update ieemoo-ai-isempty.py.

ieemoo-ai-isempty.py

@@ -7,36 +7,14 @@ import cv2, base64
 import argparse
 import sys, os
 import torch
-from gevent.pywsgi import WSGIServer
 from PIL import Image
 from torchvision import transforms
-from models.modeling import VisionTransformer, CONFIGS
-from vit_pytorch import ViT
 # import logging.config as log_config
 sys.path.insert(0, ".")
 
 #Flask对外服务接口
 
 
-# from skywalking import agent, config
-# SW_SERVER = os.environ.get('SW_AGENT_COLLECTOR_BACKEND_SERVICES')
-# SW_SERVICE_NAME = os.environ.get('SW_AGENT_NAME')
-# if SW_SERVER and SW_SERVICE_NAME:
-#     config.init() #采集服务的地址，给自己的服务起个名称
-#     #config.init(collector="123.60.56.51:11800", service='ieemoo-ai-search') #采集服务的地址，给自己的服务起个名称
-#     agent.start()
-
-# def setup_logging(path):
-#     if os.path.exists(path):
-#         with open(path, 'r') as f:
-#             config = json.load(f)
-#             log_config.dictConfig(config)
-#             print = logging.getprint("root")
-#     return print
-
-# print = setup_logging('utils/logging.json')
-
-
 app = Flask(__name__)
 #app.use_reloader=False
 
@@ -49,9 +27,8 @@ def parse_args():
     parser.add_argument('--split', type=str, default='overlap', help="Split method")
     parser.add_argument('--slide_step', type=int, default=2, help="Slide step for overlap split")
     parser.add_argument('--smoothing_value', type=float, default=0.0, help="Label smoothing value")
-    #parser.add_argument("--pretrained_model", type=str, default="output/emptyjudge5_checkpoint.bin", help="load pretrained model")
-    #parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vit_checkpoint.pth", help="load pretrained model") #使用自定义VIT
-    parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/new/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
+    #使用自定义VIT
+    parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/now/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
     opt, unknown = parser.parse_known_args()
     return opt
 
@@ -116,7 +93,7 @@ predictor = Predictor(args)
 
 @app.route("/isempty", methods=['POST'])
 def get_isempty():
-    print("begin")
+    #print("begin")
 
     data = request.get_data()
 
@@ -140,7 +117,7 @@ def getByte(path):
     return img_str
 
 if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=8092)
+    app.run(host='0.0.0.0', port=8888)
 
     # result ={}
     # imgdata = base64.b64decode(getByte("img.jpg"))