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deform_conv.py
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deform_conv.py
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# -*- coding: utf-8 -*-
from __future__ import absolute_import, division
"""
Created on Wed Mar 28 09:52:58 2018
@author: xingshuli
"""
import numpy as np
#Map the input array to new coordinates by interpolation
from scipy.ndimage.interpolation import map_coordinates as sp_map_coordinates
import tensorflow as tf
#flatten tensor
def tf_flatten(a):
return tf.reshape(a, [-1])
#Tensorflow version of np.repeat for 1D
def tf_repeat(a, repeats, axis = 0):
assert len(a.get_shape()) == 1
a = tf.expand_dims(a, -1)
a = tf.tile(a, [1, repeats])
a = tf_flatten(a)
return a
#Tensorflow version of np.repeat for 2D
def tf_repeat_2d(a, repeats):
assert len(a.get_shape()) == 2
a = tf.expand_dims(a, 0)
a = tf.tile(a, [repeats, 1, 1])
return a
#Tensorflow version of scipy.ndimage.map_coordinates
'''
Parameters:
input: tf.Tensor. shape = (s, s)
coords: tf.Tensor. shape = (n_points, 2)
coords_lt -- left-top of coordinates
coords_rb -- right-bottom of coordinates
coords_lb -- left-bottom of coordinates
coords_rt -- right-top of coordinates
for mapped_vals is calculated by bilinear interpolation
'''
def tf_map_coordinates(input, coords, order = 1):
assert order == 1 # '1' means the linear interpolation
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)
vals_lt = tf.gather_nd(input, coords_lt)
vals_rb = tf.gather_nd(input, coords_rb)
vals_lb = tf.gather_nd(input, coords_lb)
vals_rt = tf.gather_nd(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 sp_batch_map_coordinates(inputs, coords):
coords = coords.clip(0, inputs.shape[1] - 1)
mapped_vals = np.array([sp_map_coordinates(input, coord.T, mode = 'nearest', order = 1)
for input, coord in zip(inputs, coords)])
return mapped_vals
def tf_batch_map_coordinates(input, coords, order = 1):
#Batch version of tf_map_coordinates
'''
Parameter
input: tf.Tensor. shape = (b, s, s)
coords: tf.Tensor. shape = (b, n_points, 2)
Return
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)
#bilinear interpolation
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 sp_batch_map_offsets(input, offsets):
'''
Reference implementation for tf_batch_map_offsets
'''
batch_size = input.shape[0]
input_size = input.shape[1]
offsets = offsets.reshape(batch_size, -1, 2)
grid = np.stack(np.mgrid[:input_size, :input_size], -1).reshape(-1, 2)
grid = np.repeat([grid], batch_size, axis = 0)
coords = offsets + grid
coords = coords.clip(0, input_size - 1)
mapped_vals = sp_batch_map_coordinates(input, coords)
return mapped_vals
def tf_batch_map_offsets(input, offsets, order = 1):
'''
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 = grid + offsets
mapped_vals = tf_batch_map_coordinates(input, coords)
return mapped_vals