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1327f34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # Copyright 2025 The Scenic Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Preprocessing utils.
"""
from collections import abc
import functools
import tensorflow.compat.v1 as tf
def maybe_repeat(arg, n_reps):
if not isinstance(arg, abc.Sequence):
arg = (arg,) * n_reps
return arg
def tf_apply_to_image_or_images(fn, image_or_images, **map_kw):
"""Applies a function to a single image or each image in a batch of them.
Args:
fn: the function to apply, receives an image, returns an image.
image_or_images: Either a single image, or a batch of images.
**map_kw: Arguments passed through to tf.map_fn if called.
Returns:
The result of applying the function to the image or batch of images.
Raises:
ValueError: if the input is not of rank 3 or 4.
"""
static_rank = len(image_or_images.get_shape().as_list())
if static_rank == 3: # A single image: HWC
return fn(image_or_images)
elif static_rank == 4: # A batch of images: BHWC
return tf.map_fn(fn, image_or_images, **map_kw)
elif static_rank > 4: # A batch of images: ...HWC
input_shape = tf.shape(image_or_images)
h, w, c = image_or_images.get_shape().as_list()[-3:]
image_or_images = tf.reshape(image_or_images, [-1, h, w, c])
image_or_images = tf.map_fn(fn, image_or_images, **map_kw)
return tf.reshape(image_or_images, input_shape)
else:
raise ValueError("Unsupported image rank: %d" % static_rank)
class BatchedImagePreprocessing(object):
"""Decorator for preprocessing ops, which adds support for image batches.
Note: Doesn't support decorating ops which add new fields in data.
"""
def __init__(self, output_dtype=None):
self.output_dtype = output_dtype
def __call__(self, get_pp_fn):
def get_batch_pp_fn(*args, **kwargs):
"""Preprocessing function that supports batched images."""
def _batch_pp_fn(image, *a, **kw):
orig_image_pp_fn = get_pp_fn(*args, **kwargs)
orig_image_pp_fn = functools.partial(orig_image_pp_fn, *a, **kw)
return tf_apply_to_image_or_images(
orig_image_pp_fn, image, dtype=self.output_dtype)
return _batch_pp_fn
return get_batch_pp_fn
class InKeyOutKey(object):
"""Decorator for preprocessing ops, which adds `inkey` and `outkey` arguments.
Note: Only supports single-input single-output ops.
"""
def __init__(self, uses_rngkey=False, indefault="image", outdefault="image"):
self.uses_rngkey = uses_rngkey
self.indefault = indefault
self.outdefault = outdefault
def __call__(self, orig_get_pp_fn):
def get_ikok_pp_fn(*args,
key=None,
inkey=self.indefault,
outkey=self.outdefault,
**kw):
# Support legacy arg from BatchedPreprocessing
key = kw.pop("data_key", key)
orig_pp_fn = orig_get_pp_fn(*args, **kw)
def _ikok_pp_fn(data):
if not self.uses_rngkey:
data[key or outkey] = orig_pp_fn(data[key or inkey])
else:
data[key or
outkey], data["_rngkey"] = orig_pp_fn(data[key or inkey],
data["_rngkey"])
return data
return _ikok_pp_fn
return get_ikok_pp_fn
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