The tfds.decode API allows you override the default feature decoding. The main
use case is to skip the image decoding for better performance.
Usage examples
Skipping the image decoding
To keep full control over the decoding pipeline, or to apply a filter before the
images get decoded (for better performance), you can skip the image decoding
entirely. This works with both tfds.features.Image and tfds.features.Video.
ds = tfds.load('imagenet2012', split='train', decoders={
'image': tfds.decode.SkipDecoding(),
})
for example in ds.take(1):
assert example['image'].dtype == tf.string # Images are not decoded
Filter/shuffle dataset before images get decoded
Similarly to the previous example, you can use tfds.decode.SkipDecoding() to
insert additional tf.data pipeline customization before decoding the image.
That way the filtered images won't be decoded and you can use a bigger shuffle
buffer.
# Load the base dataset without decoding
ds, ds_info = tfds.load(
'imagenet2012',
split='train',
decoders={
'image': tfds.decode.SkipDecoding(), # Image won't be decoded here
},
as_supervised=True,
with_info=True,
)
# Apply filter and shuffle
ds = ds.filter(lambda image, label: label != 10)
ds = ds.shuffle(10000)
# Then decode with ds_info.features['image']
ds = ds.map(
lambda image, label: ds_info.features['image'].decode_example(image), label)
Cropping and decoding at the same time
To override the default tf.io.decode_image operation, you can create a new
tfds.decode.Decoder object using the tfds.decode.make_decoder() decorator.
@tfds.decode.make_decoder()
def decode_example(serialized_image, feature):
crop_y, crop_x, crop_height, crop_width = 10, 10, 64, 64
return tf.image.decode_and_crop_jpeg(
serialized_image,
[crop_y, crop_x, crop_height, crop_width],
channels=feature.feature.shape[-1],
)
ds = tfds.load('imagenet2012', split='train', decoders={
# With video, decoders are applied to individual frames
'image': decode_example(),
})
Which is equivalent to:
def decode_example(serialized_image, feature):
crop_y, crop_x, crop_height, crop_width = 10, 10, 64, 64
return tf.image.decode_and_crop_jpeg(
serialized_image,
[crop_y, crop_x, crop_height, crop_width],
channels=feature.shape[-1],
)
ds, ds_info = tfds.load(
'imagenet2012',
split='train',
with_info=True,
decoders={
'image': tfds.decode.SkipDecoding(), # Skip frame decoding
},
)
ds = ds.map(functools.partial(decode_example, feature=ds_info.features['image']))
Customizing video decoding
Video are Sequence(Image()). When applying custom decoders, they will be
applied to individual frames. This mean decoders for images are automatically
compatible with video.
@tfds.decode.make_decoder()
def decode_example(serialized_image, feature):
crop_y, crop_x, crop_height, crop_width = 10, 10, 64, 64
return tf.image.decode_and_crop_jpeg(
serialized_image,
[crop_y, crop_x, crop_height, crop_width],
channels=feature.feature.shape[-1],
)
ds = tfds.load('ucf101', split='train', decoders={
# With video, decoders are applied to individual frames
'video': decode_example(),
})
Which is equivalent to:
def decode_frame(serialized_image):
"""Decodes a single frame."""
crop_y, crop_x, crop_height, crop_width = 10, 10, 64, 64
return tf.image.decode_and_crop_jpeg(
serialized_image,
[crop_y, crop_x, crop_height, crop_width],
channels=ds_info.features['video'].shape[-1],
)
def decode_video(example):
"""Decodes all individual frames of the video."""
video = example['video']
video = tf.map_fn(
decode_frame,
video,
dtype=ds_info.features['video'].dtype,
parallel_iterations=10,
back_prop=False,
)
example['video'] = video
return example
ds, ds_info = tfds.load('ucf101', split='train', with_info=True, decoders={
'video': tfds.decode.SkipDecoding(), # Skip frame decoding
})
ds = ds.map(decode_video) # Decode the video