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stn.py
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stn.py
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from keras.engine.topology import Layer
import tensorflow as tf
class SpatialTransformer(Layer):
"""Spatial Transformer Layer
Implements a spatial transformer layer as described in [1]_.
Borrowed from [2]_:
downsample_fator : float
A value of 1 will keep the orignal size of the image.
Values larger than 1 will down sample the image. Values below 1 will
upsample the image.
example image: height= 100, width = 200
downsample_factor = 2
output image will then be 50, 100
References
----------
.. [1] Spatial Transformer Networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu
Submitted on 5 Jun 2015
.. [2] https://github.com/skaae/transformer_network/blob/master/transformerlayer.py
.. [3] https://github.com/EderSantana/seya/blob/keras1/seya/layers/attention.py
"""
def __init__(self,
localization_net,
output_size,
**kwargs):
self.locnet = localization_net
self.output_size = output_size
super(SpatialTransformer, self).__init__(**kwargs)
def build(self, input_shape):
self.locnet.build(input_shape)
self.trainable_weights = self.locnet.trainable_weights
# self.regularizers = self.locnet.regularizers //NOT SUER ABOUT THIS, THERE IS NO MORE SUCH PARAMETR AT self.locnet
def compute_output_shape(self, input_shape):
output_size = self.output_size
return (None,
int(output_size[0]),
int(output_size[1]),
int(input_shape[1][-1]))
def call(self, inputs):
pose = inputs[0]
X = inputs[1]
affine_transformation = self.locnet.call(pose)
output = self._transform(affine_transformation, X, self.output_size)
return output
def _repeat(self, x, num_repeats):
ones = tf.ones((1, num_repeats), dtype='int32')
x = tf.reshape(x, shape=(-1, 1))
x = tf.matmul(x, ones)
return tf.reshape(x, [-1])
def _interpolate(self, image, x, y, output_size):
batch_size = tf.shape(image)[0]
height = tf.shape(image)[1]
width = tf.shape(image)[2]
num_channels = tf.shape(image)[3]
x = tf.cast(x, dtype='float32')
y = tf.cast(y, dtype='float32')
height_float = tf.cast(height, dtype='float32')
width_float = tf.cast(width, dtype='float32')
output_height = output_size[0]
output_width = output_size[1]
x = .5 * (x + 1.0) * (width_float)
y = .5 * (y + 1.0) * (height_float)
x0 = tf.cast(tf.floor(x), 'int32')
x1 = x0 + 1
y0 = tf.cast(tf.floor(y), 'int32')
y1 = y0 + 1
max_y = tf.cast(height - 1, dtype='int32')
max_x = tf.cast(width - 1, dtype='int32')
zero = tf.zeros([], dtype='int32')
x0 = tf.clip_by_value(x0, zero, max_x)
x1 = tf.clip_by_value(x1, zero, max_x)
y0 = tf.clip_by_value(y0, zero, max_y)
y1 = tf.clip_by_value(y1, zero, max_y)
flat_image_dimensions = width * height
pixels_batch = tf.range(batch_size) * flat_image_dimensions
flat_output_dimensions = output_height * output_width
base = self._repeat(pixels_batch, flat_output_dimensions)
base_y0 = base + y0 * width
base_y1 = base + y1 * width
indices_a = base_y0 + x0
indices_b = base_y1 + x0
indices_c = base_y0 + x1
indices_d = base_y1 + x1
flat_image = tf.reshape(image, shape=(-1, num_channels))
flat_image = tf.cast(flat_image, dtype='float32')
pixel_values_a = tf.gather(flat_image, indices_a)
pixel_values_b = tf.gather(flat_image, indices_b)
pixel_values_c = tf.gather(flat_image, indices_c)
pixel_values_d = tf.gather(flat_image, indices_d)
x0 = tf.cast(x0, 'float32')
x1 = tf.cast(x1, 'float32')
y0 = tf.cast(y0, 'float32')
y1 = tf.cast(y1, 'float32')
area_a = tf.expand_dims(((x1 - x) * (y1 - y)), 1)
area_b = tf.expand_dims(((x1 - x) * (y - y0)), 1)
area_c = tf.expand_dims(((x - x0) * (y1 - y)), 1)
area_d = tf.expand_dims(((x - x0) * (y - y0)), 1)
output = tf.add_n([area_a * pixel_values_a,
area_b * pixel_values_b,
area_c * pixel_values_c,
area_d * pixel_values_d])
return output
def _meshgrid(self, height, width):
x_linspace = tf.linspace(-1., 1., width)
y_linspace = tf.linspace(-1., 1., height)
x_coordinates, y_coordinates = tf.meshgrid(x_linspace, y_linspace)
x_coordinates = tf.reshape(x_coordinates, [-1])
y_coordinates = tf.reshape(y_coordinates, [-1])
ones = tf.ones_like(x_coordinates)
indices_grid = tf.concat([x_coordinates, y_coordinates, ones], 0)
return indices_grid
def _transform(self, affine_transformation, input_shape, output_size):
batch_size = tf.shape(input_shape)[0]
height = tf.shape(input_shape)[1]
width = tf.shape(input_shape)[2]
num_channels = tf.shape(input_shape)[3]
affine_transformation = tf.reshape(affine_transformation, shape=(batch_size, 2, 3))
affine_transformation = tf.reshape(affine_transformation, (-1, 2, 3))
affine_transformation = tf.cast(affine_transformation, 'float32')
width = tf.cast(width, dtype='float32')
height = tf.cast(height, dtype='float32')
output_height = output_size[0]
output_width = output_size[1]
indices_grid = self._meshgrid(output_height, output_width)
indices_grid = tf.expand_dims(indices_grid, 0)
indices_grid = tf.reshape(indices_grid, [-1]) # flatten?
indices_grid = tf.tile(indices_grid, tf.stack([batch_size]))
indices_grid = tf.reshape(indices_grid, (batch_size, 3, -1))
transformed_grid = tf.matmul(affine_transformation, indices_grid)
x_s = tf.slice(transformed_grid, [0, 0, 0], [-1, 1, -1])
y_s = tf.slice(transformed_grid, [0, 1, 0], [-1, 1, -1])
x_s_flatten = tf.reshape(x_s, [-1])
y_s_flatten = tf.reshape(y_s, [-1])
transformed_image = self._interpolate(input_shape,
x_s_flatten,
y_s_flatten,
output_size)
transformed_image = tf.reshape(transformed_image, shape=(batch_size,
output_height,
output_width,
num_channels))
return transformed_image