tensorflow2

keras-yolo3-master/train.py

@@ -8,13 +8,16 @@ from voc import parse_voc_annotation
 from yolo import create_yolov3_model, dummy_loss
 from generator import BatchGenerator
 from utils.utils import normalize, evaluate, makedirs
-from keras.callbacks import EarlyStopping, ReduceLROnPlateau
-from keras.optimizers import Adam
+from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
+from tensorflow.keras.optimizers import Adam
 from callbacks import CustomModelCheckpoint, CustomTensorBoard
 from utils.multi_gpu_model import multi_gpu_model
 import tensorflow as tf
-import keras
-from keras.models import load_model
+from tensorflow import keras
+from tensorflow.keras.models import load_model
+
+tf.keras.backend.clear_session()
+tf.config.experimental_run_functions_eagerly(True)
 
 def create_training_instances(
     train_annot_folder,
@@ -66,28 +69,34 @@ def create_callbacks(saved_weights_name, tensorboard_logs, model_to_save):
     makedirs(tensorboard_logs)
 
     early_stop = EarlyStopping(
-        monitor     = 'loss',
+        monitor     = 'val_loss',
         min_delta   = 0.01,
         patience    = 25,
         mode        = 'min',
         verbose     = 1
     )
-    checkpoint = CustomModelCheckpoint(
+    """checkpoint = CustomModelCheckpoint(
         model_to_save   = model_to_save,
         filepath        = saved_weights_name,# + '{epoch:02d}.h5',
         monitor         = 'loss',
         verbose         = 1,
         save_best_only  = True,
         mode            = 'min',
-        period          = 1
-    )
+        save_freq       = 1
+    )"""
+    checkpoint = ModelCheckpoint(filepath=saved_weights_name,
+                                 monitor='val_loss',
+                                 save_best_only=True,
+                                 save_weights_only=True,
+                                 verbose=1)
+
     reduce_on_plateau = ReduceLROnPlateau(
-        monitor  = 'loss',
+        monitor  = 'val_loss',
         factor   = 0.5,
         patience = 15,
         verbose  = 1,
         mode     = 'min',
-        epsilon  = 0.01,
+        min_delta  = 0.01,
         cooldown = 0,
         min_lr   = 0
     )
@@ -96,7 +105,7 @@ def create_callbacks(saved_weights_name, tensorboard_logs, model_to_save):
         write_graph            = True,
         write_images           = True,
     )
-    return [early_stop, checkpoint, reduce_on_plateau, tensorboard]
+    return [early_stop, checkpoint, reduce_on_plateau]
 
 def create_model(
     nb_class,
@@ -245,21 +254,24 @@ def _main_(args):
 	backend		    = config['model']['backend']
     )
 
+
     ###############################
     #   Kick off the training
     ###############################
     callbacks = create_callbacks(config['train']['saved_weights_name'], config['train']['tensorboard_dir'], infer_model)
 
-    train_model.fit_generator(
-        generator        = train_generator,
+    train_model.fit(
+        x = train_generator,
+        validation_data = valid_generator,
         steps_per_epoch  = len(train_generator) * config['train']['train_times'],
         epochs           = config['train']['nb_epochs'] + config['train']['warmup_epochs'],
         verbose          = 2 if config['train']['debug'] else 1,
-        callbacks        = callbacks,
         workers          = 4,
-        max_queue_size   = 8
+        max_queue_size   = 8,
+        callbacks        = callbacks
     )
 
+
     # make a GPU version of infer_model for evaluation
     if multi_gpu > 1:
         infer_model = load_model(config['train']['saved_weights_name'])
@@ -284,7 +296,7 @@ def _main_(args):
         return
 
     print('mAP using the weighted average of precisions among classes: {:.4f}'.format(sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances)))
-    print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))           
+    print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))
 
 if __name__ == '__main__':
     argparser = argparse.ArgumentParser(description='train and evaluate YOLO_v3 model on any dataset')