habana_frameworks.mediapipe.fn.Neg

Class:

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

Define graph call:

• __call__(input)

Parameter:

• input - Input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT32, FLOAT16, BFLOAT16, FLOAT32.

Description:

Resultant output tensor has the sign flipped for each element in input, computed as: y = -x.

Supported backend:

• HPU

Keyword Arguments

kwargs	Description
dtype	Ouptput data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: FLOAT32 Optional: yes Supported data types: INT32 BFLOAT16 FLOAT32

Note

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

2. This operator is agnostic to the data layout.

Example: Neg Operator

The following code snippet shows usage of Neg 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 os
import numpy as np

# Create MediaPipe derived class


class myMediaPipe(MediaPipe):
    def __init__(self, device, queue_depth, batch_size, num_threads, op_device, dir):
        super(myMediaPipe, self).__init__(
            device,
            queue_depth,
            batch_size,
            num_threads,
            self.__class__.__name__)

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

        self.neg = fn.Neg(dtype=dt.FLOAT32,
                          device=op_device)

    def definegraph(self):
        input = self.input()
        output = self.neg(input)
        return output, input


def run(device, op_device):
    batch_size = 2
    queue_depth = 2
    num_threads = 1
    base_dir = os.environ['DATASET_DIR']
    dir = base_dir+"/npy_data/fp32/"

    # Create MediaPipe object
    pipe = myMediaPipe(device, queue_depth, batch_size,
                      num_threads, op_device, dir)

    # Build MediaPipe
    pipe.build()

    # Initialize MediaPipe iterator
    pipe.iter_init()

    # Run MediaPipe
    out, inp = pipe.run()

    def as_cpu(tensor):
        if (callable(getattr(tensor, "as_cpu", None))):
            tensor = tensor.as_cpu()
        return tensor
    inp = as_cpu(inp).as_nparray()
    out = as_cpu(out).as_nparray()

    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)
    return inp, out

def compare_ref(inp, out):
    ref = np.negative(inp)
    if np.array_equal(ref, out) == False:
        raise ValueError(f"Mismatch w.r.t ref for device")


if __name__ == "__main__":
    dev_opdev = {'mixed': ['hpu'],
                'legacy': ['hpu']}
    for dev in dev_opdev.keys():
        for op_dev in dev_opdev[dev]:
            inp, out = run(dev, op_dev)
            compare_ref(inp, out)

The following is the output of Neg operator:

inp tensor shape: (2, 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.]]]


[[[186. 254.  96.]
  [180.  64. 132.]]

  [[149.  50. 117.]
  [213.   6. 111.]]

  [[ 77.  11. 160.]
  [129. 102. 154.]]]]

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

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

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


[[[-186. -254.  -96.]
  [-180.  -64. -132.]]

  [[-149.  -50. -117.]
  [-213.   -6. -111.]]

  [[ -77.  -11. -160.]
  [-129. -102. -154.]]]]