313 lines
12 KiB
Python
313 lines
12 KiB
Python
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# 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 3D 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.Cropping3D")
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class Cropping3D(Layer):
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"""Cropping layer for 3D data (e.g. spatial or spatio-temporal).
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Examples:
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>>> input_shape = (2, 28, 28, 10, 3)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> y = tf.keras.layers.Cropping3D(cropping=(2, 4, 2))(x)
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>>> print(y.shape)
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(2, 24, 20, 6, 3)
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Args:
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cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
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- If int: the same symmetric cropping
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is applied to depth, height, and width.
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- If tuple of 3 ints: interpreted as two different
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symmetric cropping values for depth, height, and width:
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`(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
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- If tuple of 3 tuples of 2 ints: interpreted as
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`((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
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right_dim2_crop), (left_dim3_crop, right_dim3_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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
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while `channels_first` corresponds to inputs with shape
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`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
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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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5D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, first_axis_to_crop, second_axis_to_crop,
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third_axis_to_crop, depth)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, depth, first_axis_to_crop, second_axis_to_crop,
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third_axis_to_crop)`
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Output shape:
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5D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, first_cropped_axis, second_cropped_axis,
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third_cropped_axis, depth)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, depth, first_cropped_axis, second_cropped_axis,
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third_cropped_axis)`
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"""
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def __init__(
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self, cropping=((1, 1), (1, 1), (1, 1)), data_format=None, **kwargs
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):
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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 = (
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(cropping, cropping),
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(cropping, cropping),
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(cropping, cropping),
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)
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elif hasattr(cropping, "__len__"):
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if len(cropping) != 3:
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raise ValueError(
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f"`cropping` should have 3 elements. Received: {cropping}."
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)
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dim1_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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dim2_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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dim3_cropping = conv_utils.normalize_tuple(
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cropping[2], 2, "3rd entry of cropping", allow_zero=True
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)
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self.cropping = (dim1_cropping, dim2_cropping, dim3_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 3 ints "
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"(symmetric_dim1_crop, symmetric_dim2_crop, "
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"symmetric_dim3_crop), "
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"or a tuple of 3 tuples of 2 ints "
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"((left_dim1_crop, right_dim1_crop),"
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" (left_dim2_crop, right_dim2_crop),"
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" (left_dim3_crop, right_dim2_crop)). "
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f"Received: {cropping}."
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)
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self.input_spec = InputSpec(ndim=5)
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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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if input_shape[2] is not None:
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dim1 = (
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input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
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)
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else:
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dim1 = None
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if input_shape[3] is not None:
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dim2 = (
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input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
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)
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else:
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dim2 = None
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if input_shape[4] is not None:
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dim3 = (
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input_shape[4] - self.cropping[2][0] - self.cropping[2][1]
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)
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else:
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dim3 = None
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return tf.TensorShape(
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[input_shape[0], input_shape[1], dim1, dim2, dim3]
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)
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elif self.data_format == "channels_last":
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if input_shape[1] is not None:
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dim1 = (
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input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
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)
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else:
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dim1 = None
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if input_shape[2] is not None:
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dim2 = (
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input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
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)
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else:
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dim2 = None
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if input_shape[3] is not None:
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dim3 = (
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input_shape[3] - self.cropping[2][0] - self.cropping[2][1]
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)
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else:
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dim3 = None
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return tf.TensorShape(
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[input_shape[0], dim1, dim2, dim3, input_shape[4]]
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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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self.cropping[0][1]
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== self.cropping[1][1]
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== self.cropping[2][1]
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== 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] :,
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self.cropping[1][0] :,
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self.cropping[2][0] :,
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]
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elif self.cropping[0][1] == 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] :,
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self.cropping[1][0] :,
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self.cropping[2][0] : -self.cropping[2][1],
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]
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elif self.cropping[1][1] == self.cropping[2][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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self.cropping[2][0] :,
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]
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elif self.cropping[0][1] == self.cropping[2][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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self.cropping[2][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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self.cropping[2][0] : -self.cropping[2][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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self.cropping[2][0] : -self.cropping[2][1],
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]
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elif self.cropping[2][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] : -self.cropping[1][1],
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self.cropping[2][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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self.cropping[2][0] : -self.cropping[2][1],
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]
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else:
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if (
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self.cropping[0][1]
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== self.cropping[1][1]
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== self.cropping[2][1]
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== 0
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):
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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] :,
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self.cropping[2][0] :,
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:,
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]
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elif self.cropping[0][1] == self.cropping[1][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] :,
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self.cropping[2][0] : -self.cropping[2][1],
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:,
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]
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elif self.cropping[1][1] == self.cropping[2][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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self.cropping[2][0] :,
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:,
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]
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elif self.cropping[0][1] == self.cropping[2][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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self.cropping[2][0] :,
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:,
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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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self.cropping[2][0] : -self.cropping[2][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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self.cropping[2][0] : -self.cropping[2][1],
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:,
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]
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elif self.cropping[2][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] : -self.cropping[1][1],
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self.cropping[2][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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self.cropping[2][0] : -self.cropping[2][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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