddle_predictor(config)
-   return predictor
-
 def run(predictor, img):
-  # copy img data to input tensor
-  input_names = predictor.get_input_names()
-  for i,  name in enumerate(input_names):
-    input_tensor = predictor.get_input_tensor(name)
-    input_tensor.reshape(img[i].shape)   
-    input_tensor.copy_from_cpu(img[i].copy())
+    # copy img data to input tensor
+    input_names = predictor.get_input_names()
+    for i, name in enumerate(input_names):
+        input_tensor = predictor.get_input_handle(name)
+        input_tensor.reshape(img[i].shape)
+        input_tensor.copy_from_cpu(img[i].copy())
 
-  # do the inference
-  predictor.zero_copy_run()
+    # do the inference
+    predictor.run()
+
+    results = []
+    # get out data from output tensor
+    output_names = predictor.get_output_names()
+    for i, name in enumerate(output_names):
+        output_tensor = predictor.get_output_handle(name)
+        output_data = output_tensor.copy_to_cpu()
+        results.append(output_data)
+
+    return results
 
-  results = []
-  # get out data from output tensor
-  output_names = predictor.get_output_names()
-  for i, name in enumerate(output_names):
-    output_tensor = predictor.get_output_tensor(name)
-    output_data = output_tensor.copy_to_cpu()
-    results.append(output_data)
 
-  return results
-
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model_file", type=str, default="", help="Model filename, Specify this when your model is a combined model.")
-    parser.add_argument("--params_file", type=str, default="", help="Parameter filename, Specify this when your model is a combined model.")
-    parser.add_argument("--model_dir", type=str, default="", help="Model dir, If you load a non-combined model, specify the directory of the model.")
-    parser.add_argument("--use_gpu", type=int, default=0, help="Whether use gpu.")
+    parser.add_argument(
+        "--model_file",
+        type=str,
+        default="",
+        help="Model filename, Specify this when your model is a combined model."
+    )
+    parser.add_argument(
+        "--params_file",
+        type=str,
+        default="",
+        help=
+        "Parameter filename, Specify this when your model is a combined model."
+    )
+    parser.add_argument(
+        "--model_dir",
+        type=str,
+        default="",
+        help=
+        "Model dir, If you load a non-combined model, specify the directory of the model."
+    )
+    parser.add_argument("--use_gpu",
+                        type=int,
+                        default=0,
+                        help="Whether use gpu.")
     return parser.parse_args()
 
+
 if __name__ == '__main__':
-  args = parse_args()
-  pred = create_predictor(args) 
-  img = cv2.imread('./ILSVRC2012_val_00000247.jpeg')
-  img = preprocess(img)
-  #img = np.ones((1, 3, 224, 224)).astype(np.float32)
-  result = run(pred, [img]) 
-  print ("class index: ", np.argmax(result[0][0]))
+    args = parse_args()
+    pred = init_predictor(args)
+    img = cv2.imread('./ILSVRC2012_val_00000247.jpeg')
+    img = preprocess(img)
+    #img = np.ones((1, 3, 224, 224)).astype(np.float32)
+    result = run(pred, [img])
+    print("class index: ", np.argmax(result[0][0]))

python/yolov3/README.md

@@ -3,7 +3,7 @@
 
 ### 一：准备环境
 
-请您在环境中安装1.7或以上版本的Paddle，具体的安装方式请参照[飞桨官方页面](https://www.paddlepaddle.org.cn/)的指示方式。
+请您在环境中安装2.0或以上版本的Paddle，具体的安装方式请参照[飞桨官方页面](https://www.paddlepaddle.org.cn/)的指示方式。
 
 
 ### 二：下载模型以及测试数据