• TVM darknet yolov3算子优化与量化代码的配置方法


     TVM darknet yolov3算子优化与量化代码的配置方法

    使用以下接口函数

    l  tvm.relay.optimize

    l  quantize.quantize

    实际代码:

     

    # convert nnvm to relay

    print("convert nnvm symbols into relay function...")

    #from nnvm.to_relay import to_relay

    func, params = to_relay(sym, shape, 'float32', params=params)

    # optimization

    print("optimize relay graph...")

    with tvm.relay.build_config(opt_level=2):

        func = tvm.relay.optimize(func, target, params)

    # quantize

    print("apply quantization...")

    from tvm.relay import quantize

    with quantize.qconfig():

       func = quantize.quantize(func, params)

     

    参考链接:

    https://github.com/makihiro/tvm_yolov3_sample/blob/master/yolov3_quantize_sample.py

     

    完全代码如下

    早期版本,可以使用新的TVM版本修改。

     

     

    # 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 TVM.

    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

    """

     

    # numpy and matplotlib

    import numpy as np

    import matplotlib.pyplot as plt

    import sys

     

    # tvm, relay

    import tvm

    from tvm import te

    from tvm import relay

    from ctypes import *

    from tvm.contrib.download import download_testdata

    from tvm.relay.testing.darknet import __darknetffi__

    import tvm.relay.testing.yolo_detection

    import tvm.relay.testing.darknet

     

    ######################################################################

    # Choose the model

    # -----------------------

    # Models are: 'yolov2', 'yolov3' or 'yolov3-tiny'

     

    # 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/dmlc/web-data/blob/main/darknet/"

    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

     

    data = np.empty([batch_size, net.c, net.h, net.w], dtype)

    shape_dict = {"data": data.shape}

    print("Converting darknet to relay functions...")

    mod, params = relay.frontend.from_darknet(net, dtype=dtype, shape=data.shape)

    ######################################################################

    # Compile the model on NNVM

    # -------------------------

    # compile the model

    local = True

     

    if local:

        target = 'llvm'

        ctx = tvm.cpu(0)

    else:

        target = 'cuda'

        ctx = tvm.gpu(0)

     

    data = np.empty([batch_size, net.c, net.h, net.w], dtype)

    shape = {'data': data.shape}

     

    dtype_dict = {}

     

    # convert nnvm to relay

    print("convert nnvm symbols into relay function...")

    #from nnvm.to_relay import to_relay

    func, params = to_relay(sym, shape, 'float32', params=params)

    # optimization

    print("optimize relay graph...")

    with tvm.relay.build_config(opt_level=2):

        func = tvm.relay.optimize(func, target, params)

    # quantize

    print("apply quantization...")

    from tvm.relay import quantize

    with quantize.qconfig():

       func = quantize.quantize(func, params)

     

    # Relay build

    print("Compiling the model...")

    print(func.astext(show_meta_data=False))

    with tvm.relay.build_config(opt_level=3):

        graph, lib, params = tvm.relay.build(func, target=target, params=params)

     

    # Save the model

    tmp = util.tempdir()

    lib_fname = tmp.relpath('model.tar')

    lib.export_library(lib_fname)

     

    # NNVM

    # 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

    ######################################################################

     

    ######################################################################

    # Import the graph to Relay

    # -------------------------

    # compile the model

    target = tvm.target.Target("llvm", host="llvm")

    dev = tvm.cpu(0)

    data = np.empty([batch_size, net.c, net.h, net.w], dtype)

    shape = {"data": data.shape}

    print("Compiling the model...")

    with tvm.transform.PassContext(opt_level=3):

        lib = relay.build(mod, target=target, params=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 = REPO_URL + "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_executor

     

    m = graph_executor.GraphModule(lib["default"](dev))

     

    # set inputs

    m.set_input("data", tvm.nd.array(data.astype(dtype)))

    # execute

    print("Running the test image...")

     

    # detection

    # thresholds

    thresh = 0.5

    nms_thresh = 0.45

     

    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).numpy()

        layer_out["biases"] = m.get_output(1).numpy()

        out_shape = (layer_attr[0], layer_attr[1] // layer_attr[0], layer_attr[2], layer_attr[3])

        layer_out["output"] = m.get_output(0).numpy().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).numpy()

            layer_out["biases"] = m.get_output(i * 4 + 2).numpy()

            layer_out["mask"] = m.get_output(i * 4 + 1).numpy()

            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).numpy().reshape(out_shape)

            layer_out["classes"] = layer_attr[4]

            tvm_out.append(layer_out)

     

    elif MODEL_NAME == "yolov3-tiny":

        for i in range(2):

            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).numpy()

            layer_out["biases"] = m.get_output(i * 4 + 2).numpy()

            layer_out["mask"] = m.get_output(i * 4 + 1).numpy()

            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).numpy().reshape(out_shape)

            layer_out["classes"] = layer_attr[4]

            tvm_out.append(layer_out)

            thresh = 0.560

     

    # do the detection and bring up the bounding boxes

    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 = REPO_URL + "data/" + coco_name + "?raw=true"

    font_name = "arial.ttf"

    font_url = REPO_URL + "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.show_detections(img, dets, thresh, names, last_layer.classes)

    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()

     

    参考链接:

    https://github.com/makihiro/tvm_yolov3_sample/blob/master/yolov3_quantize_sample.py

    https://tvm.apache.org/docs/tutorials/frontend/from_darknet.html#sphx-glr-tutorials-frontend-from-darknet-py

     

    人工智能芯片与自动驾驶
  • 相关阅读:
    从Mono 4.0观C# 6.0部分新特性
    (译文)Python中的staticmethod与classmethod
    ubuntu中mysql中文乱码及用python3.x调用
    tornado学习 TCPClient 实现聊天功能
    tornado学习 TCPServer 实现聊天功能
    java基础知识3
    java基础知识5
    JSTL常用标签6
    Java基础知识2
    java基础知识4
  • 原文地址:https://www.cnblogs.com/wujianming-110117/p/15004748.html
Copyright © 2020-2023  润新知