66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
from keras_core import backend
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from keras_core.api_export import keras_core_export
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from keras_core.backend import KerasTensor
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from keras_core.backend import any_symbolic_tensors
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from keras_core.operations.operation import Operation
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class Resize(Operation):
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def __init__(
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self,
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size,
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method="bilinear",
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antialias=False,
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data_format="channels_last",
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):
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super().__init__()
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self.size = tuple(size)
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self.method = method
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self.antialias = antialias
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self.data_format = data_format
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def call(self, image):
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return backend.image.resize(
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image,
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self.shape,
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method=self.method,
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antialias=self.antialias,
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data_format=self.data_format,
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)
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def compute_output_spec(self, image):
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if len(image.shape) == 3:
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return KerasTensor(
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self.size + (image.shape[-1],), dtype=image.dtype
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)
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elif len(image.shape) == 4:
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if self.data_format == "channels_last":
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return KerasTensor(
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(image.shape[0],) + self.size + (image.shape[-1],),
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dtype=image.dtype,
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)
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else:
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return KerasTensor(
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(image.shape[0], image.shape[1]) + self.size,
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dtype=image.dtype,
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)
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raise ValueError(
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"Invalid input rank: expected rank 3 (single image) "
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"or rank 4 (batch of images). Received input with shape: "
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f"image.shape={image.shape}"
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)
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@keras_core_export("keras_core.operations.image.resize")
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def resize(
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image, size, method="bilinear", antialias=False, data_format="channels_last"
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):
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# TODO: add docstring
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if any_symbolic_tensors((image,)):
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return Resize(
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size, method=method, antialias=antialias, data_format=data_format
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).symbolic_call(image)
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return backend.image.resize(
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image, size, method=method, antialias=antialias, data_format=data_format
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)
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