From 51427477fff7697a6dec1547158f9fb190515079 Mon Sep 17 00:00:00 2001 From: jm12138 <2286040843@qq.com> Date: Fri, 16 Sep 2022 14:39:56 +0800 Subject: [PATCH] update ssd_vgg16_300_coco2017 (#1949) * update ssd_vgg16_300_model * update unittest * update unittest * update gpu config * update * add clean func * update save inference model Co-authored-by: chenjian --- .../ssd_vgg16_300_coco2017/README.md | 231 ++++++++++-------- .../ssd_vgg16_300_coco2017/README_en.md | 169 +++++++++++++ .../ssd_vgg16_300_coco2017/data_feed.py | 2 - .../ssd_vgg16_300_coco2017/module.py | 193 +++++---------- .../ssd_vgg16_300_coco2017/processor.py | 8 +- .../ssd_vgg16_300_coco2017/test.py | 108 ++++++++ .../ssd_vgg16_300_coco2017/vgg.py | 184 -------------- 7 files changed, 479 insertions(+), 416 deletions(-) create mode 100644 modules/image/object_detection/ssd_vgg16_300_coco2017/README_en.md create mode 100644 modules/image/object_detection/ssd_vgg16_300_coco2017/test.py delete mode 100644 modules/image/object_detection/ssd_vgg16_300_coco2017/vgg.py diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/README.md b/modules/image/object_detection/ssd_vgg16_300_coco2017/README.md index 85510f3e..567e575a 100644 --- a/modules/image/object_detection/ssd_vgg16_300_coco2017/README.md +++ b/modules/image/object_detection/ssd_vgg16_300_coco2017/README.md @@ -1,138 +1,169 @@ -## 命令行预测 +# ssd_vgg16_300_coco2017 -```shell -$ hub run ssd_vgg16_300_coco2017 --input_path "/PATH/TO/IMAGE" -``` +|模型名称|ssd_vgg16_300_coco2017| +| :--- | :---: | +|类别|图像 - 目标检测| +|网络|SSD| +|数据集|COCO2017| +|是否支持Fine-tuning|否| +|模型大小|139MB| +|最新更新日期|2021-03-15| +|数据指标|-| -## API -```python -def context(trainable=True, - pretrained=True, - get_prediction=False) -``` +## 一、模型基本信息 -提取特征，用于迁移学习。 +- ### 应用效果展示 + - 样例结果示例： +

+ +
+

-**参数** +- ### 模型介绍 -* trainable(bool): 参数是否可训练； -* pretrained (bool): 是否加载预训练模型； -* get\_prediction (bool): 是否执行预测。 + - Single Shot MultiBox Detector (SSD) 是一种单阶段的目标检测器。与两阶段的检测方法不同，单阶段目标检测并不进行区域推荐，而是直接从特征图回归出目标的边界框和分类概率。SSD 运用了这种单阶段检测的思想，并且对其进行改进：在不同尺度的特征图上检测对应尺度的目标。该PaddleHub Module的基网络为VGG16模型，在Pascal数据集上预训练得到，目前仅支持预测。 -**返回** -* inputs (dict): 模型的输入，keys 包括 'image', 'im\_size'，相应的取值为： - * image (Variable): 图像变量 - * im\_size (Variable): 图片的尺寸 -* outputs (dict): 模型的输出。如果 get\_prediction 为 False，输出 'head\_features'，否则输出 'bbox\_out'。 -* context\_prog (Program): 用于迁移学习的 Program. +## 二、安装 -```python -def object_detection(paths=None, - images=None, - batch_size=1, - use_gpu=False, - output_dir='detection_result', - score_thresh=0.5, - visualization=True) -``` +- ### 1、环境依赖 -预测API，检测输入图片中的所有目标的位置。 + - paddlepaddle >= 1.6.2 -**参数** + - paddlehub >= 1.6.0 | [如何安装paddlehub](../../../../docs/docs_ch/get_start/installation.rst) -* paths (list\[str\]): 图片的路径； -* images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，BGR格式； -* batch\_size (int): batch 的大小； -* use\_gpu (bool): 是否使用 GPU； -* score\_thresh (float): 识别置信度的阈值； -* visualization (bool): 是否将识别结果保存为图片文件； -* output\_dir (str): 图片的保存路径，默认设为 detection\_result； +- ### 2、安装 -**返回** + - ```shell + $ hub install ssd_vgg16_300_coco2017 + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) -* res (list\[dict\]): 识别结果的列表，列表中每一个元素为 dict，各字段为： - * data (list): 检测结果，list的每一个元素为 dict，各字段为: - * confidence (float): 识别的置信度； - * label (str): 标签； - * left (int): 边界框的左上角x坐标； - * top (int): 边界框的左上角y坐标； - * right (int): 边界框的右下角x坐标； - * bottom (int): 边界框的右下角y坐标； - * save\_path (str, optional): 识别结果的保存路径 (仅当visualization=True时存在)。 +## 三、模型API预测 -```python -def save_inference_model(dirname, - model_filename=None, - params_filename=None, - combined=True) -``` +- ### 1、命令行预测 -将模型保存到指定路径。 + - ```shell + $ hub run ssd_vgg16_300_coco2017 --input_path "/PATH/TO/IMAGE" + ``` + - 通过命令行方式实现目标检测模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst) +- ### 2、预测代码示例 -**参数** + - ```python + import paddlehub as hub + import cv2 -* dirname: 存在模型的目录名称 -* model\_filename: 模型文件名称，默认为\_\_model\_\_ -* params\_filename: 参数文件名称，默认为\_\_params\_\_(仅当`combined`为True时生效) -* combined: 是否将参数保存到统一的一个文件中 + object_detector = hub.Module(name="ssd_vgg16_300_coco2017") + result = object_detector.object_detection(images=[cv2.imread('/PATH/TO/IMAGE')]) + # or + # result = object_detector.object_detection((paths=['/PATH/TO/IMAGE']) + ``` -## 代码示例 +- ### 3、API -```python -import paddlehub as hub -import cv2 + - ```python + def object_detection(paths=None, + images=None, + batch_size=1, + use_gpu=False, + output_dir='detection_result', + score_thresh=0.5, + visualization=True) + ``` -object_detector = hub.Module(name="ssd_vgg16_300_coco2017") -result = object_detector.object_detection(images=[cv2.imread('/PATH/TO/IMAGE')]) -# or -# result = object_detector.object_detection((paths=['/PATH/TO/IMAGE']) -``` + - 预测API，检测输入图片中的所有目标的位置。 -## 服务部署 + - **参数** -PaddleHub Serving可以部署一个目标检测的在线服务。 + - paths (list\[str\]): 图片的路径；
+ - images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，BGR格式；
+ - batch\_size (int): batch 的大小；
+ - use\_gpu (bool): 是否使用 GPU；
+ - output\_dir (str): 图片的保存路径，默认设为 detection\_result；
+ - score\_thresh (float): 识别置信度的阈值；
+ - visualization (bool): 是否将识别结果保存为图片文件。 -## 第一步：启动PaddleHub Serving + **NOTE:** paths和images两个参数选择其一进行提供数据 -运行启动命令： -```shell -$ hub serving start -m ssd_vgg16_300_coco2017 -``` + - **返回** -这样就完成了一个目标检测的服务化API的部署，默认端口号为8866。 + - res (list\[dict\]): 识别结果的列表，列表中每一个元素为 dict，各字段为: + - data (list): 检测结果，list的每一个元素为 dict，各字段为: + - confidence (float): 识别的置信度 + - label (str): 标签 + - left (int): 边界框的左上角x坐标 + - top (int): 边界框的左上角y坐标 + - right (int): 边界框的右下角x坐标 + - bottom (int): 边界框的右下角y坐标 + - save\_path (str, optional): 识别结果的保存路径 (仅当visualization=True时存在) -**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 + - ```python + def save_inference_model(dirname) + ``` + - 将模型保存到指定路径。 -## 第二步：发送预测请求 + - **参数** -配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + - dirname: 模型保存路径
-```python -import requests -import json -import cv2 -import base64 +## 四、服务部署 -def cv2_to_base64(image): - data = cv2.imencode('.jpg', image)[1] - return base64.b64encode(data.tostring()).decode('utf8') +- PaddleHub Serving可以部署一个目标检测的在线服务。 +- ### 第一步：启动PaddleHub Serving -# 发送HTTP请求 -data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} -headers = {"Content-type": "application/json"} -url = "http://127.0.0.1:8866/predict/ssd_vgg16_300_coco2017" -r = requests.post(url=url, headers=headers, data=json.dumps(data)) + - 运行启动命令： + - ```shell + $ hub serving start -m ssd_vgg16_300_coco2017 + ``` -# 打印预测结果 -print(r.json()["results"]) -``` + - 这样就完成了一个目标检测的服务化API的部署，默认端口号为8866。 -### 依赖 + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 -paddlepaddle >= 1.6.2 +- ### 第二步：发送预测请求 -paddlehub >= 1.6.0 + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + import cv2 + import base64 + + + def cv2_to_base64(image): + data = cv2.imencode('.jpg', image)[1] + return base64.b64encode(data.tostring()).decode('utf8') + + # 发送HTTP请求 + data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} + headers = {"Content-type": "application/json"} + url = "http://127.0.0.1:8866/predict/ssd_vgg16_300_coco2017" + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + + # 打印预测结果 + print(r.json()["results"]) + ``` + + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + +* 1.0.2 + + 修复numpy数据读取问题 + +* 1.1.0 + + 移除 fluid api + + - ```shell + $ hub install ssd_vgg16_300_coco2017==1.1.0 + ``` diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/README_en.md b/modules/image/object_detection/ssd_vgg16_300_coco2017/README_en.md new file mode 100644 index 00000000..0d53ce2f --- /dev/null +++ b/modules/image/object_detection/ssd_vgg16_300_coco2017/README_en.md @@ -0,0 +1,169 @@ +# ssd_vgg16_300_coco2017 + +|Module Name|ssd_vgg16_300_coco2017| +| :--- | :---: | +|Category|object detection| +|Network|SSD| +|Dataset|COCO2017| +|Fine-tuning supported or not|No| +|Module Size|139MB| +|Latest update date|2021-03-15| +|Data indicators|-| + + +## I.Basic Information + +- ### Application Effect Display + - Sample results： +

+ +
+

+ +- ### Module Introduction + + - Single Shot MultiBox Detector (SSD) is a one-stage detector. Different from two-stage detector, SSD frames object detection as a re- gression problem to spatially separated bounding boxes and associated class probabilities. This module is based on VGG16, trained on COCO2017 dataset, and can be used for object detection. + + + +## II.Installation + +- ### 1、Environmental Dependence + + - paddlepaddle >= 1.6.2 + + - paddlehub >= 1.6.0 | [How to install PaddleHub](../../../../docs/docs_en/get_start/installation.rst) + +- ### 2、Installation + + - ```shell + $ hub install ssd_vgg16_300_coco2017 + ``` + - In case of any problems during installation, please refer to: [Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md) | [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md) + +## III.Module API Prediction + +- ### 1、Command line Prediction + + - ```shell + $ hub run ssd_vgg16_300_coco2017 --input_path "/PATH/TO/IMAGE" + ``` + - If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_ch/tutorial/cmd_usage.rst) +- ### 2、Prediction Code Example + + - ```python + import paddlehub as hub + import cv2 + + object_detector = hub.Module(name="ssd_vgg16_300_coco2017") + result = object_detector.object_detection(images=[cv2.imread('/PATH/TO/IMAGE')]) + # or + # result = object_detector.object_detection((paths=['/PATH/TO/IMAGE']) + ``` + +- ### 3、API + + - ```python + def object_detection(paths=None, + images=None, + batch_size=1, + use_gpu=False, + output_dir='detection_result', + score_thresh=0.5, + visualization=True) + ``` + + - Detection API, detect positions of all objects in image + + - **Parameters** + + - paths (list[str]): image path; + - images (list\[numpy.ndarray\]): image data, ndarray.shape is in the format [H, W, C], BGR; + - batch_size (int): the size of batch; + - use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU** + - output_dir (str): save path of images; + - score\_thresh (float): confidence threshold；
+ - visualization (bool): Whether to save the results as picture files; + + **NOTE:** choose one parameter to provide data from paths and images + + - **Return** + + - res (list\[dict\]): results + - data (list): detection results, each element in the list is dict + - confidence (float): the confidence of the result + - label (str): label + - left (int): the upper left corner x coordinate of the detection box + - top (int): the upper left corner y coordinate of the detection box + - right (int): the lower right corner x coordinate of the detection box + - bottom (int): the lower right corner y coordinate of the detection box + - save\_path (str, optional): output path for saving results + + - ```python + def save_inference_model(dirname) + ``` + - Save model to specific path + + - **Parameters** + + - dirname: model save path + + +## IV.Server Deployment + +- PaddleHub Serving can deploy an online service of object detection. + +- ### Step 1: Start PaddleHub Serving + + - Run the startup command： + - ```shell + $ hub serving start -m ssd_vgg16_300_coco2017 + ``` + + - The servitization API is now deployed and the default port number is 8866. + + - **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set. + +- ### Step 2: Send a predictive request + + - With a configured server, use the following lines of code to send the prediction request and obtain the result + + - ```python + import requests + import json + import cv2 + import base64 + + + def cv2_to_base64(image): + data = cv2.imencode('.jpg', image)[1] + return base64.b64encode(data.tostring()).decode('utf8') + + # Send an HTTP request + data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]} + headers = {"Content-type": "application/json"} + url = "http://127.0.0.1:8866/predict/ssd_vgg16_300_coco2017" + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + + # print prediction results + print(r.json()["results"]) + ``` + + +## V.Release Note + +* 1.0.0 + + First release + +* 1.0.2 + + Fix the problem of reading numpy + +* 1.1.0 + + Remove fluid api + + - ```shell + $ hub install ssd_vgg16_300_coco2017==1.1.0 + ``` diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/data_feed.py b/modules/image/object_detection/ssd_vgg16_300_coco2017/data_feed.py index 9fad7c95..3d3382bb 100644 --- a/modules/image/object_detection/ssd_vgg16_300_coco2017/data_feed.py +++ b/modules/image/object_detection/ssd_vgg16_300_coco2017/data_feed.py @@ -5,12 +5,10 @@ from __future__ import division import os import random -from collections import OrderedDict import cv2 import numpy as np from PIL import Image -from paddle import fluid __all__ = ['reader'] diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/module.py b/modules/image/object_detection/ssd_vgg16_300_coco2017/module.py index e0083b95..beefaf6a 100644 --- a/modules/image/object_detection/ssd_vgg16_300_coco2017/module.py +++ b/modules/image/object_detection/ssd_vgg16_300_coco2017/module.py @@ -7,39 +7,43 @@ import os from functools import partial import yaml +import paddle import numpy as np -import paddle.fluid as fluid -import paddlehub as hub -from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor +import paddle.static +from paddle.inference import Config, create_predictor from paddlehub.module.module import moduleinfo, runnable, serving -from paddlehub.common.paddle_helper import add_vars_prefix -from ssd_vgg16_300_coco2017.vgg import VGG -from ssd_vgg16_300_coco2017.processor import load_label_info, postprocess, base64_to_cv2 -from ssd_vgg16_300_coco2017.data_feed import reader +from .processor import load_label_info, postprocess, base64_to_cv2 +from .data_feed import reader @moduleinfo( name="ssd_vgg16_300_coco2017", - version="1.0.1", + version="1.1.0", type="cv/object_detection", summary="SSD with backbone VGG16, trained with dataset COCO.", author="paddlepaddle", author_email="paddle-dev@baidu.com") -class SSDVGG16(hub.Module): - def _initialize(self): - self.default_pretrained_model_path = os.path.join(self.directory, "ssd_vgg16_300_model") - self.label_names = load_label_info(os.path.join(self.directory, "label_file.txt")) +class SSDVGG16: + def __init__(self): + self.default_pretrained_model_path = os.path.join( + self.directory, "ssd_vgg16_300_model", "model") + self.label_names = load_label_info( + os.path.join(self.directory, "label_file.txt")) self.model_config = None self._set_config() def _set_config(self): - # predictor config setting. - cpu_config = AnalysisConfig(self.default_pretrained_model_path) + """ + predictor config setting. + """ + model = self.default_pretrained_model_path+'.pdmodel' + params = self.default_pretrained_model_path+'.pdiparams' + cpu_config = Config(model, params) cpu_config.disable_glog_info() cpu_config.disable_gpu() cpu_config.switch_ir_optim(False) - self.cpu_predictor = create_paddle_predictor(cpu_config) + self.cpu_predictor = create_predictor(cpu_config) try: _places = os.environ["CUDA_VISIBLE_DEVICES"] @@ -48,10 +52,10 @@ class SSDVGG16(hub.Module): except: use_gpu = False if use_gpu: - gpu_config = AnalysisConfig(self.default_pretrained_model_path) + gpu_config = Config(model, params) gpu_config.disable_glog_info() gpu_config.enable_use_gpu(memory_pool_init_size_mb=500, device_id=0) - self.gpu_predictor = create_paddle_predictor(gpu_config) + self.gpu_predictor = create_predictor(gpu_config) # model config setting. if not self.model_config: @@ -61,73 +65,6 @@ class SSDVGG16(hub.Module): self.multi_box_head_config = self.model_config['MultiBoxHead'] self.output_decoder_config = self.model_config['SSDOutputDecoder'] - def context(self, trainable=True, pretrained=True, get_prediction=False): - """ - Distill the Head Features, so as to perform transfer learning. - - Args: - trainable (bool): whether to set parameters trainable. - pretrained (bool): whether to load default pretrained model. - get_prediction (bool): whether to get prediction. - - Returns: - inputs(dict): the input variables. - outputs(dict): the output variables. - context_prog (Program): the program to execute transfer learning. - """ - context_prog = fluid.Program() - startup_program = fluid.Program() - with fluid.program_guard(context_prog, startup_program): - with fluid.unique_name.guard(): - # image - image = fluid.layers.data(name='image', shape=[3, 300, 300], dtype='float32') - # backbone - backbone = VGG(depth=16, with_extra_blocks=True, normalizations=[20., -1, -1, -1, -1, -1]) - # body_feats - body_feats = backbone(image) - # im_size - im_size = fluid.layers.data(name='im_size', shape=[2], dtype='int32') - # var_prefix - var_prefix = '@HUB_{}@'.format(self.name) - # names of inputs - inputs = {'image': var_prefix + image.name, 'im_size': var_prefix + im_size.name} - # names of outputs - if get_prediction: - locs, confs, box, box_var = fluid.layers.multi_box_head( - inputs=body_feats, image=image, num_classes=81, **self.multi_box_head_config) - pred = fluid.layers.detection_output( - loc=locs, scores=confs, prior_box=box, prior_box_var=box_var, **self.output_decoder_config) - outputs = {'bbox_out': [var_prefix + pred.name]} - else: - outputs = {'body_features': [var_prefix + var.name for var in body_feats]} - - # add_vars_prefix - add_vars_prefix(context_prog, var_prefix) - add_vars_prefix(fluid.default_startup_program(), var_prefix) - # inputs - inputs = {key: context_prog.global_block().vars[value] for key, value in inputs.items()} - outputs = { - out_key: [context_prog.global_block().vars[varname] for varname in out_value] - for out_key, out_value in outputs.items() - } - # trainable - for param in context_prog.global_block().iter_parameters(): - param.trainable = trainable - - place = fluid.CPUPlace() - exe = fluid.Executor(place) - # pretrained - if pretrained: - - def _if_exist(var): - return os.path.exists(os.path.join(self.default_pretrained_model_path, var.name)) - - fluid.io.load_vars(exe, self.default_pretrained_model_path, predicate=_if_exist) - else: - exe.run(startup_program) - - return inputs, outputs, context_prog - def object_detection(self, paths=None, images=None, @@ -160,47 +97,31 @@ class SSDVGG16(hub.Module): """ paths = paths if paths else list() data_reader = partial(reader, paths, images) - batch_reader = fluid.io.batch(data_reader, batch_size=batch_size) + batch_reader = paddle.batch(data_reader, batch_size=batch_size) res = [] for iter_id, feed_data in enumerate(batch_reader()): feed_data = np.array(feed_data) - image_tensor = PaddleTensor(np.array(list(feed_data[:, 0])).copy()) - if use_gpu: - data_out = self.gpu_predictor.run([image_tensor]) - else: - data_out = self.cpu_predictor.run([image_tensor]) - output = postprocess( - paths=paths, - images=images, - data_out=data_out, - score_thresh=score_thresh, - label_names=self.label_names, - output_dir=output_dir, - handle_id=iter_id * batch_size, - visualization=visualization) + predictor = self.gpu_predictor if use_gpu else self.cpu_predictor + input_names = predictor.get_input_names() + input_handle = predictor.get_input_handle(input_names[0]) + input_handle.copy_from_cpu(np.array(list(feed_data[:, 0]))) + + predictor.run() + output_names = predictor.get_output_names() + output_handle = predictor.get_output_handle(output_names[0]) + + output = postprocess(paths=paths, + images=images, + data_out=output_handle, + score_thresh=score_thresh, + label_names=self.label_names, + output_dir=output_dir, + handle_id=iter_id * batch_size, + visualization=visualization) res.extend(output) return res - def save_inference_model(self, dirname, model_filename=None, params_filename=None, combined=True): - if combined: - model_filename = "__model__" if not model_filename else model_filename - params_filename = "__params__" if not params_filename else params_filename - place = fluid.CPUPlace() - exe = fluid.Executor(place) - - program, feeded_var_names, target_vars = fluid.io.load_inference_model( - dirname=self.default_pretrained_model_path, executor=exe) - - fluid.io.save_inference_model( - dirname=dirname, - main_program=program, - executor=exe, - feeded_var_names=feeded_var_names, - target_vars=target_vars, - model_filename=model_filename, - params_filename=params_filename) - @serving def serving_method(self, images, **kwargs): """ @@ -220,9 +141,12 @@ class SSDVGG16(hub.Module): prog='hub run {}'.format(self.name), usage='%(prog)s', add_help=True) - self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required") + self.arg_input_group = self.parser.add_argument_group( + title="Input options", description="Input data. Required") self.arg_config_group = self.parser.add_argument_group( - title="Config options", description="Run configuration for controlling module behavior, not required.") + title="Config options", + description= + "Run configuration for controlling module behavior, not required.") self.add_module_config_arg() self.add_module_input_arg() args = self.parser.parse_args(argvs) @@ -240,17 +164,34 @@ class SSDVGG16(hub.Module): Add the command config options. """ self.arg_config_group.add_argument( - '--use_gpu', type=ast.literal_eval, default=False, help="whether use GPU or not") + '--use_gpu', + type=ast.literal_eval, + default=False, + help="whether use GPU or not") self.arg_config_group.add_argument( - '--output_dir', type=str, default='detection_result', help="The directory to save output images.") + '--output_dir', + type=str, + default='detection_result', + help="The directory to save output images.") self.arg_config_group.add_argument( - '--visualization', type=ast.literal_eval, default=False, help="whether to save output as images.") + '--visualization', + type=ast.literal_eval, + default=False, + help="whether to save output as images.") def add_module_input_arg(self): """ Add the command input options. """ - self.arg_input_group.add_argument('--input_path', type=str, help="path to image.") - self.arg_input_group.add_argument('--batch_size', type=ast.literal_eval, default=1, help="batch size.") self.arg_input_group.add_argument( - '--score_thresh', type=ast.literal_eval, default=0.5, help="threshold for object detecion.") + '--input_path', type=str, help="path to image.") + self.arg_input_group.add_argument( + '--batch_size', + type=ast.literal_eval, + default=1, + help="batch size.") + self.arg_input_group.add_argument( + '--score_thresh', + type=ast.literal_eval, + default=0.5, + help="threshold for object detecion.") diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/processor.py b/modules/image/object_detection/ssd_vgg16_300_coco2017/processor.py index ff4eb9fe..9bf96454 100644 --- a/modules/image/object_detection/ssd_vgg16_300_coco2017/processor.py +++ b/modules/image/object_detection/ssd_vgg16_300_coco2017/processor.py @@ -85,7 +85,7 @@ def load_label_info(file_path): def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True): """ - postprocess the lod_tensor produced by fluid.Executor.run + postprocess the lod_tensor produced by Executor.run Args: paths (list[str]): the path of images. @@ -108,9 +108,9 @@ def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, confidence (float): The confidence of detection result. save_path (str): The path to save output images. """ - lod_tensor = data_out[0] - lod = lod_tensor.lod[0] - results = lod_tensor.as_ndarray() + lod = data_out.lod()[0] + results = data_out.copy_to_cpu() + if handle_id < len(paths): unhandled_paths = paths[handle_id:] unhandled_paths_num = len(unhandled_paths) diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/test.py b/modules/image/object_detection/ssd_vgg16_300_coco2017/test.py new file mode 100644 index 00000000..922f3b60 --- /dev/null +++ b/modules/image/object_detection/ssd_vgg16_300_coco2017/test.py @@ -0,0 +1,108 @@ +import os +import shutil +import unittest + +import cv2 +import requests +import paddlehub as hub + + +class TestHubModule(unittest.TestCase): + @classmethod + def setUpClass(cls) -> None: + img_url = 'https://ai-studio-static-online.cdn.bcebos.com/68313e182f5e4ad9907e69dac9ece8fc50840d7ffbd24fa88396f009958f969a' + if not os.path.exists('tests'): + os.makedirs('tests') + response = requests.get(img_url) + assert response.status_code == 200, 'Network Error.' + with open('tests/test.jpg', 'wb') as f: + f.write(response.content) + cls.module = hub.Module(name="ssd_vgg16_300_coco2017") + + @classmethod + def tearDownClass(cls) -> None: + shutil.rmtree('tests') + shutil.rmtree('inference') + shutil.rmtree('detection_result') + + def test_object_detection1(self): + results = self.module.object_detection( + paths=['tests/test.jpg'] + ) + bbox = results[0]['data'][0] + label = bbox['label'] + confidence = bbox['confidence'] + left = bbox['left'] + right = bbox['right'] + top = bbox['top'] + bottom = bbox['bottom'] + + self.assertEqual(label, 'cat') + self.assertTrue(confidence > 0.5) + self.assertTrue(200 < left < 800) + self.assertTrue(2500 < right < 3500) + self.assertTrue(500 < top < 1500) + self.assertTrue(3500 < bottom < 4500) + + def test_object_detection2(self): + results = self.module.object_detection( + images=[cv2.imread('tests/test.jpg')] + ) + bbox = results[0]['data'][0] + label = bbox['label'] + confidence = bbox['confidence'] + left = bbox['left'] + right = bbox['right'] + top = bbox['top'] + bottom = bbox['bottom'] + + self.assertEqual(label, 'cat') + self.assertTrue(confidence > 0.5) + self.assertTrue(200 < left < 800) + self.assertTrue(2500 < right < 3500) + self.assertTrue(500 < top < 1500) + self.assertTrue(3500 < bottom < 4500) + + def test_object_detection3(self): + results = self.module.object_detection( + images=[cv2.imread('tests/test.jpg')], + visualization=False + ) + bbox = results[0]['data'][0] + label = bbox['label'] + confidence = bbox['confidence'] + left = bbox['left'] + right = bbox['right'] + top = bbox['top'] + bottom = bbox['bottom'] + + self.assertEqual(label, 'cat') + self.assertTrue(confidence > 0.5) + self.assertTrue(200 < left < 800) + self.assertTrue(2500 < right < 3500) + self.assertTrue(500 < top < 1500) + self.assertTrue(3500 < bottom < 4500) + + def test_object_detection4(self): + self.assertRaises( + AssertionError, + self.module.object_detection, + paths=['no.jpg'] + ) + + def test_object_detection5(self): + self.assertRaises( + cv2.error, + self.module.object_detection, + images=['test.jpg'] + ) + + def test_save_inference_model(self): + self.module.save_inference_model('./inference/model') + + self.assertTrue(os.path.exists('./inference/model.pdmodel')) + self.assertTrue(os.path.exists('./inference/model.pdiparams')) + + +if __name__ == "__main__": + unittest.main() diff --git a/modules/image/object_detection/ssd_vgg16_300_coco2017/vgg.py b/modules/image/object_detection/ssd_vgg16_300_coco2017/vgg.py deleted file mode 100644 index d950c6b5..00000000 --- a/modules/image/object_detection/ssd_vgg16_300_coco2017/vgg.py +++ /dev/null @@ -1,184 +0,0 @@ -# coding=utf-8 -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from paddle import fluid -from paddle.fluid.param_attr import ParamAttr - -__all__ = ['VGG'] - - -class VGG(object): - """ - VGG, see https://arxiv.org/abs/1409.1556 - - Args: - depth (int): the VGG net depth (16 or 19) - normalizations (list): params list of init scale in l2 norm, skip init - scale if param is -1. - with_extra_blocks (bool): whether or not extra blocks should be added - extra_block_filters (list): in each extra block, params: - [in_channel, out_channel, padding_size, stride_size, filter_size] - class_dim (int): number of class while classification - """ - - def __init__(self, - depth=16, - with_extra_blocks=False, - normalizations=[20., -1, -1, -1, -1, -1], - extra_block_filters=[[256, 512, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 0, 1, 3], - [128, 256, 0, 1, 3]], - class_dim=1000): - assert depth in [16, 19], "depth {} not in [16, 19]" - self.depth = depth - self.depth_cfg = {16: [2, 2, 3, 3, 3], 19: [2, 2, 4, 4, 4]} - self.with_extra_blocks = with_extra_blocks - self.normalizations = normalizations - self.extra_block_filters = extra_block_filters - self.class_dim = class_dim - - def __call__(self, input): - layers = [] - layers += self._vgg_block(input) - - if not self.with_extra_blocks: - return layers[-1] - - layers += self._add_extras_block(layers[-1]) - norm_cfg = self.normalizations - for k, v in enumerate(layers): - if not norm_cfg[k] == -1: - layers[k] = self._l2_norm_scale(v, init_scale=norm_cfg[k]) - - return layers - - def _vgg_block(self, input): - nums = self.depth_cfg[self.depth] - vgg_base = [64, 128, 256, 512, 512] - conv = input - res_layer = [] - layers = [] - for k, v in enumerate(vgg_base): - conv = self._conv_block(conv, v, nums[k], name="conv{}_".format(k + 1)) - layers.append(conv) - if self.with_extra_blocks: - if k == 4: - conv = self._pooling_block(conv, 3, 1, pool_padding=1) - else: - conv = self._pooling_block(conv, 2, 2) - else: - conv = self._pooling_block(conv, 2, 2) - if not self.with_extra_blocks: - fc_dim = 4096 - fc_name = ["fc6", "fc7", "fc8"] - fc1 = fluid.layers.fc( - input=conv, - size=fc_dim, - act='relu', - param_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_weights"), - bias_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_offset")) - fc2 = fluid.layers.fc( - input=fc1, - size=fc_dim, - act='relu', - param_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_weights"), - bias_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_offset")) - out = fluid.layers.fc( - input=fc2, - size=self.class_dim, - param_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_weights"), - bias_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_offset")) - out = fluid.layers.softmax(out) - res_layer.append(out) - return [out] - else: - fc6 = self._conv_layer(conv, 1024, 3, 1, 6, dilation=6, name="fc6") - fc7 = self._conv_layer(fc6, 1024, 1, 1, 0, name="fc7") - return [layers[3], fc7] - - def _add_extras_block(self, input): - cfg = self.extra_block_filters - conv = input - layers = [] - for k, v in enumerate(cfg): - assert len(v) == 5, "extra_block_filters size not fix" - conv = self._extra_block(conv, v[0], v[1], v[2], v[3], v[4], name="conv{}_".format(6 + k)) - layers.append(conv) - - return layers - - def _conv_block(self, input, num_filter, groups, name=None): - conv = input - for i in range(groups): - conv = self._conv_layer( - input=conv, - num_filters=num_filter, - filter_size=3, - stride=1, - padding=1, - act='relu', - name=name + str(i + 1)) - return conv - - def _extra_block(self, input, num_filters1, num_filters2, padding_size, stride_size, filter_size, name=None): - # 1x1 conv - conv_1 = self._conv_layer( - input=input, num_filters=int(num_filters1), filter_size=1, stride=1, act='relu', padding=0, name=name + "1") - - # 3x3 conv - conv_2 = self._conv_layer( - input=conv_1, - num_filters=int(num_filters2), - filter_size=filter_size, - stride=stride_size, - act='relu', - padding=padding_size, - name=name + "2") - return conv_2 - - def _conv_layer(self, - input, - num_filters, - filter_size, - stride, - padding, - dilation=1, - act='relu', - use_cudnn=True, - name=None): - conv = fluid.layers.conv2d( - input=input, - num_filters=num_filters, - filter_size=filter_size, - stride=stride, - padding=padding, - dilation=dilation, - act=act, - use_cudnn=use_cudnn, - param_attr=ParamAttr(name=name + "_weights"), - bias_attr=ParamAttr(name=name + "_biases") if self.with_extra_blocks else False, - name=name + '.conv2d.output.1') - return conv - - def _pooling_block(self, conv, pool_size, pool_stride, pool_padding=0, ceil_mode=True): - pool = fluid.layers.pool2d( - input=conv, - pool_size=pool_size, - pool_type='max', - pool_stride=pool_stride, - pool_padding=pool_padding, - ceil_mode=ceil_mode) - return pool - - def _l2_norm_scale(self, input, init_scale=1.0, channel_shared=False): - from paddle.fluid.layer_helper import LayerHelper - from paddle.fluid.initializer import Constant - helper = LayerHelper("Scale") - l2_norm = fluid.layers.l2_normalize(input, axis=1) # l2 norm along channel - shape = [1] if channel_shared else [input.shape[1]] - scale = helper.create_parameter( - attr=helper.param_attr, shape=shape, dtype=input.dtype, default_initializer=Constant(init_scale)) - out = fluid.layers.elementwise_mul( - x=l2_norm, y=scale, axis=-1 if channel_shared else 1, name="conv4_3_norm_scale") - return out -- GitLab