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我试图获得重复简单的模型来编译，以便它可以在珊瑚 tpu 上运行。到目前为止，我已经冻结并将文件转换为 tflite 文件，但是当我通过 Edge TPU 模型编译器运行文件时，它给了我一个相对无用的错误消息。

COMPILING FAILED
Something went wrong. Couldn't compile model.
Please make sure your model meets the requirements.
See the log below for more compilation details.
If you believe your model meets the requirements but you still receive this error,
email support at coral‑support@google.com.

我给他们发了电子邮件，他们说使用 /tensorflow/lite/tools:visualize 来查看模型有什么问题。（我也很难让它工作，但似乎我应该问一个单独的问题来获得有关 bazel 的帮助）

我已经按照这个站点对模型进行了量化感知训练，并且我已经使用随机输入运行了 tflite 文件，它似乎可以工作。我担心 TPU 模型编译器的部分问题是我在代理后面，所以我通过它运行其他人的文件并成功编译。）

这是评估图：

import pandas as pd
import sys
import tensorflow as tf
import numpy as np
from tensorflow.python.tools import inspect_checkpoint as chkp
from sklearn.model_selection import train_test_split


#test data
seed = np.random.seed(141)

features = pd.read_csv(sys.argv[1], sep=',', index_col=0)
labels = pd.read_csv(sys.argv[2], sep=',', index_col=0)
train_input, test_input, train_labels, test_labels = train_test_split(features, labels, test_size=0.2, random_state=seed)

def neuron_layer(X, n_neurons, name, activation=None):
    with tf.name_scope(name):
        n_inputs = int(X.get_shape()[1])
        W = tf.Variable(tf.zeros([n_inputs, n_neurons]), name="kernal")
        b = tf.Variable(tf.zeros([n_neurons]), name="bias")
        Z = tf.matmul(X, W) + b
        if activation is not None:
            return activation(Z)
        else:
            return Z

X = tf.placeholder(tf.float32, (1, 701), name="X")
n_outputs = 2
n_hidden1 = 700
n_hidden2 = 701
with tf.name_scope("dnn"):
    hidden1 = neuron_layer(X, n_hidden1, name="hidden1", activation=tf.nn.relu)
    # hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.relu)
    # trying only one layer
    logits = neuron_layer(hidden1, n_outputs, name="outputs")

with tf.name_scope("final_eval"):
    output = tf.argmax(logits, axis=1, name="output")


# Call the eval rewrite which rewrites the graph in-place with
# FakeQuantization nodes and fold batchnorm for eval.
g = tf.get_default_graph()
tf.contrib.quantize.create_eval_graph(input_graph=g)

# Add ops to save and restore all the variables.
saver = tf.train.Saver()
eval_graph_file = "eval_graph.pb"

#handles different tensorboard runs
from datetime import datetime
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = "tf_logs"
logdir = "eval/{}/run-{}".format(root_logdir, now)

file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
with tf.Session() as sess:
    saver.restore(sess, "./nbtf/nothing_but_tf_model.ckpt")

    # Save the checkpoint and eval graph proto to disk for freezing
    # and providing to TFLite.
    with open(eval_graph_file, 'w+') as f:
        f.write(str(g.as_graph_def()))
    saver.save(sess, "./nbtf/eval/eval.ckpt")
    pred = output.eval(feed_dict={X: [test_input.values[45]]})
    print(pred, test_labels.values[45])

然后我冻结了这个：

 freeze_graph --input_graph=eval_graph.pb --input_checkpoint=nbtf\eval\eval.ckpt --output_graph=frozen_eval_graph.pb --output_node_names=final_eval/output

然后用这个转换：

toco --graph_def_file=frozen_eval_graph.pb --output_file=tflite_model.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --inference_type=QUANTIZED_UINT8 --input_array=X --output_array=final_eval/output --std_dev_value=127 --mean_value=127

张量板图像

我只是想让这个文件编译它不必是完美的或任何东西。

感谢您的帮助。

编辑：

我已经尝试了两件事，第一件事是我从 tflite 文件中打印出张量（我试图使用 Visualize.py 工具，但我在代理后面并且在让它工作时遇到了很多麻烦。）我得到了这个：

{'name': 'X', 'index': 0, 'shape': array([  1, 701]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.007874015718698502, 127)}
{'name': 'dnn/fully_connected/MatMul_bias', 'index': 1, 'shape': array([702]), 'dtype': <class 'numpy.int32'>, 'quantization': (3.750092218979262e-05, 0)}
{'name': 'dnn/fully_connected/Relu', 'index': 2, 'shape': array([  1, 702]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.035464514046907425, 0)}
{'name': 'dnn/fully_connected/weights_quant/FakeQuantWithMinMaxVars/transpose', 'index': 3, 'shape': array([702, 701]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.004762616939842701, 121)}
{'name': 'dnn/fully_connected_1/MatMul_bias', 'index': 4, 'shape': array([703]), 'dtype': <class 'numpy.int32'>, 'quantization': (0.0001283923047594726, 0)}
{'name': 'dnn/fully_connected_1/Relu', 'index': 5, 'shape': array([  1, 703]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.019155390560626984, 0)}
{'name': 'dnn/fully_connected_1/weights_quant/FakeQuantWithMinMaxVars/transpose', 'index': 6, 'shape': array([703, 702]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.0036203034687787294, 120)}
{'name': 'dnn/outputs/MatMul_bias', 'index': 7, 'shape': array([2]), 'dtype': <class 'numpy.int32'>, 'quantization': (3.3737978810677305e-05, 0)}
{'name': 'dnn/outputs/add', 'index': 8, 'shape': array([1, 2]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.055008530616760254, 131)}
{'name': 'dnn/outputs/weights_quant/FakeQuantWithMinMaxVars/transpose', 'index': 9, 'shape': array([  2, 703]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.0017612784868106246, 110)}
{'name': 'final_eval/output', 'index': 10, 'shape': array([1, 1]), 'dtype': <class 'numpy.int64'>, 'quantization': (0.0, 0)}
{'name': 'final_eval/output/dimension', 'index': 11, 'shape': array([], dtype=int32), 'dtype': <class 'numpy.int32'>, 'quantization': (0.0, 0)}

我认为问题在于 MatMul_bias 张量没有被转换为 uint8（珊瑚 tpu 需要）。

我不知道如何解决这个问题。

我尝试的另一个改变是使用 tensorflow slim.fully_connected，而不是我自己的自定义全连接神经网络。（虽然他们有同样的问题。）

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1 回答 1

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好的，我能够编译文件没问题，我只需要使用离线编译器。似乎完全修复它。

于 2019-06-19T15:06:54.960 回答