# 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()