habana_frameworks.mediapipe.fn.BitwiseOr

Class:
  • habana_frameworks.mediapipe.fn.BitwiseOr(**kwargs)

Define graph call:
  • __call__(input1, input2)

Parameter:
  • input1 - First input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT8, INT16, INT32, UINT8, UINT16, UINT32.

  • input2 - Second input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT8, INT16, INT32, UINT8, UINT16, UINT32.

Description:

Computes the output by doing bitwise OR operation on input tensors. Output is calculated as (B = |(A)).

Supported backend:
  • HPU

Keyword Arguments

kwargs

Description

dtype

Output data type.

  • Type: habana_frameworks.mediapipe.media_types.dtype

  • Default: UINT8

  • Optional: yes

  • Supported data types:

    • UINT8

    • UINT32

    • INT8

    • INT32

Note

  1. All input/output tensors must be of the same data type and must have the same dimensionality.

  2. This operator is agnostic to the data layout.

Example: BitwiseOr Operator

The following code snippet shows usage of BitwiseOr operator:

from habana_frameworks.mediapipe import fn
from habana_frameworks.mediapipe.mediapipe import MediaPipe
from habana_frameworks.mediapipe.media_types import imgtype as it
from habana_frameworks.mediapipe.media_types import dtype as dt
import matplotlib.pyplot as plt
from numpy.random import RandomState

# Create media pipeline derived class
class myMediaPipe(MediaPipe):
    def __init__(self, device, dir, queue_depth, batch_size, img_h, img_w, img_c):
        super(
            myMediaPipe,
            self).__init__(
            device,
            queue_depth,
            batch_size,
            self.__class__.__name__)

        self.input = fn.ReadImageDatasetFromDir(shuffle=False,
                                                dir=dir,
                                                format="jpg")

        rng = RandomState(seed=100)

        self.input_data = rng.randint(0,
                                      255,
                                      size=(img_w, img_h, img_c, batch_size),
                                      dtype=dt.UINT8)

        self.input_node = fn.MediaConst(data=self.input_data,
                                        shape=[img_w, img_h, img_c, batch_size],
                                        dtype=dt.UINT8)

        # WHCN
        self.decode = fn.ImageDecoder(device="hpu",
                                      output_format=it.RGB_P,
                                      resize=[img_w, img_h])

        self.bitwise_or = fn.BitwiseOr(dtype=dt.UINT8)

        # WHCN -> CWHN
        self.transpose = fn.Transpose(permutation=[2, 0, 1, 3],
                                      tensorDim=4,
                                      dtype=dt.UINT8)

    def definegraph(self):
        images, labels = self.input()
        images1 = self.decode(images)
        images2 = self.input_node()
        images = self.bitwise_or(images1, images2)
        images = self.transpose(images)
        return images, labels

def main():
    batch_size = 6
    img_width = 200
    img_height = 200
    img_channel = 3
    img_dir = "/path/to/images"
    queue_depth = 2

    # Create media pipeline object
    pipe = myMediaPipe('hpu', img_dir, queue_depth, batch_size,
                        img_height, img_width, img_channel)

    # Build media pipeline
    pipe.build()

    # Initialize media pipeline iterator
    pipe.iter_init()

    # Run media pipeline
    images, labels = pipe.run()

    # Copy data to host from device as numpy array
    images = images.as_cpu().as_nparray()
    labels = labels.as_cpu().as_nparray()

    # Display data
    print('data:\n', images)


if __name__ == "__main__":
    main()

The following is the output of BitwiseOr operator:

data:
[[[[122 245 222]
  [255 254 127]
  [126 239  95]

  ...

  [231  57 111]
  [127 255 190]
  [188 119 126]]]]