text: """
  MIT License
  
  Copyright (c) 2020 Hyeonki Hong <hhk7734@gmail.com>
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  """
  from os import makedirs, path
  import shutil
  
  import cv2
  import numpy as np
  import tensorflow as tf
  from tensorflow.keras import backend, layers, optimizers
  
  from . import dataset, train, weights
  from .train import SaveWeightsCallback
  from ..common.base_class import BaseClass
  from ..model import yolov4
  
  physical_devices = tf.config.experimental.list_physical_devices("GPU")
  if len(physical_devices) > 0:
      tf.config.experimental.set_memory_growth(physical_devices[0], True)
      print("Call tf.config.experimental.set_memory_growth(GPU0, True)")
  
  
  class YOLOv4(BaseClass):
      def __init__(self, tiny: bool = False, tpu: bool = False):
          """
          Default configuration
          """
          super(YOLOv4, self).__init__(tiny=tiny, tpu=tpu)
  
          self.batch_size = 32
          self._has_weights = False
          self.input_size = 608
          self.model = None
  
      def make_model(
          self,
          activation0: str = "mish",
          activation1: str = "leaky",
          kernel_regularizer=tf.keras.regularizers.l2(0.0005),
      ):
          # pylint: disable=missing-function-docstring
          self._has_weights = False
          backend.clear_session()
  
          # height, width, channels
          inputs = layers.Input([self.input_size[1], self.input_size[0], 3])
          if self.tiny:
              self.model = yolov4.YOLOv4Tiny(
                  anchors=self.anchors,
                  num_classes=len(self.classes),
                  xyscales=self.xyscales,
                  activation=activation1,
                  kernel_regularizer=kernel_regularizer,
              )
          else:
              self.model = yolov4.YOLOv4(
                  anchors=self.anchors,
                  num_classes=len(self.classes),
                  xyscales=self.xyscales,
                  activation0=activation0,
                  activation1=activation1,
                  kernel_regularizer=kernel_regularizer,
              )
          self.model(inputs)
  
      def load_weights(self, weights_path: str, weights_type: str = "tf"):
          """
          Usage:
              yolo.load_weights("yolov4.weights", weights_type="yolo")
              yolo.load_weights("checkpoints")
          """
          if weights_type == "yolo":
              weights.load_weights(self.model, weights_path, tiny=self.tiny)
          elif weights_type == "tf":
              self.model.load_weights(weights_path)
  
          self._has_weights = True
  
      def save_weights(self, weights_path: str, weights_type: str = "tf"):
          """
          Usage:
              yolo.save_weights("yolov4.weights", weights_type="yolo")
              yolo.save_weights("checkpoints")
          """
          if weights_type == "yolo":
              weights.save_weights(self.model, weights_path, tiny=self.tiny)
          elif weights_type == "tf":
              self.model.save_weights(weights_path)
  
      def save_as_tflite(
          self,
          tflite_path,
          quantization=None,
          data_set=None,
          num_calibration_steps: int = 100,
      ):
          """
          Save model and weights as tflite
  
          Usage:
              yolo.save_as_tflite("yolov4.tflite")
              yolo.save_as_tflite("yolov4-float16.tflite", "float16")
              yolo.save_as_tflite("yolov4-int.tflite", "int", data_set)
          """
          converter = tf.lite.TFLiteConverter.from_keras_model(self.model)
  
          _supported_ops = [
              tf.lite.OpsSet.TFLITE_BUILTINS,
              tf.lite.OpsSet.SELECT_TF_OPS,
          ]
  
          def representative_dataset_gen():
              count = 0
              while True:
                  images, _ = next(data_set)
                  for i in range(len(images)):
                      yield [tf.cast(images[i : i + 1, ...], tf.float32)]
                      count += 1
                      if count >= num_calibration_steps:
                          return
  
          if quantization:
              converter.optimizations = [tf.lite.Optimize.DEFAULT]
  
          if quantization == "float16":
              converter.target_spec.supported_types = [tf.float16]
          elif quantization == "int":
              converter.representative_dataset = representative_dataset_gen
          elif quantization == "full_int8":
              converter.experimental_new_converter = False
              converter.representative_dataset = representative_dataset_gen
              _supported_ops += [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
              converter.inference_input_type = tf.uint8
              converter.inference_output_type = tf.uint8
          elif quantization:
              raise ValueError(
                  "YOLOv4: {} is not a valid option".format(quantization)
              )
  
          converter.target_spec.supported_ops = _supported_ops
  
          tflite_model = converter.convert()
          with tf.io.gfile.GFile(tflite_path, "wb") as fd:
              fd.write(tflite_model)
  
      #############
      # Inference #
      #############
  
      @tf.function
      def _predict(self, x):
          # s_pred, m_pred, l_pred
          # x_pred == Dim(1, output_size, output_size, anchors, (bbox))
          candidates = self.model(x, training=False)
          _candidates = []
          for candidate in candidates:
              grid_size = candidate.shape[1:3]
              _candidates.append(
                  tf.reshape(
                      candidate[0], shape=(1, grid_size[0] * grid_size[1] * 3, -1)
                  )
              )
          return tf.concat(_candidates, axis=1)
  
      def predict(
          self,
          frame: np.ndarray,
          iou_threshold: float = 0.3,
          score_threshold: float = 0.25,
      ):
          """
          Predict one frame
  
          @param frame: Dim(height, width, channels)
  
          @return pred_bboxes == Dim(-1, (x, y, w, h, class_id, probability))
          """
          # image_data == Dim(1, input_size[1], input_size[0], channels)
          image_data = self.resize_image(frame)
          image_data = image_data / 255.0
          image_data = image_data[np.newaxis, ...].astype(np.float32)
  
          candidates = self._predict(image_data)
  
          # Select 0
          pred_bboxes = self.candidates_to_pred_bboxes(
              candidates[0].numpy(),
              iou_threshold=iou_threshold,
              score_threshold=score_threshold,
          )
          pred_bboxes = self.fit_pred_bboxes_to_original(pred_bboxes, frame.shape)
          return pred_bboxes
  
      ############
      # Training #
      ############
  
      def load_dataset(
          self,
          dataset_path,
          dataset_type="converted_coco",
          label_smoothing=0.1,
          image_path_prefix=None,
          training=True,
      ):
          return dataset.Dataset(
              anchors=self.anchors,
              batch_size=self.batch_size,
              dataset_path=dataset_path,
              dataset_type=dataset_type,
              data_augmentation=training,
              input_size=self.input_size,
              label_smoothing=label_smoothing,
              num_classes=len(self.classes),
              image_path_prefix=image_path_prefix,
              strides=self.strides,
              xyscales=self.xyscales,
          )
  
      def compile(
          self,
          loss_iou_type: str = "ciou",
          loss_verbose=1,
          optimizer=optimizers.Adam(learning_rate=1e-4),
          **kwargs
      ):
          # TODO: steps_per_execution tensorflow2.4.0-rc4
          self.model.compile(
              optimizer=optimizer,
              loss=train.YOLOv4Loss(
                  batch_size=self.batch_size,
                  iou_type=loss_iou_type,
                  verbose=loss_verbose,
              ),
              **kwargs
          )
  
      def fit(
          self,
          data_set,
          epochs,
          verbose=2,
          callbacks=None,
          validation_data=None,
          initial_epoch=0,
          steps_per_epoch=None,
          validation_steps=None,
          validation_freq=1,
          **kwargs
      ):
          self.model.fit(
              data_set,
              batch_size=self.batch_size,
              epochs=epochs,
              verbose=verbose,
              callbacks=callbacks,
              validation_data=validation_data,
              initial_epoch=initial_epoch,
              steps_per_epoch=steps_per_epoch,
              validation_steps=validation_steps,
              validation_freq=validation_freq,
              **kwargs
          )
  
      def save_dataset_for_mAP(
          self, mAP_path, data_set, num_sample=None, images_optional=False,
           iou_threshold=0.3, score_threshold=0.2
      ):
          """
          gt: name left top right bottom
          dr: name confidence left top right bottom
  
          @param `mAP_path`
          @parma `data_set`
          @param `num_sample`: Number of images for mAP. If `None`, all images in
                  `data_set` are used.
          @parma `images_optional`: If `True`, images are copied to the
                  `mAP_path`.
          """
          input_path = path.join(mAP_path, "input")
  
          if path.exists(input_path):
              shutil.rmtree(input_path)
          makedirs(input_path)
  
          gt_dir_path = path.join(input_path, "ground-truth")
          dr_dir_path = path.join(input_path, "detection-results")
          makedirs(gt_dir_path)
          makedirs(dr_dir_path)
  
          if images_optional:
              img_dir_path = path.join(input_path, "images-optional")
              makedirs(img_dir_path)
  
          max_dataset_size = len(data_set)
  
          if num_sample is None:
              num_sample = max_dataset_size
  
          for i in range(num_sample):
              # image_path, [[x, y, w, h, class_id], ...]
              _dataset = data_set.dataset[i % max_dataset_size].copy()
  
              if images_optional:
                  image_path = path.join(img_dir_path, "image_{}.jpg".format(i))
                  shutil.copy(_dataset[0], image_path)
  
              image = cv2.imread(_dataset[0])
              image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
              height, width, _ = image.shape
  
              _dataset[1] = _dataset[1] * np.array(
                  [width, height, width, height, 1]
              )
  
              # ground-truth
              with open(
                  path.join(gt_dir_path, "image_{}.txt".format(i)),
                  "w",
              ) as fd:
                  for xywhc in _dataset[1]:
                      # name left top right bottom
                      class_name = self.classes[int(xywhc[4])]
                      left = int(xywhc[0] - xywhc[2] / 2)
                      top = int(xywhc[1] - xywhc[3] / 2)
                      right = int(xywhc[0] + xywhc[2] / 2)
                      bottom = int(xywhc[1] + xywhc[3] / 2)
                      fd.write(
                          "{} {} {} {} {}\n".format(
                              class_name, left, top, right, bottom
                          )
                      )
  
              pred_bboxes = self.predict(image, iou_threshold=iou_threshold, score_threshold=score_threshold)
              pred_bboxes = pred_bboxes * np.array(
                  [width, height, width, height, 1, 1]
              )
  
              # detection-results
              with open(
                  path.join(dr_dir_path, "image_{}.txt".format(i)),
                  "w",
              ) as fd:
                  for xywhcp in pred_bboxes:
                      # name confidence left top right bottom
                      class_name = self.classes[int(xywhcp[4])]
                      probability = xywhcp[5]
                      left = int(xywhcp[0] - xywhcp[2] / 2)
                      top = int(xywhcp[1] - xywhcp[3] / 2)
                      right = int(xywhcp[0] + xywhcp[2] / 2)
                      bottom = int(xywhcp[1] + xywhcp[3] / 2)
                      fd.write(
                          "{} {} {} {} {} {}\n".format(
                              class_name, probability, left, top, right, bottom
                          )
                      )
  