modify config files

RocketFlash · Dec 17, 2019 · 51a16e9 · 51a16e9
1 parent 7ad9127
commit 51a16e9
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Showing 7 changed files with 19 additions and 296 deletions.
diff --git a/configs/road_signs_resnet18.yml b/configs/road_signs_resnet18.yml
@@ -33,7 +33,7 @@ softmax_steps_per_epoch : 500
 softmax_epochs : 20
 
 #paths
-work_dir : 'work_dirs/plates/'
+work_dir : 'work_dirs/road_signs_resnet18/'
 dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
 plot_history : True
 encodings_path : 'encodings/'

diff --git a/configs/road_signs_resnet34.yml b/configs/road_signs_resnet34.yml
@@ -33,12 +33,12 @@ softmax_steps_per_epoch : 500
 softmax_epochs : 20
 
 #paths
-work_dir : 'work_dirs/plates/'
+work_dir : 'work_dirs/road_signs_resnext34/'
 dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
 plot_history : True
 encodings_path : 'encodings/'
-model_save_name : 'best_model_resnet18.h5'
-encodings_save_name: 'encodings_resnet18.pkl'
+model_save_name : 'best_model_resnet34.h5'
+encodings_save_name: 'encodings_resnet34.pkl'
 
 # encodings parameters
 save_encodings : True

diff --git a/configs/road_signs_resnext50.yml b/configs/road_signs_resnext50.yml
@@ -33,12 +33,12 @@ softmax_steps_per_epoch : 500
 softmax_epochs : 20
 
 #paths
-work_dir : 'work_dirs/plates/'
+work_dir : 'work_dirs/road_signs_resnext50/'
 dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
 plot_history : True
 encodings_path : 'encodings/'
-model_save_name : 'best_model_resnet18.h5'
-encodings_save_name: 'encodings_resnet18.pkl'
+model_save_name : 'best_model_resnext50.h5'
+encodings_save_name: 'encodings_resnext50.pkl'
 
 # encodings parameters
 save_encodings : True

diff --git a/embedding_net/backbones.py b/embedding_net/backbones.py
@@ -70,7 +70,7 @@ def get_backbone(input_shape,
         base_model = Model(
             inputs=[input_image], outputs=[encoded_output])
     else:
-        from classification_models import Classifiers
+        from classification_models.keras import Classifiers
         classifier, preprocess_input = Classifiers.get(backbone_type)
         backbone_model = classifier(input_shape=input_shape,
                                     weights=backbone_weights,
@@ -82,7 +82,7 @@ def get_backbone(input_shape,
 
         after_backbone = backbone_model.output
         x = Flatten()(after_backbone)
-        
+
         encoded_output = Dense(encodings_len, activation="relu")(x)
         if embeddings_normalization:
             encoded_output = Lambda(lambda x: K.l2_normalize(

diff --git a/embedding_net/model.py b/embedding_net/model.py
@@ -251,9 +251,8 @@ def generate_encodings(self, save_file_name='encodings.pkl', only_centers=False,
             n_neighbors=k_val)
         self.encoded_training_data['knn_classifier'].fit(self.encoded_training_data['encodings'],
                                                          self.encoded_training_data['labels'])
-        f = open(save_file_name, "wb")
-        pickle.dump(self.encoded_training_data, f)
-        f.close()
+        with open(save_file_name, "wb") as f
+            pickle.dump(self.encoded_training_data, f)
 
     def load_encodings(self, path_to_encodings):
         self.encoded_training_data = load_encodings(path_to_encodings)

diff --git a/embedding_net/pretrain_backbone_softmax.py b/embedding_net/pretrain_backbone_softmax.py
@@ -30,11 +30,12 @@ def pretrain_backbone_softmax(input_model, cfg_params):
     train_generator = image_loader.generate(batch_size, s="train")
     val_generator = image_loader.generate(batch_size, s="val")
 
+    tensorboard_save_path = os.path.join(
+        cfg_params['work_dir'], 'tf_log/pretraining_model/')
     callbacks = [
         ReduceLROnPlateau(monitor='val_loss', factor=0.1,
                           patience=4, verbose=1),
-        EarlyStopping(patience=50, verbose=1),
-        TensorBoard(log_dir='tf_log/')
+        TensorBoard(log_dir=tensorboard_save_path)
     ]
 
     history = model.fit_generator(train_generator,