saving model by steps is added to reading order and pixel wise segmentation use cases training

train.py

@@ -13,6 +13,27 @@ from tensorflow.keras.models import load_model
 from tqdm import tqdm
 import json
 from sklearn.metrics import f1_score
+from tensorflow.keras.callbacks import Callback
+
+class SaveWeightsAfterSteps(Callback):
+    def __init__(self, save_interval, save_path, _config):
+        super(SaveWeightsAfterSteps, self).__init__()
+        self.save_interval = save_interval
+        self.save_path = save_path
+        self.step_count = 0
+
+    def on_train_batch_end(self, batch, logs=None):
+        self.step_count += 1
+
+        if self.step_count % self.save_interval ==0:
+            save_file = f"{self.save_path}/model_step_{self.step_count}"
+            #os.system('mkdir '+save_file)
+
+            self.model.save(save_file)
+            
+            with open(os.path.join(os.path.join(save_path, "model_step_{self.step_count}"),"config.json"), "w") as fp:
+                json.dump(_config, fp)  # encode dict into JSON
+            print(f"saved model as steps {self.step_count} to {save_file}")
             
             
 def configuration():
@@ -93,7 +114,7 @@ def config_params():
     f1_threshold_classification = None # This threshold is used to consider models with an evaluation f1 scores bigger than it. The selected model weights undergo a weights ensembling. And avreage ensembled model will be written to output.
     classification_classes_name = None # Dictionary of classification classes names.
     backbone_type = None # As backbone we have 2 types of backbones. A vision transformer alongside a CNN and we call it "transformer" and only CNN called "nontransformer"
-    
+    save_interval = None
     dir_img_bin = None
     number_of_backgrounds_per_image = 1
     dir_rgb_backgrounds = None
@@ -112,7 +133,7 @@ def run(_config, n_classes, n_epochs, input_height,
         thetha, scaling_flip, continue_training, transformer_projection_dim,
         transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
         transformer_patchsize_x, transformer_patchsize_y,
-        transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
+        transformer_num_patches_xy, backbone_type, save_interval, flip_index, dir_eval, dir_output,
         pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
     
     if dir_rgb_backgrounds:
@@ -299,13 +320,27 @@ def run(_config, n_classes, n_epochs, input_height,
         ##img_validation_patches = os.listdir(dir_flow_eval_imgs)
         ##score_best=[]
         ##score_best.append(0)
+        
+        if save_interval:
+            save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
+            
+            
         for i in tqdm(range(index_start, n_epochs + index_start)):
-            model.fit(
+            if save_interval:
-                train_gen,
+                model.fit(
-                steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
+                    train_gen,
-                validation_data=val_gen,
+                    steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
-                validation_steps=1,
+                    validation_data=val_gen,
-                epochs=1)
+                    validation_steps=1,
+                    epochs=1, callbacks=[save_weights_callback])
+            else:
+                model.fit(
+                    train_gen,
+                    steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
+                    validation_data=val_gen,
+                    validation_steps=1,
+                    epochs=1)
+                
             model.save(os.path.join(dir_output,'model_'+str(i)))
         
             with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
@@ -392,8 +427,15 @@ def run(_config, n_classes, n_epochs, input_height,
         opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
         model.compile(loss="binary_crossentropy",
                             optimizer = opt_adam,metrics=['accuracy'])
+        
+        if save_interval:
+            save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
+            
         for i in range(n_epochs):
-            history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1)
+            if save_interval:
+                history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1, callbacks=[save_weights_callback])
+            else:
+                history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1)
             model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) ))
             
             with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp: