habana_frameworks.mediapipe.fn.Cast

Class:

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

Define graph call:

• __call__(input)

Parameter:

• input - Input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: UINT8, UINT16, UINT32, INT8, INT16, INT32, FLOAT16, BFLOAT16, FLOAT32.

Description:

This operator changes the data type of the input tensor.

Supported backend:

• HPU

Keyword Arguments

kwargs	Description
round_mode	Rounding mode selection. Type: CastF32RoundMode_t Default: 0 Optional: yes
dtype	Output data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: UINT8 Optional: yes Supported data types: INT8 UINT8 INT32 UINT32 BFLOAT16 FLOAT32

Note

1. Input and output tensors must have the different data type. Self cast is not allowed.

2. Cast between all supported data types is allowed.

Example: Cast Operator

The following code snippet shows usage of Cast operator, decoder output is casted to FLOAT32. Print of output image array shows float numbers.

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

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

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

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

        self.cast = fn.Cast(dtype=dt.FLOAT32)

    def definegraph(self):
        jpegs, labels = self.input()
        images = self.decode(jpegs)
        images = self.cast(images)
        return images, labels

def main():
    batch_size = 6
    img_width = 224
    img_height = 224
    queue_depth = 3
    img_dir="/path/to/images"

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

    # 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(images)

if __name__ == "__main__":
    main()

The following is the output of Cast operator:

[[[[122. 113.  94.]
  [121. 112.  93.]
  [119. 111.  94.]
  ...
  [108.  99. 114.]
  [108.  99. 114.]
  [109. 100. 115.]]

  ...

  [[101. 125.  78.]
  [100. 123.  78.]
  [ 97. 120.  78.]

  ...

  [ 41.  50.  44.]
  [ 42.  51.  46.]
  [ 45.  54.  49.]]]]