我训练了一个自定义的 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个解决方案，我还尝试了 tf-nightly、tf2.3 和 tf1.14，但没有一个有效（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？有谁知道如何解决这个问题？
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]'.
无论我在代码中进行什么更改，总会有相同的错误消息。我不知道这是否表明在训练期间出现了问题，但没有出现引人注目的情况。
我很乐意收到任何反馈！