TensorFlow精简版:使用toco转换为.tflite时出错

Question

我正在尝试将我的TensorFlow冻结模型转换为tflite模型。当我运行toco时，我得到如下错误消息

F tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc:982] Check failed: input_dims.size() == 4 (2 vs. 4)

下面是我调用toco的方式：

bazel-bin/tensorflow/contrib/lite/toco/toco \
--input_format=TENSORFLOW_GRAPHDEF \
--input_file=/tmp/output_graph.pb \
--output_format=TFLITE \
--output_file=/tmp/my_model.lite \
--inference_type=FLOAT \
--inference_input_type=FLOAT \
--input_arrays=input_layer \
--output_arrays=classes_tensor\
--input_shapes=1,227,227,3

下面是我在操作过程中的终端打印输出：

INFO: Analysed 0 targets (4 packages loaded).
INFO: Found 0 targets...
INFO: Elapsed time: 5.267s, Critical Path: 0.03s
INFO: Build completed successfully, 1 total action
2018-01-05 10:24:23.011483: W tensorflow/contrib/lite/toco/toco_cmdline_flags.cc:178] --input_type is deprecated. It was an ambiguous flag that set both --input_data_types and --inference_input_type. If you are trying to complement the input file with information about the type of input arrays, use --input_data_type. If you are trying to control the quantization/dequantization of real-numbers input arrays in the output file, use --inference_input_type.
2018-01-05 10:24:25.853112: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1122] Converting unsupported operation: IsVariableInitialized
2018-01-05 10:24:25.853197: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1122] Converting unsupported operation: RefSwitch
2018-01-05 10:24:25.853241: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1122] Converting unsupported operation: RandomShuffleQueueV2
2018-01-05 10:24:25.853268: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1122] Converting unsupported operation: QueueDequeueUpToV2
2018-01-05 10:24:26.207160: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 64 operators, 90 arrays (0 quantized)
2018-01-05 10:24:27.327055: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] After general graph transformations pass 1: 15 operators, 33 arrays (0 quantized)
2018-01-05 10:24:27.327262: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] After general graph transformations pass 2: 15 operators, 34 arrays (0 quantized)
2018-01-05 10:24:27.327356: F tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc:982] Check failed: input_dims.size() == 4 (2 vs. 4)
/home/olu/Dev/scratch_train_sign/freeze_graph_tf.sh: line 28:  8881 Aborted 

我查看了propagate_fixed_sizes.cc文件，在982行附近找到了下面的注释

// The current ArgMax implementation only supports 4-dimensional inputs with
// the last dimension as the axis to perform ArgMax for.

在我的训练代码中，我使用ArgMax的唯一位置如下：

 predictions = { "classes": tf.argmax(input=logits, axis=1, name="classes_tensor"), "probabilities": tf.nn.softmax(logits, name="softmax_tensor") }

你知道这个问题的解决方案是什么吗？对此的解决方案将受到很好的赞赏。

Answer 1

在GitHub上的TensorFlow bug跟踪器上提出这个问题后，答案归结为TLite目前还不完全支持ArgMax。Link