reorginize the code

configs/road_signs.yml

@@ -1,17 +1,18 @@
 input_shape :  [48, 48, 3]
-encodings_len: 4096
+encodings_len: 1024
 mode : 'triplet'
 distance_type : 'l1'
 backbone : 'simple2'
 backbone_weights : 'imagenet'
-optimizer : 'adam'
+optimizer : 'radam'
 learning_rate : 0.0001
 project_name : 'road_signs/'
 freeze_backbone : True
 
 #paths
-dataset_path : '/home/rauf/dataset/road_signs/road_signs_separated/'
+dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
 tensorboard_log_path : 'tf_log/'
 weights_save_path : 'weights/'
 plots_path : 'plots/'
-encodings_path : 'encodings/'
+encodings_path : 'encodings/'
+model_save_name : 'best_model.h5'

model.py → siamese_net/model.py

@@ -11,9 +11,9 @@
 from keras.layers import Conv2D, MaxPool2D, BatchNormalization, concatenate
 from classification_models import Classifiers
 import pickle
-from . import losses_and_accuracies as lac
 from .utils import parse_net_params, load_encodings
 from .backbones import get_backbone
+from . import losses_and_accuracies as lac
 import matplotlib.pyplot as plt
 
 
@@ -49,7 +49,8 @@ def __init__(self,  cfg_file):
         self.plots_path = params['plots_path']
         self.tensorboard_log_path = params['tensorboard_log_path']
         self.weights_save_path = params['weights_save_path']
-
+        self.model_save_name = params['model_save_name']
+
         os.makedirs(self.encodings_path, exist_ok=True)
         os.makedirs(self.plots_path, exist_ok=True)
         os.makedirs(self.tensorboard_log_path, exist_ok=True)

utils.py → siamese_net/utils.py

@@ -87,6 +87,9 @@ def parse_net_params(filename='configs/road_signs.yml'):
         optimizer = optimizers.Adam(lr=learning_rate)
     elif cfg['optimizer'] == 'rms_prop':
         optimizer = optimizers.RMSprop(lr=learning_rate)
+    elif cfg['optimizer'] == 'radam':
+        from keras_radam import RAdam
+        optimizer = RAdam()
     else:
         optimizer = optimizers.SGD(lr=learning_rate)
 
@@ -105,7 +108,7 @@ def parse_net_params(filename='configs/road_signs.yml'):
                                                   cfg['project_name'])
     params['weights_save_path'] = os.path.join(cfg['weights_save_path'],
                                                cfg['project_name'])
-
+    params['model_save_name'] = cfg['model_save_name']
     if 'dataset_path' in cfg:
         params['loader'] = SiameseImageLoader(cfg['dataset_path'],
                                               input_shape=cfg['input_shape'],

train.py

@@ -1,6 +1,6 @@
 import os
 import numpy as np
-from model import SiameseNet
+from siamese_net.model import SiameseNet
 from keras.callbacks import TensorBoard, LearningRateScheduler
 from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
 
@@ -21,7 +21,7 @@
                           decay_factor ** np.floor(x/step_size)),
     EarlyStopping(patience=50, verbose=1),
     TensorBoard(log_dir=model.tensorboard_log_path),
-    ModelCheckpoint(filepath=os.path.join(model.weights_save_path, 'best_model_4.h5'),
+    ModelCheckpoint(filepath=os.path.join(model.weights_save_path, model.model_save_name),
                     verbose=1, monitor='val_loss', save_best_only=True)
 ]