text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  """
  Compile YOLO-V2 and YOLO-V3 in DarkNet Models
  =================================
  **Author**: `Siju Samuel <https://siju-samuel.github.io/>`_
  
  This article is an introductory tutorial to deploy darknet models with NNVM.
  All the required models and libraries will be downloaded from the internet by the script.
  This script runs the YOLO-V2 and YOLO-V3 Model with the bounding boxes
  Darknet parsing have dependancy with CFFI and CV2 library
  Please install CFFI and CV2 before executing this script
  
  .. code-block:: bash
  
    pip install cffi
    pip install opencv-python
  """
  
  import nnvm
  import nnvm.frontend.darknet
  import tvm.relay.testing.yolo_detection
  import tvm.relay.testing.darknet
  import matplotlib.pyplot as plt
  import numpy as np
  import tvm
  import sys
  
  from ctypes import *
  from tvm.contrib.download import download_testdata
  from tvm.relay.testing.darknet import __darknetffi__
  
  # Model name
  MODEL_NAME = 'yolov3'
  
  ######################################################################
  # Download required files
  # -----------------------
  # Download cfg and weights file if first time.
  CFG_NAME = MODEL_NAME + '.cfg'
  WEIGHTS_NAME = MODEL_NAME + '.weights'
  REPO_URL = 'https://github.com/siju-samuel/darknet/blob/master/'
  CFG_URL = REPO_URL + 'cfg/' + CFG_NAME + '?raw=true'
  WEIGHTS_URL = 'https://pjreddie.com/media/files/' + WEIGHTS_NAME
  
  cfg_path = download_testdata(CFG_URL, CFG_NAME, module="darknet")
  weights_path = download_testdata(WEIGHTS_URL, WEIGHTS_NAME, module="darknet")
  
  # Download and Load darknet library
  if sys.platform in ['linux', 'linux2']:
      DARKNET_LIB = 'libdarknet2.0.so'
      DARKNET_URL = REPO_URL + 'lib/' + DARKNET_LIB + '?raw=true'
  elif sys.platform == 'darwin':
      DARKNET_LIB = 'libdarknet_mac2.0.so'
      DARKNET_URL = REPO_URL + 'lib_osx/' + DARKNET_LIB + '?raw=true'
  else:
      err = "Darknet lib is not supported on {} platform".format(sys.platform)
      raise NotImplementedError(err)
  
  lib_path = download_testdata(DARKNET_URL, DARKNET_LIB, module="darknet")
  
  DARKNET_LIB = __darknetffi__.dlopen(lib_path)
  net = DARKNET_LIB.load_network(cfg_path.encode('utf-8'), weights_path.encode('utf-8'), 0)
  dtype = 'float32'
  batch_size = 1
  
  print("Converting darknet to nnvm symbols...")
  sym, params = nnvm.frontend.darknet.from_darknet(net, dtype)
  
  ######################################################################
  # Compile the model on NNVM
  # -------------------------
  # compile the model
  target = 'llvm'
  ctx = tvm.cpu(0)
  data = np.empty([batch_size, net.c, net.h, net.w], dtype)
  shape = {'data': data.shape}
  print("Compiling the model...")
  dtype_dict = {}
  with nnvm.compiler.build_config(opt_level=2):
      graph, lib, params = nnvm.compiler.build(sym, target, shape, dtype_dict, params)
  
  [neth, netw] = shape['data'][2:] # Current image shape is 608x608
  ######################################################################
  # Load a test image
  # --------------------------------------------------------------------
  test_image = 'dog.jpg'
  print("Loading the test image...")
  img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + \
            test_image + '?raw=true'
  img_path = download_testdata(img_url, test_image, "data")
  
  data = tvm.relay.testing.darknet.load_image(img_path, netw, neth)
  ######################################################################
  # Execute on TVM Runtime
  # ----------------------
  # The process is no different from other examples.
  from tvm.contrib import graph_runtime
  
  m = graph_runtime.create(graph, lib, ctx)
  
  # set inputs
  m.set_input('data', tvm.nd.array(data.astype(dtype)))
  m.set_input(**params)
  # execute
  print("Running the test image...")
  
  m.run()
  # get outputs
  tvm_out = []
  if MODEL_NAME == 'yolov2':
      layer_out = {}
      layer_out['type'] = 'Region'
      # Get the region layer attributes (n, out_c, out_h, out_w, classes, coords, background)
      layer_attr = m.get_output(2).asnumpy()
      layer_out['biases'] = m.get_output(1).asnumpy()
      out_shape = (layer_attr[0], layer_attr[1]//layer_attr[0],
                   layer_attr[2], layer_attr[3])
      layer_out['output'] = m.get_output(0).asnumpy().reshape(out_shape)
      layer_out['classes'] = layer_attr[4]
      layer_out['coords'] = layer_attr[5]
      layer_out['background'] = layer_attr[6]
      tvm_out.append(layer_out)
  
  elif MODEL_NAME == 'yolov3':
      for i in range(3):
          layer_out = {}
          layer_out['type'] = 'Yolo'
          # Get the yolo layer attributes (n, out_c, out_h, out_w, classes, total)
          layer_attr = m.get_output(i*4+3).asnumpy()
          layer_out['biases'] = m.get_output(i*4+2).asnumpy()
          layer_out['mask'] = m.get_output(i*4+1).asnumpy()
          out_shape = (layer_attr[0], layer_attr[1]//layer_attr[0],
                       layer_attr[2], layer_attr[3])
          layer_out['output'] = m.get_output(i*4).asnumpy().reshape(out_shape)
          layer_out['classes'] = layer_attr[4]
          tvm_out.append(layer_out)
  
  # do the detection and bring up the bounding boxes
  thresh = 0.5
  nms_thresh = 0.45
  img = tvm.relay.testing.darknet.load_image_color(img_path)
  _, im_h, im_w = img.shape
  dets = tvm.relay.testing.yolo_detection.fill_network_boxes((netw, neth), (im_w, im_h), thresh,
                                                        1, tvm_out)
  last_layer = net.layers[net.n - 1]
  tvm.relay.testing.yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
  
  coco_name = 'coco.names'
  coco_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + coco_name + '?raw=true'
  font_name = 'arial.ttf'
  font_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + font_name + '?raw=true'
  coco_path = download_testdata(coco_url, coco_name, module='data')
  font_path = download_testdata(font_url, font_name, module='data')
  
  with open(coco_path) as f:
      content = f.readlines()
  
  names = [x.strip() for x in content]
  
  tvm.relay.testing.yolo_detection.draw_detections(font_path, img, dets, thresh, names, last_layer.classes)
  plt.imshow(img.transpose(1, 2, 0))
  plt.show()
  