habana_frameworks.mediapipe.fn.Constant

Class:

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

Define graph call:

• __call__()

Parameter:

None

Description:

Constant operator generates scalar constant value tensor.

Supported backend:

• HPU, CPU

Keyword Arguments

kwargs	Description
constant	Constant value. Type: float Default: 0.0 Optional: no
dtype	Output data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: UINT8 Optional: yes Supported data types: INT8 UINT8 BFLOAT16 FLOAT32

Example: Constant Operator

The following code snippet shows usage of Constant operator:

from habana_frameworks.mediapipe import fn
from habana_frameworks.mediapipe.mediapipe import MediaPipe
from habana_frameworks.mediapipe.media_types import dtype as dt
import numpy as np

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

        self.inp = fn.ReadNumpyDatasetFromDir(num_outputs=1,
                                              shuffle=False,
                                              dir=dir,
                                              pattern="inp_x_*.npy",
                                              dense=True,
                                              dtype=dt.FLOAT32,
                                              device=device)

        self.const = fn.Constant(constant=0.5,
                                dtype=dt.FLOAT32,
                                device=device)

        self.mul = fn.Mult(device=device)

    def definegraph(self):
        inp = self.inp()
        const = self.const()
        out = self.mul(inp, const)
        return out, inp, const


def main():
    batch_size = 1
    queue_depth = 2
    num_threads = 1
    device = 'hpu'
    dir = '/path/to/numpy/files'

    # Create media pipeline object
    pipe = myMediaPipe(device, queue_depth, batch_size, num_threads, dir)

    # Build media pipeline
    pipe.build()

    # Initialize media pipeline iterator
    pipe.iter_init()

    # Run media pipeline
    out, inp, const = pipe.run()

    if (device == 'cpu'):
        # Copy data as numpy array
        out = out.as_nparray()
        inp = inp.as_nparray()
        const = const.as_nparray()
    else:
        # Copy data to host from device as numpy array
        out = out.as_cpu().as_nparray()
        inp = inp.as_cpu().as_nparray()
        const = const.as_cpu().as_nparray()

    print("\nconst tensor shape:", const.shape)
    print("const tensor dtype:", const.dtype)
    print("const tensor data:\n", const)

    print("\ninp tensor shape:", inp.shape)
    print("inp tensor dtype:", inp.dtype)
    print("inp tensor data:\n", inp)

    print("\nout tensor shape:", out.shape)
    print("out tensor dtype:", out.dtype)
    print("out tensor data:\n", out)

    pipe.del_iter()


if __name__ == "__main__":
    main()

The following is the output of Constant operator:

const tensor shape: (1,)
const tensor dtype: float32
const tensor data:
[0.5]

inp tensor shape: (1, 3, 2, 3)
inp tensor dtype: float32
inp tensor data:
[[[[182. 227. 113.]
  [175. 128. 253.]]

  [[ 58. 140. 136.]
  [ 86.  80. 111.]]

  [[175. 196. 178.]
  [ 20. 163. 108.]]]]

out tensor shape: (1, 3, 2, 3)
out tensor dtype: float32
out tensor data:
[[[[ 91.  113.5  56.5]
  [ 87.5  64.  126.5]]

  [[ 29.   70.   68. ]
  [ 43.   40.   55.5]]

  [[ 87.5  98.   89. ]
  [ 10.   81.5  54. ]]]]