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
import glob

# Create media pipeline derived class
class myMediaPipe(MediaPipe):
    def __init__(self, device, queue_depth, patch_size, num_channels, batch_size, num_threads, img_list, lbl_list, seed):
        super(myMediaPipe, self).__init__(
            device,
            queue_depth,
            batch_size,
            num_threads,
            self.__class__.__name__)

        self.images = fn.ReadNumpyDatasetFromDir(device=device,
                                                num_outputs=1,
                                                shuffle=False,
                                                shuffle_across_dataset=False,
                                                file_list=img_list,
                                                dtype=[dt.FLOAT32],
                                                dense=False,
                                                seed=seed,
                                                num_slices=1,
                                                slice_index=0,
                                                drop_remainder=True,
                                                pad_remainder=False
                                                )

        self.labels = fn.ReadNumpyDatasetFromDir(device=device,
                                                num_outputs=1,
                                                shuffle=False,
                                                shuffle_across_dataset=False,
                                                file_list=lbl_list,
                                                dtype=[dt.UINT8],
                                                dense=False,
                                                seed=seed,
                                                num_slices=1,
                                                slice_index=0,
                                                drop_remainder=True,
                                                pad_remainder=False
                                                )

        self.crop = fn.RandomBiasedCrop(patch_size=patch_size,
                                        num_channels=num_channels,
                                        seed=seed,
                                        num_workers=4,
                                        cache_bboxes=True,
                                        device=device,
                                        )

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

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

    def definegraph(self):
        img = self.images()
        lbl = self.labels()
        img, lbl, coord = self.crop(img, lbl)
        scale = self.const_val()
        img_out = self.mul(img, scale)
        return img, scale, img_out


def main():
    batch_size = 1
    patch_size = [5, 5, 5]
    queue_depth = 2
    num_channels = 1
    num_threads = 1
    dir = "/path/to/numpy/files/"
    pattern0 = "case_*_x.npy"
    pattern1 = "case_*_y.npy"
    image_list = np.array(sorted(glob.glob(dir + "/{}".format(pattern0))))
    label_list = np.array(sorted(glob.glob(dir + "/{}".format(pattern1))))
    device = 'hpu'
    seed = 1234

    # Create media pipeline object
    pipe = myMediaPipe(device, queue_depth, patch_size,
                      num_channels, batch_size, num_threads, image_list, label_list, seed)

    # Build media pipeline
    pipe.build()

    # Initialize media pipeline iterator
    pipe.iter_init()

    # Run media pipeline
    input, constant, multiplication = pipe.run()

    if (device == 'cpu'):
        # Copy data as numpy array
        input = input.as_nparray()
        constant = constant.as_nparray()
        multiplication = multiplication.as_nparray()
    else:
        # Copy data to host from device as numpy array
        input = input.as_cpu().as_nparray()
        constant = constant.as_cpu().as_nparray()
        multiplication = multiplication.as_cpu().as_nparray()

    print("\nconstant op tensor shape:", constant.shape)
    print("\nconstant op tensor dtype:", constant.dtype)
    print("\nconstant op tensor data:", constant)


if __name__ == "__main__":
    main()

The following is the output of Constant operator:

constant tensor shape: (1,)

constant tensor dtype: float32

constant tensor data: [0.5]