Samples

# Load an already-trained .pb model to perform inference.
import tensorflow as tf
import os
import argparse
from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig
import npu_bridge
import time
import numpy as np

def parse_args():
    '''
    Set the model path, input, and output.
    '''
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--batchsize', default=1,
                        help="""batchsize""")
    parser.add_argument('--model_path', default='pb/resnet50HC.pb',
                        help="""pb path""")
    parser.add_argument('--image_path', default='image-50000',
                        help="""the data path""")
    parser.add_argument('--label_file', default='val_label.txt',
                        help="""label file""")
    parser.add_argument('--input_tensor_name', default='input_data:0',
                        help="""input_tensor_name""")
    parser.add_argument('--output_tensor_name', default='resnet_model/final_dense:0',
                        help="""output_tensor_name""")
    args, unknown_args = parser.parse_known_args()
    if len(unknown_args) > 0:
        for bad_arg in unknown_args:
            print("ERROR: Unknown command line arg: %s" % bad_arg)
        raise ValueError("Invalid command line arg(s)")
    return args

def read_file(image_name, path):
    '''
    Read image information from the tag file.
    '''
    with open(path, 'r') as cs:
        rs_list = cs.readlines()
        for name in rs_list:
            if image_name in str(name):
                num = str(name).split(" ")[1]
                break
    return int(num) + 1

def normalize(inputs):
    '''
   Normalize input images.
    '''
    mean = [121.0, 115.0, 100.0]
    std =  [70.0, 68.0, 71.0]
    mean = tf.expand_dims(tf.expand_dims(mean, 0), 0)
    std = tf.expand_dims(tf.expand_dims(std, 0), 0)
    inputs = inputs - mean
    inputs = inputs * (1.0 / std)
    return inputs

def image_process(image_path, label_file):
    '''
    Preprocess input images.
    '''
    imagelist = []
    labellist = []
    images_count = 0
    for file in os.listdir(image_path):
        with tf.Session().as_default():
            image_file = os.path.join(image_path, file)
            image_name = image_file.split('/')[-1].split('.')[0]
            #images preprocessing
            image= tf.gfile.FastGFile(image_file, 'rb').read()
            img = tf.image.decode_jpeg(image, channels=3)
            bbox = tf.constant([0.1,0.1,0.9,0.9])
            img = tf.image.crop_and_resize(img[None, :, :, :], bbox[None, :], [0], [224, 224])[0]
            img = tf.clip_by_value(img, 0., 255.)
            img = normalize(img)
            img = tf.cast(img, tf.float16)
            images_count = images_count + 1
            img = img.eval()
            imagelist.append(img)
            tf.reset_default_graph()

            # read image label from label_file
            label = read_file(image_name, label_file)
            labellist.append(label)

    return np.array(imagelist), np.array(labellist),images_count


class Classifier(object):
    #set batchsize:
    args = parse_args()
    batch_size = int(args.batchsize)

    def __init__(self):

        # Configurations of model compilation and tuning on the Ascend AI Processor
        config = tf.ConfigProto()
        custom_op = config.graph_options.rewrite_options.custom_optimizers.add()
        custom_op.name = "NpuOptimizer"
        # Configuration 1: Schedule the inference job to the Ascend AI Processor.
        custom_op.parameter_map["use_off_line"].b = True
       # Configuration 2: In the online inference scenario, you are advised to retain the default precision selection force_fp16 to achieve better performance.
        custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("force_fp16")
        # Configuration 3: Select the graph run mode. Set this parameter to 0 in the inference scenario or retain the default value 1 in the training scenario.
        custom_op.parameter_map["graph_run_mode"].i = 0
        # Configuration 4: Disable remapping and MemoryOptimizer.
        config.graph_options.rewrite_options.remapping = RewriterConfig.OFF
        config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF
        # Load the model and set the input and output nodes of the model.
        args = parse_args()
        self.graph = self.__load_model(args.model_path)
        self.input_tensor = self.graph.get_tensor_by_name(args.input_tensor_name)
        self.output_tensor = self.graph.get_tensor_by_name(args.output_tensor_name)

        # Model compilation is triggered when the sess.run() method is called for the first time, which takes a long time. You can tie the session to the object's lifetime.
        self.sess = tf.Session(config=config, graph=self.graph)

    def __load_model(self, model_file):
        """
        Load a static graph.
        """
        with tf.gfile.GFile(model_file, "rb") as gf:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(gf.read())

        with tf.Graph().as_default() as graph:
            tf.import_graph_def(graph_def, name="")

        return graph

    def do_infer(self, batch_data):
        """
        Start inference.
        """
        out_list = []
        total_time = 0
        i = 0
        for data in batch_data:
            t = time.time()
            out = self.sess.run(self.output_tensor, feed_dict={self.input_tensor: data})
            if i > 0:
                total_time = total_time + time.time() - t
            i = i + 1
            out_list.append(out)
        return np.array(out_list), total_time

    def batch_process(self, image_data, label_data):
        """
        Batch processing
        """
        # Obtain the batch information of the current input data and automatically adjust the data to a fixed batch.
        n_dim = image_data.shape[0]
        batch_size = self.batch_size

        # If the data is insufficient for the entire batch, pad the data.
        m = n_dim % batch_size
        if m < batch_size and m > 0:
            # The part without data is padded with 0s.
            pad = np.zeros((batch_size - m, 224, 224, 3)).astype(np.float32)
            image_data = np.concatenate((image_data, pad), axis=0)

        # Define the minimum batch that can be divided.
        mini_batch = []
        mini_label = []
        i = 0
        while i < n_dim:
            # Define the Minis that can be divided into several batches
            mini_batch.append(image_data[i: i + batch_size, :, :, :])
            mini_label.append(label_data[i: i + batch_size])
            i += batch_size

        return mini_batch, mini_label

def main():
    args = parse_args()
    top1_count = 0
    top5_count = 0
    # Data preprocessing
    tf.reset_default_graph()
    print("########NOW Start Preprocess!!!#########")
    images, labels, images_count = image_process(args.image_path, args.label_file)
    # Batch processing
    print("########NOW Start Batch!!!#########")
    classifier = Classifier()
    batch_images, batch_labels= classifier.batch_process(images, labels)
    # Start inference.
    print("########NOW Start inference!!!#########")
    batch_logits, total_time = classifier.do_infer(batch_images)
    # Compute the accuracy.
    batchsize = int(args.batchsize)
    total_step = int(images_count / batchsize)
    print("########NOW Start Compute Accuracy!!!#########")
    for i in range(total_step):
        top1acc = tf.reduce_sum(tf.cast(tf.equal(tf.argmax(batch_logits[i], 1), batch_labels[i]), tf.float32))
        top5acc = tf.reduce_sum(tf.cast(tf.nn.in_top_k(batch_logits[i], batch_labels[i], 5), tf.float32))
        with tf.Session().as_default():
            tf.reset_default_graph()
            top1_count += top1acc.eval()
            top5_count += top5acc.eval()

    print('+----------------------------------------+')
    print('the correct num is {}, total num is {}.'.format(top1_count, total_step * batchsize))
    print('Top1 accuracy:', top1_count / (total_step * batchsize) * 100)
    print('Top5 accuracy:', top5_count / (total_step * batchsize) * 100)
    print('images number = ', total_step * batchsize)
    print('images/sec = ', (total_step * batchsize) / total_time)
    print('+----------------------------------------+')

if __name__ == '__main__':
    main()