continue training, losses and etc

train.py

@@ -9,6 +9,7 @@ from models import *
 from utils import *
 from metrics import *
 from keras.models import load_model
+from tqdm import tqdm
 
 def configuration():
     keras.backend.clear_session()
@@ -61,19 +62,24 @@ def config_params():
     blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation.
     scales= [ 0.5, 2 ] # Scale patches with these scales. Used for augmentation.
     flip_index=[0,1,-1] # Flip image. Used for augmentation.
-
+    continue_training = False # If 
+    index_start = 0
+    dir_of_start_model = ''
+    is_loss_soft_dice = False
+    weighted_loss = False
+    data_is_provided = False
 
 @ex.automain
 def run(n_classes,n_epochs,input_height,
-        input_width,weight_decay,
+        input_width,weight_decay,weighted_loss,
+        index_start,dir_of_start_model,is_loss_soft_dice,
         n_batch,patches,augmentation,flip_aug
         ,blur_aug,scaling, binarization,
-        blur_k,scales,dir_train,
+        blur_k,scales,dir_train,data_is_provided,
         scaling_bluring,scaling_binarization,rotation,
-        rotation_not_90,thetha,scaling_flip,
+        rotation_not_90,thetha,scaling_flip,continue_training,
         flip_index,dir_eval ,dir_output,pretraining,learning_rate):
     
-    data_is_provided = False
     
     if data_is_provided:
         dir_train_flowing=os.path.join(dir_output,'train')
@@ -143,12 +149,43 @@ def run(n_classes,n_epochs,input_height,
                         augmentation=False,patches=patches)
         
 
-    continue_train = False
+    
+    if weighted_loss:
+        weights=np.zeros(n_classes)
+        if data_is_provided:
+            for obj in os.listdir(dir_flow_train_labels):
+                try:
+                    label_obj=cv2.imread(dir_flow_train_labels+'/'+obj)
+                    label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
+                    weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
+                except:
+                    pass
+        else:
+            
+            for obj in os.listdir(dir_seg):
+                try:
+                    label_obj=cv2.imread(dir_seg+'/'+obj)
+                    label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
+                    weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
+                except:
+                    pass
+            
 
-    if continue_train:
-        model_dir_start = '/home/vahid/Documents/struktur_full_data/output_multi/model_0.h5'
-        model = load_model (model_dir_start, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss})
-        index_start = 1
+        weights=1.00/weights
+        
+        weights=weights/float(np.sum(weights))
+        weights=weights/float(np.min(weights))
+        weights=weights/float(np.sum(weights))
+        
+        
+        
+    if continue_training:
+        if is_loss_soft_dice:
+            model = load_model (dir_of_start_model, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss})
+        if weighted_loss:
+            model = load_model (dir_of_start_model, compile = True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
+        if not is_loss_soft_dice and not weighted_loss:
+            model = load_model (dir_of_start_model, compile = True)
     else:
         #get our model.
         index_start = 0
@@ -158,12 +195,16 @@ def run(n_classes,n_epochs,input_height,
     #model.summary()
     
 
+    if not is_loss_soft_dice and not weighted_loss:
+        model.compile(loss='categorical_crossentropy',
+                            optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
+    if is_loss_soft_dice:                    
+        model.compile(loss=soft_dice_loss,
+                            optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
     
-    #model.compile(loss='categorical_crossentropy',
-                        #optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
-                        
-    model.compile(loss=soft_dice_loss,
-                        optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
+    if weighted_loss:
+        model.compile(loss=weighted_categorical_crossentropy(weights),
+                            optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
     
     #generating train and evaluation data
     train_gen = data_gen(dir_flow_train_imgs,dir_flow_train_labels, batch_size =  n_batch,
@@ -171,7 +212,7 @@ def run(n_classes,n_epochs,input_height,
     val_gen = data_gen(dir_flow_eval_imgs,dir_flow_eval_labels, batch_size =  n_batch,
                          input_height=input_height, input_width=input_width,n_classes=n_classes  )
     
-    for i in range(index_start, n_epochs+index_start):
+    for i in tqdm(range(index_start, n_epochs+index_start)):
         model.fit_generator(
             train_gen,
             steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1,
@@ -181,8 +222,8 @@ def run(n_classes,n_epochs,input_height,
         model.save(dir_output+'/'+'model_'+str(i)+'.h5')
     
 
-    os.system('rm -rf '+dir_train_flowing)
-    os.system('rm -rf '+dir_eval_flowing)
+    #os.system('rm -rf '+dir_train_flowing)
+    #os.system('rm -rf '+dir_eval_flowing)
 
     #model.save(dir_output+'/'+'model'+'.h5')