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deformable_conv.py
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from __future__ import absolute_import, division
import tensorflow as tf
def tf_flatten(a):
"""Flatten tensor"""
return tf.reshape(a, [-1])
def tf_repeat(a, repeats, axis=0):
"""TensorFlow version of np.repeat for 1D"""
# https://github.com/tensorflow/tensorflow/issues/8521
assert len(a.get_shape()) == 1
a = tf.expand_dims(a, -1)
a = tf.tile(a, [1, repeats])
a = tf_flatten(a)
return a
def tf_repeat_2d(a, repeats):
"""Tensorflow version of np.repeat for 2D"""
assert len(a.get_shape()) == 2
a = tf.expand_dims(a, 0)
a = tf.tile(a, [repeats, 1, 1])
return a
def tf_batch_map_coordinates(input, coords, order=1):
"""Batch version of tf_map_coordinates
Only supports 2D feature maps
Parameters
----------
input : tf.Tensor. shape = (b, s, s)
coords : tf.Tensor. shape = (b, n_points, 2)
Returns
-------
tf.Tensor. shape = (b, s, s)
"""
input_shape = tf.shape(input)
batch_size = input_shape[0]
input_size = input_shape[1]
n_coords = tf.shape(coords)[1]
coords = tf.clip_by_value(coords, 0, tf.cast(input_size, 'float32') - 1)
coords_lt = tf.cast(tf.floor(coords), 'int32')
coords_rb = tf.cast(tf.ceil(coords), 'int32')
coords_lb = tf.stack([coords_lt[..., 0], coords_rb[..., 1]], axis=-1)
coords_rt = tf.stack([coords_rb[..., 0], coords_lt[..., 1]], axis=-1)
idx = tf_repeat(tf.range(batch_size), n_coords)
def _get_vals_by_coords(input, coords):
indices = tf.stack([
idx, tf_flatten(coords[..., 0]), tf_flatten(coords[..., 1])
], axis=-1)
vals = tf.gather_nd(input, indices)
vals = tf.reshape(vals, (batch_size, n_coords))
return vals
vals_lt = _get_vals_by_coords(input, coords_lt)
vals_rb = _get_vals_by_coords(input, coords_rb)
vals_lb = _get_vals_by_coords(input, coords_lb)
vals_rt = _get_vals_by_coords(input, coords_rt)
coords_offset_lt = coords - tf.cast(coords_lt, 'float32')
vals_t = vals_lt + (vals_rt - vals_lt) * coords_offset_lt[..., 0]
vals_b = vals_lb + (vals_rb - vals_lb) * coords_offset_lt[..., 0]
mapped_vals = vals_t + (vals_b - vals_t) * coords_offset_lt[..., 1]
return mapped_vals
def tf_batch_map_offsets(input, offsets, order=1):
"""Batch map offsets into input
Parameters
---------
input : tf.Tensor. shape = (b, s, s)
offsets: tf.Tensor. shape = (b, s, s, 2)
Returns
-------
tf.Tensor. shape = (b, s, s)
"""
input_shape = tf.shape(input)
batch_size = input_shape[0]
input_size = input_shape[1]
offsets = tf.reshape(offsets, (batch_size, -1, 2))
grid = tf.meshgrid(
tf.range(input_size), tf.range(input_size), indexing='ij'
)
grid = tf.stack(grid, axis=-1)
grid = tf.cast(grid, 'float32')
grid = tf.reshape(grid, (-1, 2))
grid = tf_repeat_2d(grid, batch_size)
coords = offsets + grid
mapped_vals = tf_batch_map_coordinates(input, coords)
return mapped_vals
def to_bc_h_w_2(x, x_shape):
"""(b, h, w, 2c) -> (b*c, h, w, 2)"""
x = tf.transpose(x, [0, 3, 1, 2])
x = tf.reshape(x, (-1, int(x_shape[1]), int(x_shape[2]), 2))
return x
def to_bc_h_w(x, x_shape):
"""(b, h, w, c) -> (b*c, h, w)"""
x = tf.transpose(x, [0, 3, 1, 2])
x = tf.reshape(x, (-1, int(x_shape[1]), int(x_shape[2])))
return x
def to_b_h_w_c(x, x_shape):
"""(b*c, h, w) -> (b, h, w, c)"""
x = tf.reshape(
x, (-1, int(x_shape[3]), int(x_shape[1]), int(x_shape[2]))
)
x = tf.transpose(x, [0, 2, 3, 1])
return x
def deformable_convolution(x, offsets):
"""Return the deformed featured map"""
x_shape = x.get_shape()
# offsets: (b*c, h, w, 2)
offsets = to_bc_h_w_2(offsets, x_shape)
# x: (b*c, h, w)
x = to_bc_h_w(x, x_shape)
# X_offset: (b*c, h, w)
x_offset = tf_batch_map_offsets(x, offsets)
# x_offset: (b, h, w, c)
x_offset = to_b_h_w_c(x_offset, x_shape)
return x_offset