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本篇文章译自英文文档 Compile Tensorflow Models。
更多 TVM 中文文档可拜访 →TVM 中文站。
本文介绍了如何用 TVM 部署 TensorFlow 模型。
首先装置 TensorFlow Python 模块（可参考 https://www.tensorflow.org/install）。
# 导入 tvm 和 relay
import tvm
from tvm import te
from tvm import relay
# 导入 os 和 numpy
import numpy as np
import os.path
# 导入 TensorFlow
import tensorflow as tf
# 让 TensorFlow 将 GPU 内存限度为理论须要的内存，而非占用所有可用的内存。# https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth
# 本教程这样做，对 sphinx-gallery 更敌对。gpus = tf.config.list_physical_devices("GPU")
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
print("tensorflow will use experimental.set_memory_growth(True)")
except RuntimeError as e:
print("experimental.set_memory_growth option is not available: {}".format(e))
try:
tf_compat_v1 = tf.compat.v1
except ImportError:
tf_compat_v1 = tf
# TensorFlow 实用函数
import tvm.relay.testing.tf as tf_testing
# 模型相干文件的根本地位
repo_base = "https://github.com/dmlc/web-data/raw/main/tensorflow/models/InceptionV1/"
# 测试图像
img_name = "elephant-299.jpg"
image_url = os.path.join(repo_base, img_name)教程
参考 docs/frontend/tensorflow.md，获取 TensorFlow 中各种模型的更多信息。
model_name = "classify_image_graph_def-with_shapes.pb"
model_url = os.path.join(repo_base, model_name)
# 图像标签图
map_proto = "imagenet_2012_challenge_label_map_proto.pbtxt"
map_proto_url = os.path.join(repo_base, map_proto)
# 可读的标签文本
label_map = "imagenet_synset_to_human_label_map.txt"
label_map_url = os.path.join(repo_base, label_map)
# target 设置
# 用上面这些正文为 cuda 构建
# target = tvm.target.Target("cuda", host="llvm")
# layout = "NCHW"
# dev = tvm.cuda(0)
target = tvm.target.Target("llvm", host="llvm")
layout = None
dev = tvm.cpu(0)下载所需文件
下载上述列出的文件：
from tvm.contrib.download import download_testdata
img_path = download_testdata(image_url, img_name, module="data")
model_path = download_testdata(model_url, model_name, module=["tf", "InceptionV1"])
map_proto_path = download_testdata(map_proto_url, map_proto, module="data")
label_path = download_testdata(label_map_url, label_map, module="data")导入模型
从 protobuf 文件创建 TensorFlow 计算图定义。
with tf_compat_v1.gfile.GFile(model_path, "rb") as f:
graph_def = tf_compat_v1.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name="")
# 调用函数将计算图定义导入默认计算图。graph_def = tf_testing.ProcessGraphDefParam(graph_def)
# 给计算图增加 shape
with tf_compat_v1.Session() as sess:
graph_def = tf_testing.AddShapesToGraphDef(sess, "softmax")解码图像
备注
TensorFlow 前端导入不反对 JpegDecode 等预处理操作。JpegDecode 被绕过（只返回源节点），因而咱们只向 TVM 提供解码后的帧。
from PIL import Image
image = Image.open(img_path).resize((299, 299))
x = np.array(image)将计算图导入 Relay
将 TensorFlow 计算图定义导入到 Relay 前端。
后果：
- sym：给定 TensorFlow protobuf 的 Relay 表达式。
- params：从 TensorFlow 参数 (tensor protobuf) 转换而来的参数。
shape_dict = {"DecodeJpeg/contents": x.shape}
dtype_dict = {"DecodeJpeg/contents": "uint8"}
mod, params = relay.frontend.from_tensorflow(graph_def, layout=layout, shape=shape_dict)
print("Tensorflow protobuf imported to relay frontend.")输入后果：
/workspace/python/tvm/relay/frontend/tensorflow.py:535: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
"will be used for operator %s." % node.name
/workspace/python/tvm/relay/frontend/tensorflow_ops.py:1009: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
Tensorflow protobuf imported to relay frontend.Relay 构建
用给定的输出标准，将计算图编译为 LLVM target。
后果：
- graph：编译后的最终计算图。
- params：编译后的最终参数。
- lib：target 库（可用 TVM runtime 部署到 target 上）。
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target, params=params)输入后果：
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated."在 TVM 上执行可移植计算图
接下来在 target 上部署编译好的模型：
from tvm.contrib import graph_executor
dtype = "uint8"
m = graph_executor.GraphModule(lib["default"](dev))
# 设置输出
m.set_input("DecodeJpeg/contents", tvm.nd.array(x.astype(dtype)))
# 执行
m.run()
# 失去输入
tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), "float32"))解决输入
将 InceptionV1 模型的输入解决为人类可读文本。
predictions = tvm_output.numpy()
predictions = np.squeeze(predictions)
# 创立节点 ID --> 英文字符串查找
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path, uid_lookup_path=label_path)
# 打印 TVM 输入的前 5 个预测。top_k = predictions.argsort()[-5:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print("%s (score = %.5f)" % (human_string, score))输入后果：
African elephant, Loxodonta africana (score = 0.61481)
tusker (score = 0.30387)
Indian elephant, Elephas maximus (score = 0.03343)
banana (score = 0.00023)
rapeseed (score = 0.00021)在 TensorFlow 上推理
在 TensorFlow 上运行对应的模型：
def create_graph():
"""从已保留的 GraphDef 文件创建一个计算图，并返回 saver。"""
# 从已保留的 graph_def.pb 创立图形
with tf_compat_v1.gfile.GFile(model_path, "rb") as f:
graph_def = tf_compat_v1.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name="")
# 调用函数将计算图定义导入默认计算图。graph_def = tf_testing.ProcessGraphDefParam(graph_def)
def run_inference_on_image(image):
""" 在图像上进行推理。参数
----------
image: String 类型
图像文件名。返回值
-------
无
"""
if not tf_compat_v1.gfile.Exists(image):
tf.logging.fatal("File does not exist %s", image)
image_data = tf_compat_v1.gfile.GFile(image, "rb").read()
# 从已保留的 GraphDef 创立计算图。create_graph()
with tf_compat_v1.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name("softmax:0")
predictions = sess.run(softmax_tensor, {"DecodeJpeg/contents:0": image_data})
predictions = np.squeeze(predictions)
# 创立节点 ID --> 英文字符查找
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path, uid_lookup_path=label_path)
# 打印 TensorFlow 的前 5 个预测。top_k = predictions.argsort()[-5:][::-1]
print("===== TENSORFLOW RESULTS =======")
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print("%s (score = %.5f)" % (human_string, score))
run_inference_on_image(img_path)输入后果：
===== TENSORFLOW RESULTS =======
African elephant, Loxodonta africana (score = 0.58394)
tusker (score = 0.33909)
Indian elephant, Elephas maximus (score = 0.03186)
banana (score = 0.00022)
desk (score = 0.00019)脚本总运行时长：（1 分 6.352 秒）
下载 Python 源代码：from_tensorflow.py
下载 Jupyter Notebook：from_tensorflow.ipynb
正文完
