From 4e794c6779e725afefc759160a643e537681f23a Mon Sep 17 00:00:00 2001 From: sjtubinlong Date: Thu, 28 Nov 2019 16:01:48 +0800 Subject: [PATCH] update python inferences codes and docs --- README.md | 3 +- deploy/README.md | 10 + {inference => deploy/cpp}/CMakeLists.txt | 0 {inference => deploy/cpp}/CMakeSettings.json | 0 {inference => deploy/cpp}/INSTALL.md | 0 {inference => deploy/cpp}/LICENSE | 0 {inference => deploy/cpp}/README.md | 6 +- {inference => deploy/cpp}/conf/humanseg.yaml | 0 {inference => deploy/cpp}/demo.cpp | 0 {inference => deploy/cpp}/docs/demo.jpg | Bin {inference => deploy/cpp}/docs/demo_jpg.png | Bin {inference => deploy/cpp}/docs/linux_build.md | 0 {inference => deploy/cpp}/docs/vis.md | 0 {inference => deploy/cpp}/docs/vis_result.png | Bin .../cpp}/docs/windows_vs2015_build.md | 0 .../cpp}/docs/windows_vs2019_build.md | 0 .../cpp}/external-cmake/yaml-cpp.cmake | 0 .../cpp}/images/humanseg/demo1.jpeg | Bin .../cpp}/images/humanseg/demo2.jpeg | Bin 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mode 100644 inference/python_inference.md delete mode 100644 serving/imgs/GF1_PMS1_sino0_0.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_1.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_2.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_3.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_4.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_5.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_6.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_7.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_8.png delete mode 100644 serving/imgs/GF1_PMS1_sino0_9.png diff --git a/README.md b/README.md index a2456c92..a17a7cf8 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,8 @@ $ pip install -r requirements.txt ### 预测部署 * [模型导出](./docs/model_export.md) -* [C++预测库使用](./inference) +* [使用Python预测](./deploy/python/) +* [使用C++预测](./deploy/cpp/) ### 高级功能 diff --git a/deploy/README.md b/deploy/README.md new file mode 100644 index 00000000..fc06306b --- /dev/null +++ b/deploy/README.md @@ -0,0 +1,10 @@ +# PaddleSeg 预测部署 + +`PaddleSeg`目前支持使用`Python`和`C++`部署在`Windows` 和`Linux` 上, 也可以集成`PaddleServing`服务化部署在 `Linux` 上。 + +[1. Python预测(支持 Linux 和 Windows)](./python/) + +[2. C++预测(支持 Linux 和 Windows)](./cpp/) + +[3. 服务化部署(仅支持 Linux)](./serving) + diff --git a/inference/CMakeLists.txt b/deploy/cpp/CMakeLists.txt similarity index 100% rename from inference/CMakeLists.txt rename to deploy/cpp/CMakeLists.txt diff --git a/inference/CMakeSettings.json b/deploy/cpp/CMakeSettings.json similarity index 100% rename from inference/CMakeSettings.json rename to deploy/cpp/CMakeSettings.json diff --git a/inference/INSTALL.md b/deploy/cpp/INSTALL.md similarity index 100% rename from inference/INSTALL.md rename to deploy/cpp/INSTALL.md diff --git a/inference/LICENSE b/deploy/cpp/LICENSE similarity index 100% rename from inference/LICENSE rename to deploy/cpp/LICENSE diff --git a/inference/README.md b/deploy/cpp/README.md similarity index 94% rename from inference/README.md rename to deploy/cpp/README.md index 4bad993c..c9d1c08d 100644 --- a/inference/README.md +++ b/deploy/cpp/README.md @@ -11,7 +11,7 @@ ## 1.说明 -本目录提供一个跨平台的图像分割模型的C++、Python预测部署方案,用户通过一定的配置,加上少量的代码,即可把模型集成到自己的服务中,完成图像分割的任务。 +本目录提供一个跨平台`PaddlePaddle`图像分割模型的`C++`预测部署方案,用户通过一定的配置,加上少量的代码,即可把模型集成到自己的服务中,完成图像分割的任务。 主要设计的目标包括以下四点: - 跨平台,支持在 windows 和 Linux 完成编译、开发和部署 @@ -19,6 +19,8 @@ - 可扩展性,支持用户针对新模型开发自己特殊的数据预处理、后处理等逻辑 - 高性能,除了`PaddlePaddle`自身带来的性能优势,我们还针对图像分割的特点对关键步骤进行了性能优化 +**注意** 如需要使用`Python`的预测部署方法,请参考:[Python预测部署](../python/) + ## 2.主要目录和文件 @@ -57,8 +59,6 @@ inference `Windows`上推荐使用最新的`Visual Studio 2019 Community`直接编译`CMake`项目。 -针对Python的预测部署方法,可参考以下链接:[Python预测部署方法](python_inference.md) - ## 4.预测并可视化结果 完成编译后,便生成了需要的可执行文件和链接库,然后执行以下步骤: diff --git a/inference/conf/humanseg.yaml b/deploy/cpp/conf/humanseg.yaml similarity index 100% rename from inference/conf/humanseg.yaml rename to deploy/cpp/conf/humanseg.yaml diff --git a/inference/demo.cpp b/deploy/cpp/demo.cpp similarity index 100% rename from inference/demo.cpp rename to deploy/cpp/demo.cpp diff --git a/inference/docs/demo.jpg b/deploy/cpp/docs/demo.jpg similarity index 100% rename from inference/docs/demo.jpg rename to deploy/cpp/docs/demo.jpg diff --git a/inference/docs/demo_jpg.png b/deploy/cpp/docs/demo_jpg.png similarity index 100% rename from inference/docs/demo_jpg.png rename to deploy/cpp/docs/demo_jpg.png diff --git a/inference/docs/linux_build.md b/deploy/cpp/docs/linux_build.md similarity index 100% rename from inference/docs/linux_build.md rename to deploy/cpp/docs/linux_build.md diff --git a/inference/docs/vis.md b/deploy/cpp/docs/vis.md similarity index 100% rename from inference/docs/vis.md rename to deploy/cpp/docs/vis.md diff --git a/inference/docs/vis_result.png b/deploy/cpp/docs/vis_result.png similarity index 100% rename from inference/docs/vis_result.png rename to deploy/cpp/docs/vis_result.png diff --git a/inference/docs/windows_vs2015_build.md b/deploy/cpp/docs/windows_vs2015_build.md similarity index 100% rename from inference/docs/windows_vs2015_build.md rename to deploy/cpp/docs/windows_vs2015_build.md diff --git a/inference/docs/windows_vs2019_build.md b/deploy/cpp/docs/windows_vs2019_build.md similarity index 100% rename from inference/docs/windows_vs2019_build.md rename to deploy/cpp/docs/windows_vs2019_build.md diff --git a/inference/external-cmake/yaml-cpp.cmake b/deploy/cpp/external-cmake/yaml-cpp.cmake similarity index 100% rename from inference/external-cmake/yaml-cpp.cmake rename to deploy/cpp/external-cmake/yaml-cpp.cmake diff --git a/inference/images/humanseg/demo1.jpeg b/deploy/cpp/images/humanseg/demo1.jpeg similarity index 100% rename from inference/images/humanseg/demo1.jpeg rename to deploy/cpp/images/humanseg/demo1.jpeg diff --git a/inference/images/humanseg/demo2.jpeg b/deploy/cpp/images/humanseg/demo2.jpeg similarity index 100% rename from inference/images/humanseg/demo2.jpeg rename to deploy/cpp/images/humanseg/demo2.jpeg diff --git a/inference/images/humanseg/demo2.jpeg_result.png b/deploy/cpp/images/humanseg/demo2.jpeg_result.png similarity index 100% rename from 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--git a/inference/preprocessor/preprocessor.cpp b/deploy/cpp/preprocessor/preprocessor.cpp similarity index 100% rename from inference/preprocessor/preprocessor.cpp rename to deploy/cpp/preprocessor/preprocessor.cpp diff --git a/inference/preprocessor/preprocessor.h b/deploy/cpp/preprocessor/preprocessor.h similarity index 100% rename from inference/preprocessor/preprocessor.h rename to deploy/cpp/preprocessor/preprocessor.h diff --git a/inference/preprocessor/preprocessor_seg.cpp b/deploy/cpp/preprocessor/preprocessor_seg.cpp similarity index 100% rename from inference/preprocessor/preprocessor_seg.cpp rename to deploy/cpp/preprocessor/preprocessor_seg.cpp diff --git a/inference/preprocessor/preprocessor_seg.h b/deploy/cpp/preprocessor/preprocessor_seg.h similarity index 100% rename from inference/preprocessor/preprocessor_seg.h rename to deploy/cpp/preprocessor/preprocessor_seg.h diff --git a/inference/tools/visualize.py b/deploy/cpp/tools/visualize.py similarity index 100% rename from inference/tools/visualize.py rename to deploy/cpp/tools/visualize.py diff --git a/inference/utils/seg_conf_parser.h b/deploy/cpp/utils/seg_conf_parser.h similarity index 100% rename from inference/utils/seg_conf_parser.h rename to deploy/cpp/utils/seg_conf_parser.h diff --git a/inference/utils/utils.h b/deploy/cpp/utils/utils.h similarity index 100% rename from inference/utils/utils.h rename to deploy/cpp/utils/utils.h diff --git a/deploy/python/README.md b/deploy/python/README.md new file mode 100644 index 00000000..3c762517 --- /dev/null +++ b/deploy/python/README.md @@ -0,0 +1,94 @@ +# PaddleSeg Python 预测部署方案 + +本文档旨在提供一个`PaddlePaddle`跨平台图像分割模型的`Python`预测部署方案,用户通过一定的配置,加上少量的代码,即可把模型集成到自己的服务中,完成图像分割的任务。 + +## 前置条件 + +* Python2.7+,Python3+ +* pip,pip3 + +## 主要目录和文件 + +``` +├── infer.py # 核心代码,完成分割模型的预测以及结果可视化 +├── requirements.txt # 依赖的Python包 +└── README.md # 说明文档 +``` + +### Step1:安装PaddlePaddle + +如何选择合适版本的`PaddlePaddle`版本进行安装,可参考: [PaddlePaddle安装教程](https://www.paddlepaddle.org.cn/install/doc/) + +### Step2:安装Python依赖包 + +2.1 在**当前**目录下, 使用`pip`安装`Python`依赖包 +```bash +pip install -r requirements.txt +``` + +2.2 安装`OpenCV` 相关依赖库 +预测代码中需要使用`OpenCV`,所以还需要`OpenCV`安装相关的动态链接库。 +以`Ubuntu` 和`CentOS` 为例,命令如下: + +`Ubuntu`下安装相关链接库: +```bash +apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev +``` + +CentOS 下安装相关链接库: +```bash +yum install -y libXext libSM libXrender +``` +### Step3:预测 +进行预测前, 请使用[模型导出工具](../../docs/model_export.md) 导出您的模型(或点击下载我们的[人像分割样例模型](https://bj.bcebos.com/paddleseg/inference/human_freeze_model.zip)用于测试)。 + +导出的模型目录通常包括三个文件,除了模型文件`models` 和参数文件`params`,还会生成对应的配置文件`deploy.yaml`用于`C++`和`Python` 预测, 主要字段及其含义如下: +```yaml +DEPLOY: + # 是否使用GPU预测 + USE_GPU: 1 + # 模型和参数文件所在目录路径 + MODEL_PATH: "/root/projects/models/deeplabv3p_xception65_humanseg" + # 模型文件名 + MODEL_FILENAME: "__model__" + # 参数文件名 + PARAMS_FILENAME: "__params__" + # 预测图片的的标准输入尺寸,输入尺寸不一致会做resize + EVAL_CROP_SIZE: (513, 513) + # 均值 + MEAN: [0.5, 0.5, 0.5] + # 方差 + STD: [0.5, 0.5, 0.5] + # 分类类型数 + NUM_CLASSES: 2 + # 图片通道数 + CHANNELS : 3 + # 预测模式,支持 NATIVE 和 ANALYSIS + PREDICTOR_MODE: "ANALYSIS" + # 每次预测的 batch_size + BATCH_SIZE : 3 +``` + +模型文件就绪后,在终端输入以下命令进行预测: +``` +python infer.py --conf=/path/to/deploy.yaml --input_dir/path/to/images_directory --use_pr=False +``` +参数说明如下: + +| 参数 | 是否必须|含义 | +|-------|-------|----------| +| conf | YES|模型配置的Yaml文件路径 | +| input_dir |YES| 需要预测的图片目录 | +| use_pr |NO|是否使用优化模型,默认为False| + +* 优化模型:使用`PaddleSeg 0.3.0`版导出的为优化模型, 此前版本导出的模型即为未优化版本。优化模型把图像的预处理以及后处理部分融入到模型网络中使用`GPU` 完成,相比原来`CPU` 中的处理提升了计算性能。 + + +运行后会扫描`input_dir` 目录下所有指定格式图片,生成`预测mask`和`可视化的结果`。 +对于图片`a.jpeg`, `预测mask` 存在`a_jpeg.png` 中,而可视化结果则在`a_jpeg_result.png` 中。 + +输入样例: +![avatar](../cpp/images/humanseg/demo2.jpeg) + +输出结果: +![avatar](../cpp/images/humanseg/demo2.jpeg_result.png) diff --git a/deploy/python/infer.py b/deploy/python/infer.py new file mode 100644 index 00000000..705f78d5 --- /dev/null +++ b/deploy/python/infer.py @@ -0,0 +1,291 @@ +# coding: utf8 +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import ast +import time + +import gflags +import yaml +import cv2 + +import numpy as np +import paddle.fluid as fluid + +from concurrent.futures import ThreadPoolExecutor, as_completed + +gflags.DEFINE_string("conf", default="", help="Configuration File Path") +gflags.DEFINE_string("input_dir", default="", help="Directory of Input Images") +gflags.DEFINE_boolean("use_pr", default=False, help="Use optimized model") +gflags.DEFINE_string("trt_mode", default="", help="Use optimized model") +gflags.FLAGS = gflags.FLAGS + +# ColorMap for visualization +color_map = [[128, 64, 128], [244, 35, 231], [69, 69, 69], [102, 102, 156], + [190, 153, 153], [153, 153, 153], [250, 170, 29], [219, 219, 0], + [106, 142, 35], [152, 250, 152], [69, 129, 180], [219, 19, 60], + [255, 0, 0], [0, 0, 142], [0, 0, 69], [0, 60, 100], [0, 79, 100], + [0, 0, 230], [119, 10, 32]] + +# Paddle-TRT Precision Map +trt_precision_map = { + "int8": fluid.core.AnalysisConfig.Precision.Int8, + "fp32": fluid.core.AnalysisConfig.Precision.Float32, + "fp16": fluid.core.AnalysisConfig.Precision.Half +} + +# scan a directory and get all images with support extensions +def get_images_from_dir(img_dir, support_ext=".jpg|.jpeg"): + if (not os.path.exists(img_dir) or not os.path.isdir(img_dir)): + raise Exception("Image Directory [%s] invalid" % img_dir) + imgs = [] + for item in os.listdir(img_dir): + ext = os.path.splitext(item)[1][1:].strip().lower() + if (len(ext) > 0 and ext in support_ext): + item_path = os.path.join(img_dir, item) + imgs.append(item_path) + return imgs + +# Deploy Configuration File Parser +class DeployConfig: + def __init__(self, conf_file): + if not os.path.exists(conf_file): + raise Exception('Config file path [%s] invalid!' % conf_file) + + with open(conf_file) as fp: + configs = yaml.load(fp, Loader=yaml.FullLoader) + deploy_conf = configs["DEPLOY"] + # 1. get eval_crop_size + self.eval_crop_size = ast.literal_eval(deploy_conf["EVAL_CROP_SIZE"]) + # 2. get mean + self.mean = deploy_conf["MEAN"] + # 3. get std + self.std = deploy_conf["STD"] + # 4. get class_num + self.class_num = deploy_conf["NUM_CLASSES"] + # 5. get paddle model and params file path + self.model_file = os.path.join( + deploy_conf["MODEL_PATH"], deploy_conf["MODEL_FILENAME"]) + self.param_file = os.path.join( + deploy_conf["MODEL_PATH"], deploy_conf["PARAMS_FILENAME"]) + # 6. use_gpu + self.use_gpu = deploy_conf["USE_GPU"] + # 7. predictor_mode + self.predictor_mode = deploy_conf["PREDICTOR_MODE"] + # 8. batch_size + self.batch_size = deploy_conf["BATCH_SIZE"] + # 9. channels + self.channels = deploy_conf["CHANNELS"] + +class ImageReader: + def __init__(self, configs): + self.config = configs + self.threads_pool = ThreadPoolExecutor(configs.batch_size) + + # image processing thread worker + def process_worker(self, imgs, idx, use_pr=False): + image_path = imgs[idx] + im = cv2.imread(image_path, -1) + channels = im.shape[2] + ori_h = im.shape[0] + ori_w = im.shape[1] + if channels == 1: + im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) + channels = im.shape[2] + if channels != 3 and channels != 4: + print("Only support rgb(gray) or rgba image.") + return -1 + + # resize to eval_crop_size + eval_crop_size = self.config.eval_crop_size + if (ori_h != eval_crop_size[0] or ori_w != eval_crop_size[1]): + im = cv2.resize( + im, eval_crop_size, fx=0, fy=0, interpolation=cv2.INTER_LINEAR) + + # if use models with no pre-processing/post-processing op optimizations + if not use_pr: + im_mean = np.array(self.config.mean).reshape((3, 1, 1)) + im_std = np.array(self.config.std).reshape((3, 1, 1)) + # HWC -> CHW, don't use transpose((2, 0, 1)) + im = im.swapaxes(1, 2) + im = im.swapaxes(0, 1) + im = im[:, :, :].astype('float32') / 255.0 + im -= im_mean + im /= im_std + im = im[np.newaxis,:,:,:] + info = [image_path, im, (ori_w, ori_h)] + return info + + # process multiple images with multithreading + def process(self, imgs, use_pr=False): + imgs_data = [] + with ThreadPoolExecutor(max_workers=self.config.batch_size) as exec: + tasks = [exec.submit(self.process_worker, imgs, idx, use_pr) + for idx in range(len(imgs))] + for task in as_completed(tasks): + imgs_data.append(task.result()) + return imgs_data + +class Predictor: + def __init__(self, conf_file): + self.config = DeployConfig(conf_file) + self.image_reader = ImageReader(self.config) + if self.config.predictor_mode == "NATIVE": + predictor_config = fluid.core.NativeConfig() + predictor_config.prog_file = self.config.model_file + predictor_config.param_file = self.config.param_file + predictor_config.use_gpu = config.use_gpu + predictor_config.device = 0 + predictor_config.fraction_of_gpu_memory = 0 + elif self.config.predictor_mode == "ANALYSIS": + predictor_config = fluid.core.AnalysisConfig( + self.config.model_file, self.config.param_file) + if self.config.use_gpu: + predictor_config.enable_use_gpu(100, 0) + predictor_config.switch_ir_optim(True) + if gflags.FLAGS.trt_mode != "": + precision_type = trt_precision_map[gflags.FLAGS.trt_mode] + use_calib = (gflags.FLAGS.trt_mode == "int8") + predictor_config.enable_tensorrt_engine( + workspace_size=1<<30, + max_batch_size=self.config.batch_size, + min_subgraph_size=40, + precision_mode=precision_type, + use_static=False, + use_calib_mode=use_calib) + else: + predictor_config.disable_gpu() + predictor_config.switch_specify_input_names(True) + predictor_config.enable_memory_optim() + self.predictor = fluid.core.create_paddle_predictor(predictor_config) + + def create_tensor(self, inputs, batch_size, use_pr=False): + im_tensor = fluid.core.PaddleTensor() + im_tensor.name = "image" + if not use_pr: + im_tensor.shape = [batch_size, + self.config.channels, + self.config.eval_crop_size[1], + self.config.eval_crop_size[0]] + else: + im_tensor.shape = [batch_size, + self.config.eval_crop_size[1], + self.config.eval_crop_size[0], + self.config.channels] + im_tensor.dtype = fluid.core.PaddleDType.FLOAT32 + im_tensor.data = fluid.core.PaddleBuf(inputs.ravel().astype("float32")) + return [im_tensor] + + # save prediction results and visualization them + def output_result(self, imgs_data, infer_out, use_pr=False): + for idx in range(len(imgs_data)): + img_name = imgs_data[idx][0] + ori_shape = imgs_data[idx][2] + mask = infer_out[idx] + if not use_pr: + mask = np.argmax(mask, axis=0) + mask = mask.astype('uint8') + mask_png = mask + score_png = mask_png[:, :, np.newaxis] + score_png = np.concatenate([score_png] * 3, axis=2) + # visualization score png + for i in range(score_png.shape[0]): + for j in range(score_png.shape[1]): + score_png[i, j] = color_map[score_png[i, j, 0]] + # save the mask + # mask of xxx.jpeg will be saved as xxx_jpeg_mask.png + ext_pos = img_name.rfind(".") + img_name_fix = img_name[:ext_pos] + "_" + img_name[ext_pos + 1:] + mask_save_name = img_name_fix + "_mask.png" + cv2.imwrite(mask_save_name, mask_png, [cv2.CV_8UC1]) + # save the visualized result + # result of xxx.jpeg will be saved as xxx_jpeg_result.png + vis_result_name = img_name_fix + "_result.png" + result_png = score_png + # if not use_pr: + result_png = cv2.resize(result_png, ori_shape, fx=0, fy=0, + interpolation=cv2.INTER_CUBIC) + cv2.imwrite(vis_result_name, result_png, [cv2.CV_8UC1]) + print("save result of [" + img_name + "] done.") + + def predict(self, images): + # image reader preprocessing time cost + reader_time = 0 + # inference time cost + infer_time = 0 + # post_processing: generate mask and visualize it + post_time = 0 + # total time cost: preprocessing + inference + postprocessing + total_runtime = 0 + + # record starting time point + total_start = time.time() + batch_size = self.config.batch_size + for i in range(0, len(images), batch_size): + real_batch_size = batch_size + if i + batch_size >= len(images): + real_batch_size = len(images) - i + reader_start = time.time() + img_datas = self.image_reader.process(images[i: i + real_batch_size]) + input_data = np.concatenate([item[1] for item in img_datas]) + input_data = self.create_tensor( + input_data, real_batch_size, use_pr=gflags.FLAGS.use_pr) + reader_end = time.time() + infer_start = time.time() + output_data = self.predictor.run(input_data)[0] + infer_end = time.time() + reader_time += (reader_end - reader_start) + infer_time += (infer_end - infer_start) + output_data = output_data.as_ndarray() + post_start = time.time() + self.output_result(img_datas, output_data, gflags.FLAGS.use_pr) + post_end = time.time() + post_time += (post_end - post_start) + + # finishing process all images + total_end = time.time() + # compute whole processing time + total_runtime = (total_end - total_start) + print("images_num=[%d],preprocessing_time=[%f],infer_time=[%f],postprocessing_time=[%f],total_runtime=[%f]" + % (len(images), reader_time, infer_time, post_time, total_runtime)) + +def run(deploy_conf, imgs_dir, support_extensions=".jpg|.jpeg"): + # 1. scan and get all images with valid extensions in directory imgs_dir + imgs = get_images_from_dir(imgs_dir) + if len(imgs) == 0: + print("No Image (with extensions : %s) found in [%s]" + % (support_extensions, imgs_dir)) + return -1 + # 2. create a predictor + seg_predictor = Predictor(deploy_conf) + # 3. do a inference on images + seg_predictor.predict(imgs) + return 0 + +if __name__ == "__main__": + # 0. parse the arguments + gflags.FLAGS(sys.argv) + if (gflags.FLAGS.conf == "" or gflags.FLAGS.input_dir == ""): + print("Usage: python infer.py --conf=/config/path/to/your/model " + +"--input_dir=/directory/of/your/input/images [--use_pr=True]") + exit(-1) + # set empty to turn off as default + trt_mode = gflags.FLAGS.trt_mode + if (trt_mode != "" and trt_mode not in trt_precision_map): + print("Invalid trt_mode [%s], only support[int8, fp16, fp32]" % trt_mode) + exit(-1) + # run inference + run(gflags.FLAGS.conf, gflags.FLAGS.input_dir) diff --git a/inference/requirements.txt b/deploy/python/requirements.txt similarity index 100% rename from inference/requirements.txt rename to deploy/python/requirements.txt diff --git a/serving/COMPILE_GUIDE.md b/deploy/serving/COMPILE_GUIDE.md similarity index 100% rename from serving/COMPILE_GUIDE.md rename to deploy/serving/COMPILE_GUIDE.md diff --git a/serving/README.md b/deploy/serving/README.md similarity index 100% rename from serving/README.md rename to deploy/serving/README.md diff --git a/serving/UBUNTU.md b/deploy/serving/UBUNTU.md similarity index 100% rename from serving/UBUNTU.md rename to deploy/serving/UBUNTU.md diff --git a/serving/requirements.txt b/deploy/serving/requirements.txt similarity index 100% rename from serving/requirements.txt rename to deploy/serving/requirements.txt diff --git a/serving/seg-serving/CMakeLists.txt b/deploy/serving/seg-serving/CMakeLists.txt similarity index 100% rename from serving/seg-serving/CMakeLists.txt rename to deploy/serving/seg-serving/CMakeLists.txt diff --git a/serving/seg-serving/conf/gflags.conf b/deploy/serving/seg-serving/conf/gflags.conf similarity index 100% rename from serving/seg-serving/conf/gflags.conf rename to deploy/serving/seg-serving/conf/gflags.conf diff --git a/serving/seg-serving/conf/model_toolkit.prototxt b/deploy/serving/seg-serving/conf/model_toolkit.prototxt similarity index 100% rename from serving/seg-serving/conf/model_toolkit.prototxt rename to deploy/serving/seg-serving/conf/model_toolkit.prototxt diff --git a/serving/seg-serving/conf/resource.prototxt b/deploy/serving/seg-serving/conf/resource.prototxt similarity index 100% rename from serving/seg-serving/conf/resource.prototxt rename to deploy/serving/seg-serving/conf/resource.prototxt diff --git a/serving/seg-serving/conf/seg_conf.yaml b/deploy/serving/seg-serving/conf/seg_conf.yaml similarity index 100% rename from serving/seg-serving/conf/seg_conf.yaml rename to deploy/serving/seg-serving/conf/seg_conf.yaml diff --git a/serving/seg-serving/conf/seg_conf2.yaml b/deploy/serving/seg-serving/conf/seg_conf2.yaml similarity index 100% rename from serving/seg-serving/conf/seg_conf2.yaml rename to deploy/serving/seg-serving/conf/seg_conf2.yaml diff --git a/serving/seg-serving/conf/service.prototxt b/deploy/serving/seg-serving/conf/service.prototxt similarity index 100% rename from serving/seg-serving/conf/service.prototxt rename to deploy/serving/seg-serving/conf/service.prototxt diff --git a/serving/seg-serving/conf/workflow.prototxt b/deploy/serving/seg-serving/conf/workflow.prototxt similarity index 100% rename from serving/seg-serving/conf/workflow.prototxt rename to deploy/serving/seg-serving/conf/workflow.prototxt diff --git a/serving/seg-serving/data/model/paddle/fluid_reload_flag b/deploy/serving/seg-serving/data/model/paddle/fluid_reload_flag similarity index 100% rename from serving/seg-serving/data/model/paddle/fluid_reload_flag rename to deploy/serving/seg-serving/data/model/paddle/fluid_reload_flag diff --git a/serving/seg-serving/data/model/paddle/fluid_time_file b/deploy/serving/seg-serving/data/model/paddle/fluid_time_file similarity index 100% rename from serving/seg-serving/data/model/paddle/fluid_time_file rename to deploy/serving/seg-serving/data/model/paddle/fluid_time_file diff --git a/serving/seg-serving/op/CMakeLists.txt b/deploy/serving/seg-serving/op/CMakeLists.txt similarity index 100% rename from serving/seg-serving/op/CMakeLists.txt rename to deploy/serving/seg-serving/op/CMakeLists.txt diff --git a/serving/seg-serving/op/image_seg_op.cpp b/deploy/serving/seg-serving/op/image_seg_op.cpp similarity index 100% rename from serving/seg-serving/op/image_seg_op.cpp rename to deploy/serving/seg-serving/op/image_seg_op.cpp diff --git a/serving/seg-serving/op/image_seg_op.h b/deploy/serving/seg-serving/op/image_seg_op.h similarity index 100% rename from serving/seg-serving/op/image_seg_op.h rename to deploy/serving/seg-serving/op/image_seg_op.h diff --git a/serving/seg-serving/op/reader_op.cpp b/deploy/serving/seg-serving/op/reader_op.cpp similarity index 100% rename from serving/seg-serving/op/reader_op.cpp rename to deploy/serving/seg-serving/op/reader_op.cpp diff --git a/serving/seg-serving/op/reader_op.h b/deploy/serving/seg-serving/op/reader_op.h similarity index 100% rename from serving/seg-serving/op/reader_op.h rename to deploy/serving/seg-serving/op/reader_op.h diff --git a/serving/seg-serving/op/seg_conf.cpp b/deploy/serving/seg-serving/op/seg_conf.cpp similarity index 100% rename from serving/seg-serving/op/seg_conf.cpp rename to deploy/serving/seg-serving/op/seg_conf.cpp diff --git a/serving/seg-serving/op/seg_conf.h b/deploy/serving/seg-serving/op/seg_conf.h similarity index 100% rename from serving/seg-serving/op/seg_conf.h rename to deploy/serving/seg-serving/op/seg_conf.h diff --git a/serving/seg-serving/op/write_json_op.cpp b/deploy/serving/seg-serving/op/write_json_op.cpp similarity index 100% rename from serving/seg-serving/op/write_json_op.cpp rename to deploy/serving/seg-serving/op/write_json_op.cpp diff --git a/serving/seg-serving/op/write_json_op.h b/deploy/serving/seg-serving/op/write_json_op.h similarity index 100% rename from serving/seg-serving/op/write_json_op.h rename to deploy/serving/seg-serving/op/write_json_op.h diff --git a/serving/seg-serving/proto/CMakeLists.txt b/deploy/serving/seg-serving/proto/CMakeLists.txt similarity index 100% rename from serving/seg-serving/proto/CMakeLists.txt rename to deploy/serving/seg-serving/proto/CMakeLists.txt diff --git a/serving/seg-serving/proto/image_seg.proto b/deploy/serving/seg-serving/proto/image_seg.proto similarity index 100% rename from serving/seg-serving/proto/image_seg.proto rename to deploy/serving/seg-serving/proto/image_seg.proto diff --git a/serving/seg-serving/scripts/start.sh b/deploy/serving/seg-serving/scripts/start.sh similarity index 100% rename from serving/seg-serving/scripts/start.sh rename to deploy/serving/seg-serving/scripts/start.sh diff --git a/serving/tools/image_seg_client.py b/deploy/serving/tools/image_seg_client.py similarity index 100% rename from serving/tools/image_seg_client.py rename to deploy/serving/tools/image_seg_client.py diff --git a/serving/tools/images/1.jpg b/deploy/serving/tools/images/1.jpg similarity index 100% rename from serving/tools/images/1.jpg rename to deploy/serving/tools/images/1.jpg diff --git a/serving/tools/images/2.jpg b/deploy/serving/tools/images/2.jpg similarity index 100% rename from serving/tools/images/2.jpg rename to deploy/serving/tools/images/2.jpg diff --git a/serving/tools/images/3.jpg b/deploy/serving/tools/images/3.jpg similarity index 100% rename from serving/tools/images/3.jpg rename to deploy/serving/tools/images/3.jpg diff --git a/docs/model_export.md b/docs/model_export.md index eab31fdd..e2b050f3 100644 --- a/docs/model_export.md +++ b/docs/model_export.md @@ -18,4 +18,4 @@ python pdseg/export_model.py --cfg configs/unet_pet.yaml TEST.TEST_MODEL test/saved_models/unet_pet/final ``` -预测模型会导出到`freeze_model`目录,用于C++预测的模型配置会导出到`freeze_model/deploy.yaml`下 +预测模型会导出到`freeze_model`目录,用于`C++`或者`Python`预测的模型配置会导出到`freeze_model/deploy.yaml`下 diff --git a/inference/infer.py b/inference/infer.py deleted file mode 100644 index a3f123e5..00000000 --- a/inference/infer.py +++ /dev/null @@ -1,349 +0,0 @@ -# coding: utf8 -# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys -import time - -import gflags -import yaml -import numpy as np -import cv2 - -from paddle.fluid.core import AnalysisConfig -from paddle.fluid.core import NativeConfig -from paddle.fluid.core import create_paddle_predictor -from paddle.fluid.core import PaddleTensor -from paddle.fluid.core import PaddleBuf -from paddle.fluid.core import PaddleDType -from concurrent.futures import ThreadPoolExecutor, as_completed - -gflags.DEFINE_string("conf", default="", help="Configuration File Path") -gflags.DEFINE_string("input_dir", default="", help="Directory of Input Images") -gflags.DEFINE_boolean("use_pr", default=False, help="Use optimized model") -Flags = gflags.FLAGS - -# ColorMap for visualization more clearly -color_map = [[128, 64, 128], [244, 35, 231], [69, 69, 69], [102, 102, 156], - [190, 153, 153], [153, 153, 153], [250, 170, 29], [219, 219, 0], - [106, 142, 35], [152, 250, 152], [69, 129, 180], [219, 19, 60], - [255, 0, 0], [0, 0, 142], [0, 0, 69], [0, 60, 100], [0, 79, 100], - [0, 0, 230], [119, 10, 32]] - - -class ConfItemsNotFoundError(Exception): - def __init__(self, message): - super().__init__(message + " item not Found") - - -class Config: - def __init__(self, config_dict): - if "DEPLOY" not in config_dict: - raise ConfItemsNotFoundError("DEPLOY") - deploy_dict = config_dict["DEPLOY"] - if "EVAL_CROP_SIZE" not in deploy_dict: - raise ConfItemsNotFoundError("EVAL_CROP_SIZE") - # 1. get resize - self.resize = [int(value) for value in - deploy_dict["EVAL_CROP_SIZE"].strip("()").split(",")] - - # 2. get mean - if "MEAN" not in deploy_dict: - raise ConfItemsNotFoundError("MEAN") - self.mean = deploy_dict["MEAN"] - - # 3. get std - if "STD" not in deploy_dict: - raise ConfItemsNotFoundError("STD") - self.std = deploy_dict["STD"] - - # 4. get image type - if "IMAGE_TYPE" not in deploy_dict: - raise ConfItemsNotFoundError("IMAGE_TYPE") - self.img_type = deploy_dict["IMAGE_TYPE"] - - # 5. get class number - if "NUM_CLASSES" not in deploy_dict: - raise ConfItemsNotFoundError("NUM_CLASSES") - self.class_num = deploy_dict["NUM_CLASSES"] - - # 7. set model path - if "MODEL_PATH" not in deploy_dict: - raise ConfItemsNotFoundError("MODEL_PATH") - self.model_path = deploy_dict["MODEL_PATH"] - - # 8. get model file_name - if "MODEL_FILENAME" not in deploy_dict: - self.model_file_name = "__model__" - else: - self.model_file_name = deploy_dict["MODEL_FILENAME"] - - # 9. get model param file name - if "PARAMS_FILENAME" not in deploy_dict: - self.param_file_name = "__params__" - else: - self.param_file_name = deploy_dict["PARAMS_FILENAME"] - - # 10. get pre_processor - if "PRE_PROCESSOR" not in deploy_dict: - raise ConfItemsNotFoundError("PRE_PROCESSOR") - self.pre_processor = deploy_dict["PRE_PROCESSOR"] - - # 11. use_gpu - if "USE_GPU" not in deploy_dict: - self.use_gpu = 0 - else: - self.use_gpu = deploy_dict["USE_GPU"] - - # 12. predictor_mode - if "PREDICTOR_MODE" not in deploy_dict: - raise ConfItemsNotFoundError("PREDICTOR_MODE") - self.predictor_mode = deploy_dict["PREDICTOR_MODE"] - - # 13. batch_size - if "BATCH_SIZE" not in deploy_dict: - raise ConfItemsNotFoundError("BATCH_SIZE") - self.batch_size = deploy_dict["BATCH_SIZE"] - - # 14. channels - if "CHANNELS" not in deploy_dict: - raise ConfItemsNotFoundError("CHANNELS") - self.channels = deploy_dict["CHANNELS"] - - -class PreProcessor: - def __init__(self, config): - self.resize_size = (config.resize[0], config.resize[1]) - self.mean = config.mean - self.std = config.std - - def process(self, image_file, im_list, ori_h_list, ori_w_list, idx, use_pr=False): - start = time.time() - im = cv2.imread(image_file, -1) - end = time.time() - print("imread spent %fs" % (end - start)) - channels = im.shape[2] - ori_h = im.shape[0] - ori_w = im.shape[1] - if channels == 1: - im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) - channels = im.shape[2] - if channels != 3 and channels != 4: - print("Only support rgb(gray) or rgba image.") - return -1 - - if ori_h != self.resize_size[0] or ori_w != self.resize_size[1]: - start = time.time() - im = cv2.resize(im, self.resize_size, fx=0, fy=0, interpolation=cv2.INTER_LINEAR) - end = time.time() - print("resize spent %fs" % (end - start)) - if not use_pr: - start = time.time() - im_mean = np.array(self.mean).reshape((3, 1, 1)) - im_std = np.array(self.std).reshape((3, 1, 1)) - - # HWC -> CHW, don't use transpose((2, 0, 1)) - im = im.swapaxes(1, 2) - im = im.swapaxes(0, 1) - im = im[:, :, :].astype('float32') / 255.0 - im -= im_mean - im /= im_std - end = time.time() - print("preprocessing spent %fs" % (end-start)) - im = im[np.newaxis,:,:,:] - im_list[idx] = im - ori_h_list[idx] = ori_h - ori_w_list[idx] = ori_w - return im, ori_h, ori_w - - -class Predictor: - def __init__(self, config): - self.config = config - model_file = os.path.join(config.model_path, config.model_file_name) - param_file = os.path.join(config.model_path, config.param_file_name) - if config.predictor_mode == "NATIVE": - predictor_config = NativeConfig() - predictor_config.prog_file = model_file - predictor_config.param_file = param_file - predictor_config.use_gpu = config.use_gpu - predictor_config.device = 0 - predictor_config.fraction_of_gpu_memory = 0 - elif config.predictor_mode == "ANALYSIS": - predictor_config = AnalysisConfig(model_file, param_file) - if config.use_gpu: - predictor_config.enable_use_gpu(100, 0) - else: - predictor_config.disable_gpu() - # need to use zero copy run - # predictor_config.switch_use_feed_fetch_ops(False) - # predictor_config.enable_tensorrt_engine( - # workspace_size=1<<30, - # max_batch_size=1, - # min_subgraph_size=3, - # precision_mode=AnalysisConfig.Precision.Int8, - # use_static=False, - # use_calib_mode=True - # ) - predictor_config.switch_specify_input_names(True) - predictor_config.enable_memory_optim() - - self.predictor = create_paddle_predictor(predictor_config) - self.preprocessor = PreProcessor(config) - self.threads_pool = ThreadPoolExecutor(config.batch_size) - - def make_tensor(self, inputs, batch_size, use_pr=False): - im_tensor = PaddleTensor() - im_tensor.name = "image" - if not use_pr: - im_tensor.shape = [batch_size, self.config.channels, - self.config.resize[1], self.config.resize[0]] - else: - im_tensor.shape = [batch_size, self.config.resize[1], - self.config.resize[0], self.config.channels] - print(im_tensor.shape) - im_tensor.dtype = PaddleDType.FLOAT32 - start = time.time() - im_tensor.data = PaddleBuf(inputs.ravel().astype("float32")) - print("flatten time: %f" % (time.time() - start)) - return [im_tensor] - - def output_result(self, image_name, output, ori_h, ori_w, use_pr=False): - mask = output - if not use_pr: - mask = np.argmax(output, axis=0) - mask = mask.astype('uint8') - mask_png = mask - score_png = mask_png[:, :, np.newaxis] - score_png = np.concatenate([score_png] * 3, axis=2) - for i in range(score_png.shape[0]): - for j in range(score_png.shape[1]): - score_png[i, j] = color_map[score_png[i, j, 0]] - - mask_save_name = image_name + ".png" - cv2.imwrite(mask_save_name, mask_png, [cv2.CV_8UC1]) - - result_name = image_name + "_result.png" - result_png = score_png - # if not use_pr: - result_png = cv2.resize(result_png, (ori_w, ori_h), fx=0, fy=0, - interpolation=cv2.INTER_CUBIC) - cv2.imwrite(result_name, result_png, [cv2.CV_8UC1]) - - print("save result of [" + image_name + "] done.") - - def predict(self, images): - batch_size = self.config.batch_size - total_runtime = 0 - total_imwrite_time = 0 - for i in range(0, len(images), batch_size): - start = time.time() - bz = batch_size - if i + batch_size >= len(images): - bz = len(images) - i - im_list = [0] * bz - ori_h_list = [0] * bz - ori_w_list = [0] * bz - tasks = [self.threads_pool.submit(self.preprocessor.process, - images[i + j], im_list, - ori_h_list, ori_w_list, - j, Flags.use_pr) - for j in range(bz)] - # join all running threads - for t in as_completed(tasks): - pass - input_data = np.concatenate(im_list) - input_data = self.make_tensor(input_data, bz, use_pr=Flags.use_pr) - inference_start = time.time() - output_data = self.predictor.run(input_data)[0] - end = time.time() - print("inference time = %fs " % (end - inference_start)) - print("runtime = %fs " % (end - start)) - total_runtime += (end - start) - output_data = output_data.as_ndarray() - - output_start = time.time() - for j in range(bz): - self.output_result(images[i + j], output_data[j], - ori_h_list[j], ori_w_list[j], Flags.use_pr) - output_end = time.time() - total_imwrite_time += output_end - output_start - print("total time = %fs" % total_runtime) - print("total imwrite time = %fs" % total_imwrite_time) - - -def usage(): - print("Usage: python infer.py --conf=/config/path/to/your/model " + - "--input_dir=/directory/of/your/input/images [--use_pr=True]") - - -def read_conf(conf_file): - if not os.path.exists(conf_file): - raise FileNotFoundError("Can't find the configuration file path," + - " please check whether the configuration" + - " path is correctly set.") - f = open(conf_file) - config_dict = yaml.load(f, Loader=yaml.FullLoader) - config = Config(config_dict) - return config - - -def read_input_dir(input_dir, ext=".jpg|.jpeg"): - if not os.path.exists(input_dir): - raise FileNotFoundError("This input directory doesn't exist, please" + - " check whether the input directory is" + - " correctly set.") - if not os.path.isdir(input_dir): - raise NotADirectoryError("This input directory in not a directory," + - " please check whether the input directory" + - " is correctly set.") - files_list = [] - ext_list = ext.split("|") - files = os.listdir(input_dir) - for file in files: - for ext_suffix in ext_list: - if file.endswith(ext_suffix): - full_path = os.path.join(input_dir, file) - files_list.append(full_path) - break - return files_list - - -def main(argv): - # 0. parse the argument - Flags(argv) - if Flags.conf == "" or Flags.input_dir == "": - usage() - return -1 - try: - # 1. get a conf dictionary - seg_deploy_configs = read_conf(Flags.conf) - # 2. get all the images path with extension '.jpeg' at input_dir - images = read_input_dir(Flags.input_dir) - if len(images) == 0: - print("No Images Found! Please check whether the images format" + - " is correct. Supporting format: [.jpeg|.jpg].") - print(images) - except Exception as e: - print(e) - return -1 - - # 3. init predictor and predict - seg_predictor = Predictor(seg_deploy_configs) - seg_predictor.predict(images) - - -if __name__ == "__main__": - main(sys.argv) diff --git a/inference/python_inference.md b/inference/python_inference.md deleted file mode 100644 index 8ae42119..00000000 --- a/inference/python_inference.md +++ /dev/null @@ -1,59 +0,0 @@ -# PaddleSeg Python 预测部署方案 - -本说明文档旨在提供一个跨平台的图像分割模型的Python预测部署方案,用户通过一定的配置,加上少量的代码,即可把模型集成到自己的服务中,完成图像分割的任务。 - -## 前置条件 - -* Python2.7+,Python3+ -* pip,pip3 - -## 主要目录和文件 - -``` -inference -├── infer.py # 完成预测、可视化的Python脚本 -└── requirements.txt # 预测部署脚本所依赖的库 -``` - -### Step1:安装PaddlePaddle - -可参考以下链接,选择合适版本的PaddlePaddle进行安装。[PaddlePaddle安装教程](https://www.paddlepaddle.org.cn/install/doc/) - -### Step2:安装Python依赖包 - -在inference目录下,安装相应的Python预测依赖包 -```bash -pip install -r requirements.txt -``` -因为预测部署中需要使用opencv,所以还需要安装相关的动态链接库。相关操作如下: - -Ubuntu 下安装相关链接库: -```bash -apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev -``` - -CentOS 下安装相关链接库: -```bash -yum install -y libXext libSM libXrender -``` -### Step3:预测 - -在终端输入以下命令进行预测。 -``` -python infer.py --conf=/path/to/XXX.yaml --input_dir/path/to/images_directory --use_pr=False -``` - -预测使用的三个命令参数说明如下: - -| 参数 | 含义 | -|-------|----------| -| conf | 模型配置的Yaml文件路径 | -| input_dir | 需要预测的图片目录 | -| use_pr | 是否使用优化模型,默认为False| - -* 优化模型:对于图像分割模型,由于模型输入的数据需要使用CPU对读取的图像数据进行预处理,预处理时长较长,为了降低在使用GPU进行端到端预测时的延时,优化模型把预处理部分融入到模型当中。在使用GPU进行预测时,优化模型的预处理部分将会在GPU上进行,大大降低了端到端延时。可使用新版的模型导出工具导出优化模型。 - -![avatar](images/humanseg/demo2.jpeg) - -输出预测结果 -![avatar](images/humanseg/demo2.jpeg_result.png) \ No newline at end of file diff --git a/serving/imgs/GF1_PMS1_sino0_0.png b/serving/imgs/GF1_PMS1_sino0_0.png deleted file mode 100644 index d62d41898634ec174da3fd129cec9f177639b033..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 330545 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