diff --git a/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py b/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py index f345ed272f781c5f11c81ea65cc95e18a10f9859..1a7505dd69866308bee30529c20daa212e26e98a 100644 --- a/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py +++ b/demo/serving/module_serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py @@ -6,7 +6,7 @@ import os if __name__ == "__main__": # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = ["../img/man.png"] + file_list = ["../../../../docs/imgs/man.png"] files = [("image", (open(item, "rb"))) for item in file_list] # 为每张图片对应指定info和style data = {"info": ["Male,Black_Hair"], "style": ["Bald"]} diff --git a/demo/serving/module_serving/GAN_stgan_celeba/stgan_output/Bald_man.png b/demo/serving/module_serving/GAN_stgan_celeba/stgan_output/Bald_man.png index e5b17a741668a5e1a3d1df7a4fb519b447a36e31..56ef052d9f5d7cf123136989babc582402bb5f21 100644 Binary files a/demo/serving/module_serving/GAN_stgan_celeba/stgan_output/Bald_man.png and b/demo/serving/module_serving/GAN_stgan_celeba/stgan_output/Bald_man.png differ diff --git a/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py b/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py index 2927bfb16097acd2fe1087643570d89a121d0707..6cd7e36e34631943d8ba84e7e8f50c5b45999c92 100644 --- a/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py +++ b/demo/serving/module_serving/classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py @@ -4,7 +4,9 @@ import json if __name__ == "__main__": # 指定要预测的图片并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = ["../img/cat.jpg", "../img/flower.jpg"] + file_list = [ + "../../../../docs/imgs/cat.jpg", "../../../../docs/imgs/flower.jpg" + ] files = [("image", (open(item, "rb"))) for item in file_list] # 指定预测方法为vgg11_imagenet并发送post请求 url = "http://127.0.0.1:8866/predict/image/vgg11_imagenet" diff --git a/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/yolov3_darknet53_coco2017_serving_demo.py b/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/yolov3_darknet53_coco2017_serving_demo.py index 8b85f223ada1116ddefa8000fadb42b55eb02369..a653cbc1ea3aedbb8484a0f0f39d13a8c0aa8a78 100644 --- a/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/yolov3_darknet53_coco2017_serving_demo.py +++ b/demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017/yolov3_darknet53_coco2017_serving_demo.py @@ -6,7 +6,9 @@ import os if __name__ == "__main__": # 指定要检测的图片并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = ["../img/cat.jpg", "../img/dog.jpg"] + file_list = [ + "../../../../docs/imgs/cat.jpg", "../../../../docs/imgs/dog.jpg" + ] files = [("image", (open(item, "rb"))) for item in file_list] # 指定检测方法为yolov3_coco2017并发送post请求 url = "http://127.0.0.1:8866/predict/image/yolov3_darknet53_coco2017" diff --git a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py b/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py index 1c08c318865ef1f953be9b85a417f954468af739..96b201de4eaac6c6e931517291b98ab9960d171b 100644 --- a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py +++ b/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py @@ -6,7 +6,7 @@ import os if __name__ == "__main__": # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ] - file_list = ["../img/girl.jpg"] + file_list = ["../../../../docs/imgs/girl.jpg"] files = [("image", (open(item, "rb"))) for item in file_list] # 指定图片分割方法为deeplabv3p_xception65_humanseg并发送post请求 url = "http://127.0.0.1:8866/predict/image/deeplabv3p_xception65_humanseg" diff --git a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/output/girl.png b/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/output/girl.png index 667e9e70e5e8f0cf84cc5db7728eada13bf14607..9059ede00e10a410446bce9b5324531dbf5a515a 100644 Binary files a/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/output/girl.png and b/demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg/output/girl.png differ diff --git a/docs/tutorial/bert_service.md b/docs/tutorial/bert_service.md index 7c3e314ed0ec2bdf0a66c7120978f66e8ca69946..3f96e155dab73d69d3fed97f891653f24db6a585 100644 --- a/docs/tutorial/bert_service.md +++ b/docs/tutorial/bert_service.md @@ -9,7 +9,7 @@
-BS流程图 +BS流程图
@@ -203,7 +203,7 @@ result = bc.get_result(input_text=input_text) ```python [[0.9993321895599361, 0.9994612336158751, 0.9999646544456481, 0.732795298099517, -0.34387934207916204, ... ]] ``` -客户端代码demo文件见[示例](../demo/serving/bert_service/bert_service_client.py)。 +客户端代码demo文件见[示例](../../demo/serving/bert_service/bert_service_client.py)。 运行命令如下： ```shell $ python bert_service_client.py diff --git a/docs/tutorial/serving.md b/docs/tutorial/serving.md index c754a1b7a2737458b84326c347a5295520d76afe..932400984ef4504b77ad3f2967397ab197d20820 100644 --- a/docs/tutorial/serving.md +++ b/docs/tutorial/serving.md @@ -81,7 +81,7 @@ http://0.0.0.0:8866/predict//\

- +

@@ -117,7 +117,7 @@ $ hub serving start -c serving_config.json

- +

@@ -171,41 +171,41 @@ if __name__ == "__main__": } ``` -此Demo的具体信息和代码请参见[LAC Serving](../demo/serving/module_serving/lexical_analysis_lac)。另外，下面展示了一些其他的一键服务部署Demo。 +此Demo的具体信息和代码请参见[LAC Serving](../../demo/serving/module_serving/lexical_analysis_lac)。另外，下面展示了一些其他的一键服务部署Demo。 ## Demo——其他模型的一键部署服务 获取其他PaddleHub Serving的一键服务部署场景示例，可参见下列demo -* [图像分类-基于vgg11_imagent](../demo/serving/module_serving/classification_vgg11_imagenet) +* [图像分类-基于vgg11_imagent](../../demo/serving/module_serving/classification_vgg11_imagenet)   该示例展示了利用vgg11_imagent完成图像分类服务化部署和在线预测，获取图像分类结果。 -* [图像生成-基于stgan_celeba](../demo/serving/module_serving/GAN_stgan_celeba) +* [图像生成-基于stgan_celeba](../../demo/serving/module_serving/GAN_stgan_celeba)   该示例展示了利用stgan_celeba生成图像服务化部署和在线预测，获取指定风格的生成图像。 -* [文本审核-基于porn_detection_lstm](../demo/serving/module_serving/text_censorship_porn_detection_lstm) +* [文本审核-基于porn_detection_lstm](../../demo/serving/module_serving/text_censorship_porn_detection_lstm)   该示例展示了利用porn_detection_lstm完成中文文本黄色敏感信息鉴定的服务化部署和在线预测，获取文本是否敏感及其置信度。 -* [中文词法分析-基于lac](../demo/serving/module_serving/lexical_analysis_lac) +* [中文词法分析-基于lac](../../demo/serving/module_serving/lexical_analysis_lac)   该示例展示了利用lac完成中文文本分词服务化部署和在线预测，获取文本的分词结果，并可通过用户自定义词典干预分词结果。 -* [目标检测-基于yolov3_darknet53_coco2017](../demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017) +* [目标检测-基于yolov3_darknet53_coco2017](../../demo/serving/module_serving/object_detection_yolov3_darknet53_coco2017)   该示例展示了利用yolov3_darknet53_coco2017完成目标检测服务化部署和在线预测，获取检测结果和覆盖识别框的图片。 -* [中文语义分析-基于simnet_bow](../demo/serving/module_serving/semantic_model_simnet_bow) +* [中文语义分析-基于simnet_bow](../../demo/serving/module_serving/semantic_model_simnet_bow)   该示例展示了利用simnet_bow完成中文文本相似度检测服务化部署和在线预测，获取文本的相似程度。 -* [图像分割-基于deeplabv3p_xception65_humanseg](../demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg) +* [图像分割-基于deeplabv3p_xception65_humanseg](../../demo/serving/module_serving/semantic_segmentation_deeplabv3p_xception65_humanseg)   该示例展示了利用deeplabv3p_xception65_humanseg完成图像分割服务化部署和在线预测，获取识别结果和分割后的图像。 -* [中文情感分析-基于simnet_bow](../demo/serving/module_serving/semantic_model_simnet_bow) +* [中文情感分析-基于simnet_bow](../../demo/serving/module_serving/semantic_model_simnet_bow)   该示例展示了利用senta_lstm完成中文文本情感分析服务化部署和在线预测，获取文本的情感分析结果。 diff --git a/paddlehub/commands/serving.py b/paddlehub/commands/serving.py index 16b22d2b04459e124f9d3b31616124d699272df5..c4a1043f350f45bc39818e4f857bcc9f982718ff 100644 --- a/paddlehub/commands/serving.py +++ b/paddlehub/commands/serving.py @@ -103,6 +103,8 @@ class ServingCommand(BaseCommand): self.parser.add_argument("--config", "-c", nargs="?") self.parser.add_argument("--port", "-p", nargs="?", default=8866) self.parser.add_argument("--gpu", "-i", nargs="?", default=0) + self.parser.add_argument( + "--use_singleprocess", action="store_true", default=False) def dump_pid_file(self): pid = os.getpid() @@ -336,12 +338,13 @@ class ServingCommand(BaseCommand): def start_serving(self): config_file = self.args.config + single_mode = self.args.use_singleprocess if config_file is not None: if os.path.exists(config_file): with open(config_file, "r") as fp: configs = json.load(fp) use_multiprocess = configs.get("use_multiprocess", False) - if platform.system() == "Windows": + if single_mode is True or platform.system() == "Windows": print( "Warning: Windows cannot use multiprocess working " "mode, PaddleHub Serving will switch to single process mode" @@ -357,7 +360,7 @@ class ServingCommand(BaseCommand): else: print("config_file ", config_file, "not exists.") else: - if platform.system() == "Windows": + if single_mode is True or platform.system() == "Windows": print( "Warning: Windows cannot use multiprocess working " "mode, PaddleHub Serving will switch to single process mode" diff --git a/paddlehub/common/utils.py b/paddlehub/common/utils.py index 45c02a427b078537872ad5b3113a3965f917fbd2..7bd4e74a001ff68aac4d792404bbb04878b3b2f6 100644 --- a/paddlehub/common/utils.py +++ b/paddlehub/common/utils.py @@ -22,9 +22,12 @@ import os import multiprocessing import hashlib import platform +import base64 import paddle.fluid as fluid import six +import numpy as np +import cv2 from paddlehub.module import module_desc_pb2 from paddlehub.common.logger import logger @@ -51,6 +54,42 @@ def version_compare(version1, version2): return len(version1) > len(version2) +def base64s_to_cvmats(base64s): + for index, value in enumerate(base64s): + value = bytes(value, encoding="utf8") + value = base64.b64decode(value) + value = np.fromstring(value, np.uint8) + value = cv2.imdecode(value, 1) + + base64s[index] = value + return base64s + + +def handle_mask_results(results): + result = [] + if len(results) <= 0: + return results + _id = results[0]["id"] + _item = { + "data": [], + "path": results[0].get("path", ""), + "id": results[0]["id"] + } + for item in results: + if item["id"] == _id: + _item["data"].append(item["data"]) + else: + result.append(_item) + _id = _id + 1 + _item = { + "data": [item["data"]], + "path": item.get("path", ""), + "id": item.get("id", _id) + } + result.append(_item) + return result + + def get_platform(): return platform.platform() diff --git a/paddlehub/serving/app_single.py b/paddlehub/serving/app_single.py index 96fd56f6cd5fcf45a22239c690e309b74b87608f..88d504869113bdc063c75ac3fe10a0eec142463e 100644 --- a/paddlehub/serving/app_single.py +++ b/paddlehub/serving/app_single.py @@ -18,6 +18,7 @@ import time import os import base64 import logging +import shutil cv_module_method = { "vgg19_imagenet": "predict_classification", @@ -47,7 +48,9 @@ cv_module_method = { "faster_rcnn_coco2017": "predict_object_detection", "cyclegan_cityscapes": "predict_gan", "deeplabv3p_xception65_humanseg": "predict_semantic_segmentation", - "ace2p": "predict_semantic_segmentation" + "ace2p": "predict_semantic_segmentation", + "pyramidbox_lite_server_mask": "predict_mask", + "pyramidbox_lite_mobile_mask": "predict_mask" } @@ -132,6 +135,59 @@ def predict_gan(module, input_img, id, batch_size, extra={}): return results_pack +def predict_mask(module, input_img, id, batch_size, extra=None, r_img=False): + output_folder = "detection_result" + global use_gpu + method_name = module.desc.attr.map.data['default_signature'].s + predict_method = getattr(module, method_name) + try: + data = {} + if input_img is not None: + input_img = {"image": input_img} + data.update(input_img) + if extra is not None: + data.update(extra) + r_img = True if "r_img" in extra.keys() else False + results = predict_method( + data=data, use_gpu=use_gpu, batch_size=batch_size) + results = utils.handle_mask_results(results) + except Exception as err: + curr = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())) + print(curr, " - ", err) + return {"result": "Please check data format!"} + finally: + base64_list = [] + results_pack = [] + if input_img is not None: + if r_img is False: + shutil.rmtree(output_folder) + for index in range(len(results)): + results[index]["path"] = "" + results_pack = results + else: + input_img = input_img.get("image", []) + for index in range(len(input_img)): + item = input_img[index] + with open(os.path.join(output_folder, item), "rb") as fp: + b_head = "data:image/" + item.split(".")[-1] + ";base64" + b_body = base64.b64encode(fp.read()) + b_body = str(b_body).replace("b'", "").replace("'", "") + b_img = b_head + "," + b_body + base64_list.append(b_img) + results[index]["path"] = results[index]["path"].replace( + id + "_", "") if results[index]["path"] != "" \ + else "" + + results[index].update({"base64": b_img}) + results_pack.append(results[index]) + os.remove(item) + os.remove(os.path.join(output_folder, item)) + else: + results_pack = results + + return results_pack + + def predict_object_detection(module, input_img, id, batch_size, extra={}): output_folder = "detection_result" global use_gpu @@ -253,6 +309,14 @@ def create_app(init_flag=False, configs=None): extra_info = {} for item in list(request.form.keys()): extra_info.update({item: request.form.getlist(item)}) + + for key in extra_info.keys(): + if isinstance(extra_info[key], list): + extra_info[key] = utils.base64s_to_cvmats( + eval(extra_info[key][0])["b64s"]) if isinstance( + extra_info[key][0], str + ) and "b64s" in extra_info[key][0] else extra_info[key] + file_name_list = [] if img_base64 != []: for item in img_base64: @@ -260,7 +324,7 @@ def create_app(init_flag=False, configs=None): if ext not in ["jpeg", "jpg", "png"]: return {"result": "Unrecognized file type"} filename = req_id + "_" \ - + utils.md5(str(time.time())+item[0:20]) \ + + utils.md5(str(time.time()) + item[0:20]) \ + "." \ + ext img_data = base64.b64decode(item.split(',')[-1]) @@ -281,6 +345,10 @@ def create_app(init_flag=False, configs=None): module_type = module.type.split("/")[-1].replace("-", "_").lower() predict_func = eval("predict_" + module_type) batch_size = batch_size_dict.get(module_name, 1) + if file_name_list == []: + file_name_list = None + if extra_info == {}: + extra_info = None results = predict_func(module, file_name_list, req_id, batch_size, extra_info) r = {"results": str(results)}