我有一台带有少量 NVidia GPU 的计算机，使用数据包 'segmentation_models' 并在 Unet 的基础上构建 NN:

import segmentation_models as sm
import keras.backend as K
from keras import optimizers
from keras.utils import multi_gpu_model

lr = 2e-4
NUM_GPUS = 3
learning_rate = lr * NUM_GPUS

adam = optimizers.Adam(lr=learning_rate)

def dice_coef(y_true, y_pred, smooth=1):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

model = sm.Unet('efficientnetb3', encoder_weights='imagenet', classes=4, activation='softmax', encoder_freeze=False)
parallel_model = multi_gpu_model(model, gpus=NUM_GPUS)
model = parallel_model
model.compile(adam, 'categorical_crossentropy', [dice_coef])
history = model.fit_generator(
        generator=train_gen, steps_per_epoch=len(train_gen), \
        validation_data=validation_gen, \
        epochs=50, callbacks=[clr, checkpoints, csv_logger],
        initial_epoch=0)

训练后我保存权重以备将来在 cpu 模式下使用:

single_gpu_model = model.layers[-2]
single_gpu_model.save(single_proc_model_path_1_kernel)

我尝试使用这些权重:

import keras
model1 = keras.models.load_model(single_proc_model_path_1_kernel)
...
pr_mask = self.model1.predict(img_exp)

• NN 训练机器:Ubuntu 16.04.4 LTS，3 x K80 GPU； python 3.6.7、tensorflow 1.12.0 - 所有代码都在这里工作。
  
• Win10 和 1 GeForce GTX 1080； python 3.7.3、tensorflow-gpu 1.13.1 - 代码也可以在这里工作。
  
• 没有 NVidia GPU 的 Win10； tensorflow-gpu 1.13.1 - 加载模型时出错:
  

tensorflow/stream_executor/cuda/cuda_driver.cc:300] 调用 cuInit 失败:CUDA_ERROR_NO_DEVICE:未检测到支持 CUDA 的设备

• docker 与 Ubuntu 18.04.3 LTS； python 3.6.9，tensorflow 2.1.0。
  加载模型时出错:
  

tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. Segmentation Models: using keras framework. tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303) I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (b36a4cf2df2e): /proc/driver/nvidia/version does not exist

我应该更改什么以强制代码在只有 CPU 的机器上工作？

最佳答案

Tensorflow 1.15 解决了所有问题。谢谢。

https://stackoverflow.com/questions/60706122/