update

ieemoo-ai-isempty.py

@@ -12,7 +12,27 @@ from torchvision import transforms
 # import logging.config as log_config
 sys.path.insert(0, ".")
 
+<<<<<<< HEAD
 #Flask对外服务接口
+=======
+import logging.config
+from skywalking import agent, config
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
+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)
+            logging.config.dictConfig(config)
+            logger = logging.getLogger("root")
+    return logger
+logger = setup_logging('utils/logging.json')
+>>>>>>> develop
 
 
 app = Flask(__name__)

train.py

@@ -57,9 +57,29 @@ def reduce_mean(tensor, nprocs):
 
 #保存模型
 def save_model(args, model):
+<<<<<<< HEAD
     model_checkpoint = os.path.join("../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin")
     torch.save(model, model_checkpoint)
     logger.info("Saved model checkpoint to [File: %s]", "../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin")
+=======
+    model_to_save = model.module if hasattr(model, 'module') else model
+    model_checkpoint = os.path.join(args.output_dir, "%s_checkpoint.bin" % args.name)
+    checkpoint = {
+        'model': model_to_save.state_dict(),
+    }
+    torch.save(checkpoint, model_checkpoint)
+    logger.info("Saved model checkpoint to [DIR: %s]", args.output_dir)
+
+def save_eve_model(args, model, eve_name):
+    model_to_save = model.module if hasattr(model, 'module') else model
+    model_checkpoint = os.path.join(args.output_dir, "%s_checkpoint.bin" % eve_name)
+    checkpoint = {
+        'model': model_to_save.state_dict(),
+    }
+    torch.save(checkpoint, model_checkpoint)
+    logger.info("Saved model checkpoint to [DIR: %s]", args.output_dir)
+
+>>>>>>> develop
 
 #根据数据集配置模型
 def setup(args):
@@ -249,6 +269,8 @@ def train(args, model):
                             save_model(args, model)
                             best_acc = accuracy
                         logger.info("best accuracy so far: %f" % best_acc)
+                    if int(global_step)%10000 == 0:
+                        save_eve_model(args, model, str(global_step))
                     model.train()
 
                 if global_step % t_total == 0:
@@ -280,8 +302,13 @@ def main():
     parser.add_argument("--name", type=str, default='ieemooempty',
                         help="Name of this run. Used for monitoring.")
     parser.add_argument("--dataset", choices=["CUB_200_2011", "car", "dog", "nabirds", "INat2017", "emptyJudge5", "emptyJudge4"],
+<<<<<<< HEAD
                         default="emptyJudge5", help="Which dataset.")
     parser.add_argument('--data_root', type=str, default='./')
+=======
+                        default="CUB_200_2011", help="Which dataset.")
+    parser.add_argument('--data_root', type=str, default='/data/pfc/fineGrained')
+>>>>>>> develop
     parser.add_argument("--model_type", choices=["ViT-B_16", "ViT-B_32", "ViT-L_16", "ViT-L_32", "ViT-H_14"],
                         default="ViT-B_16",help="Which variant to use.")
     parser.add_argument("--pretrained_dir", type=str, default="./preckpts/ViT-B_16.npz",
@@ -297,7 +324,11 @@ def main():
                         help="Total batch size for training.")
     parser.add_argument("--eval_batch_size", default=8, type=int,
                         help="Total batch size for eval.")
+<<<<<<< HEAD
     parser.add_argument("--eval_every", default=786, type=int,
+=======
+    parser.add_argument("--eval_every", default=200, type=int, #200
+>>>>>>> develop
                         help="Run prediction on validation set every so many steps."
                              "Will always run one evaluation at the end of training.")
 
@@ -305,7 +336,11 @@ def main():
                         help="The initial learning rate for SGD.")
     parser.add_argument("--weight_decay", default=0.00001, type=float,
                         help="Weight deay if we apply some.")
+<<<<<<< HEAD
     parser.add_argument("--num_steps", default=78600, type=int,    #100000
+=======
+    parser.add_argument("--num_steps", default=40000, type=int,    #100000
+>>>>>>> develop
                         help="Total number of training epochs to perform.")
     parser.add_argument("--decay_type", choices=["cosine", "linear"], default="cosine",
                         help="How to decay the learning rate.")
@@ -331,8 +366,12 @@ def main():
     # Setup CUDA, GPU & distributed training
     if args.local_rank == -1:
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+<<<<<<< HEAD
+=======
+        #print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count())
+>>>>>>> develop
         args.n_gpu = torch.cuda.device_count()
-        print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count()) 
+        #print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count()) 
     else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.cuda.set_device(args.local_rank)
         device = torch.device("cuda", args.local_rank)