编译 TensorFlow 模型

# 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 内存限制为实际需要的内存，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 更友好。
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
# 模型相关文件的基本位置
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)

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
# 用下面这些注释为 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)

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")

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)
    # 给计算图添加 shape
    with tf_compat_v1.Session() as sess:
        graph_def = tf_testing.AddShapesToGraphDef(sess, "softmax")

from PIL import Image
image = Image.open(img_path).resize((299, 299))
x = np.array(image)

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.")

/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.

with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target, params=params)

/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. "

from tvm.contrib import graph_executor
dtype = "uint8"
m = graph_executor.GraphModule(lib["default"](dev))
# 设置输入
m.set_input("DecodeJpeg/contents", tvm.nd.array(x.astype(dtype)))
# 执行
m.run()
# 得到输出
tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), "float32"))

predictions = tvm_output.numpy()
predictions = np.squeeze(predictions)
# 创建节点 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 输出的前 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))

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)

def create_graph():
    """从已保存的 GraphDef 文件创建一个计算图，并返回 saver。"""
    # 从已保存的 graph_def.pb 创建图形
    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()
    # 从已保存的 GraphDef 创建计算图。
    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)
        # 创建节点 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)

===== 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)