164 lines
6.5 KiB
Python
164 lines
6.5 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 zero-padding layer for 3D input."""
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import tensorflow.compat.v2 as tf
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from keras import backend
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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.ZeroPadding3D")
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class ZeroPadding3D(Layer):
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"""Zero-padding layer for 3D data (spatial or spatio-temporal).
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Examples:
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>>> input_shape = (1, 1, 2, 2, 3)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> y = tf.keras.layers.ZeroPadding3D(padding=2)(x)
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>>> print(y.shape)
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(1, 5, 6, 6, 3)
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Args:
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padding: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
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- If int: the same symmetric padding
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is applied to height and width.
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- If tuple of 3 ints:
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interpreted as two different
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symmetric padding values for height and width:
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`(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
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- If tuple of 3 tuples of 2 ints:
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interpreted as
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`((left_dim1_pad, right_dim1_pad), (left_dim2_pad,
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right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
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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_pad, second_axis_to_pad,
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third_axis_to_pad, depth)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, depth, first_axis_to_pad, second_axis_to_pad,
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third_axis_to_pad)`
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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_padded_axis, second_padded_axis,
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third_axis_to_pad, depth)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, depth, first_padded_axis, second_padded_axis,
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third_axis_to_pad)`
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"""
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def __init__(self, padding=(1, 1, 1), 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(padding, int):
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self.padding = (
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(padding, padding),
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(padding, padding),
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(padding, padding),
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)
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elif hasattr(padding, "__len__"):
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if len(padding) != 3:
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raise ValueError(
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f"`padding` should have 3 elements. Received: {padding}."
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)
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dim1_padding = conv_utils.normalize_tuple(
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padding[0], 2, "1st entry of padding", allow_zero=True
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)
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dim2_padding = conv_utils.normalize_tuple(
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padding[1], 2, "2nd entry of padding", allow_zero=True
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)
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dim3_padding = conv_utils.normalize_tuple(
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padding[2], 2, "3rd entry of padding", allow_zero=True
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)
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self.padding = (dim1_padding, dim2_padding, dim3_padding)
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else:
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raise ValueError(
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"`padding` should be either an int, "
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"a tuple of 3 ints "
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"(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), "
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"or a tuple of 3 tuples of 2 ints "
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"((left_dim1_pad, right_dim1_pad),"
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" (left_dim2_pad, right_dim2_pad),"
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" (left_dim3_pad, right_dim2_pad)). "
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f"Received: {padding}."
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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 = input_shape[2] + self.padding[0][0] + self.padding[0][1]
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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 = input_shape[3] + self.padding[1][0] + self.padding[1][1]
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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 = input_shape[4] + self.padding[2][0] + self.padding[2][1]
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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 = input_shape[1] + self.padding[0][0] + self.padding[0][1]
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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 = input_shape[2] + self.padding[1][0] + self.padding[1][1]
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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 = input_shape[3] + self.padding[2][0] + self.padding[2][1]
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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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return backend.spatial_3d_padding(
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inputs, padding=self.padding, data_format=self.data_format
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)
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def get_config(self):
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config = {"padding": self.padding, "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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