213 lines
8.5 KiB
Python
213 lines
8.5 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.layers.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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@keras_core_export("keras_core.layers.Cropping2D")
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class Cropping2D(Layer):
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"""Cropping layer for 2D input (e.g. picture).
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It crops along spatial dimensions, i.e. height and width.
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Examples:
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>>> input_shape = (2, 28, 28, 3)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> y = keras_core.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
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>>> y.shape
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(2, 24, 20, 3)
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Args:
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cropping: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
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- If int: the same symmetric cropping is applied to height and
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width.
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- If tuple of 2 ints: interpreted as two different symmetric
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cropping values for height and width:
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`(symmetric_height_crop, symmetric_width_crop)`.
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- If tuple of 2 tuples of 2 ints: interpreted as
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`((top_crop, bottom_crop), (left_crop, right_crop))`
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data_format: A string, one of `"channels_last"` (default) or
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`"channels_first"`. The ordering of the dimensions in the inputs.
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`"channels_last"` corresponds to inputs with shape
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`(batch_size, height, width, channels)` while `"channels_first"`
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corresponds to inputs with shape
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`(batch_size, channels, height, width)`.
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When unspecified, uses `image_data_format` value found in your Keras
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config file at `~/.keras/keras.json` (if exists). Defaults to
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`"channels_last"`.
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Input shape:
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4D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, height, width, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, height, width)`
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Output shape:
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4D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, cropped_height, cropped_width, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, cropped_height, cropped_width)`
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"""
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def __init__(
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self, cropping=((0, 0), (0, 0)), data_format=None, name=None, dtype=None
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):
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super().__init__(name=name, dtype=dtype)
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self.data_format = backend.standardize_data_format(data_format)
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if isinstance(cropping, int):
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self.cropping = ((cropping, cropping), (cropping, cropping))
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elif hasattr(cropping, "__len__"):
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if len(cropping) != 2:
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raise ValueError(
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"`cropping` should have two elements. "
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f"Received: cropping={cropping}."
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)
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height_cropping = cropping[0]
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if isinstance(height_cropping, int):
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height_cropping = (height_cropping, height_cropping)
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width_cropping = cropping[1]
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if isinstance(width_cropping, int):
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width_cropping = (width_cropping, width_cropping)
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self.cropping = (height_cropping, width_cropping)
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else:
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raise ValueError(
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"`cropping` should be either an int, a tuple of 2 ints "
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"(symmetric_height_crop, symmetric_width_crop), "
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"or a tuple of 2 tuples of 2 ints "
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"((top_crop, bottom_crop), (left_crop, right_crop)). "
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f"Received: cropping={cropping}."
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)
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self.input_spec = InputSpec(ndim=4)
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def compute_output_shape(self, input_shape):
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if self.data_format == "channels_first":
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if (
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input_shape[2] is not None
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and sum(self.cropping[0]) >= input_shape[2]
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) or (
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input_shape[3] is not None
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and sum(self.cropping[1]) >= input_shape[3]
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):
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raise ValueError(
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"Values in `cropping` argument should be greater than the "
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"corresponding spatial dimension of the input. Received: "
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f"input_shape={input_shape}, cropping={self.cropping}"
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)
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return (
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input_shape[0],
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input_shape[1],
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input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
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if input_shape[2] is not None
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else None,
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input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
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if input_shape[3] is not None
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else None,
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)
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else:
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if (
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input_shape[1] is not None
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and sum(self.cropping[0]) >= input_shape[1]
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) or (
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input_shape[2] is not None
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and sum(self.cropping[1]) >= input_shape[2]
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):
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raise ValueError(
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"Values in `cropping` argument should be greater than the "
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"corresponding spatial dimension of the input. Received: "
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f"input_shape={input_shape}, cropping={self.cropping}"
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)
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return (
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input_shape[0],
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input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
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if input_shape[1] is not None
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else None,
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input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
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if input_shape[2] is not None
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else None,
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input_shape[3],
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)
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def call(self, inputs):
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if self.data_format == "channels_first":
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if (
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inputs.shape[2] is not None
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and sum(self.cropping[0]) >= inputs.shape[2]
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) or (
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inputs.shape[3] is not None
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and sum(self.cropping[1]) >= inputs.shape[3]
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):
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raise ValueError(
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"Values in `cropping` argument should be greater than the "
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"corresponding spatial dimension of the input. Received: "
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f"inputs.shape={inputs.shape}, cropping={self.cropping}"
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)
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if self.cropping[0][1] == self.cropping[1][1] == 0:
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return inputs[
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:, :, self.cropping[0][0] :, self.cropping[1][0] :
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]
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elif self.cropping[0][1] == 0:
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return inputs[
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:,
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:,
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self.cropping[0][0] :,
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self.cropping[1][0] : -self.cropping[1][1],
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]
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elif self.cropping[1][1] == 0:
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return inputs[
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:,
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:,
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self.cropping[0][0] : -self.cropping[0][1],
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self.cropping[1][0] :,
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]
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return inputs[
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:,
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:,
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self.cropping[0][0] : -self.cropping[0][1],
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self.cropping[1][0] : -self.cropping[1][1],
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]
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else:
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if (
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inputs.shape[1] is not None
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and sum(self.cropping[0]) >= inputs.shape[1]
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) or (
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inputs.shape[2] is not None
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and sum(self.cropping[1]) >= inputs.shape[2]
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):
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raise ValueError(
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"Values in `cropping` argument should be greater than the "
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"corresponding spatial dimension of the input. Received: "
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f"inputs.shape={inputs.shape}, cropping={self.cropping}"
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)
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if self.cropping[0][1] == self.cropping[1][1] == 0:
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return inputs[
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:, self.cropping[0][0] :, self.cropping[1][0] :, :
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]
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elif self.cropping[0][1] == 0:
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return inputs[
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:,
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self.cropping[0][0] :,
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self.cropping[1][0] : -self.cropping[1][1],
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:,
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]
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elif self.cropping[1][1] == 0:
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return inputs[
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:,
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self.cropping[0][0] : -self.cropping[0][1],
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self.cropping[1][0] :,
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:,
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]
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return inputs[
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:,
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self.cropping[0][0] : -self.cropping[0][1],
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self.cropping[1][0] : -self.cropping[1][1],
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:,
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]
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def get_config(self):
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config = {"cropping": self.cropping, "data_format": self.data_format}
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base_config = super().get_config()
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return {**base_config, **config}
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