update ieemoo-ai-isempty.py.

ieemoo-ai-isempty.py

@@ -7,14 +7,36 @@ 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
 
@@ -23,12 +45,12 @@ print(torch.__version__)
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--img_size", default=600, type=int, help="Resolution size")
+    parser.add_argument("--img_size", default=320, type=int, help="Resolution size")
     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")
-    #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")
+    parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/now/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
     opt, unknown = parser.parse_known_args()
     return opt
 
@@ -43,7 +65,7 @@ class Predictor(object):
         self.num_classes = 0
         self.model = None
         self.prepare_model()
-        self.test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
+        self.test_transform = transforms.Compose([transforms.Resize((320, 320), Image.BILINEAR),
                                              transforms.ToTensor(),
                                              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
 
@@ -62,8 +84,7 @@ class Predictor(object):
         #         self.model = torch.load(self.args.pretrained_model,map_location='cpu')
         self.model = torch.load(self.args.pretrained_model,map_location=torch.device('cpu'))
         self.model.eval()
-        if torch.cuda.is_available():
-            self.model.to("cuda")
+        self.model.to("cuda")
         
     def normal_predict(self, img_data, result):
         # img = Image.open(img_path)
@@ -74,8 +95,8 @@ class Predictor(object):
         else:
             with torch.no_grad():
                 x = self.test_transform(img_data)
-                if torch.cuda.is_available():
-                    x = x.cuda()
+                # if torch.cuda.is_available():
+                #     x = x.cuda()
                 part_logits = self.model(x.unsqueeze(0))
                 probs = torch.nn.Softmax(dim=-1)(part_logits)
                 topN = torch.argsort(probs, dim=-1, descending=True).tolist()
@@ -93,7 +114,7 @@ predictor = Predictor(args)
 
 @app.route("/isempty", methods=['POST'])
 def get_isempty():
-    #print("begin")
+    print("begin")
 
     data = request.get_data()