98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Keras cropping layer for 1D input."""
|
|
|
|
|
|
import tensorflow.compat.v2 as tf
|
|
|
|
from keras.engine.base_layer import Layer
|
|
from keras.engine.input_spec import InputSpec
|
|
from keras.utils import conv_utils
|
|
|
|
# isort: off
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
@keras_export("keras.layers.Cropping1D")
|
|
class Cropping1D(Layer):
|
|
"""Cropping layer for 1D input (e.g. temporal sequence).
|
|
|
|
It crops along the time dimension (axis 1).
|
|
|
|
Examples:
|
|
|
|
>>> input_shape = (2, 3, 2)
|
|
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
|
|
>>> print(x)
|
|
[[[ 0 1]
|
|
[ 2 3]
|
|
[ 4 5]]
|
|
[[ 6 7]
|
|
[ 8 9]
|
|
[10 11]]]
|
|
>>> y = tf.keras.layers.Cropping1D(cropping=1)(x)
|
|
>>> print(y)
|
|
tf.Tensor(
|
|
[[[2 3]]
|
|
[[8 9]]], shape=(2, 1, 2), dtype=int64)
|
|
|
|
Args:
|
|
cropping: Int or tuple of int (length 2)
|
|
How many units should be trimmed off at the beginning and end of
|
|
the cropping dimension (axis 1).
|
|
If a single int is provided, the same value will be used for both.
|
|
|
|
Input shape:
|
|
3D tensor with shape `(batch_size, axis_to_crop, features)`
|
|
|
|
Output shape:
|
|
3D tensor with shape `(batch_size, cropped_axis, features)`
|
|
"""
|
|
|
|
def __init__(self, cropping=(1, 1), **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.cropping = conv_utils.normalize_tuple(
|
|
cropping, 2, "cropping", allow_zero=True
|
|
)
|
|
self.input_spec = InputSpec(ndim=3)
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
input_shape = tf.TensorShape(input_shape).as_list()
|
|
if input_shape[1] is not None:
|
|
length = input_shape[1] - self.cropping[0] - self.cropping[1]
|
|
else:
|
|
length = None
|
|
return tf.TensorShape([input_shape[0], length, input_shape[2]])
|
|
|
|
def call(self, inputs):
|
|
if (
|
|
inputs.shape[1] is not None
|
|
and sum(self.cropping) >= inputs.shape[1]
|
|
):
|
|
raise ValueError(
|
|
"cropping parameter of Cropping layer must be "
|
|
"greater than the input shape. Received: inputs.shape="
|
|
f"{inputs.shape}, and cropping={self.cropping}"
|
|
)
|
|
if self.cropping[1] == 0:
|
|
return inputs[:, self.cropping[0] :, :]
|
|
else:
|
|
return inputs[:, self.cropping[0] : -self.cropping[1], :]
|
|
|
|
def get_config(self):
|
|
config = {"cropping": self.cropping}
|
|
base_config = super().get_config()
|
|
return dict(list(base_config.items()) + list(config.items()))
|