habana_frameworks.mediapipe.fn.Constant
habana_frameworks.mediapipe.fn.Constant¶
- Class:
habana_frameworks.mediapipe.fn.Constant(**kwargs)
- Define graph call:
__call__(input)
- Parameter:
input - Input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT8, UINT8, BFLOAT16, FLOAT32.
Description:
The output tensor is filled with the data from the input tensor. The shape of the output tensor is determined by the shape of the input tensor.
- Supported backend:
HPU
Keyword Arguments
kwargs |
Description |
---|---|
dtype |
Output data type.
|
Note
The input and the output tensors must be of the same data type.
The input tensor should be contiguous.
Example: Constant Operator
The following code snippet shows usage of Constant 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
# 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")
# WHCN
self.decode = fn.ImageDecoder(device="hpu",
output_format=it.RGB_P,
resize=[img_w, img_h])
self.constant = fn.Constant(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()
images = self.decode(images)
const_data = self.constant(images)
return const_data, 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
const_data, labels = pipe.run()
# Copy data to host from device as numpy array
const_data = const_data.as_cpu().as_nparray()
labels = labels.as_cpu().as_nparray()
# Display shape, dtype, data
print('constant tensor shape:', const_data.shape)
print('constant tensor dtype:', const_data.dtype)
print('data:\n', const_data)
if __name__ == "__main__":
main()
The following is the output of Constant operator:
constant tensor shape: (6, 3, 200, 200)
constant tensor dtype: uint8
data:
[[[[122 121 118 ... 109 108 109]
[119 118 115 ... 113 110 106]
[121 120 117 ... 110 110 108]
...
[ 78 75 75 ... 39 44 46]
[ 73 72 74 ... 44 46 48]
[ 79 77 78 ... 44 46 48]]]]