python - API 函数返回 404 错误。试了多次

Question

在创建 API 时，我是一个完整的新手。我有一个 TF 模型并想使用 API Endpoint 访问它，但我不断收到 404 错误。有人可以帮我找出错误在哪里以及为什么是错误吗？这是一个用 py 编写的机器学习模型，我想使用 api 端点对其进行测试。

我尝试了 3-4 种方法来纠正它，甚至将其更改为 localhost 服务器（127.0.0.1），但仍然出现相同的错误。

import time
from absl import app, logging
import cv2
import numpy as np
import tensorflow as tf
from yolov3_tf2.models import (
    YoloV3, YoloV3Tiny
)
from yolov3_tf2.dataset import transform_images, load_tfrecord_dataset
from yolov3_tf2.utils import draw_outputs
from flask import Flask, request, Response, jsonify, send_from_directory, abort
import os
​
# customize your API through the following parameters
classes_path = './data/labels/coco.names'
weights_path = './weights/yolov3.tf'
tiny = False                    # set to True if using a Yolov3 Tiny model
size = 416                      # size images are resized to for model
output_path = './detections/'   # path to output folder where images with detections are saved
num_classes = 80                # number of classes in model
​
# load in weights and classes
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
    tf.config.experimental.set_memory_growth(physical_devices[0], True)
​
if tiny:
    yolo = YoloV3Tiny(classes=num_classes)
else:
    yolo = YoloV3(classes=num_classes)
​
yolo.load_weights(weights_path).expect_partial()
print('weights loaded')
​
class_names = [c.strip() for c in open(classes_path).readlines()]
print('classes loaded')
​
# Initialize Flask application
app = Flask(__name__)
​
# API that returns JSON with classes found in images
@app.route('/detections', methods=['POST'])
def get_detections():
    raw_images = []
    images = request.files.getlist("images")
    image_names = []
    for image in images:
        image_name = image.filename
        image_names.append(image_name)
        image.save(os.path.join(os.getcwd(), image_name))
        img_raw = tf.image.decode_image(
            open(image_name, 'rb').read(), channels=3)
        raw_images.append(img_raw)
        
    num = 0
    
    # create list for final response
    response = []
​
    for j in range(len(raw_images)):
        # create list of responses for current image
        responses = []
        raw_img = raw_images[j]
        num+=1
        img = tf.expand_dims(raw_img, 0)
        img = transform_images(img, size)
​
        t1 = time.time()
        boxes, scores, classes, nums = yolo(img)
        t2 = time.time()
        print('time: {}'.format(t2 - t1))
​
        print('detections:')
        for i in range(nums[0]):
            print('\t{}, {}, {}'.format(class_names[int(classes[0][i])],
                                            np.array(scores[0][i]),
                                            np.array(boxes[0][i])))
            responses.append({
                "class": class_names[int(classes[0][i])],
                "confidence": float("{0:.2f}".format(np.array(scores[0][i])*100))
            })
        response.append({
            "image": image_names[j],
            "detections": responses
        })
        img = cv2.cvtColor(raw_img.numpy(), cv2.COLOR_RGB2BGR)
        img = draw_outputs(img, (boxes, scores, classes, nums), class_names)
        cv2.imwrite(output_path + 'detection' + str(num) + '.jpg', img)
        print('output saved to: {}'.format(output_path + 'detection' + str(num) + '.jpg'))
​
    #remove temporary images
    for name in image_names:
        os.remove(name)
    try:
        return jsonify({"response":response}), 200
    except FileNotFoundError:
        abort(404)
​
# API that returns image with detections on it
@app.route('/image', methods= ['POST'])
def get_image():
    image = request.files["images"]
    image_name = image.filename
    image.save(os.path.join(os.getcwd(), image_name))
    img_raw = tf.image.decode_image(
        open(image_name, 'rb').read(), channels=3)
    img = tf.expand_dims(img_raw, 0)
    img = transform_images(img, size)
​
    t1 = time.time()
    boxes, scores, classes, nums = yolo(img)
    t2 = time.time()
    print('time: {}'.format(t2 - t1))
​
    print('detections:')
    for i in range(nums[0]):
        print('\t{}, {}, {}'.format(class_names[int(classes[0][i])],
                                        np.array(scores[0][i]),
                                        np.array(boxes[0][i])))
    img = cv2.cvtColor(img_raw.numpy(), cv2.COLOR_RGB2BGR)
    img = draw_outputs(img, (boxes, scores, classes, nums), class_names)
    cv2.imwrite(output_path + 'detection.jpg', img)
    print('output saved to: {}'.format(output_path + 'detection.jpg'))
    
    # prepare image for response
    _, img_encoded = cv2.imencode('.png', img)
    response = img_encoded.tostring()
    
    #remove temporary image
    os.remove(image_name)
​
    try:
        return Response(response=response, status=200, mimetype='image/png')
    except FileNotFoundError:
        abort(404)
if __name__ == '__main__':
    app.run(debug=True, host = '0.0.0.0', port=5000) #5000

错误堆栈

PS C:\Users\ritpr\downloads\happymonk> cd Object-Detection/API/Object-Detection-API
PS C:\Users\ritpr\downloads\happymonk\Object-Detection\API\Object-Detection-API> python app.py
2020-09-04 15:58:49.616474: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cud
art64_100.dll not found
2020-09-04 15:58:49.621871: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machin
e.
2020-09-04 15:58:54.280416: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dl
l not found
2020-09-04 15:58:54.286614: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2020-09-04 15:58:54.293859: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ritprasin
2020-09-04 15:58:54.301395: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ritprasin
WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable._
_init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
2020-09-04 15:58:58.851284: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compile
d to use: AVX2
weights loaded
classes loaded
 * Serving Flask app "app" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on
 * Restarting with windowsapi reloader
2020-09-04 15:59:02.401745: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cud
art64_100.dll not found
2020-09-04 15:59:02.407205: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machin
e.
2020-09-04 15:59:06.954279: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dl
l not found
2020-09-04 15:59:06.965616: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2020-09-04 15:59:06.972812: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ritprasin
2020-09-04 15:59:06.979463: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ritprasin
WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable._
_init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
2020-09-04 15:59:11.402905: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compile
d to use: AVX2
weights loaded
classes loaded
 * Debugger is active!
 * Debugger PIN: 291-509-420
 * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)

标签： pythonapiflask