python - 我如何获得 TOCO tf_convert 的卡住 Tensorflow 模型的 input_shape

Question

我正在尝试转换我从 davidsandberg/facenet 获得的卡住模型使用 TF Lite Converter 到 Ubuntu 18.04.1 LTS (VirtualBox) 上的 .tflite (this is the specific model i am using) .当我尝试运行命令时:

/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

我收到以下错误:

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'.

由于我自己没有训练过模型，所以我不知道输入的确切形状。可能可以从 classifier.py 和 facenet.py 中提取它，在 David Sandberg 的 GitHubRep. 中找到，位于 facenet/src，但我对代码的理解不足以让我自己做到这一点。我什至尝试通过张量板分析图表。反正我想不通，但也许你可以:Tensorboard-Screenshot正如您可能已经注意到的那样，我对 Ubuntu、Tensorflow 和所有相关的东西都很陌生，所以我很乐意就这个问题接受任何建议。提前致谢!

这是 classifier.py 的相关部分，模型在此处加载和设置:

 # 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)

Answer 1

谢谢你的帮助，我确实像 Alan Chiao 说的那样跟着 load_data() 到 facenet.py，我最终找到了形状 [1,160, 160, 3]。另外，Tensorflow's command line reference for the tf lite converter向我展示了我必须注意的事项:

--input_shapes. Type: colon-separated list of comma-separated lists of integers. Each comma-separated list of integers gives the shape of one of the input arrays specified in TensorFlow convention.

Example: --input_shapes=1,60,80,3 for a typical vision model means a batch size of 1, an input image height of 60, an input image widthof 80, and an input image depth of 3 (representing RGB channels).