tvm编译tensorflow模型

2022-03-02

实现官方demo并保存转换后的tvm模型，重读并推理

上面的操作，代码比较分散，其他编译器把所有代码一起编译就ok了

官方文档

1.导入头文件

# tvm, relay
import tvm
from tvm import te
from tvm import relay
# os and numpy
import numpy as np
import os.path
# Tensorflow imports
import tensorflow as tf
try:
    tf_compat_v1 = tf.compat.v1
except ImportError:
    tf_compat_v1 = tf
# Tensorflow utility functions
import tvm.relay.testing.tf as tf_testing
from tensorflow.keras.datasets import mnist
from tensorflow.python.platform import gfile

2.设置下载路径和配置文件参数（CPU支持型号，llvm编译）

repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
# Test image
img_name = 'elephant-299.jpg'
image_url = os.path.join(repo_base, img_name)
######################################################################
# Tutorials
# ---------
# Please refer docs/frontend/tensorflow.md for more details for various models
# from tensorflow.

model_name = 'classify_image_graph_def-with_shapes.pb'
model_url = os.path.join(repo_base, model_name)
# Image label map
map_proto = 'imagenet_2012_challenge_label_map_proto.pbtxt'
map_proto_url = os.path.join(repo_base, map_proto)
# Human readable text for labels
label_map = 'imagenet_synset_to_human_label_map.txt'
label_map_url = os.path.join(repo_base, label_map)
# Target settings
# Use these commented settings to build for cuda.
#target = 'cuda'
#target_host = 'llvm'
#layout = "NCHW"
#ctx = tvm.gpu(0)
target = 'llvm'
target_host = 'llvm'
layout = None
ctx = tvm.context(target, 0)#tvm.cpu(0)

3.下载需要的资源，如果下载失败，根据错误提示手动下载资源，放到对应目录下

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') 
print(model_path)

4.读入模型

with tf_compat_v1.gfile.FastGFile(model_path, 'rb') as f:
    graph_def = tf_compat_v1.GraphDef()
    graph_def.ParseFromString(f.read())
    graph = tf.import_graph_def(graph_def, name='')
    # Call the utility to import the graph definition into default graph.
    graph_def = tf_testing.ProcessGraphDefParam(graph_def)
    # Add shapes to the graph.
    with tf_compat_v1.Session() as sess:
        graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax')

5.处理训练数据并读入模型

from PIL import Image
image = Image.open(img_path).resize((299, 299))
x = np.array(image)
######################################################################
# Import the graph to Relay
# -------------------------
# Import tensorflow graph definition to relay frontend.
#
# Results:
#   sym: relay expr for given tensorflow protobuf.
#   params: params converted from tensorflow params (tensor protobuf).
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.")
print(mod.astext(show_meta_data=False))

是没有问题

6.开始编译

with relay.build_config(opt_level=3):
    lib = relay.build(mod,
                                     target=target,
                                     target_host=target_host,
                                     params=params)

一堆，没什么大不了的

7. 推理

from tvm.contrib import graph_runtime
dtype = 'uint8'
m = graph_runtime.GraphModule(lib["default"](ctx))
# set inputs
m.set_input("DecodeJpeg/contents", tvm.nd.array(x.astype(dtype)))
# execute
m.run()
# get outputs
tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
predictions = tvm_output.asnumpy()
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path,
                                    uid_lookup_path=label_path)
# Print top 5 predictions from TVM output.
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))

可以正常推理，转换成功

8.保存

from tvm.contrib import utils
temp=utils.tempdir()
path_lib=temp.relpath("/home/aiteam/tiwang/tvm_code/inceptionV2.1_lib.tar")
lib.export_library(path_lib)

已成功保存

9.读入模型并推断

loaded_lib=tvm.runtime.load_module(path_lib)
input_data=tvm.nd.array(x.astype(dtype))
mm=graph_runtime.GraphModule(loaded_lib["default"](ctx))
mm.run(data=input_data)
out_deploy = mm.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
predictions = out_deploy.asnumpy()
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path,
                                    uid_lookup_path=label_path)
# Print top 5 predictions from TVM output.
top_k = predictions.argsort()[-10:][::-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))

整个过程没问题

但是，在转换自己的模型时仍然存在很多问题。首先是取自 tf- 的演示模型在第 4 步会出现解码错误。

参考

修改

with tf_compat_v1.gfile.FastGFile(model_dir+model_name, 'rb') as f:
    data=compat.as_bytes(f.read())
    graph_def=saved_model_pb2.SavedModel()
    graph_def.ParseFromString(data)
    graph_def=graph_def.meta_graphs[0].graph_def
    
    #graph_def = tf_compat_v1.GraphDef()
    #graph_def.ParseFromString(f.read())
    graph = tf.import_graph_def(graph_def, name='')
    # Call the utility to import the graph definition into default graph.
    graph_def = tf_testing.ProcessGraphDefParam(graph_def)
    # Add shapes to the graph.
    with tf_compat_v1.Session() as sess:
        graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax')

可以正常解码，但是后面会有问题

: 不在图中

这里还需要看源码，了解graph和

另外我自己写了一个mnist模型，可以正常读取，但是这里还是出现同样的错误。根据其他博客，我认为第一个错误是模型保存时调用函数的问题。文件格式相同但编码不同，因为里面的解决方案

compat.as_bytes(f.read())

明显转换

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