"The Conv2D op currently only supports the NHWC tensor format on the CPU" Error despite NHWC Format (YOLO 3)

Question

I am trying to get to run a bit of sample code from github in order to learn Working with Tensorflow 2 and the YOLO Framework. My Laptop has a M1000M Graphics Card and I installed the CUDA Platform from NVIDIA from here.

So the Code in question is this bit:

tf.compat.v1.disable_eager_execution()

_MODEL_SIZE = (416, 416)
_CLASS_NAMES_FILE = './data/labels/coco.names'
_MAX_OUTPUT_SIZE = 20


def main(type, iou_threshold, confidence_threshold, input_names):
    class_names = load_class_names(_CLASS_NAMES_FILE)
    n_classes = len(class_names)

    model = Yolo_v3(n_classes=n_classes, model_size=_MODEL_SIZE,
                    max_output_size=_MAX_OUTPUT_SIZE,
                    iou_threshold=iou_threshold,
                    confidence_threshold=confidence_threshold)

    if type == 'images':
        batch_size = len(input_names)
        batch = load_images(input_names, model_size=_MODEL_SIZE)
        inputs = tf.compat.v1.placeholder(tf.float32, [batch_size, *_MODEL_SIZE, 3])
        detections = model(inputs, training=False)
        saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables(scope='yolo_v3_model'))

        with tf.compat.v1.Session() as sess:
            saver.restore(sess, './weights/model.ckpt')
            detection_result = sess.run(detections, feed_dict={inputs: batch})

        draw_boxes(input_names, detection_result, class_names, _MODEL_SIZE)

        print('Detections have been saved successfully.')

While executing this (also wondering why starting the detection.py doesnt use GPU in the first place), I get the Error Message:

File "C:\SDKs etc\Python 3.8\lib\site-packages\tensorflow\python\client\session.py", line 1451, in _call_tf_sessionrun
    return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.UnimplementedError: The Conv2D op currently only supports the NHWC tensor format on the CPU. The op was given the format: NCHW
         [[{{node yolo_v3_model/conv2d/Conv2D}}]]

Full Log see here.

If I am understanding this correctly, the format of inputs = tf.compat.v1.placeholder(tf.float32, [batch_size, *_MODEL_SIZE, 3]) is already NHWC (Model Size is a tuple of 2 Numbers) and I don't know how I need to change things in Code to get this running on CPU.

Answer 1

If I am understanding this correctly, the format of inputs = tf.compat.v1.placeholder(tf.float32, [batch_size, *_MODEL_SIZE, 3]) is already NHWC (Model Size is a tuple of 2 Numbers) and I don't know how I need to change things in Code to get this running on CPU.

Yes you are. But look here:

def __init__(self, n_classes, model_size, max_output_size, iou_threshold,
                 confidence_threshold, data_format=None):
        """Creates the model.
        Args:
            n_classes: Number of class labels.
            model_size: The input size of the model.
            max_output_size: Max number of boxes to be selected for each class.
            iou_threshold: Threshold for the IOU.
            confidence_threshold: Threshold for the confidence score.
            data_format: The input format.
        Returns:
            None.
        """
        if not data_format:
            if tf.test.is_built_with_cuda():
                data_format = 'channels_first'
            else:
                data_format = 'channels_last'

And later:

def __call__(self, inputs, training):
    """Add operations to detect boxes for a batch of input images.
    Args:
        inputs: A Tensor representing a batch of input images.
        training: A boolean, whether to use in training or inference mode.
    Returns:
        A list containing class-to-boxes dictionaries
            for each sample in the batch.
    """
    with tf.compat.v1.variable_scope('yolo_v3_model'):
        if self.data_format == 'channels_first':
            inputs = tf.transpose(inputs, [0, 3, 1, 2])

Solution:

1. check tf.test.is_built_with_cuda() work as expected

2. if not - set order manually when create model:

   model = Yolo_v3(n_classes=n_classes, model_size=_MODEL_SIZE,
max_output_size=_MAX_OUTPUT_SIZE,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold,
data_format = 'channels_last')