如何获取TOCO tf_convert的冻结Tensorflow模型的input_shape
我正在尝试使用TF Lite Converter (this is the specific model i am using)在Ubuntu18.04.1LTS (VirtualBox)上将我从davidsandberg/facenet获得的冻结模型转换为.tflite。当我尝试运行该命令时：
/home/nils/.local/bin/tflite_convert
--output_file=/home/nils/Documents/frozen.tflite
--graph_def_file=/home/nils/Documents/20180402-114759/20180402-114759.pb
--input_arrays=input --output_array=embeddings我得到以下错误：
2018-11-29 16:36:21.774098: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX2
Traceback (most recent call last):
File "/home/nils/.local/bin/tflite_convert", line 11, in <module>
sys.exit(main())
File
"/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib /lite/python/tflite_convert.py",
line 412, in main
app.run(main=run_main, argv=sys.argv[:1])
File
"/home/nils/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py",
line 125, in run
_sys.exit(main(argv))
File
"/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/tflite_convert.py",
line 408, in run_main
_convert_model(tflite_flags)
File
"/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/tflite_convert.py",
line 162, in _convert_model
output_data = converter.convert()
File
"/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/lite.py",
line 404, in convert
"'{0}'.".format(_tensor_name(tensor)))
ValueError: Provide an input shape for input array 'input'.由于我没有亲自训练模型，我不知道输入的确切形状。也许可以从David Sandberg的GitHubRep.中找到的classifier.py和facenet.py中提取出来，位于facenet/src，但我对代码的理解还不足以让我自己这么做。我甚至试着通过tensorboard来分析图表。我无论如何都搞不清楚，但也许你可以：Tensorboard-Screenshot正如你可能已经注意到的，我对Ubuntu，Tensorflow和所有相关的东西都是新手，所以我很乐意在这个问题上接受任何建议。提前谢谢你！
这是classifier.py的相关部分，模型在这里加载和设置：
# Load the model
print('Loading feature extraction model')
facenet.load_model(args.model)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
nrof_images = len(paths)
nrof_batches_per_epoch = int(math.ceil(1.0*nrof_images / args.batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
start_index = i*args.batch_size
end_index = min((i+1)*args.batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, args.image_size)
feed_dict = { images_placeholder:images, phase_train_placeholder:False }
emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
classifier_filename_exp = os.path.expanduser(args.classifier_filename)回答 2
Stack Overflow用户
发布于 2018-12-13 04:04:06
如果你再次启动Tensorboard，回到你看到的图表，应该有一个搜索图标(我想在左上角)，在那里你可以输入" input“并找到输入张量。它会给你想要的形状。我猜它应该是'1，image_size，image_size，3‘的形式。
或者，您可以检查代码
feed_dict = { images_placeholder:images, phase_train_placeholder:False }请注意，我们将“图像”对象输入到images_placeholder中，该对象映射到"input:0“张量。实际上，您需要的是图像对象的形状。
图像来自对facenet.load_data()的调用。如果您进入facenet.py并检查load_data函数，您可以观察到其形状类似于我上面建议的形状。如果您打印image_size值，它应该与您在Tensorboard中看到的值相匹配。
Stack Overflow用户
发布于 2019-01-10 15:45:41
我看过了tflite转换器的代码。我发现您需要以{"input_tensor_name": [input shape]}格式将输入形状作为字典提供。
下面是一个解决这个问题的例子：
`graph_def_file = "20180402-114759/20180402-114759.pb"
input_arrays = ["input"]
output_arrays = ["embeddings"]
converter = tf.lite.TFLiteConverter.from_frozen_graph(
graph_def_file, input_arrays, output_arrays,input_shapes={"input":[1,160,160,3]})
tflite_model = converter.convert()
open("model.tflite", "wb").write(tflite_model)
`https://stackoverflow.com/questions/53543872
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