服务

helper/page_detection/pdf_detection.py

@@ -770,13 +770,10 @@ def load_predictor(model_dir,
             precision_mode=precision_map[run_mode],
             use_static=False,
             use_calib_mode=trt_calib_mode)
-        if FLAGS.collect_trt_shape_info:
-            config.collect_shape_range_info(FLAGS.tuned_trt_shape_file)
-        elif os.path.exists(FLAGS.tuned_trt_shape_file):
-            print(f'Use dynamic shape file: '
-                  f'{FLAGS.tuned_trt_shape_file} for TRT...')
-            config.enable_tuned_tensorrt_dynamic_shape(
-                FLAGS.tuned_trt_shape_file, True)
+    if os.path.exists('shape_range_info.pbtxt'):
+        print('Use dynamic shape file: shape_range_info.pbtxt for TRT...')
+        config.enable_tuned_tensorrt_dynamic_shape(
+            'shape_range_info.pbtxt', True)
 
         if use_dynamic_shape:
             min_input_shape = {
@@ -849,20 +846,10 @@ def visualize(image_list, result, labels, output_dir='output/', threshold=0.5):
         print("save result to: " + out_path)
 
 
-def print_arguments(args):
-    print('-----------  Running Arguments -----------')
-    for arg, value in sorted(vars(args).items()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------')
-
-
 class Pipeline(object):
 
     def __init__(self, model_dir):
-        if FLAGS.use_fd_format:
-            deploy_file = os.path.join(model_dir, 'inference.yml')
-        else:
-            deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
         with open(deploy_file) as f:
             yml_conf = yaml.safe_load(f)
         arch = yml_conf['arch']
@@ -876,40 +863,27 @@ class Pipeline(object):
 
         self.detector = eval(detector_func)(
             model_dir,
-            device=FLAGS.device,
-            run_mode=FLAGS.run_mode,
-            batch_size=FLAGS.batch_size,
-            trt_min_shape=FLAGS.trt_min_shape,
-            trt_max_shape=FLAGS.trt_max_shape,
-            trt_opt_shape=FLAGS.trt_opt_shape,
-            trt_calib_mode=FLAGS.trt_calib_mode,
-            cpu_threads=FLAGS.cpu_threads,
-            enable_mkldnn=FLAGS.enable_mkldnn,
-            enable_mkldnn_bfloat16=FLAGS.enable_mkldnn_bfloat16,
-            threshold=FLAGS.threshold,
-            output_dir=FLAGS.output_dir,
-            use_fd_format=FLAGS.use_fd_format)
+            device='GPU',
+            run_mode='paddle',
+            batch_size=1,
+            trt_min_shape=1,
+            trt_max_shape=1280,
+            trt_opt_shape=640,
+            trt_calib_mode=False,
+            cpu_threads=1,
+            enable_mkldnn=False,
+            enable_mkldnn_bfloat16=False,
+            threshold=0.5,
+            output_dir='output',
+            use_fd_format=False)
 
     def __call__(self, image):
         if isinstance(image, np.ndarray):
             image = [image]
         results = self.detector.predict_image(
             image,
-            visual=FLAGS.save_images)
+            visual=False)
         return results
             
 
 paddle.enable_static()
-parser = argsparser()
-FLAGS = parser.parse_args()
-# print_arguments(FLAGS)
-FLAGS.device = 'GPU'
-FLAGS.save_images = False
-FLAGS.device = FLAGS.device.upper()
-assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU', 'MLU', 'GCU'
-                        ], "device should be CPU, GPU, XPU, MLU, NPU or GCU"
-assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
-
-assert not (
-    FLAGS.enable_mkldnn == False and FLAGS.enable_mkldnn_bfloat16 == True
-), 'To enable mkldnn bfloat, please turn on both enable_mkldnn and enable_mkldnn_bfloat16'

server.py

@@ -1,19 +1,27 @@
 from flask import Flask, request, jsonify
 import requests
 from pipeline import pdf2markdown_pipeline
+import concurrent.futures
+from loguru import logger
 
 
 app = Flask(__name__)
+executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
+
+
+def pdf2markdown_task(pdf_paths, callback_url):
+    for pdf_path in pdf_paths:
+        process_status, pdf_id = pdf2markdown_pipeline(pdf_path)
+        requests.post(callback_url, json={'pdfId': pdf_id, 'processStatus': process_status})
 
 
 @app.route('/pdf-qa-server/pdf-to-md', methods=['POST'])
 def pdf2markdown():
     data = request.json
+    logger.info(f'request params: {data}')
     pdf_paths = data['pathList']
     callback_url = data['webhookUrl']
-    for pdf_path in pdf_paths:
-        process_status, pdf_id = pdf2markdown_pipeline(pdf_path)
-        requests.post(callback_url, json={'pdfId': pdf_id, 'processStatus': process_status})
+    executor.submit(pdf2markdown_task, pdf_paths, callback_url)
     return jsonify({})
 
 

server.sh

@@ -0,0 +1,3 @@
+#! /bin/bash
+
+gunicorn -w 1 -b 0.0.0.0:8000 server:app