# This sample uses a UFF MNIST model to create a TensorRT Inference Engine
from random import randint
from PIL import Image
import numpy as np

import pycuda.driver as cuda
# This import causes pycuda to automatically manage CUDA context creation and cleanup.
import pycuda.autoinit

import tensorrt as trt
import time

import sys, os
sys.path.insert(1, os.path.join(sys.path[0], ".."))
import common

# You can set the logger severity higher to suppress messages (or lower to display more messages).
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

batch_size = 128

class ModelData(object):
    MODEL_FILE = os.path.join(os.path.dirname(__file__), "model2/frozen_model.uff")
    INPUT_NAME ="input_1"
    INPUT_SHAPE = (3, 256, 256)
    OUTPUT_NAME = 'predictions/Softmax'
    DTYPE = trt.float32

def build_engine(model_file):
    # For more information on TRT basics, refer to the introductory samples.
    with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:
        builder.max_batch_size = batch_size
        builder.max_workspace_size = common.GiB(1)
        # Parse the Uff Network
        parser.register_input(ModelData.INPUT_NAME, ModelData.INPUT_SHAPE)
        parser.register_output(ModelData.OUTPUT_NAME)
        parser.parse(model_file, network)
        # Build and return an engine.
        return builder.build_cuda_engine(network)

# Loads a test case into the provided pagelocked_buffer.
def load_normalized_test_case(data_path, pagelocked_buffer, case_num=randint(0, 9)):
#    test_case_path = os.path.join(data_path, str(case_num) + ".pgm")
    # Flatten the image into a 1D array, normalize, and copy to pagelocked memory.
    def normalize_image(image):
        # Resize, antialias and transpose the image to CHW.
        c, h, w = ModelData.INPUT_SHAPE
        return np.asarray(image.resize((w, h), Image.ANTIALIAS)).transpose([2, 0, 1]).astype(trt.nptype(ModelData.DTYPE))
    test_case_path = "lena.jpg"
    img = normalize_image(Image.open(test_case_path))
    img_array = []
    for i in range(batch_size):
        img_array.append(img)
    img_array = np.array(img_array, dtype=trt.nptype(ModelData.DTYPE))
    img_array = img_array.ravel()
    np.copyto(pagelocked_buffer, img_array)
    return case_num

def main():
#    data_path = common.find_sample_data(description="Runs an MNIST network using a UFF model file", subfolder="mnist")
    data_path = "/home/bjxiangboren/tools/TensorRT-5.0.2.6/data/mnist/"
    model_file = ModelData.MODEL_FILE

#    with open("inception_batch.engine", "rb") as f, trt.Runtime(TRT_LOGGER) as runtime: 
#        engine = runtime.deserialize_cuda_engine(f.read())
    with build_engine(model_file) as engine:
        # Build an engine, allocate buffers and create a stream.
        # For more information on buffer allocation, refer to the introductory samples.
        with open("inception_batch.engine", "wb") as f:
            f.write(engine.serialize())
        inputs, outputs, bindings, stream = common.allocate_buffers(engine)
        with engine.create_execution_context() as context:
            case_num = load_normalized_test_case(data_path, pagelocked_buffer=inputs[0].host)
            # For more information on performing inference, refer to the introductory samples.
            # The common.do_inference function will return a list of outputs - we only have one in this case.
            while True:
                start_time = time.time()
                [output] = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, batch_size=batch_size)
                end_time = time.time()
                print("time dis is %s" % (end_time - start_time))
#            output = output.reshape((30,1001))
#            print output
#            print output.shape
#            print np.argmax(output, axis=1)
#                pred = np.argmax(output)
#                print("Test Case: " + str(case_num))
#                print("Prediction: " + str(pred))

if __name__ == '__main__':
    main()

1、首先將pb轉(zhuǎn)為uff格式的模型

python /usr/lib/python3.5/dist-packages/uff/bin/convert_to_uff.py --input_file models/lenet5.pb

2、使用trt engine加速

這個加速還是挺明顯的，但轉(zhuǎn)換后的模型無法使用tfservign了，只能用tensorrt自己的engine。

參考：https://devtalk.nvidia.com/default/topic/1044466/tensorrt/uff-inference-time-large-than-pb-time-when-process-vgg-19/

https://blog.csdn.net/zong596568821xp/article/details/86077553

https://blog.csdn.net/g11d111/article/details/92061884

https://mp.weixin.qq.com/s/Ps49ZTfJprcOYrc6xo-gLg?

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