【发布时间】：2020-10-22 13:32:00
【问题描述】：
我训练了一个自定义 MobileNetV2 SSD 模型来进行对象检测。我保存了 .pb 文件，现在我想将其转换为 .tflite 文件，以便与 Coral edge-tpu 一起使用。
我在 CPU 上的 Windows 10 上使用 Tensorflow 2.2。
我正在使用的代码：
import tensorflow as tf
saved_model_dir = r"C:/Tensorflow/Backup_Training/my_MobileNetV2_fpnlite_320x320/saved_model"
num_calibration_steps = 100
def representative_dataset_gen():
for _ in range(num_calibration_steps):
# Get sample input data as a numpy array
yield [np.random.uniform(0.0, 1.0, size=(1,416,416, 3)).astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [
#tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS
]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_quant_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
我尝试了其他线程提出的几种解决方案，还尝试了 tf-nightly、tf2.3 和 tf1.14，但都没有奏效（总是有另一个我无法处理的错误消息）。由于我使用 tf2.2 进行了培训，因此我认为继续使用 tf2.2 可能是个好主意。
由于我是 Tensorflow 的新手，我有几个问题：输入张量到底是什么，我在哪里定义它？是否有可能查看或提取此输入张量？ 有人知道如何解决这个问题吗？
整个错误信息：
(tf22) C:\Tensorflow\Backup_Training>python full_int_quant.py
2020-10-22 14:51:20.460948: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-10-22 14:51:20.466366: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-10-22 14:51:29.231404: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2020-10-22 14:51:29.239003: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)
2020-10-22 14:51:29.250497: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ip3536
2020-10-22 14:51:29.258432: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ip3536
2020-10-22 14:51:29.269261: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-10-22 14:51:29.291457: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2ae2ac3ffc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-22 14:51:29.298043: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-10-22 14:52:03.785341: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2020-10-22 14:52:03.790251: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2020-10-22 14:52:04.559832: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:797] Optimization results for grappler item: graph_to_optimize
2020-10-22 14:52:04.564529: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799] function_optimizer: Graph size after: 3672 nodes (3263), 5969 edges (5553), time = 136.265ms.
2020-10-22 14:52:04.570187: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799] function_optimizer: function_optimizer did nothing. time = 2.637ms.
2020-10-22 14:52:10.742013: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2020-10-22 14:52:10.746868: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2020-10-22 14:52:12.358897: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:797] Optimization results for grappler item: graph_to_optimize
2020-10-22 14:52:12.363657: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799] constant_folding: Graph size after: 1714 nodes (-1958), 2661 edges (-3308), time = 900.347ms.
2020-10-22 14:52:12.369137: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:799] constant_folding: Graph size after: 1714 nodes (0), 2661 edges (0), time = 60.628ms.
Traceback (most recent call last):
File "full_int_quant.py", line 40, in <module>
tflite_model = converter.convert()
File "C:\Users\schulzyk\Anaconda3\envs\tf22\lib\site-packages\tensorflow\lite\python\lite.py", line 480, in convert
raise ValueError(
ValueError: None is only supported in the 1st dimension. Tensor 'input_tensor' has invalid shape '[1, None, None, 3]'.
无论我对代码进行什么更改，总是会出现相同的错误消息。我不知道这是否表明在训练过程中出现了问题但没有引人注目的事件发生。
我很乐意收到任何反馈！
【问题讨论】：
标签： tensorflow tensorflow-lite google-coral edge-tpu