无法获得卷积算法这可能是因为cuDNN无法初始化，

如何解决无法获得卷积算法这可能是因为cuDNN无法初始化，

我遇到了此错误，并通过从系统中卸载所有CUDA和cuDNN版本修复了该错误。然后，我为 安装了 （没有任何补丁）和 。

解决方法

在Tensorflow /Keras中，从https://github.com/pierluigiferrari/ssd_keras运行代码时，请使用估算器：ssd300_evaluation。我收到此错误。

无法获得卷积算法。这可能是因为cuDNN无法初始化，所以请尝试查看上面是否打印了警告日志消息。

我正在运行的问题：

的Python：3.6.4。

Tensorflow版本：1.12.0。

Keras版本：2.2.4。

CUDA：V10.0。

cuDNN：V7.4.1.5。

NVIDIA GeForce GTX 1080。

我也跑了：

import tensorflow as tf
with tf.device('/gpu:0'):
      a = tf.constant([1.0,2.0,3.0,4.0,5.0,6.0],shape=[2,3],name='a')
      b = tf.constant([1.0,shape=[3,2],name='b')
      c = tf.matmul(a,b)
with tf.Session() as sess:
print (sess.run(c))

没有错误或问题。

极简示例是：

 from keras import backend as K
 from keras.models import load_model
 from keras.optimizers import Adam
 from scipy.misc import imread
 import numpy as np
 from matplotlib import pyplot as plt

 from models.keras_ssd300 import ssd_300
 from keras_loss_function.keras_ssd_loss import SSDLoss
 from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes
 from keras_layers.keras_layer_DecodeDetections import DecodeDetections
 from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast
 from keras_layers.keras_layer_L2Normalization import L2Normalization
 from data_generator.object_detection_2d_data_generator import DataGenerator
 from eval_utils.average_precision_evaluator import Evaluator
 import tensorflow as tf
 %matplotlib inline
 import keras
 keras.__version__



 # Set a few configuration parameters.
 img_height = 300
 img_width = 300
 n_classes = 20
 model_mode = 'inference'


 K.clear_session() # Clear previous models from memory.

 model = ssd_300(image_size=(img_height,img_width,3),n_classes=n_classes,mode=model_mode,l2_regularization=0.0005,scales=[0.1,0.2,0.37,0.54,0.71,0.88,1.05],# The scales 
 for MS COCO [0.07,0.15,0.33,0.51,0.69,0.87,1.05]
            aspect_ratios_per_layer=[[1.0,0.5],[1.0,0.5,1.0/3.0],0.5]],two_boxes_for_ar1=True,steps=[8,16,32,64,100,300],offsets=[0.5,clip_boxes=False,variances=[0.1,0.1,0.2],normalize_coords=True,subtract_mean=[123,117,104],swap_channels=[2,1,0],confidence_thresh=0.01,iou_threshold=0.45,top_k=200,nms_max_output_size=400)

 # 2: Load the trained weights into the model.

 # TODO: Set the path of the trained weights.
 weights_path = 'C:/Users/USAgData/TF SSD 
 Keras/weights/VGG_VOC0712Plus_SSD_300x300_iter_240000.h5'

 model.load_weights(weights_path,by_name=True)

 # 3: Compile the model so that Keras won't complain the next time you load it.

 adam = Adam(lr=0.001,beta_1=0.9,beta_2=0.999,epsilon=1e-08,decay=0.0)

 ssd_loss = SSDLoss(neg_pos_ratio=3,alpha=1.0)

 model.compile(optimizer=adam,loss=ssd_loss.compute_loss)


dataset = DataGenerator()

# TODO: Set the paths to the dataset here.
dir= "C:/Users/USAgData/TF SSD Keras/VOC/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/"
Pascal_VOC_dataset_images_dir = dir+ 'JPEGImages'
Pascal_VOC_dataset_annotations_dir = dir + 'Annotations/'
Pascal_VOC_dataset_image_set_filename = dir+'ImageSets/Main/test.txt'

# The XML parser needs to now what object class names to look for and in which order to map them to integers.
classes = ['background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']

dataset.parse_xml(images_dirs=[Pascal_VOC_dataset_images_dir],image_set_filenames=[Pascal_VOC_dataset_image_set_filename],annotations_dirs=[Pascal_VOC_dataset_annotations_dir],classes=classes,include_classes='all',exclude_truncated=False,exclude_difficult=False,ret=False)



evaluator = Evaluator(model=model,data_generator=dataset,model_mode=model_mode)



results = evaluator(img_height=img_height,img_width=img_width,batch_size=8,data_generator_mode='resize',round_confidences=False,matching_iou_threshold=0.5,border_pixels='include',sorting_algorithm='quicksort',average_precision_mode='sample',num_recall_points=11,ignore_neutral_boxes=True,return_precisions=True,return_recalls=True,return_average_precisions=True,verbose=True)