Function Differences with tf.image.random_crop
tf.image.random_crop
tf.image.random_crop(
value,
size,
seed=None,
name=None
)
For more information, see tf.image.random_crop.
mindspore.dataset.vision.RandomCrop
class mindspore.dataset.vision.RandomCrop(
size,
padding=None,
pad_if_needed=False,
fill_value=0,
padding_mode=Border.CONSTANT
)
For more information, see mindspore.dataset.vision.RandomCrop.
Differences
TensorFlow: Crop the image at a random position with the specified random seed.
MindSpore: Crop the image at a random position and pad if needed. The global random seed can be set through mindspore.dataset.config.set_seed
.
Code Example
# The following implements RandomCrop with MindSpore.
import numpy as np
import mindspore.dataset as ds
ds.config.set_seed(57)
image = np.random.random((28, 28, 3))
result = ds.vision.RandomCrop((5, 5))(image)
print(result.shape)
# (5, 5, 3)
# The following implements random_crop with TensorFlow.
import tensorflow as tf
image = tf.random.normal((28, 28, 3))
result = tf.image.random_crop(image, (5, 5, 3), seed=57)
print(result.shape)
# (5, 5, 3)