我使用load_model和model.save将.h5文件转换为.pb文件，如下所示
model = load_model("model_190-1.00.h5")
model.summary()
model.save("saved_model.pb")
converted = load_model(r"C:UsersHsienDesktopNCS2OCT")
converted.summary()
然后我使用model.summary()来确保我的原始.h5文件和转换后的.pb文件共享相同的结构。
但当我试图使用mo_tf.py将.pb文件传输到IR格式的时，我收到了一条错误消息
hcl-lab@hcllab-SYS-5049A-TR:/opt/intel/openvino_2021.4.689/deployment_tools/model_optimizer$ sudo python mo_tf.py --saved_model_dir /home/hcl-lab/NCS2/OCT --input_shape [1,256,256,3] --data_type FP16
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: None
- Path for generated IR: /opt/intel/openvino_2021.4.689/deployment_tools/model_optimizer/.
- IR output name: saved_model
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: [1,256,256,3]
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP16
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: None
- Reverse input channels: False
TensorFlow specific parameters:
- Input model in text protobuf format: False
- Path to model dump for TensorBoard: None
- List of shared libraries with TensorFlow custom layers implementation: None
- Update the configuration file with input/output node names: None
- Use configuration file used to generate the model with Object Detection API: None
- Use the config file: None
- Inference Engine found in: /opt/intel/openvino_2021.4.689/python/python3.6/openvino
Inference Engine version: 2021.4.1-3926-14e67d86634-releases/2021/4
Model Optimizer version: 2021.4.1-3926-14e67d86634-releases/2021/4
2021-11-24 02:43:53.158753: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py:22: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
2021-11-24 02:43:54.273204: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-11-24 02:43:54.273881: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-11-24 02:43:54.306060: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:1b:00.0 name: RTX A5000 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 64 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 715.34GiB/s
2021-11-24 02:43:54.306553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 1 with properties:
pciBusID: 0000:1c:00.0 name: RTX A4000 computeCapability: 8.6
coreClock: 1.56GHz coreCount: 48 deviceMemorySize: 15.74GiB deviceMemoryBandwidth: 417.29GiB/s
2021-11-24 02:43:54.306574: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-11-24 02:43:54.308779: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-11-24 02:43:54.308821: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-11-24 02:43:54.309547: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-11-24 02:43:54.309755: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-11-24 02:43:54.311122: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-11-24 02:43:54.311697: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-11-24 02:43:54.311815: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-11-24 02:43:54.314219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0, 1
2021-11-24 02:43:54.314443: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-11-24 02:43:54.315010: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-11-24 02:43:54.438124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:1b:00.0 name: RTX A5000 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 64 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 715.34GiB/s
2021-11-24 02:43:54.438626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 1 with properties:
pciBusID: 0000:1c:00.0 name: RTX A4000 computeCapability: 8.6
coreClock: 1.56GHz coreCount: 48 deviceMemorySize: 15.74GiB deviceMemoryBandwidth: 417.29GiB/s
2021-11-24 02:43:54.438656: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-11-24 02:43:54.438675: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-11-24 02:43:54.438683: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-11-24 02:43:54.438690: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-11-24 02:43:54.438698: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-11-24 02:43:54.438706: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-11-24 02:43:54.438714: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-11-24 02:43:54.438722: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-11-24 02:43:54.440675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0, 1
2021-11-24 02:43:54.440721: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-11-24 02:43:55.014738: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-11-24 02:43:55.014773: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 1
2021-11-24 02:43:55.014779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N N
2021-11-24 02:43:55.014782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 1: N N
2021-11-24 02:43:55.017101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22272 MB memory) -> physical GPU (device: 0, name: RTX A5000, pci bus id: 0000:1b:00.0, compute capability: 8.6)
2021-11-24 02:43:55.018332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14704 MB memory) -> physical GPU (device: 1, name: RTX A4000, pci bus id: 0000:1c:00.0, compute capability: 8.6)
[ FRAMEWORK ERROR ] Cannot load input model: SavedModel format load failure: '_UserObject' object has no attribute 'add_slot'
我尝试在Windows 10和Ubuntu 18.04中使用openvino_2021.4.689和openvino_202 1.4.752执行相同的过程。
(我已将.pb相关文件放在适当的文件夹中。(
我有可能用错误的方式转换这两个文件(.h5到.pb(吗？
感谢英特尔开发人员的帮助。
最后我找到了鞋子夹的地方。
当将.h5文件传输到.pb文件时，我应该使用tf.saved_model.save而不是model.save。
前者将只创建saved_model.pb文件，而后者将同时创建keras_metadata.pb和saved_model.pb两个文件，这将导致具有mo_tf.py的SavedModel format load failure。
您需要将saved_model.pb文件保存在saved_model文件夹中，因为--saved_model_dir参数必须提供指向SavedModel目录的路径。
例如，您的当前位置是C:\Users\Hsien\Desktop\NCS2\OCT，请将模型移动到C:\Users\Hseen\Deskt\NCS2\saved_model。