habana_frameworks.mediapipe.fn.BitwiseAnd
habana_frameworks.mediapipe.fn.BitwiseAnd¶
- Class:
habana_frameworks.mediapipe.fn.BitwiseAnd(**kwargs)
- Define graph call:
__call__(input1, input2)
- Parameter:
input1 - First input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT8, INT16, INT32, UINT8, UINT16, UINT32.
input2 - Second input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT8, INT16, INT32, UINT8, UINT16, UINT32.
Description:
Computes the output by doing bitwise AND
operation on input tensors. Output is calculated as B = &(A).
- Supported backend:
HPU
Keyword Arguments
kwargs |
Description |
---|---|
dtype |
Output data type.
|
Note
All input/output tensors must be of the same data type and must have the same dimensionality.
This operator is agnostic to the data layout.
Example: BitwiseAnd Operator
The following code snippet shows usage of BitwiseAnd 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 matplotlib.pyplot as plt
from numpy.random import RandomState
# Create media pipeline derived class
class myMediaPipe(MediaPipe):
def __init__(self, device, dir, queue_depth, batch_size, img_h, img_w, img_c):
super(
myMediaPipe,
self).__init__(
device,
queue_depth,
batch_size,
self.__class__.__name__)
self.input = fn.ReadImageDatasetFromDir(shuffle=False,
dir=dir,
format="jpg")
rng = RandomState(seed=100)
self.input_data = rng.randint(0,
255,
size=(img_w, img_h, img_c, batch_size),
dtype=dt.UINT8)
self.input_node = fn.MediaConst(data=self.input_data,
shape=[img_w, img_h, img_c, batch_size],
dtype=dt.UINT8)
# WHCN
self.decode = fn.ImageDecoder(device="hpu",
output_format=it.RGB_P,
resize=[img_w, img_h])
self.bitwise_and = fn.BitwiseAnd(dtype=dt.UINT8)
# WHCN -> CWHN
self.transpose = fn.Transpose(permutation=[2, 0, 1, 3],
tensorDim=4,
dtype=dt.UINT8)
def definegraph(self):
images, labels = self.input()
images1 = self.decode(images)
images2 = self.input_node()
images = self.bitwise_and(images1, images2)
images = self.transpose(images)
return images, labels
def main():
batch_size = 6
img_width = 200
img_height = 200
img_channel = 3
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, img_channel)
# 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('data:\n', images)
if __name__ == "__main__":
main()
The following is the output of BitwiseAnd operator: