text: """
  Retrain the YOLO model for your own dataset.
  """
  
  import numpy as np
  import keras.backend as K
  from keras.layers import Input, Lambda
  from keras.models import Model
  from keras.optimizers import Adam
  from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
  
  from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
  from yolo3.utils import get_random_data
  from keras.utils import multi_gpu_model
  
  
  def _main():
      annotation_path = '/data/saakur/ActionDetection-TrecVID/Sathya/4Channel/TrecVID_PC_Train_4Channel_Keras.txt'
      log_dir = 'logs/000/'
      classes_path = 'model_data/TrecVID.txt'
      anchors_path = 'model_data/yolo_anchors_TrecVID.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
  
      is_tiny_version = len(anchors)==6 # default setting
      if is_tiny_version:
          model = create_tiny_model(input_shape, anchors, num_classes,
              freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
      else:
          model = create_model(input_shape, anchors, num_classes,
              freeze_body=2, weights_path='model_data/yolo_weights.h5', load_pretrained=False) # make sure you know what you freeze
  
      num_gpus = 4
      model = multi_gpu_model(model, gpus=num_gpus)
  
      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:
          model.layers[0].trainable = True
          model.compile(optimizer=Adam(lr=1e-3), loss={
              # use custom yolo_loss Lambda layer.
              'yolo_loss': lambda y_true, y_pred: y_pred[0]})
  
          batch_size = 32
          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 = 16 # 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, 4))
      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)))
  
      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
  
  def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
              weights_path='model_data/tiny_yolo_weights.h5'):
      '''create the training model, for Tiny YOLOv3'''
      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}[l], w//{0:32, 1:16}[l], \
          num_anchors//2, num_classes+5)) for l in range(2)]
  
      model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)
      print('Create Tiny 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 the darknet body or freeze all but 2 output layers.
              num = (20, len(model_body.layers)-2)[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)))
  
      model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
          arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(
          [*model_body.output, *y_true])
      model = Model([model_body.input, *y_true], model_loss)
  
      return model
  
  def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
      '''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:
                  np.random.shuffle(annotation_lines)
              # image, box = get_random_data(annotation_lines[i], input_shape, random=True, scaled=False)
              image, box = get_random_data(annotation_lines[i], input_shape, random=True, scaled=True)
              image_data.append(image)
              box_data.append(box)
              i = (i+1) % n
          image_data = np.array(image_data)
          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):
      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)
  
  if __name__ == '__main__':
      _main()
  