data prefecther issue

out of memory

README.md

@@ -14,7 +14,23 @@ Paper link: https://arxiv.org/abs/2003.11337
 For vehicles equipped with the automatic parking system, the accuracy and speed of the parking slot detection are crucial. But the high accuracy is obtained at the price of low speed or expensive computation equipment, which are sensitive for many car manufacturers. In this paper, we proposed a detector using CNN(convolutional neural networks) for faster speed and smaller model size while keeps accuracy. To achieve the optimal balance, we developed a strategy to select the best receptive fields and prune the redundant channels automatically after each training epoch. The proposed model is capable of jointly detecting corners and line features of parking slots while running efficiently in real time on average processors. The model has a frame rate of about 30 FPS on a 2.3 GHz CPU core, yielding parking slot corner localization error of 1.51±2.14 cm (std. err.) and slot detection accuracy of 98%, generally satisfying the requirements in both speed and accuracy on onboard mobile terminals.
 
 ## Usage
-Detailed instructions will be given soon.
+
+
+
+1. 데이터 셋 구성
+./data/train/
+./data/train_raw_label/
+./data/test/all/
+./data/test/test_raw_label/
+
+
+
+
+
+
+
+
+
 
 ## Performance
 The training and test data set is https://cslinzhang.github.io/deepps/

SPFCN/__init__.py

@@ -13,18 +13,18 @@ def setup(seed):
     cudnn.deterministic = True
 
 
-def slot_network_training(device_id=1):
+def slot_network_training(device_id=0):
     # Initial
     setup(19960229)
     net = SlotNetwork([32, 44, 64, 92, 128], device_id=device_id)
 
     # Train
-    auto_train(get_training_set(6535, 50, 224, device_id), net, device_id=device_id,
+    auto_train(get_training_set(6535, 12, 224, device_id), net, device_id=device_id,
                epoch_limit=1000, save_path="parameters/")
 
 
 # TODO
-def slot_network_testing(model_path, device_id=1):
+def slot_network_testing(model_path, device_id=0):
     # Initial
     setup(19960229)
 

SPFCN/dataset/__init__.py

@@ -12,15 +12,17 @@ def get_training_set(data_size: int,
     assert 0 < data_size < 6596 and 0 < batch_size and 0 < resolution
 
     vps_set = VisionParkingSlotDataset(
-        image_path="/data/training/",
-        label_path="/data/training_raw_label/",
+        image_path="./data/training/image/",
+        label_path="./data/training/label/",
         data_size=data_size,
         resolution=resolution)
-    if device_id < 0:
-        return DataLoader(dataset=vps_set, shuffle=True, batch_size=batch_size, num_workers=4)
-    else:
-        return DataPrefetcher(device=torch.device('cuda:%d' % device_id),
-                              dataset=vps_set, batch_size=batch_size, shuffle=True)
+
+    return DataLoader(dataset=vps_set, shuffle=True, batch_size=batch_size, num_workers=4)
+    # if device_id < 0:
+    #     return DataLoader(dataset=vps_set, shuffle=True, batch_size=batch_size, num_workers=4)
+    # else:
+    #     return DataPrefetcher(device=torch.device('cuda:%d' % device_id),
+    #                           dataset=vps_set, batch_size=batch_size, shuffle=True)
 
 
 def get_validating_set(data_size: int,
@@ -29,8 +31,8 @@ def get_validating_set(data_size: int,
                        device_id: int = 0):
     assert 0 < data_size < 1538 and 0 < batch_size and 0 < resolution
     vps_set = VisionParkingSlotDataset(
-        image_path="/data/testing/all/all/",
-        label_path="/data/testing/all/raw_label/",
+        image_path="./data/testing/image/",
+        label_path="./data/testing/label/",
         data_size=data_size,
         resolution=resolution)
     if device_id < 0:
@@ -47,8 +49,8 @@ def get_testing_set(data_size: int,
                        device_id: int = 0):
     assert 0 < data_size < 1538 and 0 < batch_size and 0 < resolution
     vps_set = VisionParkingSlotDataset(
-        image_path="/data/testing/all/all/",
-        label_path="/data/testing/all/raw_label/",
+        image_path="./data/testing/image/",
+        label_path="./data/testing/label/",
         data_size=data_size,
         resolution=resolution)
     if device_id < 0: