habana_frameworks.mediapipe.fn.ReduceMin

Class:

• habana_frameworks.mediapipe.fn.ReduceMin(**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:

Computes the minimum of the input tensor’s elements along the provided dimension. Along with reduced output tensor, the index tensor (Retained-Index) is also produced. The resultant tensor stores the index of minimum element.

Supported backend:

• HPU

Keyword Arguments

kwargs	Description
reductionDimension	The dimension in which to perform the reduction operation. Type: int Default: 0 Optional: yes
dtype	Output data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: UINT8 Optional: yes Supported data types: INT32 BFLOAT16 FLOAT32

Note

1. All input/output tensors must be of the same data type.

2. All tensors should have the same dimensionality.

3. Size of the reduction dimension in the output tensor must be 1.

4. The size of the index tensor must be equal to the size of the output tensor.

Example: ReduceMin Operator

The following code snippet shows usage of ReduceMin 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

# 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])

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

        self.min = fn.ReduceMin(reductionDimension=0,
                                dtype=dt.FLOAT32)

    def definegraph(self):
        images, label = self.input()
        images = self.decode(images)
        images = self.pre_cast(images)
        images, images_i = self.min(images)
        return images, images_i

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

    # 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
    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 shape, data
    print('shape:', images.shape)
    print('data:\n', images)

if __name__ == "__main__":
    main()

The following is the output of ReduceMin operator:

shape: (6, 3, 200, 1)
data:
[[[[47.]
  [47.]
  [48.]

  ...

  [31.]
  [33.]
  [40.]]]]