habana_frameworks.mediapipe.fn.MediaConst

Class:

• habana_frameworks.mediapipe.fn.MediaConst(**kwargs)

Define graph call:

• __call__()

Parameter:

• None

Description:

This operator generates constant data as per given shape and data type on every iteration.

Supported backend:

• CPU

Keyword Arguments

kwargs	Description
data	Data to be used as constant. Type: Numpy array Default: None Optional: no
layout	Layout of data. Type: str Default: ‘’ Optional: yes Supported types: NA = ‘’ NHWC = ‘CWHN’ NCHW = ‘WHCN’ FHWC = ‘CWHC’
shape	Shape of the output tensor. Type: list[int] Default: [1] Optional: no

Note

Produces one output.

Example: MediaConst Operator

The following code snippet shows usage of MediaConst operator. In this example the indices which are input to GatherND operation are generated using MediaConst 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
import numpy as np

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

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

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

        indices_data = np.array([[5], [4], [3], [2], [1], [0]], dtype='int32')

        self.indices = fn.MediaConst(data=indices_data,
                                     shape=[1, batch_size],
                                     dtype=dt.INT32)

        self.gather_nd = fn.GatherND(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()
        images = self.decode(images)
        indices = self.indices()
        images = self.gather_nd(images, indices)
        images = self.transpose(images)
        return images, labels

def display_images(images, batch_size, cols):
    rows = (batch_size + 1) // cols
    plt.figure(figsize=(10, 10))
    for i in range(batch_size):
        ax = plt.subplot(rows, cols, i + 1)
        plt.imshow(images[i])
        plt.axis("off")
    plt.show()

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

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

    # Build media pipeline
    pipe.build()

    # Initialize media pipeline iterator
    pipe.iter_init()

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

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

    # Display images
    display_images(images, batch_size, columns)

if __name__ == "__main__":
    main()

MediaConst() Generated Images from GatherND 1

1

Licensed under a CC BY SA 4.0 license. The images used here are taken from https://data.caltech.edu/records/mzrjq-6wc02.