VGGImageConverter
classkeras_hub.layers.VGGImageConverter(
image_size=None,
scale=None,
offset=None,
crop_to_aspect_ratio=True,
interpolation="bilinear",
data_format=None,
**kwargs
)
Preprocess raw images into model ready inputs.
This class converts from raw images to model ready inputs. This conversion proceeds in the following steps:
image_size
. If image_size
is None
, this
step will be skipped.scale
, which can be either global
or per channel. If scale
is None
, this step will be skipped.offset
, which can be either global
or per channel. If offset
is None
, this step will be skipped.The layer will take as input a raw image tensor in the channels last or channels first format, and output a preprocessed image input for modeling. This tensor can be batched (rank 4), or unbatched (rank 3).
This layer can be used with the from_preset()
constructor to load a layer
that will rescale and resize an image for a specific pretrained model.
Using the layer this way allows writing preprocessing code that does not
need updating when switching between model checkpoints.
Arguments
(int, int)
tuple or None
. The output size of the image,
not including the channels axis. If None
, the input will not be
resized.None
. The scale to apply to the
inputs. If scale
is a single float, the entire input will be
multiplied by scale
. If scale
is a tuple, it's assumed to
contain per-channel scale value multiplied against each channel of
the input images. If scale
is None
, no scaling is applied.None
. The offset to apply to the
inputs. If offset
is a single float, the entire input will be
summed with offset
. If offset
is a tuple, it's assumed to
contain per-channel offset value summed against each channel of the
input images. If offset
is None
, no scaling is applied.True
, resize the images without aspect
ratio distortion. When the original aspect ratio differs
from the target aspect ratio, the output image will be
cropped so as to return the
largest possible window in the image (of size (height, width)
)
that matches the target aspect ratio. By default
(crop_to_aspect_ratio=False
), aspect ratio may not be preserved."bilinear"
, "nearest"
, "bicubic"
,
"lanczos3"
, "lanczos5"
. Defaults to "bilinear"
."channels_last"
or "channels_first"
.
The ordering of the dimensions in the inputs. "channels_last"
corresponds to inputs with shape (batch, height, width, channels)
while "channels_first"
corresponds to inputs with shape
(batch, channels, height, width)
. It defaults to the
image_data_format
value found in your Keras config file at
~/.keras/keras.json
. If you never set it, then it will be
"channels_last"
.Examples
# Resize raw images and scale them to [0, 1].
converter = keras_hub.layers.ImageConverter(
image_size=(128, 128),
scale=1. / 255,
)
converter(np.random.randint(0, 256, size=(2, 512, 512, 3)))
# Resize images to the specific size needed for a PaliGemma preset.
converter = keras_hub.layers.ImageConverter.from_preset(
"pali_gemma_3b_224"
)
converter(np.random.randint(0, 256, size=(2, 512, 512, 3)))
from_preset
methodVGGImageConverter.from_preset(preset, **kwargs)
Instantiate a keras_hub.layers.ImageConverter
from a model preset.
A preset is a directory of configs, weights and other file assets used
to save and load a pre-trained model. The preset
can be passed as
one of:
'pali_gemma_3b_224'
'kaggle://user/paligemma/keras/pali_gemma_3b_224'
'hf://user/pali_gemma_3b_224'
'./pali_gemma_3b_224'
You can run cls.presets.keys()
to list all built-in presets available
on the class.
Arguments
True
, the weights will be loaded into the
model architecture. If False
, the weights will be randomly
initialized.Examples
batch = np.random.randint(0, 256, size=(2, 512, 512, 3))
# Resize images for `"pali_gemma_3b_224"`.
converter = keras_hub.layers.ImageConverter.from_preset(
"pali_gemma_3b_224"
)
converter(batch) # # Output shape (2, 224, 224, 3)
# Resize images for `"pali_gemma_3b_448"` without cropping.
converter = keras_hub.layers.ImageConverter.from_preset(
"pali_gemma_3b_448",
crop_to_aspect_ratio=False,
)
converter(batch) # # Output shape (2, 448, 448, 3)
Preset name | Parameters | Description |
---|---|---|
vgg_11_imagenet | 9.22M | 11-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
vgg_13_imagenet | 9.40M | 13-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
vgg_16_imagenet | 14.71M | 16-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
vgg_19_imagenet | 20.02M | 19-layer vgg model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |