update ieemoo-ai-isempty.py.

ieemoo-ai-isempty.py

@@ -11,36 +11,40 @@ from gevent.pywsgi import WSGIServer
 from PIL import Image
 from torchvision import transforms
 from models.modeling import VisionTransformer, CONFIGS
+# import logging.config as log_config
 sys.path.insert(0, ".")
 
-import logging.config
+#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')
+# from skywalking import agent, config
-if SW_SERVER and SW_SERVICE_NAME:
+# SW_SERVER = os.environ.get('SW_AGENT_COLLECTOR_BACKEND_SERVICES')
-    config.init() #采集服务的地址，给自己的服务起个名称
+# SW_SERVICE_NAME = os.environ.get('SW_AGENT_NAME')
-    #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
+# if SW_SERVER and SW_SERVICE_NAME:
-    agent.start()
+#     config.init() #采集服务的地址，给自己的服务起个名称
-def setup_logging(path):
+#     #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
-    if os.path.exists(path):
+#     agent.start()
-        with open(path, 'r') as f:
+
-            config = json.load(f)
+# def setup_logging(path):
-            logging.config.dictConfig(config)
+#     if os.path.exists(path):
-            logger = logging.getLogger("root")
+#         with open(path, 'r') as f:
-    return logger
+#             config = json.load(f)
-logger = setup_logging('utils/logging.json')
+#             log_config.dictConfig(config)
+#             print = logging.getprint("root")
+#     return print
+
+# print = setup_logging('utils/logging.json')
 
 
 app = Flask(__name__)
 app.use_reloader=False
 
 
-def parse_args(model_file="../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin"):
+def parse_args(model_file="./output/ieemooempty_checkpoint_good.pth"):
-#def parse_args(model_file="output/emptyjudge5_checkpoint.bin"):
     parser = argparse.ArgumentParser()
-    parser.add_argument("--img_size", default=448, type=int, help="Resolution size")
+    parser.add_argument("--img_size", default=600, type=int, help="Resolution size")
     parser.add_argument('--split', type=str, default='overlap', help="Split method")
-    parser.add_argument('--slide_step', type=int, default=12, help="Slide step for overlap split")
+    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=model_file, help="load pretrained model")
     opt, unknown = parser.parse_known_args()
@@ -57,7 +61,7 @@ class Predictor(object):
         self.num_classes = 0
         self.model = None
         self.prepare_model()
-        self.test_transform = transforms.Compose([transforms.Resize((448, 448), Image.BILINEAR),
+        self.test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
                                              transforms.ToTensor(),
                                              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
 
@@ -65,27 +69,22 @@ class Predictor(object):
         config = CONFIGS["ViT-B_16"]
         config.split = self.args.split
         config.slide_step = self.args.slide_step
-        model_name = os.path.basename(self.args.pretrained_model).replace("_checkpoint.bin", "")
-        #print("use model_name: ", model_name)
         self.num_classes = 5
         self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
         self.model = VisionTransformer(config, self.args.img_size, zero_head=True, num_classes=self.num_classes, smoothing_value=self.args.smoothing_value)
         if self.args.pretrained_model is not None:
             if not torch.cuda.is_available():
-                pretrained_model = torch.load(self.args.pretrained_model, map_location=torch.device('cpu'))['model']
+                self.model = torch.load(self.args.pretrained_model)
-                self.model.load_state_dict(pretrained_model)
             else:
-                pretrained_model = torch.load(self.args.pretrained_model)['model']
+                self.model = torch.load(self.args.pretrained_model,map_location='cpu')
-                self.model.load_state_dict(pretrained_model)
         self.model.eval()
         self.model.to(self.args.device)
-        #self.model.eval()
         
     def normal_predict(self, img_data, result):
         # img = Image.open(img_path)
         if img_data is None:
             #print('error, img data is None')
-            logger.warning('error, img data is None')
+            print('error, img data is None')
             return result
         else:
             with torch.no_grad():
@@ -103,8 +102,7 @@ class Predictor(object):
                 return result
 
 
-model_file ="../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin"
+model_file ="./output/ieemooempty_checkpoint_good.pth"
-#model_file ="output/emptyjudge5_checkpoint.bin"
 args = parse_args(model_file)
 predictor = Predictor(args)
 
@@ -116,7 +114,7 @@ def get_isempty():
     data = request.get_data()
     ip = request.remote_addr
     #print('------ ip = %s ------' % ip)
-    logger.info(ip)
+    print(ip)
 
     json_data = json.loads(data.decode("utf-8"))
     getdateend = time.time()
@@ -133,10 +131,10 @@ def get_isempty():
         img_data = Image.fromarray(np.uint8(img_src))
         result = predictor.normal_predict(img_data, result)  # 1==empty, 0==nonEmpty
     except Exception as e:
-        logger.warning(e)
+        print(e)
         return repr(result)
-    logger.info(repr(result))
+    print(repr(result))
     return repr(result)
 
 if __name__ == "__main__":
-    app.run(host='192.168.1.142', port=8000)
+    app.run(host='0.0.0.0', port=14465)