habana_frameworks.mediapipe.fn.CoinFlip
habana_frameworks.mediapipe.fn.CoinFlip¶
- Class:
habana_frameworks.mediapipe.fn.CoinFlip(**kwargs)
- Define graph call:
__call__(input, seed)
- Parameter:
probability - Input probability tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: FLOAT16, BFLOAT16, FLOAT32.
(optional) seed - Seed to the random number generator. This is a scalar value. Supported dimensions: minimum = 1, maximum = 1. Supported data types: UINT32.
Description:
Outputs a tensor with Random numbers from a Bernoulli distribution. Bernoulli distribution is the discrete probability distribution of a random variable which takes the value 1 with probability p and the value 0 with probability q = 1 - p.
- Supported backend:
HPU
Keyword Arguments
kwargs |
Description |
---|---|
seed |
Seed to the random number generator, only positive integer.
|
dtype |
Output data type.
|
Note
Input type BF16/F16 maps to output type INT16 and Input type FLOAT32 maps to Output type INT32.
Seed can be given either as a static attribute or as a input tensor. If given as input tensor, the static parameter will be ignored.
Example: CoinFlip Operator
The following code snippet shows usage of CoinFlip operator. The predicate tensor generated by CoinFlip operator is fed to RandomFlip operator for flipping the images:
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
import numpy as np
# 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.input_data = np.ones(shape=[batch_size],
dtype=np.float32) * 0.3
self.input_node = fn.MediaConst(data=self.input_data,
shape=[batch_size],
dtype='float32')
self.random_bernoulli = fn.CoinFlip(seed=100,
dtype=dt.INT32)
self.random_flip = fn.RandomFlip(horizontal=1)
# 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)
input_node = self.input_node()
predicate = self.random_bernoulli(input_node)
images = self.random_flip(images, predicate)
images = self.transpose(images)
return images, labels, predicate
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, predicate = pipe.run()
# Copy data to host from device as numpy array
images = images.as_cpu().as_nparray()
labels = labels.as_cpu().as_nparray()
predicate = predicate.as_cpu().as_nparray()
# Display images
plt.figure(figsize=(10, 10))
for i in range(batch_size):
ax = plt.subplot(2, 3, i + 1)
plt.imshow(images[i].astype("uint8"))
title = "flipped" if predicate[i] else "not flipped"
plt.title(title)
plt.axis("off")
plt.show()
print('predicate dtype:', predicate.dtype)
print('predicate:\n', predicate)
if __name__ == "__main__":
main()
The following is the output for CoinFlip operator:
predicate dtype: uint8
predicate:
[1 1 0 1 1 0]