update best checkpoint and config show

train.py

@@ -14,7 +14,7 @@
 from utils.callbacks import (ExponentDecayScheduler, LossHistory,
                              ParallelModelCheckpoint)
 from utils.dataloader import YoloDatasets
-from utils.utils import get_anchors, get_classes
+from utils.utils import get_anchors, get_classes, show_config
 
 '''
 训练自己的目标检测模型一定需要注意以下几点：
@@ -230,6 +230,12 @@
     num_train   = len(train_lines)
     num_val     = len(val_lines)
 
+    show_config(
+        classes_path = classes_path, anchors_path = anchors_path, anchors_mask = anchors_mask, model_path = model_path, input_shape = input_shape, \
+        Init_Epoch = Init_Epoch, Freeze_Epoch = Freeze_Epoch, UnFreeze_Epoch = UnFreeze_Epoch, Freeze_batch_size = Freeze_batch_size, Unfreeze_batch_size = Unfreeze_batch_size, Freeze_Train = Freeze_Train, \
+        Init_lr = Init_lr, Min_lr = Min_lr, optimizer_type = optimizer_type, momentum = momentum, lr_decay_type = lr_decay_type, \
+        save_period = save_period, save_dir = save_dir, num_workers = num_workers, num_train = num_train, num_val = num_val
+    )
     #-----------------------------------------------#
     #   总训练世代指的是遍历全部数据的总次数
     #   总训练步长指的是梯度下降的总次数 
@@ -238,10 +244,10 @@
     wanted_step = 5e4 if optimizer_type == "sgd" else 1.5e4
     total_step  = num_train // Unfreeze_batch_size * UnFreeze_Epoch
     if total_step <= wanted_step:
-        wanted_epoch = wanted_step // (num_train // Unfreeze_batch_size)
-        print("\033[1;33;40m\n[Warning] 使用%s优化器时，建议将训练总步长设置到%d以上。\033[0m"%(optimizer_type, wanted_step))
-        print("\033[1;33;40m[Warning] 本次运行的总训练数据量为%d，Unfreeze_batch_size为%d，共训练%d个Epoch，计算出总训练步长为%d。\033[0m"%(num_train, Unfreeze_batch_size, UnFreeze_Epoch, total_step))
-        print("\033[1;33;40m[Warning] 由于总训练步长为%d，小于建议总步长%d，建议设置总世代为%d。\033[0m"%(total_step, wanted_step, wanted_epoch))
+        wanted_epoch = wanted_step // (num_train // Unfreeze_batch_size) + 1
+        print("\n\033[1;33;44m[Warning] 使用%s优化器时，建议将训练总步长设置到%d以上。\033[0m"%(optimizer_type, wanted_step))
+        print("\033[1;33;44m[Warning] 本次运行的总训练数据量为%d，Unfreeze_batch_size为%d，共训练%d个Epoch，计算出总训练步长为%d。\033[0m"%(num_train, Unfreeze_batch_size, UnFreeze_Epoch, total_step))
+        print("\033[1;33;44m[Warning] 由于总训练步长为%d，小于建议总步长%d，建议设置总世代为%d。\033[0m"%(total_step, wanted_step, wanted_epoch))
 
     for layer in model_body.layers:
         if isinstance(layer, DepthwiseConv2D):
@@ -315,14 +321,18 @@
                                     monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = save_period)
             checkpoint_last = ParallelModelCheckpoint(model_body, os.path.join(save_dir, "last_epoch_weights.h5"), 
                                     monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = 1)
+            checkpoint_best = ParallelModelCheckpoint(model_body, os.path.join(save_dir, "best_epoch_weights.h5"), 
+                                    monitor = 'val_loss', save_weights_only = True, save_best_only = True, period = 1)
         else:
             checkpoint      = ModelCheckpoint(os.path.join(save_dir, "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5"), 
                                     monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = save_period)
             checkpoint_last = ModelCheckpoint(os.path.join(save_dir, "last_epoch_weights.h5"), 
                                     monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = 1)
+            checkpoint_best = ModelCheckpoint(os.path.join(save_dir, "best_epoch_weights.h5"), 
+                                    monitor = 'val_loss', save_weights_only = True, save_best_only = True, period = 1)
         early_stopping  = EarlyStopping(monitor='val_loss', min_delta = 0, patience = 10, verbose = 1)
         lr_scheduler    = LearningRateScheduler(lr_scheduler_func, verbose = 1)
-        callbacks       = [logging, loss_history, checkpoint, checkpoint_last, lr_scheduler]
+        callbacks       = [logging, loss_history, checkpoint, checkpoint_last, checkpoint_best, lr_scheduler]
 
         if start_epoch < end_epoch:
             print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
@@ -359,7 +369,7 @@
             #---------------------------------------#
             lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
             lr_scheduler    = LearningRateScheduler(lr_scheduler_func, verbose = 1)
-            callbacks       = [logging, loss_history, checkpoint, lr_scheduler]
+            callbacks       = [logging, loss_history, checkpoint, checkpoint_last, checkpoint_best, lr_scheduler]
 
             for i in range(len(model_body.layers)): 
                 model_body.layers[i].trainable = True

utils/utils.py

@@ -63,6 +63,15 @@ def preprocess_input(image):
     image /= 255.0
     return image
 
+def show_config(**kwargs):
+    print('Configurations:')
+    print('-' * 70)
+    print('|%25s | %40s|' % ('keys', 'values'))
+    print('-' * 70)
+    for key, value in kwargs.items():
+        print('|%25s | %40s|' % (str(key), str(value)))
+    print('-' * 70)
+
 #-------------------------------------------------------------------------------------------------------------------------------#
 #   From https://github.com/ckyrkou/Keras_FLOP_Estimator 
 #   Fix lots of bugs

yolo.py

@@ -8,7 +8,7 @@
 
 from nets.yolo import yolo_body
 from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input,
-                         resize_image)
+                         resize_image, show_config)
 from utils.utils_bbox import DecodeBox
 
 
@@ -65,6 +65,7 @@ def get_defaults(cls, n):
     #---------------------------------------------------#
     def __init__(self, **kwargs):
         self.__dict__.update(self._defaults)
+        show_config(**self._defaults)
         for name, value in kwargs.items():
             setattr(self, name, value)