machine based reading order training is integrated

models.py

@@ -544,4 +544,59 @@ def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=
     
 
 
+    return model
+
+def machine_based_reading_order_model(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
+    assert input_height%32 == 0
+    assert input_width%32 == 0
+
+    img_input = Input(shape=(input_height,input_width , 3 ))
+
+    if IMAGE_ORDERING == 'channels_last':
+        bn_axis = 3
+    else:
+        bn_axis = 1
+
+    x1 = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
+    x1 = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x1)
+
+    x1 = BatchNormalization(axis=bn_axis, name='bn_conv1')(x1)
+    x1 = Activation('relu')(x1)
+    x1 = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x1)
+    
+    x1 = conv_block(x1, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
+    x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='b')
+    x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='c')
+
+    x1 = conv_block(x1, 3, [128, 128, 512], stage=3, block='a')
+    x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='b')
+    x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='c')
+    x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='d')
+
+    x1 = conv_block(x1, 3, [256, 256, 1024], stage=4, block='a')
+    x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='b')
+    x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='c')
+    x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='d')
+    x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='e')
+    x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='f')
+
+    x1 = conv_block(x1, 3, [512, 512, 2048], stage=5, block='a')
+    x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='b')
+    x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='c')
+    
+    if pretraining:
+        Model(img_input , x1).load_weights(resnet50_Weights_path)
+    
+    x1 = AveragePooling2D((7, 7), name='avg_pool1')(x1)
+    flattened = Flatten()(x1)
+    
+    o = Dense(256, activation='relu', name='fc512')(flattened)
+    o=Dropout(0.2)(o)
+    
+    o = Dense(256, activation='relu', name='fc512a')(o)
+    o=Dropout(0.2)(o)
+
+    o = Dense(n_classes, activation='sigmoid', name='fc1000')(o)
+    model = Model(img_input , o)
+
     return model

train.py

@@ -313,4 +313,35 @@ def run(_config, n_classes, n_epochs, input_height,
             
         with open(os.path.join( os.path.join(dir_output,'model_best'), "config.json"), "w") as fp:
             json.dump(_config, fp)  # encode dict into JSON
+            
+    elif task=='reading_order':
+        configuration()
+        model = machine_based_reading_order_model(n_classes,input_height,input_width,weight_decay,pretraining)
+        
+        dir_flow_train_imgs = os.path.join(dir_train, 'images')
+        dir_flow_train_labels = os.path.join(dir_train, 'labels')
+        
+        classes = os.listdir(dir_flow_train_labels)
+        num_rows =len(classes)
+        #ls_test = os.listdir(dir_flow_train_labels)
+
+        #f1score_tot = [0]
+        indexer_start = 0
+        opt = SGD(lr=0.01, momentum=0.9)
+        opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
+        model.compile(loss="binary_crossentropy",
+                            optimizer = opt_adam,metrics=['accuracy'])
+        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), 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:
+                json.dump(_config, fp)  # encode dict into JSON
+            '''
+            if f1score>f1score_tot[0]:
+                f1score_tot[0] = f1score
+                model_dir = os.path.join(dir_out,'model_best')
+                model.save(model_dir)
+            '''
+
     

utils.py

@@ -268,6 +268,29 @@ def IoU(Yi, y_predi):
     #print("Mean IoU: {:4.3f}".format(mIoU))
     return mIoU
 
+def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes):
+    all_labels_files = os.listdir(classes_file_dir)
+    ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
+    ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
+    batchcount = 0
+    while True:
+        for i in all_labels_files:
+            file_name = i.split('.')[0]
+            img = cv2.imread(os.path.join(modal_dir,file_name+'.png'))
+
+            label_class = int( np.load(os.path.join(classes_file_dir,i)) )
+
+            ret_x[batchcount, :,:,0] = img[:,:,0]/3.0
+            ret_x[batchcount, :,:,2] = img[:,:,2]/3.0
+            ret_x[batchcount, :,:,1] = img[:,:,1]/5.0
+
+            ret_y[batchcount, :] =  label_class
+            batchcount+=1
+            if batchcount>=batchsize:
+                yield (ret_x, ret_y)
+                ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
+                ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
+                batchcount = 0
 
 def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
     c = 0