Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,

Question

In Tensorflow/ Keras when running the code from https://github.com/pierluigiferrari/ssd_keras, use the estimator: ssd300_evaluation. I received this error.

Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

This is very similar to the unsolved question: Google Colab Error : Failed to get convolution algorithm.This is probably because cuDNN failed to initialize

With the issue I'm running:

python: 3.6.4.

Tensorflow Version: 1.12.0.

Keras Version: 2.2.4.

CUDA: V10.0.

cuDNN: V7.4.1.5.

NVIDIA GeForce GTX 1080.

Also I ran:

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, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
      c = tf.matmul(a, b)
with tf.Session() as sess:
print (sess.run(c))

With no errors or issues.

The minimalist example is:

 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, 2.0, 0.5],
                                     [1.0, 2.0, 0.5, 3.0, 1.0/3.0],
                                     [1.0, 2.0, 0.5, 3.0, 1.0/3.0],
                                     [1.0, 2.0, 0.5, 3.0, 1.0/3.0],
                                     [1.0, 2.0, 0.5],
                                     [1.0, 2.0, 0.5]],
            two_boxes_for_ar1=True,
            steps=[8, 16, 32, 64, 100, 300],
            offsets=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
            clip_boxes=False,
            variances=[0.1, 0.1, 0.2, 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,
                      n_classes=n_classes,
                      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)

Answer 1

I've seen this error message for three different reasons, with different solutions:

1. You have cache issues

I regularly work around this error by shutting down my python process, removing the ~/.nv directory (on linux, rm -rf ~/.nv), and restarting the Python process. I don't exactly know why this works. It's probably at least partly related to the second option:

2. You're out of memory

The error can also show up if you run out of graphics card RAM. With an nvidia GPU you can check graphics card memory usage with nvidia-smi. This will give you a readout of how much GPU RAM you have in use (something like 6025MiB / 6086MiB if you're almost at the limit) as well as a list of what processes are using GPU RAM.

If you've run out of RAM, you'll need to restart the process (which should free up the RAM) and then take a less memory-intensive approach. A few options are:

• reducing your batch size
• using a simpler model
• using less data
• limit TensorFlow GPU memory fraction: For example, the following will make sure TensorFlow uses <= 90% of your RAM:

import keras
import tensorflow as tf

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9  # 0.6 sometimes works better for folks
keras.backend.tensorflow_backend.set_session(tf.Session(config=config))

This can slow down your model evaluation if not used together with the items above, presumably since the large data set will have to be swapped in and out to fit into the small amount of memory you've allocated.

A second option is to have TensorFlow start out using only a minimum amount of memory and then allocate more as needed (documented here):

os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

3. You have incompatible versions of CUDA, TensorFlow, NVIDIA drivers, etc.

If you've never had similar models working, you're not running out of VRAM and your cache is clean, I'd go back and set up CUDA + TensorFlow using the best available installation guide - I have had the most success with following the instructions at https://www.tensorflow.org/install/gpu rather than those on the NVIDIA / CUDA site. Lambda Stack is also a good way to go.

Answer 2

I had the same issue, I solved it thanks to that :

os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

or

physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
   tf.config.experimental.set_memory_growth(physical_devices[0], True)

Answer 3

I had this error and I fixed it by uninstalling all CUDA and cuDNN versions from my system. Then I installed CUDA Toolkit 9.0 (without any patches) and cuDNN v7.4.1 for CUDA 9.0.