我正在尝试在Google Coral上部署对象检测模型。我使用以下config file训练了模型，我尝试将其与来自here的docker映像中的演示配置文件紧密匹配。
我成功地训练了模型，然后似乎成功运行了脚本./convert_checkpoint_to_edgetpu_tflite.sh，并显示了以下输出：
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator.py:183: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
2019-09-12 11:15:11.539092: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-12 11:15:11.707588: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x68382b0 executing computations on platform CUDA. Devices:
2019-09-12 11:15:11.707625: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-09-12 11:15:11.728473: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3298290000 Hz
2019-09-12 11:15:11.729431: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x68a1b90 executing computations on platform Host. Devices:
2019-09-12 11:15:11.729473: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2019-09-12 11:15:11.729783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:05:00.0
totalMemory: 10.73GiB freeMemory: 10.34GiB
2019-09-12 11:15:11.729823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-12 11:15:11.732474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-12 11:15:11.732509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-12 11:15:11.732523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-12 11:15:11.732730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10057 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5)
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/tools/freeze_graph.py:127: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
2019-09-12 11:15:15.451695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-12 11:15:15.451741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-12 11:15:15.451748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-12 11:15:15.451753: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-12 11:15:15.451857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10057 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5)
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/tools/freeze_graph.py:232: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.graph_util.convert_variables_to_constants
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/framework/graph_util_impl.py:245: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.graph_util.extract_sub_graph
2019-09-12 11:15:17.880135: I tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying strip_unused_nodes
CONVERTING frozen graph to TF Lite file...
2019-09-12 11:15:19.959403: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-12 11:15:20.105331: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3f28f50 executing computations on platform CUDA. Devices:
2019-09-12 11:15:20.105370: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-09-12 11:15:20.124476: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3298290000 Hz
2019-09-12 11:15:20.125267: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3f92630 executing computations on platform Host. Devices:
2019-09-12 11:15:20.125297: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2019-09-12 11:15:20.125542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:05:00.0
totalMemory: 10.73GiB freeMemory: 10.34GiB
2019-09-12 11:15:20.125569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-12 11:15:20.127390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-12 11:15:20.127411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-12 11:15:20.127420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-12 11:15:20.127553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10057 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5)
TFLite graph generated at model_exported/output_tflite_graph.tflite
然后我运行edgetpu_compiler output_tflite_graph.tflite似乎也成功，并显示以下输出：
Edge TPU Compiler version 2.0.258810407
INFO: Initialized TensorFlow Lite runtime.
Model compiled successfully in 383 ms.
Input model: model_exported/output_tflite_graph.tflite
Input size: 1.65MiB
Output model: output_tflite_graph_edgetpu.tflite
Output size: 2.33MiB
On-chip memory available for caching model parameters: 7.00MiB
On-chip memory used for caching model parameters: 2.11MiB
Off-chip memory used for streaming uncached model parameters: 0.00B
Number of Edge TPU subgraphs: 1
Total number of operations: 115
Operation log: output_tflite_graph_edgetpu.log
Model successfully compiled but not all operations are supported by the Edge TPU. A percentage of the model will instead run on the CPU, which is slower. If possible, consider updating your model to use only operations supported by the Edge TPU. For details, visit g.co/coral/model-reqs.
Number of operations that will run on Edge TPU: 114
Number of operations that will run on CPU: 1
See the operation log file for individual operation details.
以及以下output_tflite_graph_edgetpu.log文件：
Edge TPU Compiler version 2.0.258810407
Input: output_tflite_graph.tflite
Output: output_tflite_graph_edgetpu.tflite
Operator Count Status
DEPTHWISE_CONV_2D 33 Mapped to Edge TPU
RESHAPE 13 Mapped to Edge TPU
LOGISTIC 1 Mapped to Edge TPU
CUSTOM 1 Operation is working on an unsupported data type
ADD 10 Mapped to Edge TPU
CONCATENATION 2 Mapped to Edge TPU
CONV_2D 55 Mapped to Edge TPU
最后，我将转换后的output_tflite_graph_edgetpu.tflite放在珊瑚上，并出现以下错误：
Traceback (most recent call last):
File "main.py", line 224, in <module>
main()
File "main.py", line 221, in main
run_app(add_render_gen_args, render_gen)
File "/home/mendel/projects/DARTS/object_detection/edge_tpu_vision/edgetpuvision/apps.py", line 75, in run_app
display=args.displaymode):
File "/home/mendel/projects/DARTS/object_detection/edge_tpu_vision/edgetpuvision/gstreamer.py", line 243, in run_gen
inference_size = render_overlay_gen.send(None) # Initialize.
File "main.py", line 154, in render_gen
engines, titles = utils.make_engines(args.model, DetectionEngine)
File "/home/mendel/projects/DARTS/object_detection/edge_tpu_vision/edgetpuvision/utils.py", line 53, in make_engines
engine = engine_class(model_path)
File "/usr/lib/python3/dist-packages/edgetpu/detection/engine.py", line 55, in __init__
super().__init__(model_path)
File "/usr/lib/python3/dist-packages/edgetpu/swig/edgetpu_cpp_wrapper.py", line 300, in __init__
this = _edgetpu_cpp_wrapper.new_BasicEngine(*args)
RuntimeError: Failed to allocate tensors.
我在做什么错了？
谢谢！
PS：我意识到这可能更适合git问题，但是我不确定在git上发布google-coral问题的地方...
答案 0 :(得分：1)
在按照本教程重新训练对象检测模型之后，我在珊瑚板上也遇到了完全相同的问题。
对我来说，问题是由于编译器针对的运行库版本与我在Coral板上的运行库版本不同。在珊瑚板上，您可以像这样检查运行时版本：
python3 -c "import edgetpu.basic.edgetpu_utils; print(edgetpu.basic.edgetpu_utils.GetRuntimeVersion())"
在我的主板上，运行时版本为 10 ，与编译器的默认版本为 12 不同。如果确实存在相同的问题，则可以使用以下命令编译针对该版本的模型：
edgetpu_compiler --min_runtime_version 10 your_model.tflite
来源： https://coral.withgoogle.com/docs/edgetpu/compiler/#compiler-and-runtime-versions
答案 1 :(得分：0)
您可以尝试使用最新的编译器并进行检查吗？我知道编译器自身中的一些错误已修复。指南在这里： https://coral.withgoogle.com/news/updates-09-2019/