text: """
  Retrain the YOLO model for your own dataset.
  """
  import os
  import numpy as np
  import tensorflow.keras.backend as K
  from tensorflow.keras.layers import Input, Lambda
  from tensorflow.keras.models import Model
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
  
  from yolo4.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
  from yolo4.utils import get_random_data
  
  
  def _main():
      #annotation_path = 'train.txt'
      annotation_path = '2017_train.txt'
      log_dir = 'logs/000/'
      classes_path = 'model_data/coco_classes.txt'
      anchors_path = 'model_data/yolo_anchors.txt'
      class_names = get_classes(classes_path)
      num_classes = len(class_names)
      anchors = get_anchors(anchors_path)
  
      input_shape = (416,416) # multiple of 32, hw
  
      model, bottleneck_model, last_layer_model = create_model(input_shape, anchors, num_classes,
              #freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze
              freeze_body=2, weights_path='yolo_weights.h5') # make sure you know what you freeze
  
      logging = TensorBoard(log_dir=log_dir)
      checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
          monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
      reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
      early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
  
      val_split = 0.1
      with open(annotation_path) as f:
          lines = f.readlines()
      np.random.seed(10101)
      np.random.shuffle(lines)
      np.random.seed(None)
      num_val = int(len(lines)*val_split)
      num_train = len(lines) - num_val
  
      # Train with frozen layers first, to get a stable loss.
      # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
      if True:
          # perform bottleneck training
          if not os.path.isfile("bottlenecks.npz"):
              print("calculating bottlenecks")
              batch_size=8
              bottlenecks=bottleneck_model.predict_generator(data_generator_wrapper(lines, batch_size, input_shape, anchors, num_classes, random=False, verbose=True),
               steps=(len(lines)//batch_size)+1, max_queue_size=1)
              np.savez("bottlenecks.npz", bot0=bottlenecks[0], bot1=bottlenecks[1], bot2=bottlenecks[2])
      
          # load bottleneck features from file
          dict_bot=np.load("bottlenecks.npz")
          bottlenecks_train=[dict_bot["bot0"][:num_train], dict_bot["bot1"][:num_train], dict_bot["bot2"][:num_train]]
          bottlenecks_val=[dict_bot["bot0"][num_train:], dict_bot["bot1"][num_train:], dict_bot["bot2"][num_train:]]
  
          # train last layers with fixed bottleneck features
          batch_size=8
          print("Training last layers with bottleneck features")
          print('with {} samples, val on {} samples and batch size {}.'.format(num_train, num_val, batch_size))
          last_layer_model.compile(optimizer='adam', loss={'yolo_loss': lambda y_true, y_pred: y_pred})
          last_layer_model.fit_generator(bottleneck_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, bottlenecks_train),
                  steps_per_epoch=max(1, num_train//batch_size),
                  validation_data=bottleneck_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, bottlenecks_val),
                  validation_steps=max(1, num_val//batch_size),
                  epochs=30,
                  initial_epoch=0, max_queue_size=1)
          model.save_weights(log_dir + 'trained_weights_stage_0.h5')
          
          # train last layers with random augmented data
          model.compile(optimizer=Adam(lr=1e-3), loss={
              # use custom yolo_loss Lambda layer.
              'yolo_loss': lambda y_true, y_pred: y_pred})
          batch_size = 16
          print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
          model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
                  steps_per_epoch=max(1, num_train//batch_size),
                  validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
                  validation_steps=max(1, num_val//batch_size),
                  epochs=50,
                  initial_epoch=0,
                  callbacks=[logging, checkpoint])
          model.save_weights(log_dir + 'trained_weights_stage_1.h5')
  
      # Unfreeze and continue training, to fine-tune.
      # Train longer if the result is not good.
      if True:
          for i in range(len(model.layers)):
              model.layers[i].trainable = True
          model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
          print('Unfreeze all of the layers.')
  
          batch_size = 4 # note that more GPU memory is required after unfreezing the body
          print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
          model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
              steps_per_epoch=max(1, num_train//batch_size),
              validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
              validation_steps=max(1, num_val//batch_size),
              epochs=100,
              initial_epoch=50,
              callbacks=[logging, checkpoint, reduce_lr, early_stopping])
          model.save_weights(log_dir + 'trained_weights_final.h5')
  
      # Further training if needed.
  
  
  def get_classes(classes_path):
      '''loads the classes'''
      with open(classes_path) as f:
          class_names = f.readlines()
      class_names = [c.strip() for c in class_names]
      return class_names
  
  def get_anchors(anchors_path):
      '''loads the anchors from a file'''
      with open(anchors_path) as f:
          anchors = f.readline()
      anchors = [float(x) for x in anchors.split(',')]
      return np.array(anchors).reshape(-1, 2)
  
  
  def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
              weights_path='model_data/yolo_weights.h5'):
      '''create the training model'''
      K.clear_session() # get a new session
      image_input = Input(shape=(None, None, 3))
      h, w = input_shape
      num_anchors = len(anchors)
  
      y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
          num_anchors//3, num_classes+5)) for l in range(3)]
  
      model_body = yolo_body(image_input, num_anchors//3, num_classes)
      print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
  
      if load_pretrained:
          model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
          print('Load weights {}.'.format(weights_path))
          if freeze_body in [1, 2]:
              # Freeze darknet53 body or freeze all but 3 output layers.
              num = (185, len(model_body.layers)-3)[freeze_body-1]
              for i in range(num): model_body.layers[i].trainable = False
              print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
  
      # get output of second last layers and create bottleneck model of it
      out1=model_body.layers[246].output
      out2=model_body.layers[247].output
      out3=model_body.layers[248].output
      bottleneck_model = Model([model_body.input, *y_true], [out1, out2, out3])
  
      # create last layer model of last layers from yolo model
      in0 = Input(shape=bottleneck_model.output[0].shape[1:].as_list()) 
      in1 = Input(shape=bottleneck_model.output[1].shape[1:].as_list())
      in2 = Input(shape=bottleneck_model.output[2].shape[1:].as_list())
      last_out0=model_body.layers[249](in0)
      last_out1=model_body.layers[250](in1)
      last_out2=model_body.layers[251](in2)
      model_last=Model(inputs=[in0, in1, in2], outputs=[last_out0, last_out1, last_out2])
      model_loss_last =Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
          arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
          [*model_last.output, *y_true])
      last_layer_model = Model([in0,in1,in2, *y_true], model_loss_last)
  
      
      model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
          arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
          [*model_body.output, *y_true])
      model = Model([model_body.input, *y_true], model_loss)
  
      return model, bottleneck_model, last_layer_model
  
  def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False):
      '''data generator for fit_generator'''
      n = len(annotation_lines)
      i = 0
      while True:
          image_data = []
          box_data = []
          for b in range(batch_size):
              if i==0 and random:
                  np.random.shuffle(annotation_lines)
              image, box = get_random_data(annotation_lines[i], input_shape, random=random)
              image_data.append(image)
              box_data.append(box)
              i = (i+1) % n
          image_data = np.array(image_data)
          if verbose:
              print("Progress: ",i,"/",n)
          box_data = np.array(box_data)
          y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
          yield [image_data, *y_true], np.zeros(batch_size)
  
  def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False):
      n = len(annotation_lines)
      if n==0 or batch_size<=0: return None
      return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random, verbose)
  
  def bottleneck_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, bottlenecks):
      n = len(annotation_lines)
      i = 0
      while True:
          box_data = []
          b0=np.zeros((batch_size,bottlenecks[0].shape[1],bottlenecks[0].shape[2],bottlenecks[0].shape[3]))
          b1=np.zeros((batch_size,bottlenecks[1].shape[1],bottlenecks[1].shape[2],bottlenecks[1].shape[3]))
          b2=np.zeros((batch_size,bottlenecks[2].shape[1],bottlenecks[2].shape[2],bottlenecks[2].shape[3]))
          for b in range(batch_size):
              _, box = get_random_data(annotation_lines[i], input_shape, random=False, proc_img=False)
              box_data.append(box)
              b0[b]=bottlenecks[0][i]
              b1[b]=bottlenecks[1][i]
              b2[b]=bottlenecks[2][i]
              i = (i+1) % n
          box_data = np.array(box_data)
          y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
          yield [b0, b1, b2, *y_true], np.zeros(batch_size)
  
  if __name__ == '__main__':
      _main()
  