text: """YOLO_v3 Model Defined in Keras."""
  
  from functools import wraps
  
  import numpy as np
  import tensorflow as tf
  from keras import backend as K
  from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D
  from keras.layers.advanced_activations import LeakyReLU
  from keras.layers.normalization import BatchNormalization
  from keras.models import Model
  from keras.regularizers import l2
  
  from ..yolo3.utils import compose
  
  
  @wraps(Conv2D)
  def DarknetConv2D(*args, **kwargs):
      """Wrapper to set Darknet parameters for Convolution2D."""
      darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
      darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides')==(2,2) else 'same'
      darknet_conv_kwargs.update(kwargs)
      return Conv2D(*args, **darknet_conv_kwargs)
  
  def DarknetConv2D_BN_Leaky(*args, **kwargs):
      """Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
      no_bias_kwargs = {'use_bias': False}
      no_bias_kwargs.update(kwargs)
      return compose(
          DarknetConv2D(*args, **no_bias_kwargs),
          BatchNormalization(),
          LeakyReLU(alpha=0.1))
  
  def resblock_body(x, num_filters, num_blocks):
      '''A series of resblocks starting with a downsampling Convolution2D'''
      # Darknet uses left and top padding instead of 'same' mode
      x = ZeroPadding2D(((1,0),(1,0)))(x)
      x = DarknetConv2D_BN_Leaky(num_filters, (3,3), strides=(2,2))(x)
      for i in range(num_blocks):
          y = compose(
                  DarknetConv2D_BN_Leaky(num_filters//2, (1,1)),
                  DarknetConv2D_BN_Leaky(num_filters, (3,3)))(x)
          x = Add()([x,y])
      return x
  
  def darknet_body(x):
      '''Darknent body having 52 Convolution2D layers'''
      x = DarknetConv2D_BN_Leaky(32, (3,3))(x)
      x = resblock_body(x, 64, 1)
      x = resblock_body(x, 128, 2)
      x = resblock_body(x, 256, 8)
      x = resblock_body(x, 512, 8)
      x = resblock_body(x, 1024, 4)
      return x
  
  def make_last_layers(x, num_filters, out_filters):
      '''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
      x = compose(
              DarknetConv2D_BN_Leaky(num_filters, (1,1)),
              DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
              DarknetConv2D_BN_Leaky(num_filters, (1,1)),
              DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
              DarknetConv2D_BN_Leaky(num_filters, (1,1)))(x)
      y = compose(
              DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
              DarknetConv2D(out_filters, (1,1)))(x)
      return x, y
  
  
  def yolo_body(inputs, num_anchors, num_classes):
      """Create YOLO_V3 model CNN body in Keras."""
      darknet = Model(inputs, darknet_body(inputs))
      x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5))
  
      x = compose(
              DarknetConv2D_BN_Leaky(256, (1,1)),
              UpSampling2D(2))(x)
      x = Concatenate()([x,darknet.layers[152].output])
      x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5))
  
      x = compose(
              DarknetConv2D_BN_Leaky(128, (1,1)),
              UpSampling2D(2))(x)
      x = Concatenate()([x,darknet.layers[92].output])
      x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))
  
      return Model(inputs, [y1,y2,y3])
  
  def tiny_yolo_body(inputs, num_anchors, num_classes):
      '''Create Tiny YOLO_v3 model CNN body in keras.'''
      x1 = compose(
              DarknetConv2D_BN_Leaky(16, (3,3)),
              MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
              DarknetConv2D_BN_Leaky(32, (3,3)),
              MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
              DarknetConv2D_BN_Leaky(64, (3,3)),
              MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
              DarknetConv2D_BN_Leaky(128, (3,3)),
              MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
              DarknetConv2D_BN_Leaky(256, (3,3)))(inputs)
      x2 = compose(
              MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
              DarknetConv2D_BN_Leaky(512, (3,3)),
              MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
              DarknetConv2D_BN_Leaky(1024, (3,3)),
              DarknetConv2D_BN_Leaky(256, (1,1)))(x1)
      y1 = compose(
              DarknetConv2D_BN_Leaky(512, (3,3)),
              DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)
  
      x2 = compose(
              DarknetConv2D_BN_Leaky(128, (1,1)),
              UpSampling2D(2))(x2)
      y2 = compose(
              Concatenate(),
              DarknetConv2D_BN_Leaky(256, (3,3)),
              DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])
  
      return Model(inputs, [y1,y2])
  
  
  def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
      """Convert final layer features to bounding box parameters."""
      num_anchors = len(anchors)
      # Reshape to batch, height, width, num_anchors, box_params.
      anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])
  
      grid_shape = K.shape(feats)[1:3] # height, width
      grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
          [1, grid_shape[1], 1, 1])
      grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
          [grid_shape[0], 1, 1, 1])
      grid = K.concatenate([grid_x, grid_y])
      grid = K.cast(grid, K.dtype(feats))
  
      feats = K.reshape(
          feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
  
      # Adjust preditions to each spatial grid point and anchor size.
      box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[...,::-1], K.dtype(feats))
      box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[...,::-1], K.dtype(feats))
      box_confidence = K.sigmoid(feats[..., 4:5])
      box_class_probs = K.sigmoid(feats[..., 5:])
  
      if calc_loss == True:
          return grid, feats, box_xy, box_wh
      return box_xy, box_wh, box_confidence, box_class_probs
  
  
  def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
      '''Get corrected boxes'''
      box_yx = box_xy[..., ::-1]
      box_hw = box_wh[..., ::-1]
      input_shape = K.cast(input_shape, K.dtype(box_yx))
      image_shape = K.cast(image_shape, K.dtype(box_yx))
      new_shape = K.round(image_shape * K.min(input_shape/image_shape))
      offset = (input_shape-new_shape)/2./input_shape
      scale = input_shape/new_shape
      box_yx = (box_yx - offset) * scale
      box_hw *= scale
  
      box_mins = box_yx - (box_hw / 2.)
      box_maxes = box_yx + (box_hw / 2.)
      boxes =  K.concatenate([
          box_mins[..., 0:1],  # y_min
          box_mins[..., 1:2],  # x_min
          box_maxes[..., 0:1],  # y_max
          box_maxes[..., 1:2]  # x_max
      ])
  
      # Scale boxes back to original image shape.
      boxes *= K.concatenate([image_shape, image_shape])
      return boxes
  
  
  def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
      '''Process Conv layer output'''
      box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats,
          anchors, num_classes, input_shape)
      boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
      boxes = K.reshape(boxes, [-1, 4])
      box_scores = box_confidence * box_class_probs
      box_scores = K.reshape(box_scores, [-1, num_classes])
      return boxes, box_scores
  
  
  def yolo_eval(yolo_outputs,
                anchors,
                num_classes,
                image_shape,
                max_boxes=20,
                score_threshold=.6,
                iou_threshold=.5):
      """Evaluate YOLO model on given input and return filtered boxes."""
      num_layers = len(yolo_outputs)
      anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]] # default setting
      input_shape = K.shape(yolo_outputs[0])[1:3] * 32
      boxes = []
      box_scores = []
      for l in range(num_layers):
          _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l],
              anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
          boxes.append(_boxes)
          box_scores.append(_box_scores)
      boxes = K.concatenate(boxes, axis=0)
      box_scores = K.concatenate(box_scores, axis=0)
  
      mask = box_scores >= score_threshold
      max_boxes_tensor = K.constant(max_boxes, dtype='int32')
      boxes_ = []
      scores_ = []
      classes_ = []
      for c in range(num_classes):
          # TODO: use keras backend instead of tf.
          class_boxes = tf.boolean_mask(boxes, mask[:, c])
          class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])
          nms_index = tf.image.non_max_suppression(
              class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=iou_threshold)
          class_boxes = K.gather(class_boxes, nms_index)
          class_box_scores = K.gather(class_box_scores, nms_index)
          classes = K.ones_like(class_box_scores, 'int32') * c
          boxes_.append(class_boxes)
          scores_.append(class_box_scores)
          classes_.append(classes)
      boxes_ = K.concatenate(boxes_, axis=0)
      scores_ = K.concatenate(scores_, axis=0)
      classes_ = K.concatenate(classes_, axis=0)
  
      return boxes_, scores_, classes_
  
  
  def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
      '''Preprocess true boxes to training input format
  
      Parameters
      ----------
      true_boxes: array, shape=(m, T, 5)
          Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape.
      input_shape: array-like, hw, multiples of 32
      anchors: array, shape=(N, 2), wh
      num_classes: integer
  
      Returns
      -------
      y_true: list of array, shape like yolo_outputs, xywh are reletive value
  
      '''
      assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
      num_layers = len(anchors)//3 # default setting
      anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
  
      true_boxes = np.array(true_boxes, dtype='float32')
      input_shape = np.array(input_shape, dtype='int32')
      boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2
      boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
      true_boxes[..., 0:2] = boxes_xy/input_shape[::-1]
      true_boxes[..., 2:4] = boxes_wh/input_shape[::-1]
  
      m = true_boxes.shape[0]
      grid_shapes = [input_shape//{0:32, 1:16, 2:8}[l] for l in range(num_layers)]
      y_true = [np.zeros((m,grid_shapes[l][0],grid_shapes[l][1],len(anchor_mask[l]),5+num_classes),
          dtype='float32') for l in range(num_layers)]
  
      # Expand dim to apply broadcasting.
      anchors = np.expand_dims(anchors, 0)
      anchor_maxes = anchors / 2.
      anchor_mins = -anchor_maxes
      valid_mask = boxes_wh[..., 0]>0
  
      for b in range(m):
          # Discard zero rows.
          wh = boxes_wh[b, valid_mask[b]]
          if len(wh)==0: continue
          # Expand dim to apply broadcasting.
          wh = np.expand_dims(wh, -2)
          box_maxes = wh / 2.
          box_mins = -box_maxes
  
          intersect_mins = np.maximum(box_mins, anchor_mins)
          intersect_maxes = np.minimum(box_maxes, anchor_maxes)
          intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
          intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
          box_area = wh[..., 0] * wh[..., 1]
          anchor_area = anchors[..., 0] * anchors[..., 1]
          iou = intersect_area / (box_area + anchor_area - intersect_area)
  
          # Find best anchor for each true box
          best_anchor = np.argmax(iou, axis=-1)
  
          for t, n in enumerate(best_anchor):
              for l in range(num_layers):
                  if n in anchor_mask[l]:
                      i = np.floor(true_boxes[b,t,0]*grid_shapes[l][1]).astype('int32')
                      j = np.floor(true_boxes[b,t,1]*grid_shapes[l][0]).astype('int32')
                      k = anchor_mask[l].index(n)
                      c = true_boxes[b,t, 4].astype('int32')
                      y_true[l][b, j, i, k, 0:4] = true_boxes[b,t, 0:4]
                      y_true[l][b, j, i, k, 4] = 1
                      y_true[l][b, j, i, k, 5+c] = 1
  
      return y_true
  
  
  def box_iou(b1, b2):
      '''Return iou tensor
  
      Parameters
      ----------
      b1: tensor, shape=(i1,...,iN, 4), xywh
      b2: tensor, shape=(j, 4), xywh
  
      Returns
      -------
      iou: tensor, shape=(i1,...,iN, j)
  
      '''
  
      # Expand dim to apply broadcasting.
      b1 = K.expand_dims(b1, -2)
      b1_xy = b1[..., :2]
      b1_wh = b1[..., 2:4]
      b1_wh_half = b1_wh/2.
      b1_mins = b1_xy - b1_wh_half
      b1_maxes = b1_xy + b1_wh_half
  
      # Expand dim to apply broadcasting.
      b2 = K.expand_dims(b2, 0)
      b2_xy = b2[..., :2]
      b2_wh = b2[..., 2:4]
      b2_wh_half = b2_wh/2.
      b2_mins = b2_xy - b2_wh_half
      b2_maxes = b2_xy + b2_wh_half
  
      intersect_mins = K.maximum(b1_mins, b2_mins)
      intersect_maxes = K.minimum(b1_maxes, b2_maxes)
      intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
      intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
      b1_area = b1_wh[..., 0] * b1_wh[..., 1]
      b2_area = b2_wh[..., 0] * b2_wh[..., 1]
      iou = intersect_area / (b1_area + b2_area - intersect_area)
  
      return iou
  
  
  def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, print_loss=False):
      '''Return yolo_loss tensor
  
      Parameters
      ----------
      yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body
      y_true: list of array, the output of preprocess_true_boxes
      anchors: array, shape=(N, 2), wh
      num_classes: integer
      ignore_thresh: float, the iou threshold whether to ignore object confidence loss
  
      Returns
      -------
      loss: tensor, shape=(1,)
  
      '''
      num_layers = len(anchors)//3 # default setting
      yolo_outputs = args[:num_layers]
      y_true = args[num_layers:]
      anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
      input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0]))
      grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)]
      loss = 0
      m = K.shape(yolo_outputs[0])[0] # batch size, tensor
      mf = K.cast(m, K.dtype(yolo_outputs[0]))
  
      for l in range(num_layers):
          object_mask = y_true[l][..., 4:5]
          true_class_probs = y_true[l][..., 5:]
  
          grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l],
               anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True)
          pred_box = K.concatenate([pred_xy, pred_wh])
  
          # Darknet raw box to calculate loss.
          raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid
          raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1])
          raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf
          box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4]
  
          # Find ignore mask, iterate over each of batch.
          ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True)
          object_mask_bool = K.cast(object_mask, 'bool')
          def loop_body(b, ignore_mask):
              true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0])
              iou = box_iou(pred_box[b], true_box)
              best_iou = K.max(iou, axis=-1)
              ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))
              return b+1, ignore_mask
          _, ignore_mask = K.control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
          ignore_mask = ignore_mask.stack()
          ignore_mask = K.expand_dims(ignore_mask, -1)
  
          # K.binary_crossentropy is helpful to avoid exp overflow.
          xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[...,0:2], from_logits=True)
          wh_loss = object_mask * box_loss_scale * 0.5 * K.square(raw_true_wh-raw_pred[...,2:4])
          confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \
              (1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask
          class_loss = object_mask * K.binary_crossentropy(true_class_probs, raw_pred[...,5:], from_logits=True)
  
          xy_loss = K.sum(xy_loss) / mf
          wh_loss = K.sum(wh_loss) / mf
          confidence_loss = K.sum(confidence_loss) / mf
          class_loss = K.sum(class_loss) / mf
          loss += xy_loss + wh_loss + confidence_loss + class_loss
          if print_loss:
              loss = tf.Print(loss, [loss, xy_loss, wh_loss, confidence_loss, class_loss, K.sum(ignore_mask)], message='loss: ')
      return loss
  