# Copyright 2016-2023 The Van Valen Lab at the California Institute of
# Technology (Caltech), with support from the Paul Allen Family Foundation,
# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
# All rights reserved.
#
# Licensed under a modified Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
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#
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# For any other use of the Work, including commercial use, please contact:
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# prior written permission.
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# ==============================================================================
"""Layers to encode location data"""
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer
from keras.utils import conv_utils
from tensorflow.python.framework import tensor_shape
logger = tf.get_logger()
[docs]
class Location2D(Layer):
"""Location Layer for 2D cartesian coordinate locations.
Args:
data_format (str): 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, height, width, channels)`` while ``channels_first``
corresponds to inputs with shape
``(batch, channels, height, width)``.
"""
def __init__(self, data_format=None, **kwargs):
in_shape = kwargs.pop('in_shape', None)
if in_shape is not None:
logger.warn('in_shape (from deepcell.layerse.location) is '
'deprecated and will be removed in a future version.')
super().__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
[docs]
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
channel_axis = 1 if self.data_format == 'channels_first' else 3
input_shape[channel_axis] = 2
return tensor_shape.TensorShape(input_shape)
[docs]
def call(self, inputs):
input_shape = K.shape(inputs)
if self.data_format == 'channels_first':
x = K.arange(0, input_shape[2], dtype=inputs.dtype)
y = K.arange(0, input_shape[3], dtype=inputs.dtype)
else:
x = K.arange(0, input_shape[1], dtype=inputs.dtype)
y = K.arange(0, input_shape[2], dtype=inputs.dtype)
x = x / K.max(x)
y = y / K.max(y)
loc_x, loc_y = tf.meshgrid(x, y, indexing='ij')
if self.data_format == 'channels_first':
loc = K.stack([loc_x, loc_y], axis=0)
else:
loc = K.stack([loc_x, loc_y], axis=-1)
location = K.expand_dims(loc, axis=0)
if self.data_format == 'channels_first':
location = K.permute_dimensions(location, pattern=[0, 2, 3, 1])
location = tf.tile(location, [input_shape[0], 1, 1, 1])
if self.data_format == 'channels_first':
location = K.permute_dimensions(location, pattern=[0, 3, 1, 2])
return location
[docs]
def get_config(self):
config = {
'data_format': self.data_format
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
[docs]
class Location3D(Layer):
"""Location Layer for 3D cartesian coordinate locations.
Args:
data_format (str): 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, height, width, channels)`` while ``channels_first``
corresponds to inputs with shape
``(batch, channels, height, width)``.
"""
def __init__(self, data_format=None, **kwargs):
in_shape = kwargs.pop('in_shape', None)
if in_shape is not None:
logger.warn('in_shape (from deepcell.layerse.location) is '
'deprecated and will be removed in a future version.')
super().__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
[docs]
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
channel_axis = 1 if self.data_format == 'channels_first' else 4
input_shape[channel_axis] = 3
return tensor_shape.TensorShape(input_shape)
[docs]
def call(self, inputs):
input_shape = K.shape(inputs)
if self.data_format == 'channels_first':
z = K.arange(0, input_shape[2], dtype=inputs.dtype)
x = K.arange(0, input_shape[3], dtype=inputs.dtype)
y = K.arange(0, input_shape[4], dtype=inputs.dtype)
else:
z = K.arange(0, input_shape[1], dtype=inputs.dtype)
x = K.arange(0, input_shape[2], dtype=inputs.dtype)
y = K.arange(0, input_shape[3], dtype=inputs.dtype)
x = x / K.max(x)
y = y / K.max(y)
z = z / K.max(z)
loc_z, loc_x, loc_y = tf.meshgrid(z, x, y, indexing='ij')
if self.data_format == 'channels_first':
loc = K.stack([loc_z, loc_x, loc_y], axis=0)
else:
loc = K.stack([loc_z, loc_x, loc_y], axis=-1)
location = K.expand_dims(loc, axis=0)
if self.data_format == 'channels_first':
location = K.permute_dimensions(location, pattern=[0, 2, 3, 4, 1])
location = tf.tile(location, [input_shape[0], 1, 1, 1, 1])
if self.data_format == 'channels_first':
location = K.permute_dimensions(location, pattern=[0, 4, 1, 2, 3])
return location
[docs]
def get_config(self):
config = {
'data_format': self.data_format
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))