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Merged
merged 1 commit into from
Jun 11, 2020
Merged

use raw ops #1914

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47 changes: 3 additions & 44 deletions tensorflow_addons/image/transform_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,13 +16,11 @@

import tensorflow as tf
from tensorflow_addons.image import utils as img_utils
from tensorflow_addons.utils.resource_loader import LazySO
from tensorflow_addons.utils.types import TensorLike
from tensorflow_addons.image.utils import wrap, unwrap

from typing import Optional

_image_so = LazySO("custom_ops/image/_image_ops.so")

_IMAGE_DTYPES = {
tf.dtypes.uint8,
Expand All @@ -34,7 +32,6 @@
}


@tf.function
def transform(
images: TensorLike,
transforms: TensorLike,
Expand Down Expand Up @@ -108,10 +105,10 @@ def transform(
% len(transforms.get_shape())
)

output = _image_so.ops.addons_image_projective_transform_v2(
images,
output_shape=output_shape,
output = tf.raw_ops.ImageProjectiveTransformV2(
images=images,
transforms=transforms,
output_shape=output_shape,
interpolation=interpolation.upper(),
)
return img_utils.from_4D_image(output, original_ndims)
Expand Down Expand Up @@ -271,44 +268,6 @@ def angles_to_projective_transforms(
)


@tf.RegisterGradient("Addons>ImageProjectiveTransformV2")
def _image_projective_transform_grad(op, grad):
"""Computes the gradient for ImageProjectiveTransform."""
images = op.inputs[0]
transforms = op.inputs[1]
interpolation = op.get_attr("interpolation")

image_or_images = tf.convert_to_tensor(images, name="images")
transform_or_transforms = tf.convert_to_tensor(
transforms, name="transforms", dtype=tf.dtypes.float32
)

if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
raise ValueError("Invalid dtype %s." % image_or_images.dtype)
if len(transform_or_transforms.get_shape()) == 1:
transforms = transform_or_transforms[None]
elif len(transform_or_transforms.get_shape()) == 2:
transforms = transform_or_transforms
else:
transforms = transform_or_transforms
raise ValueError(
"transforms should have rank 1 or 2, but got rank %d"
% len(transforms.get_shape())
)

# Invert transformations
transforms = flat_transforms_to_matrices(transforms=transforms)
inverse = tf.linalg.inv(transforms)
transforms = matrices_to_flat_transforms(inverse)
output = _image_so.ops.addons_image_projective_transform_v2(
images=grad,
transforms=transforms,
output_shape=tf.shape(image_or_images)[1:3],
interpolation=interpolation,
)
return [output, None, None]


def rotate(
images: TensorLike,
angles: TensorLike,
Expand Down