Fix bugs on saving model.

Author: wangermeng2021

README.md

@@ -1,8 +1,14 @@
 
 # PVT-tensorflow2
+[![Python 3.7](https://img.shields.io/badge/Python-3.7-3776AB)](https://www.python.org/downloads/release/python-360/)
+[![TensorFlow 2.4](https://img.shields.io/badge/TensorFlow-2.4-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0)
+
 A Tensorflow2.x implementation of Pyramid Vision Transformer as described in [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122)
 
 ## Update Log
+[2021-06-29]
+* Fix bug on saving model
+  
 [2021-03-20] 
 * Add PVT-tiny,PVT-small,PVT-medium,PVT-large. 
 

model/PVT.py

@@ -101,6 +101,14 @@ def build(self, input_shape):
     def call(self, x):
         return x+self.pos_embed
 
+    def get_config(self):
+
+        config = super().get_config().copy()
+        config.update({
+            'img_len': self.img_len,
+        })
+        return config
+
 def get_pvt(img_size,num_classes,block_depth,mlp_ratio,drop_path_rate,first_level_patch_size,embed_dims,num_heads,sr_ratio,attention_drop_rate,drop_rate):
     block_drop_path_rate = np.linspace(0, drop_path_rate, sum(block_depth))
     block_depth_index = 0

train.py

@@ -49,18 +49,18 @@ def main(args):
         os.makedirs(args.checkpoints)
     # lr_cb = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=20, verbose=1, min_lr=0)
     lr_cb = tf.keras.callbacks.LearningRateScheduler(lr_scheduler)
-    model_checkpoint_cb = tf.keras.callbacks.ModelCheckpoint(filepath=args.checkpoints+'/best_weight_{epoch}_{accuracy:.3f}_{val_accuracy:.3f}',
-                                 monitor='val_accuracy',mode='max',
-                                 verbose=1,save_best_only=True)
+    model_checkpoint_cb = tf.keras.callbacks.ModelCheckpoint(filepath=args.checkpoints+'/best_weight_{epoch}_{accuracy:.3f}_{val_accuracy:.3f}.h5',
+                                                             monitor='val_accuracy',mode='max',
+                                                             verbose=1,save_best_only=True,save_weights_only=True)
     cbs=[lr_cb,
-         # model_checkpoint_cb
+         model_checkpoint_cb
          ]
     model.compile(optimizer,loss_object,metrics=["accuracy"],)
     model.fit(train_generator,
                           validation_data=val_generator,
                           epochs=args.epochs,
                           callbacks=cbs,
-                          verbose=2,
+                          verbose=1,
                           )
 
 if __name__== "__main__":