219 lines
8.2 KiB
Python
219 lines
8.2 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras cropping layer for 2D input."""
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import tensorflow.compat.v2 as tf
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from keras.engine.base_layer import Layer
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from keras.engine.input_spec import InputSpec
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from keras.utils import conv_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.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 = tf.keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
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>>> print(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
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is applied to height and width.
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- If tuple of 2 ints:
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interpreted as two different
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symmetric 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:
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interpreted as
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`((top_crop, bottom_crop), (left_crop, right_crop))`
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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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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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "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, rows, cols, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, rows, cols)`
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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_rows, cropped_cols, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, cropped_rows, cropped_cols)`
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"""
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def __init__(self, cropping=((0, 0), (0, 0)), data_format=None, **kwargs):
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super().__init__(**kwargs)
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self.data_format = conv_utils.normalize_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}."
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)
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height_cropping = conv_utils.normalize_tuple(
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cropping[0], 2, "1st entry of cropping", allow_zero=True
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)
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width_cropping = conv_utils.normalize_tuple(
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cropping[1], 2, "2nd entry of cropping", allow_zero=True
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)
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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, "
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"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}."
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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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input_shape = tf.TensorShape(input_shape).as_list()
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if self.data_format == "channels_first":
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return tf.TensorShape(
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[
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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]
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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]
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else None,
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]
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)
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else:
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return tf.TensorShape(
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[
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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]
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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]
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else None,
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input_shape[3],
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]
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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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"Argument `cropping` must be "
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"greater than the input shape. Received: inputs.shape="
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f"{inputs.shape}, and 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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"Argument `cropping` must be "
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"greater than the input shape. Received: inputs.shape="
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f"{inputs.shape}, and 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 dict(list(base_config.items()) + list(config.items()))
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