【TVM帮助文档学习】使用Python接口编译和优化模型(AutoTVM)
本文翻译自：Compiling and Optimizing a Model with the Python Interface (AutoTVM) — tvm 0.9.dev0 documentation
在Compiling and Optimizing a Model with TVMC — tvm 0.9.dev0 documentation中，我们介绍了如何使用TVM的命令行界面来编译、运行和调优预训练的视觉模型ResNet-50 v2。TVM不仅仅是一个命令行工具，它还是一个优化框架，带有许多不同语言的api，在使用机器学习模型时为您提供了极大的灵活性。
在本教程中，我们将讨论与TVMC相同的内容，但将展示如何使用Python API完成。本节完成后，我们将使用TVM的Python API完成以下任务:
- 为TVM运行时编译一个预训练的ResNet-50 v2模型。
- 使用编译后的模型中运行一张真实的图片，并解释输出和模型性能。
- 使用TVM调优CPU上的模型。
- 使用TVM收集的调优数据重新编译优化模型。
- 使用优化后的模型推理图片，并比较输出和模型性能。
本节的目标是向您概述TVM的功能，以及如何通过Python API使用它们。
TVM是一个深度学习编译器框架，具有许多不同的模块，可用于处理深度学习模型和算子。在本教程中，我们将学习如何使用Python API加载、编译和优化模型。
我们先导入依赖包,包括onnx加载和转换模型,下载测试数据的辅助工具,处理图像数据的Python Image Library,图像数据的预处理和后处理的numpy,TVM Relay框架, TVM图执行器等。
import onnx
from tvm.contrib.download import download_testdata
from PIL import Image
import numpy as np
import tvm.relay as relay
import tvm
from tvm.contrib import graph_executor下载和加载ONNX模型
在本教程中，我们将使用ResNet-50 v2。ResNet-50是一个深度为50层的卷积神经网络，旨在对图像进行分类。我们将使用的模型已经在1000种不同分类的100多万张图像上进行了预先训练。该网络的输入图像尺寸为224x224。如果您对ResNet-50模型的结构感兴趣，我们建议下载Netron，这是一个免费的ML模型查看器。
TVM提供了一个辅助库来下载预先训练的模型。通过模块提供模型URL、文件名和模型类型，TVM将下载模型并保存到磁盘。对于某个ONNX模型的实例，你可以使用ONNX运行时将其加载到内存中。
model_url = (
"https://github.com/onnx/models/raw/main/"
"vision/classification/resnet/model/"
"resnet50-v2-7.onnx"
)
model_path = download_testdata(model_url, "resnet50-v2-7.onnx", module="onnx")
onnx_model = onnx.load(model_path)TVM支持许多流行的模型格式。可以在TVM文档的编译深度学习模型（Compile Deep Learning Models — tvm 0.9.dev0 documentation）部分找到一个列表。
下载、预处理和加载测试图片
每个模型都有特定的张量形状、格式和数据类型。所以大多数模型需要一些预处理和后处理，以确保输入正确，并解释输出。TVMC的输入和输出数据都采用了NumPy的.npz格式。这是一种支持良好的NumPy格式，可以将多个数组序列化存入到一个文件中。
作为本教程的输入，我们将使用一只猫的图像，您也可以替换为其他任何图像。
下载图片，并将它转换为numpy数组作为模型的输入：
img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
img_path = download_testdata(img_url, "imagenet_cat.png", module="data")
# Resize it to 224x224
resized_image = Image.open(img_path).resize((224, 224))
img_data = np.asarray(resized_image).astype("float32")
# Our input image is in HWC layout while ONNX expects CHW input, so convert the array
img_data = np.transpose(img_data, (2, 0, 1))
# Normalize according to the ImageNet input specification
imagenet_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
imagenet_stddev = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
norm_img_data = (img_data / 255 - imagenet_mean) / imagenet_stddev
# Add the batch dimension, as we are expecting 4-dimensional input: NCHW.
img_data = np.expand_dims(norm_img_data, axis=0)使用Relay编译模型
接下来是编译ResNet模型。我们首先使用from_onnx接口将模型导入到Relay。然后我们使用标准优化将模型构建为一个TVM库。最后，我们使用库创建一个TVM图形运行时模块。
target = "llvm"正确定义target：指定正确的目标可能会对编译模块的性能产生巨大影响，因为它可以利用目标上可用的硬件特性。有关更多信息，请参阅x86 CPU的卷积网络自动调优（Auto-tuning a Convolutional Network for x86 CPU — tvm 0.9.dev0 documentation）。我们建议确定您运行的是哪个CPU，以及可选的特性，并适当地设置目标。例如，对于某些具有AVX-512向量指令集的处理器，target = "llvm -mcpu=skylake"，或者target = "llvm -mcpu=skylake-avx512"。
# The input name may vary across model types. You can use a tool
# like Netron to check input names
input_name = "data"
shape_dict = {input_name: img_data.shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))在TVM运行时上执行
现在我们已经编译好了模型，我们可以使用TVM运行时对其进行预测。为了使用TVM运行模型并进行预测，我们需要两个条件:
- 我们刚才编译好的模型。
- 对模型进行预测的有效输入。
dtype = "float32"
module.set_input(input_name, img_data)
module.run()
output_shape = (1, 1000)
tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()收集基本性能数据
我们在这里收集一些基本的性能数据，用来与稍后调优后的模型进行比较。为了帮助解释CPU噪声，我们以多batch、多次重复运行计算，然后收集关于平均值、中值和标准偏差的一些基本统计信息。
import timeit
timing_number = 10
timing_repeat = 10
unoptimized = (
np.array(timeit.Timer(lambda: module.run()).repeat(repeat=timing_repeat, number=timing_number))
* 1000
/ timing_number
)
unoptimized = {
"mean": np.mean(unoptimized),
"median": np.median(unoptimized),
"std": np.std(unoptimized),
}
print(unoptimized)输出：
{'mean': 496.2511969099978, 'median': 495.80396929999324, 'std': 0.7997811122746795}输出后处理
正如前面提到的，每个模型都有自己特定的输出张量
在我们的示例中，我们需要对esNet-50 v2的输出做一些后处理，使用为模型提供的查找表，使其呈现为更便于人类阅读的形式。
from scipy.special import softmax
# Download a list of labels
labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
labels_path = download_testdata(labels_url, "synset.txt", module="data")
with open(labels_path, "r") as f:
labels = [l.rstrip() for l in f]
# Open the output and read the output tensor
scores = softmax(tvm_output)
scores = np.squeeze(scores)
ranks = np.argsort(scores)[::-1]
for rank in ranks[0:5]:
print("class='%s' with probability=%f" % (labels[rank], scores[rank]))输出：
class='n02123045 tabby, tabby cat' with probability=0.621103
class='n02123159 tiger cat' with probability=0.356379
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262调优
前吻是将模型编译为在TVM运行时上运行，但是不包括任何平台相关的优化。在本节中，我们将向您展示如何使用TVM构建一个针对您的工作平台的优化模型。
在某些情况下，当使用编译后的模块运行推断时，可能无法获得预期的性能。在这种情况下，我们可以使用自动调优器，为我们的模型找到更好的配置，从而提高性能。TVM中的调优是指对模型进行优化，使其在给定目标上运行得更快的过程。这与训练或微调不同，因为它不会影响模型的准确性，而只会影响运行时性能。作为调优过程的一部分，TVM将尝试运行算子的许多不同的实现变体，以查看哪一种性能最好。这些运行的结果存储在一个调优记录文件中。
以最简单的形式来说，调优需要提供以下三件事:
- 您打算在其上运行此模型的设备的目标规格
- 输出文件的路径，调优记录将存储在该文件中
- 要调优的模型的路径。
import tvm.auto_scheduler as auto_scheduler
from tvm.autotvm.tuner import XGBTuner
from tvm import autotvm为运行器设置一些基本参数。运行器执行由一组特定参数编译生成的代码，并测量它的性能。number指定我们将测试的不同配置的数量，而repeat指定我们将对每个配置进行多少次测量。min_repeat_ms是一个值，用于指定运行配置测试所需的时间。如果重复次数低于这个时间，则会增加。这个选项对于精确的gpu调优是必需的，而对于CPU调优则不是必需的。将该值设置为0将禁用它。超时设置了每个测试配置运行训练代码的时间上限。
number = 10
repeat = 1
min_repeat_ms = 0 # since we're tuning on a CPU, can be set to 0
timeout = 10 # in seconds
# create a TVM runner
runner = autotvm.LocalRunner(
number=number,
repeat=repeat,
timeout=timeout,
min_repeat_ms=min_repeat_ms,
enable_cpu_cache_flush=True,
)创建一个简单的结构来保存调优选项。我们使用XGBoost算法来指导搜索。对于生产作业，您需要将试验次数设置为大于此处使用的值10。对于CPU我们推荐1500，对于GPU我们推荐3000-4000。所需的试验次数可能取决于特定的模型和处理器，因此值得花一些时间综合一系列值评估性能，以找到调优时间和模型优化之间的最佳平衡。因为运行调优是时间密集型的，所以我们将试验次数设置为10次，但不建议设置这么小的值。early_stopping参数是在应用满足提前停止搜索的条件之前，要运行的最小实验次数。measure_option选项指示将在哪里构建实验代码，以及在哪里运行它。在本例中，我们使用刚刚创建的LocalRunner和一个LocalBuilder。tuning_records选项指定要将调优数据写入的文件。
tuning_option = {
"tuner": "xgb",
"trials": 10,
"early_stopping": 100,
"measure_option": autotvm.measure_option(
builder=autotvm.LocalBuilder(build_func="default"), runner=runner
),
"tuning_records": "resnet-50-v2-autotuning.json",
}定义调优搜索算法：默认情况下，使用XGBoost Grid算法引导搜索。根据模型的复杂性和可用时间，您可能想要选择不同的算法。
设置调优参数：在本例中，出于时间考虑，我们将试验次数(trails)和提前停止（early_stopping）的数量设置为10。如果将这些值设置得更大，您可能会看到更多的性能改进，但这是以调优时间为代价的。得到一个兼顾各种条件的结果所需的试验次数，将取决于模型和目标平台的具体情况。
# begin by extracting the tasks from the onnx model
tasks = autotvm.task.extract_from_program(mod["main"], target=target, params=params)
# Tune the extracted tasks sequentially.
for i, task in enumerate(tasks):
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
tuner_obj = XGBTuner(task, loss_type="rank")
tuner_obj.tune(
n_trial=min(tuning_option["trials"], len(task.config_space)),
early_stopping=tuning_option["early_stopping"],
measure_option=tuning_option["measure_option"],
callbacks=[
autotvm.callback.progress_bar(tuning_option["trials"], prefix=prefix),
autotvm.callback.log_to_file(tuning_option["tuning_records"]),
],
)输出：
[Task 1/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 1/25] Current/Best: 15.08/ 19.42 GFLOPS | Progress: (4/10) | 7.45 s
[Task 1/25] Current/Best: 16.95/ 19.42 GFLOPS | Progress: (8/10) | 11.76 s
[Task 1/25] Current/Best: 17.05/ 19.42 GFLOPS | Progress: (10/10) | 12.64 s Done.
[Task 2/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 2/25] Current/Best: 12.61/ 20.46 GFLOPS | Progress: (4/10) | 2.48 s
[Task 2/25] Current/Best: 13.28/ 20.46 GFLOPS | Progress: (8/10) | 3.59 s
[Task 2/25] Current/Best: 13.12/ 20.46 GFLOPS | Progress: (10/10) | 4.42 s Done.
[Task 3/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 3/25] Current/Best: 17.04/ 17.04 GFLOPS | Progress: (4/10) | 2.95 s
[Task 3/25] Current/Best: 23.87/ 23.87 GFLOPS | Progress: (8/10) | 6.39 s
[Task 3/25] Current/Best: 17.69/ 23.87 GFLOPS | Progress: (10/10) | 7.17 s Done.
[Task 4/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 4/25] Current/Best: 17.23/ 18.64 GFLOPS | Progress: (4/10) | 2.52 s
[Task 4/25] Current/Best: 13.85/ 22.40 GFLOPS | Progress: (8/10) | 4.04 s
[Task 4/25] Current/Best: 10.79/ 22.40 GFLOPS | Progress: (10/10) | 9.28 s Done.
[Task 5/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 5/25] Current/Best: 12.48/ 21.12 GFLOPS | Progress: (4/10) | 2.49 s
[Task 5/25] Current/Best: 14.24/ 21.12 GFLOPS | Progress: (8/10) | 4.88 s
[Task 5/25] Current/Best: 17.85/ 21.12 GFLOPS | Progress: (10/10) | 5.63 s Done.
[Task 6/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 6/25] Current/Best: 11.62/ 11.62 GFLOPS | Progress: (4/10) | 3.61 s
[Task 6/25] Current/Best: 14.97/ 19.32 GFLOPS | Progress: (8/10) | 5.40 s
[Task 6/25] Current/Best: 4.89/ 19.32 GFLOPS | Progress: (10/10) | 6.80 s Done.
[Task 7/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 7/25] Current/Best: 15.88/ 15.88 GFLOPS | Progress: (4/10) | 3.25 s
[Task 7/25] Current/Best: 13.90/ 15.88 GFLOPS | Progress: (8/10) | 5.41 s
[Task 7/25] Current/Best: 16.98/ 20.14 GFLOPS | Progress: (10/10) | 6.19 s Done.
[Task 8/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 8/25] Current/Best: 15.27/ 15.27 GFLOPS | Progress: (4/10) | 9.10 s
[Task 8/25] Current/Best: 9.71/ 15.27 GFLOPS | Progress: (8/10) | 12.85 s
[Task 8/25] Current/Best: 5.26/ 19.76 GFLOPS | Progress: (10/10) | 13.95 s Done.
[Task 9/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 9/25] Current/Best: 21.00/ 23.28 GFLOPS | Progress: (4/10) | 2.26 s
[Task 9/25] Current/Best: 6.83/ 23.28 GFLOPS | Progress: (8/10) | 4.53 s
[Task 9/25] Current/Best: 8.35/ 23.28 GFLOPS | Progress: (10/10) | 5.16 s Done.
[Task 10/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 10/25] Current/Best: 4.24/ 13.50 GFLOPS | Progress: (4/10) | 2.65 s
[Task 10/25] Current/Best: 18.35/ 18.35 GFLOPS | Progress: (8/10) | 4.04 s
[Task 10/25] Current/Best: 7.82/ 18.35 GFLOPS | Progress: (10/10) | 4.73 s Done.
[Task 11/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 11/25] Current/Best: 12.36/ 15.69 GFLOPS | Progress: (4/10) | 3.37 s
[Task 11/25] Current/Best: 15.19/ 23.20 GFLOPS | Progress: (8/10) | 4.97 s
[Task 11/25] Current/Best: 14.85/ 23.33 GFLOPS | Progress: (10/10) | 5.73 s Done.
[Task 12/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 12/25] Current/Best: 15.70/ 20.95 GFLOPS | Progress: (4/10) | 3.11 s
[Task 12/25] Current/Best: 13.96/ 20.95 GFLOPS | Progress: (8/10) | 6.27 s
[Task 12/25] Current/Best: 6.09/ 20.95 GFLOPS | Progress: (10/10) | 7.64 s Done.
[Task 13/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 13/25] Current/Best: 11.49/ 19.01 GFLOPS | Progress: (4/10) | 3.13 s
[Task 13/25] Current/Best: 9.63/ 19.01 GFLOPS | Progress: (8/10) | 6.25 s
[Task 13/25] Current/Best: 10.28/ 19.01 GFLOPS | Progress: (10/10) | 7.64 s Done.
[Task 14/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 14/25] Current/Best: 15.85/ 15.85 GFLOPS | Progress: (4/10) | 3.17 s
[Task 14/25] Current/Best: 5.76/ 18.55 GFLOPS | Progress: (8/10) | 6.54 s
[Task 14/25] Current/Best: 13.77/ 18.55 GFLOPS | Progress: (10/10) | 7.30 s
[Task 15/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 15/25] Current/Best: 18.00/ 19.72 GFLOPS | Progress: (4/10) | 2.77 s
[Task 15/25] Current/Best: 1.72/ 23.47 GFLOPS | Progress: (8/10) | 4.84 s
[Task 15/25] Current/Best: 7.08/ 23.47 GFLOPS | Progress: (10/10) | 5.56 s
[Task 16/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 16/25] Current/Best: 7.06/ 11.45 GFLOPS | Progress: (4/10) | 4.34 s
[Task 16/25] Current/Best: 17.36/ 20.81 GFLOPS | Progress: (8/10) | 5.45 s
[Task 16/25] Current/Best: 11.77/ 20.81 GFLOPS | Progress: (10/10) | 7.54 s Done.
[Task 17/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 17/25] Current/Best: 17.96/ 21.18 GFLOPS | Progress: (4/10) | 3.15 s Done.
Done.
[Task 17/25] Current/Best: 16.94/ 21.18 GFLOPS | Progress: (8/10) | 6.81 s
[Task 17/25] Current/Best: 18.82/ 21.18 GFLOPS | Progress: (10/10) | 7.71 s Done.
[Task 18/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 18/25] Current/Best: 7.06/ 21.85 GFLOPS | Progress: (4/10) | 6.66 s
[Task 18/25] Current/Best: 13.46/ 21.85 GFLOPS | Progress: (8/10) | 8.50 s
[Task 18/25] Current/Best: 4.30/ 21.85 GFLOPS | Progress: (10/10) | 10.63 s Done.
[Task 19/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 19/25] Current/Best: 19.72/ 19.72 GFLOPS | Progress: (4/10) | 3.58 s
[Task 19/25] Current/Best: 11.06/ 19.72 GFLOPS | Progress: (8/10) | 8.72 s
[Task 19/25] Current/Best: 20.04/ 20.04 GFLOPS | Progress: (10/10) | 10.02 s Done.
[Task 20/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 20/25] Current/Best: 13.85/ 17.00 GFLOPS | Progress: (4/10) | 2.22 s
[Task 20/25] Current/Best: 6.31/ 20.17 GFLOPS | Progress: (8/10) | 7.44 s
[Task 20/25] Current/Best: 15.74/ 20.17 GFLOPS | Progress: (10/10) | 8.15 s
[Task 21/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 21/25] Current/Best: 14.35/ 19.50 GFLOPS | Progress: (4/10) | 2.63 s
[Task 21/25] Current/Best: 16.28/ 19.50 GFLOPS | Progress: (8/10) | 5.50 s
[Task 21/25] Current/Best: 10.70/ 19.50 GFLOPS | Progress: (10/10) | 6.82 s
[Task 22/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 22/25] Current/Best: 10.63/ 19.50 GFLOPS | Progress: (4/10) | 3.26 s
[Task 22/25] Current/Best: 2.71/ 19.50 GFLOPS | Progress: (8/10) | 5.71 s
[Task 22/25] Current/Best: 17.89/ 19.50 GFLOPS | Progress: (10/10) | 6.49 s Done.
[Task 23/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 23/25] Current/Best: 20.28/ 20.28 GFLOPS | Progress: (4/10) | 4.05 s
[Task 23/25] Current/Best: 22.31/ 22.31 GFLOPS | Progress: (8/10) | 6.62 s
[Task 23/25] Current/Best: 12.03/ 22.31 GFLOPS | Progress: (10/10) | 7.65 s Done.
[Task 24/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
Done.
[Task 24/25] Current/Best: 3.68/ 3.68 GFLOPS | Progress: (4/10) | 50.27 s
[Task 24/25] Current/Best: 2.41/ 9.14 GFLOPS | Progress: (8/10) | 73.54 s
[Task 24/25] Current/Best: 5.75/ 9.14 GFLOPS | Progress: (10/10) | 75.42 s
[Task 25/25] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (0/10) | 0.00 s
[Task 25/25] Current/Best: 5.65/ 5.65 GFLOPS | Progress: (4/10) | 23.04 s
[Task 25/25] Current/Best: 3.50/ 8.90 GFLOPS | Progress: (8/10) | 25.65 s
[Task 25/25] Current/Best: 2.99/ 8.90 GFLOPS | Progress: (10/10) | 26.52 s
T这个调优过程的输出如下所示:
# [Task 1/24] Current/Best: 10.71/ 21.08 GFLOPS | Progress: (60/1000) | 111.77 s Done.
# [Task 1/24] Current/Best: 9.32/ 24.18 GFLOPS | Progress: (192/1000) | 365.02 s Done.
# [Task 2/24] Current/Best: 22.39/ 177.59 GFLOPS | Progress: (960/1000) | 976.17 s Done.
# [Task 3/24] Current/Best: 32.03/ 153.34 GFLOPS | Progress: (800/1000) | 776.84 s Done.
# [Task 4/24] Current/Best: 11.96/ 156.49 GFLOPS | Progress: (960/1000) | 632.26 s Done.
# [Task 5/24] Current/Best: 23.75/ 130.78 GFLOPS | Progress: (800/1000) | 739.29 s Done.
# [Task 6/24] Current/Best: 38.29/ 198.31 GFLOPS | Progress: (1000/1000) | 624.51 s Done.
# [Task 7/24] Current/Best: 4.31/ 210.78 GFLOPS | Progress: (1000/1000) | 701.03 s Done.
# [Task 8/24] Current/Best: 50.25/ 185.35 GFLOPS | Progress: (972/1000) | 538.55 s Done.
# [Task 9/24] Current/Best: 50.19/ 194.42 GFLOPS | Progress: (1000/1000) | 487.30 s Done.
# [Task 10/24] Current/Best: 12.90/ 172.60 GFLOPS | Progress: (972/1000) | 607.32 s Done.
# [Task 11/24] Current/Best: 62.71/ 203.46 GFLOPS | Progress: (1000/1000) | 581.92 s Done.
# [Task 12/24] Current/Best: 36.79/ 224.71 GFLOPS | Progress: (1000/1000) | 675.13 s Done.
# [Task 13/24] Current/Best: 7.76/ 219.72 GFLOPS | Progress: (1000/1000) | 519.06 s Done.
# [Task 14/24] Current/Best: 12.26/ 202.42 GFLOPS | Progress: (1000/1000) | 514.30 s Done.
# [Task 15/24] Current/Best: 31.59/ 197.61 GFLOPS | Progress: (1000/1000) | 558.54 s Done.
# [Task 16/24] Current/Best: 31.63/ 206.08 GFLOPS | Progress: (1000/1000) | 708.36 s Done.
# [Task 17/24] Current/Best: 41.18/ 204.45 GFLOPS | Progress: (1000/1000) | 736.08 s Done.
# [Task 18/24] Current/Best: 15.85/ 222.38 GFLOPS | Progress: (980/1000) | 516.73 s Done.
# [Task 19/24] Current/Best: 15.78/ 203.41 GFLOPS | Progress: (1000/1000) | 587.13 s Done.
# [Task 20/24] Current/Best: 30.47/ 205.92 GFLOPS | Progress: (980/1000) | 471.00 s Done.
# [Task 21/24] Current/Best: 46.91/ 227.99 GFLOPS | Progress: (308/1000) | 219.18 s Done.
# [Task 22/24] Current/Best: 13.33/ 207.66 GFLOPS | Progress: (1000/1000) | 761.74 s Done.
# [Task 23/24] Current/Best: 53.29/ 192.98 GFLOPS | Progress: (1000/1000) | 799.90 s Done.
# [Task 24/24] Current/Best: 25.03/ 146.14 GFLOPS | Progress: (1000/1000) | 1112.55 s Done.使用调优数据编译一个优化的模型
上述调优过程的输出（即调优记录）存储在resnet-50-v2-autotuning.json中。编译器将使用它们为您指定的目标上的模型生成高性能代码。
现在已经收集了模型的调优数据，我们可以使用优化的算子重新编译模型，以加快计算速度。
with autotvm.apply_history_best(tuning_option["tuning_records"]):
with tvm.transform.PassContext(opt_level=3, config={}):
lib = relay.build(mod, target=target, params=params)
dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))输出：
Done.运行优化后的模型，验证优化前后输出是一致的:
dtype = "float32"
module.set_input(input_name, img_data)
module.run()
output_shape = (1, 1000)
tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()
scores = softmax(tvm_output)
scores = np.squeeze(scores)
ranks = np.argsort(scores)[::-1]
for rank in ranks[0:5]:
print("class='%s' with probability=%f" % (labels[rank], scores[rank]))输出：
class='n02123045 tabby, tabby cat' with probability=0.621104
class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262可以看到输出结果和优化前一致：
# class='n02123045 tabby, tabby cat' with probability=0.610550
# class='n02123159 tiger cat' with probability=0.367181
# class='n02124075 Egyptian cat' with probability=0.019365
# class='n02129604 tiger, Panthera tigris' with probability=0.001273
# class='n04040759 radiator' with probability=0.000261比较优化和未优化的模型
我们希望收集当前优化模型的一些基本性能数据，以便与优化前进行比较。通过比较您应该会看到性能改进，提升多少取决于底层硬件、迭代次数以及其他因素。
import timeit
timing_number = 10
timing_repeat = 10
optimized = (
np.array(timeit.Timer(lambda: module.run()).repeat(repeat=timing_repeat, number=timing_number))
* 1000
/ timing_number
)
optimized = {"mean": np.mean(optimized), "median": np.median(optimized), "std": np.std(optimized)}
print("optimized: %s" % (optimized))
print("unoptimized: %s" % (unoptimized))输出：
optimized: {'mean': 426.5695632400002, 'median': 426.31598235000183, 'std': 0.8991986364530805}
unoptimized: {'mean': 496.2511969099978, 'median': 495.80396929999324, 'std': 0.7997811122746795}小结
在本教程中，我们给出了一个关于如何使用TVM Python API来编译、运行和调优模型的简短示例。我们还讨论了对输入和输出进行预处理和后处理的必要性。在调优过程之后，我们演示了如何比较未优化模型和优化模型的性能。
这里我们给出了一个在本地使用ResNet-50 v2的简单示例。但是，TVM支持更多的特性，包括交叉编译、远程执行和分析/基准测试。
脚本的总运行时间:(7分钟48.959秒)