habana_frameworks.mediapipe.fn.Neg
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.
|
Note
All input/output tensors must be of the same data type and they must have the same dimensionality.
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
# 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")
self.cast = fn.Cast(dtype=dt.FLOAT32, round_mode=0)
# WHCN
self.decode = fn.ImageDecoder(device="hpu",
output_format=it.RGB_P,
resize=[img_w, img_h])
self.neg = fn.Neg(dtype=dt.FLOAT32)
# WHCN -> CWHN
self.transpose = fn.Transpose(permutation=[2, 0, 1, 3],
tensorDim=4,
dtype=dt.FLOAT32)
def definegraph(self):
images, labels = self.input()
images = self.decode(images)
images = self.cast(images)
images = self.neg(images)
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
columns = 3
# 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 Neg operator:
data: [[[[-122. -113. -94.] [-121. -112. -93.] [-118. -110. -95.] ... [ -38. -49. -44.] [ -42. -51. -46.] [ -44. -53. -48.]]]]