From 3cbb58833b6c81229fcbaba803062f0ab04849a6 Mon Sep 17 00:00:00 2001 From: jiweibo Date: Thu, 3 Dec 2020 10:21:53 +0000 Subject: [PATCH] update demo with 2.0 api. --- README.md | 2 + python/ELMo/README.md | 2 +- python/ELMo/infer.py | 83 ++++++++++----------- python/resnet50/README.md | 2 +- python/resnet50/img_preprocess.py | 27 ++++--- python/resnet50/infer_resnet.py | 117 +++++++++++++++++------------ python/yolov3/README.md | 2 +- python/yolov3/infer_yolov3.py | 120 ++++++++++++++++++------------ python/yolov3/utils.py | 21 +++--- 9 files changed, 211 insertions(+), 165 deletions(-) diff --git a/README.md b/README.md index 5164b6f3e62345..ffbb0db5b68d30 100644 --- a/README.md +++ b/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) diff --git a/python/ELMo/README.md b/python/ELMo/README.md index bdd97b4a589d9e..63a0c45198c159 100644 --- a/python/ELMo/README.md +++ b/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 diff --git a/python/ELMo/infer.py b/python/ELMo/infer.py index 4e7ec1bb637c9b..f185538fa69c5e 100644 --- a/python/ELMo/infer.py +++ b/python/ELMo/infer.py @@ -6,8 +6,8 @@ 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(): @@ -15,36 +15,30 @@ 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()} diff --git a/python/resnet50/README.md b/python/resnet50/README.md index f1f504f69d301f..f186e305f90803 100644 --- a/python/resnet50/README.md +++ b/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 diff --git a/python/resnet50/img_preprocess.py b/python/resnet50/img_preprocess.py index 42c6451f8d9956..34321de022caac 100644 --- a/python/resnet50/img_preprocess.py +++ b/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, :] diff --git a/python/resnet50/infer_resnet.py b/python/resnet50/infer_resnet.py index 309bfc36e2d04c..0ddda146e36cc4 100644 --- a/python/resnet50/infer_resnet.py +++ b/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])) diff --git a/python/yolov3/README.md b/python/yolov3/README.md index bd03944f6cc4e1..a5c7c28baced57 100644 --- a/python/yolov3/README.md +++ b/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 diff --git a/python/yolov3/infer_yolov3.py b/python/yolov3/infer_yolov3.py index d9816f79accf07..ef96c9720ec8f9 100644 --- a/python/yolov3/infer_yolov3.py +++ b/python/yolov3/infer_yolov3.py @@ -3,68 +3,90 @@ import cv2 from PIL import Image -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 utils import preprocess, draw_bbox -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.run() - # do the inference - predictor.zero_copy_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() - img_name = 'kite.jpg' - save_img_name = 'res.jpg' - im_size = 608 - pred = create_predictor(args) - img = cv2.imread(img_name) - data = preprocess(img, im_size) - im_shape = np.array([im_size, im_size]).reshape((1,2)).astype(np.int32) - result = run(pred, [data, im_shape]) - img = Image.open(img_name).convert('RGB').resize((im_size, im_size)) - draw_bbox(img, result[0], save_name=save_img_name) + args = parse_args() + img_name = 'kite.jpg' + save_img_name = 'res.jpg' + im_size = 608 + pred = init_predictor(args) + img = cv2.imread(img_name) + data = preprocess(img, im_size) + im_shape = np.array([im_size, im_size]).reshape((1, 2)).astype(np.int32) + result = run(pred, [data, im_shape]) + img = Image.open(img_name).convert('RGB').resize((im_size, im_size)) + draw_bbox(img, result[0], save_name=save_img_name) diff --git a/python/yolov3/utils.py b/python/yolov3/utils.py index 2059a623f387d6..115f9b906b97c7 100644 --- a/python/yolov3/utils.py +++ b/python/yolov3/utils.py @@ -2,6 +2,7 @@ import numpy as np from PIL import Image, ImageDraw + def resize(img, target_size): """resize to target size""" if not isinstance(img, np.ndarray): @@ -11,9 +12,10 @@ def resize(img, target_size): im_size_max = np.max(im_shape[0:2]) im_scale_x = float(target_size) / float(im_shape[1]) im_scale_y = float(target_size) / float(im_shape[0]) - img = cv2.resize(img, None,None,fx=im_scale_x, fy=im_scale_y) + img = cv2.resize(img, None, None, fx=im_scale_x, fy=im_scale_y) return img + def normalize(img, mean, std): img = img / 255.0 mean = np.array(mean)[np.newaxis, np.newaxis, :] @@ -22,15 +24,17 @@ def normalize(img, mean, std): img /= std return img + def preprocess(img, img_size): - mean = [0.485, 0.456, 0.406] + mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] img = resize(img, img_size) - img = img[:, :, ::-1].astype('float32') # bgr -> rgb + img = img[:, :, ::-1].astype('float32') # bgr -> rgb img = normalize(img, mean, std) - img = img.transpose((2,0,1)) # hwc -> chw + img = img.transpose((2, 0, 1)) # hwc -> chw return img[np.newaxis, :] + def draw_bbox(img, result, threshold=0.5, save_name='res.jpg'): """draw bbox""" draw = ImageDraw.Draw(img) @@ -39,10 +43,9 @@ def draw_bbox(img, result, threshold=0.5, save_name='res.jpg'): if score < threshold: continue xmin, ymin, xmax, ymax = bbox - draw.line( - [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), - (xmin, ymin)], - width=2, - fill=(255, 0, 0)) + draw.line([(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), + (xmin, ymin)], + width=2, + fill=(255, 0, 0)) print('category id is {}, bbox is {}'.format(cat_id, bbox)) img.save(save_name, quality=95)