fix some bug

config.py

@@ -18,7 +18,7 @@ def get_classify_config():
     parser = argparse.ArgumentParser()
 
     # -----------------------------------------超参数设置-----------------------------------------
-    parser.add_argument('--batch_size', type=int, default=72, help='batch size')
+    parser.add_argument('--batch_size', type=int, default=48, help='batch size')
     parser.add_argument('--epoch', type=int, default=100, help='epoch')
     parser.add_argument('--lr', type=float, default=1e-3, help='init lr')
     parser.add_argument('--weight_decay', type=float, default=0, help='weight_decay in optimizer')

demo.py

@@ -143,7 +143,7 @@ def __prepare__(self, label_json_path):
     mean = (0.485, 0.456, 0.406)
     std = (0.229, 0.224, 0.225)
     transforms = None
-    config.image_size = [416, 416]
+    config.selected_fold = [0]
 
     weight_path = os.path.join('checkpoints', model_type)
     lists = os.listdir(weight_path)  # 获得文件夹内所有文件

train_classifier.py

@@ -98,7 +98,7 @@ def __init__(self, config, fold, train_labels_number):
         # 初始化分类度量准则类
         with open("online-service/model/label_id_name.json", 'r', encoding='utf-8') as json_file:
             self.class_names = list(json.load(json_file).values())
-        self.classification_metric = ClassificationMetric(self.class_names, self.model_path)
+        self.classification_metric = ClassificationMetric(self.class_names, self.model_path, text_flag=0)
 
         self.max_accuracy_valid = 0
 

train_classifier_online.py

@@ -157,7 +157,7 @@ def __init__(self, config, fold, train_labels_number):
         # 初始化分类度量准则类
         with open(config.local_data_root+'label_id_name.json', 'r', encoding='utf-8') as json_file:
             self.class_names = list(json.load(json_file).values())
-        self.classification_metric = ClassificationMetric(self.class_names, self.model_path)
+        self.classification_metric = ClassificationMetric(self.class_names, self.model_path, text_flag=0)
 
         self.max_accuracy_valid = 0
 
@@ -306,7 +306,6 @@ def validation(self, valid_loader):
                     oa,
                     average_accuracy,
                     kappa,
-                    text_flag=0,
                     font_fname="../font/simhei.ttf"
                 )
             else:

utils/rm_deleted_files.py

@@ -0,0 +1,45 @@
+import json
+import os
+
+delete_files_path = 'data/huawei_data/delete_bak'
+dataset_split_file = 'data/huawei_data/dataset_split.json'
+
+delete_files = [f for f in os.listdir(delete_files_path) if f.endswith('jpg')]
+with open(dataset_split_file, 'r') as f:
+    train_list, val_list = json.load(f)
+
+delete_files_count = []
+undelete_train_list = []
+for fold_index, fold_list in enumerate(train_list):
+    fold_sample_list = fold_list[0]
+    fold_label_list = fold_list[1]
+    undelete_fold_sample = []
+    undelete_fold_label = []
+    for sample_index, sample in enumerate(fold_sample_list):
+        if sample not in delete_files:
+            # 不在被删除的文件中
+            undelete_fold_sample.append(sample)
+            undelete_fold_label.append(fold_label_list[sample_index])
+        else:
+            delete_files_count.append([sample, fold_label_list[sample_index]])
+            print('[Train Fold %d] Remove: %s, Label: %d' % (fold_index, sample, fold_label_list[sample_index]))
+    undelete_train_list.append([undelete_fold_sample, undelete_fold_label])
+
+undelete_val_list = []
+for fold_index, fold_list in enumerate(val_list):
+    fold_sample_list = fold_list[0]
+    fold_label_list = fold_list[1]
+    undelete_fold_sample = []
+    undelete_fold_label = []
+    for sample_index, sample in enumerate(fold_sample_list):
+        if sample not in delete_files:
+            # 不在被删除的文件中
+            undelete_fold_sample.append(sample)
+            undelete_fold_label.append(fold_label_list[sample_index])
+        else:
+            delete_files_count.append([sample, fold_label_list[sample_index]])
+            print('[Val Fold %d] Remove: %s, Label: %d' % (fold_index, sample, fold_label_list[sample_index]))
+    undelete_val_list.append([undelete_fold_sample, undelete_fold_label])
+
+with open('dataset_split_delete.json', 'w') as f:
+    json.dump([undelete_train_list, undelete_val_list], f, ensure_ascii=False)