【深度學(xué)習(xí)】基于web端和C++的兩種深度學(xué)習(xí)模型部署方式

tensorflow 1.14keras 2.2.4flask 1.1.1redis 3.3.8

redis-server

IMAGE_WIDTH = 224IMAGE_HEIGHT = 224IMAGE_CHANS = 3IMAGE_DTYPE = "float32"IMAGE_QUEUE = "image_queue"BATCH_SIZE = 32SERVER_SLEEP = 0.25CLIENT_SLEEP = 0.25

app = flask.Flask(__name__)db = redis.StrictRedis(host="localhost", port=6379, db=0)model = None

def base64_encode_image(img):    return base64.b64encode(img).decode("utf-8")
def base64_decode_image(img, dtype, shape):    if sys.version_info.major == 3:        img = bytes(img, encoding="utf-8")    img = np.frombuffer(base64.decodebytes(img), dtype=dtype)    img = img.reshape(shape)    return img

def prepare_image(image, target):    # if the image mode is not RGB, convert it    if image.mode != "RGB":        image = image.convert("RGB")    # resize the input image and preprocess it    image = image.resize(target)    image = img_to_array(image)    # expand image as one batch like shape (1, c, w, h)    image = np.expand_dims(image, axis=0)    image = imagenet_utils.preprocess_input(image)    # return the processed image    return image

def classify_process():    # 導(dǎo)入模型    print("* Loading model...")    model = ResNet50(weights="imagenet")    print("* Model loaded")    while True:        # 從數(shù)據(jù)庫(kù)中創(chuàng)建預(yù)測(cè)圖像隊(duì)列        queue = db.lrange(IMAGE_QUEUE, 0, BATCH_SIZE - 1)        imageIDs = []        batch = None        # 遍歷隊(duì)列        for q in queue:            # 獲取隊(duì)列中的圖像并反序列化解碼            q = json.loads(q.decode("utf-8"))            image = base64_decode_image(q["image"], IMAGE_DTYPE,                                        (1, IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANS))            # 檢查batch列表是否為空            if batch is None:                batch = image            # 合并batch            else:                batch = np.vstack([batch, image])            # 更新圖像ID            imageIDs.append(q["id"])         if len(imageIDs) > 0:            print("* Batch size: {}".format(batch.shape))            preds = model.predict(batch)            results = imagenet_utils.decode_predictions(preds)            # 遍歷圖像ID和預(yù)測(cè)結(jié)果并打印            for (imageID, resultSet) in zip(imageIDs, results):                # initialize the list of output predictions                output = []                # loop over the results and add them to the list of                # output predictions                for (imagenetID, label, prob) in resultSet:                    r = {"label": label, "probability": float(prob)}                    output.append(r)                # 保存結(jié)果到數(shù)據(jù)庫(kù)                db.set(imageID, json.dumps(output))            # 從隊(duì)列中刪除已預(yù)測(cè)過的圖像            db.ltrim(IMAGE_QUEUE, len(imageIDs), -1)        time.sleep(SERVER_SLEEP)

@app.route("/predict", methods=["POST"])def predict():    # 初始化數(shù)據(jù)字典    data = {"success": False}    # 確保圖像上傳方式正確    if flask.request.method == "POST":        if flask.request.files.get("image"):            # 讀取圖像數(shù)據(jù)            image = flask.request.files["image"].read()            image = Image.open(io.BytesIO(image))            image = prepare_image(image, (IMAGE_WIDTH, IMAGE_HEIGHT))            # 將數(shù)組以C語(yǔ)言存儲(chǔ)順序存儲(chǔ)            image = image.copy(order="C")            # 生成圖像ID            k = str(uuid.uuid4())            d = {"id": k, "image": base64_encode_image(image)}            db.rpush(IMAGE_QUEUE, json.dumps(d))            # 運(yùn)行服務(wù)            while True:                # 獲取輸出結(jié)果                output = db.get(k)                if output is not None:                    output = output.decode("utf-8")                    data["predictions"] = json.loads(output)                    db.delete(k)                    break                time.sleep(CLIENT_SLEEP)            data["success"] = True        return flask.jsonify(data)

curl -X POST -F image=@test.jpg 'http://127.0.0.1:5000/predict'

from threading import Threadimport requestsimport time# 請(qǐng)求的URLKERAS_REST_API_URL = "http://127.0.0.1:5000/predict"# 測(cè)試圖片IMAGE_PATH = "test.jpg"# 并發(fā)數(shù)NUM_REQUESTS = 500# 請(qǐng)求間隔SLEEP_COUNT = 0.05def call_predict_endpoint(n):    # 上傳圖像    image = open(IMAGE_PATH, "rb").read()    payload = {"image": image}    # 提交請(qǐng)求    r = requests.post(KERAS_REST_API_URL, files=payload).json()    # 確認(rèn)請(qǐng)求是否成功    if r["success"]:        print("[INFO] thread {} OK".format(n))    else:        print("[INFO] thread {} FAILED".format(n))# 多線程進(jìn)行for i in range(0, NUM_REQUESTS):    # 創(chuàng)建線程來調(diào)用api    t = Thread(target=call_predict_endpoint, args=(i,))    t.daemon = True    t.start()    time.sleep(SLEEP_COUNT)time.sleep(300)

class?MyModel(torch.nn.Module):????def?__init__(self):????????super(MyModel,?self).__init__()        self.linear = torch.nn.Linear(4, 4)
????def?forward(self,?x,?h):????????new_h?=?torch.tanh(self.linear(x)?+?h)        return new_h, new_h
#?創(chuàng)建模型實(shí)例?my_model?=?MyModel()#?輸入示例x,?h?=?torch.rand(3,?4),?torch.rand(3,?4)#?torch.jit.trace方法對(duì)模型構(gòu)建TorchScripttraced_model?=?torch.jit.trace(my_model,?(x,?h))#?保存轉(zhuǎn)換后的模型traced_model.save('model.pt')

print(traced_model.graph)

graph(%self.1?:?__torch__.torch.nn.modules.module.___torch_mangle_1.Module,??????%input?:?Float(3,?4),??????%h?:?Float(3,?4)):??%19?:?__torch__.torch.nn.modules.module.Module?=?prim::GetAttr[name="linear"](%self.1)??%21?:?Tensor?=?prim::CallMethod[name="forward"](%19,?%input)??%12?:?int?=?prim::Constant[value=1]()?#?/var/lib/jenkins/workspace/beginner_source/Intro_to_TorchScript_tutorial.py:188:0??%13?:?Float(3,?4)?=?aten::add(%21,?%h,?%12)?#?/var/lib/jenkins/workspace/beginner_source/Intro_to_TorchScript_tutorial.py:188:0??%14?:?Float(3,?4)?=?aten::tanh(%13)?#?/var/lib/jenkins/workspace/beginner_source/Intro_to_TorchScript_tutorial.py:188:0??%15?:?(Float(3,?4),?Float(3,?4))?=?prim::TupleConstruct(%14,?%14)  return (%15)

print(traced_cell.code)

def?forward(self,????input:?Tensor,????h:?Tensor)?->?Tuple[Tensor,?Tensor]:??_0?=?torch.add((self.linear).forward(input,?),?h,?alpha=1)??_1?=?torch.tanh(_0)  return (_1, _1)

scripted_model?=?torch.jit.script(MyModel)scripted_model.save('model.pt')

import torchimport torchvision.models as modelsvgg16 = models.vgg16()example = torch.rand(1, 3, 224, 224).cuda() model = model.eval()traced_script_module = torch.jit.trace(model, example)output = traced_script_module(torch.ones(1,3,224,224).cuda())traced_script_module.save('vgg16-trace.pt')print(output)

tensor([[?-0.8301,?-35.6095,?12.4716]],?device='cuda:0',        grad_fn=<AddBackward0>)

cmake_minimum_required(VERSION?3.0.0?FATAL_ERROR)project(libtorch_test)find_package(Torch?REQUIRED)message(STATUS?"Pytorch?status:")message(STATUS?"libraries:?${TORCH_LIBRARIES}")add_executable(libtorch_test?test.cpp)target_link_libraries(libtorch_test?"${TORCH_LIBRARIES}")set_property(TARGET libtorch_test PROPERTY CXX_STANDARD 11)

#include?"torch/script.h"#include?"torch/torch.h"#include?#include?using?namespace?std;
int?main(int?argc,?const?char*?argv[]){????if?(argc?!=?2)?{????????std::cerr?<"usage:?example-app?\n";????????return?-1;    }
????//?讀取TorchScript轉(zhuǎn)化后的模型????torch::jit::script::Module?module;????try?{????????module?=?torch::jit::load(argv[1]);    }
????catch?(const?c10::Error&?e)?{????????std::cerr?<"error?loading?the?model\n";????????return?-1;    }
????module->to(at::kCUDA);????assert(module?!=?nullptr);    std::cout << "ok\n";
????//?構(gòu)建示例輸入????std::vector?inputs;    inputs.push_back(torch::ones({1, 3, 224, 224}).to(at::kCUDA));
????//?執(zhí)行模型推理并輸出tensor????at::Tensor?output?=?module->forward(inputs).toTensor();    std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << '\n';}

ok-0.8297,?-35.6048,?12.4823[Variable[CUDAFloatType]{1,3}]

import?torchfrom?torchvision.models?import?resnet18model?=resnet18()example?=?torch.rand(1,?3,?224,?224)tracing.traced_script_module = torch.jit.trace(model, example)

traced_script_module.save("traced_resnet_model.pt")

cmake_minimum_required(VERSION?3.0?FATAL_ERROR)project(custom_ops)find_package(Torch?REQUIRED)add_executable(example-app?example-app.cpp)target_link_libraries(example-app?"${TORCH_LIBRARIES}")set_property(TARGET example-app PROPERTY CXX_STANDARD 14)

#include??//?torch頭文件.#include #include 
int?main(int?argc,?const?char*?argv[])?{??if?(argc?!=?2)?{????std::cerr?<"usage:?example-app?\n";????return?-1;  }
??torch::jit::script::Module?module;??try?{????//?反序列化：導(dǎo)入TorchScript模型????module?=?torch::jit::load(argv[1]);  }
??catch?(const?c10::Error&?e)?{????std::cerr?<"error?loading?the?model\n";????return?-1;  }  std::cout << "ok\n";}

mkdir?example_testcd?example_testcmake?-DCMAKE_PREFIX_PATH=/path/to/libtorch?..cmake --example_test . --config Release

std::vector?inputs;inputs.push_back(torch::ones({1,?3,?224,?224}));//?執(zhí)行推理并將模型轉(zhuǎn)化為Tensoroutput = module.forward(inputs).toTensor();std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << '\n';