# 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 3D 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.Cropping3D") class Cropping3D(Layer): """Cropping layer for 3D data (e.g. spatial or spatio-temporal). Examples: >>> input_shape = (2, 28, 28, 10, 3) >>> x = np.arange(np.prod(input_shape)).reshape(input_shape) >>> y = tf.keras.layers.Cropping3D(cropping=(2, 4, 2))(x) >>> print(y.shape) (2, 24, 20, 6, 3) Args: cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints. - If int: the same symmetric cropping is applied to depth, height, and width. - If tuple of 3 ints: interpreted as two different symmetric cropping values for depth, height, and width: `(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`. - If tuple of 3 tuples of 2 ints: interpreted as `((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))` data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". Input shape: 5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)` - If `data_format` is `"channels_first"`: `(batch_size, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)` Output shape: 5D tensor with shape: - If `data_format` is `"channels_last"`: `(batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)` - If `data_format` is `"channels_first"`: `(batch_size, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)` """ def __init__( self, cropping=((1, 1), (1, 1), (1, 1)), data_format=None, **kwargs ): super().__init__(**kwargs) self.data_format = conv_utils.normalize_data_format(data_format) if isinstance(cropping, int): self.cropping = ( (cropping, cropping), (cropping, cropping), (cropping, cropping), ) elif hasattr(cropping, "__len__"): if len(cropping) != 3: raise ValueError( "`cropping` should have 3 elements. " f"Received: {cropping}." ) dim1_cropping = conv_utils.normalize_tuple( cropping[0], 2, "1st entry of cropping", allow_zero=True ) dim2_cropping = conv_utils.normalize_tuple( cropping[1], 2, "2nd entry of cropping", allow_zero=True ) dim3_cropping = conv_utils.normalize_tuple( cropping[2], 2, "3rd entry of cropping", allow_zero=True ) self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping) else: raise ValueError( "`cropping` should be either an int, " "a tuple of 3 ints " "(symmetric_dim1_crop, symmetric_dim2_crop, " "symmetric_dim3_crop), " "or a tuple of 3 tuples of 2 ints " "((left_dim1_crop, right_dim1_crop)," " (left_dim2_crop, right_dim2_crop)," " (left_dim3_crop, right_dim2_crop)). " f"Received: {cropping}." ) self.input_spec = InputSpec(ndim=5) def compute_output_shape(self, input_shape): input_shape = tf.TensorShape(input_shape).as_list() if self.data_format == "channels_first": if input_shape[2] is not None: dim1 = ( input_shape[2] - self.cropping[0][0] - self.cropping[0][1] ) else: dim1 = None if input_shape[3] is not None: dim2 = ( input_shape[3] - self.cropping[1][0] - self.cropping[1][1] ) else: dim2 = None if input_shape[4] is not None: dim3 = ( input_shape[4] - self.cropping[2][0] - self.cropping[2][1] ) else: dim3 = None return tf.TensorShape( [input_shape[0], input_shape[1], dim1, dim2, dim3] ) elif self.data_format == "channels_last": if input_shape[1] is not None: dim1 = ( input_shape[1] - self.cropping[0][0] - self.cropping[0][1] ) else: dim1 = None if input_shape[2] is not None: dim2 = ( input_shape[2] - self.cropping[1][0] - self.cropping[1][1] ) else: dim2 = None if input_shape[3] is not None: dim3 = ( input_shape[3] - self.cropping[2][0] - self.cropping[2][1] ) else: dim3 = None return tf.TensorShape( [input_shape[0], dim1, dim2, dim3, input_shape[4]] ) def call(self, inputs): if self.data_format == "channels_first": if ( self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0 ): return inputs[ :, :, self.cropping[0][0] :, self.cropping[1][0] :, self.cropping[2][0] :, ] elif self.cropping[0][1] == self.cropping[1][1] == 0: return inputs[ :, :, self.cropping[0][0] :, self.cropping[1][0] :, self.cropping[2][0] : -self.cropping[2][1], ] elif self.cropping[1][1] == self.cropping[2][1] == 0: return inputs[ :, :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] :, self.cropping[2][0] :, ] elif self.cropping[0][1] == self.cropping[2][1] == 0: return inputs[ :, :, self.cropping[0][0] :, self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] :, ] elif self.cropping[0][1] == 0: return inputs[ :, :, self.cropping[0][0] :, self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] : -self.cropping[2][1], ] elif self.cropping[1][1] == 0: return inputs[ :, :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] :, self.cropping[2][0] : -self.cropping[2][1], ] elif self.cropping[2][1] == 0: return inputs[ :, :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] :, ] return inputs[ :, :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] : -self.cropping[2][1], ] else: if ( self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0 ): return inputs[ :, self.cropping[0][0] :, self.cropping[1][0] :, self.cropping[2][0] :, :, ] elif self.cropping[0][1] == self.cropping[1][1] == 0: return inputs[ :, self.cropping[0][0] :, self.cropping[1][0] :, self.cropping[2][0] : -self.cropping[2][1], :, ] elif self.cropping[1][1] == self.cropping[2][1] == 0: return inputs[ :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] :, self.cropping[2][0] :, :, ] elif self.cropping[0][1] == self.cropping[2][1] == 0: return inputs[ :, self.cropping[0][0] :, self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] :, :, ] elif self.cropping[0][1] == 0: return inputs[ :, self.cropping[0][0] :, self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] : -self.cropping[2][1], :, ] elif self.cropping[1][1] == 0: return inputs[ :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] :, self.cropping[2][0] : -self.cropping[2][1], :, ] elif self.cropping[2][1] == 0: return inputs[ :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] :, :, ] return inputs[ :, self.cropping[0][0] : -self.cropping[0][1], self.cropping[1][0] : -self.cropping[1][1], self.cropping[2][0] : -self.cropping[2][1], :, ] def get_config(self): config = {"cropping": self.cropping, "data_format": self.data_format} base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))