add pytengine examples with timvx (#1300)

* std::max_element in range [first, last)

* add pytengine examples with timvx

examples/classification_timvx.py

+from tengine import tg
+import numpy as np
+import cv2
+import argparse
+import os
+import time
+import numpy as np
+import time
+
+DEFAULT_LABEL_FILE = "./synset_words.txt"
+DEFAULT_IMG_H = 224
+DEFAULT_IMG_W = 224
+DEFAULT_SCALE = 0.017
+DEFAULT_MEAN1 = 104.007
+DEFAULT_MEAN2 = 116.669
+DEFAULT_MEAN3 = 122.679
+
+parser = argparse.ArgumentParser(description='classification')
+parser.add_argument('-m', '--model', default='./mobilenet_uint8.tmfile', type=str)
+parser.add_argument('-i', '--image', default='./cat.jpg', type=str)
+parser.add_argument('-g', '--image-size', default=f'{DEFAULT_IMG_H},{DEFAULT_IMG_W}', type=str, help='image size: height, width')
+parser.add_argument('-w', '--mean-value', default=f'{DEFAULT_MEAN1},{DEFAULT_MEAN2},{DEFAULT_MEAN3}', type=str, help='mean value: mean1, mean2, mean3')
+parser.add_argument('-s', '--scale', default=f'{DEFAULT_SCALE}', type=str)
+parser.add_argument('-l', '--label', default=f'{DEFAULT_LABEL_FILE}', type=str, help='the default path of labels.txt (e.g. synset_words.txt)')
+
+def get_current_time():
+    return time.time() * 1000
+
+def read_labels_file(fname):
+    outs = []
+    with open(fname, 'r') as fin:
+        for line in fin.readlines():
+            outs.append(line.strip())
+    return outs
+
+def main(args):
+    image_file = args.image
+    tm_file = args.model
+    assert os.path.exists(args.label), f'Label File: {args.label} not found'
+    assert os.path.exists(image_file), f'Image: {image_file} not found'
+    assert os.path.exists(tm_file), f'Model: {tm_file} not found'
+    labels = read_labels_file(args.label)
+
+    img_h, img_w = map(int, args.image_size.split(','))
+    context = tg.Context("timvx", 1)
+    context.addDev("TIMVX")
+    graph = tg.Graph(context, 'tengine', tm_file)
+    input_tensor = graph.getInputTensor(0, 0)
+    dims = [1, 3, img_h, img_w]
+    input_tensor.shape = dims
+    graph.preRun()
+
+    input_scale, input_zero_point = input_tensor.getQuantParam(1)
+    scale = float(args.scale)
+    mean_value = list(map(float, args.mean_value.split(',')))
+    assert len(mean_value) == 3, 'The number of mean_value should be 3, e.g. 104.007,116.669,122.679'
+    img_mean = np.array(mean_value).reshape((1, 1, 3))
+    data = cv2.imread(image_file)
+    data = cv2.resize(data, (img_w, img_h))
+    data = (((data - img_mean) * scale)/ input_scale + input_zero_point).astype(np.uint8)
+    data = np.ascontiguousarray(data.transpose((2, 0, 1)))
+    assert data.dtype == np.uint8
+    data = data.copy()
+
+    input_tensor.buf = data
+    begin = time.time()
+    graph.run(1) # 1 is blocking
+    print("time:",(time.time() - begin)*1000, "ms")
+    output_tensor = graph.getOutputTensor(0, 0)
+    output_scale, output_zero_point = output_tensor.getQuantParam(1)
+    output = np.array(output_tensor.buf)
+    output = (output - output_zero_point) * output_scale
+
+    k = 5
+    idx = output.argsort()[-1:-k-1:-1]
+    for i in idx:
+        print(labels[i], output[i])
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)