def run_bilateral_slice_apply(self,
dev,
grid_data,
guide_data,
input_data,
has_offset=False):
with tf.device(dev):
grid_tensor = tf.convert_to_tensor(grid_data,
name='grid',
dtype=tf.float32)
guide_tensor = tf.convert_to_tensor(guide_data,
name='guide',
dtype=tf.float32)
input_tensor = tf.convert_to_tensor(input_data,
name='input',
dtype=tf.float32)
output_tensor = ops.bilateral_slice_apply(grid_tensor,
guide_tensor,
input_tensor,
has_offset=has_offset)
with self.test_session() as sess:
output_data = sess.run(output_tensor)
return output_data
def bilateral_slice_apply(grid, guide, input_image, has_offset=True, name=None):
"""Slices into a bilateral grid using the guide map.
Args:
grid: (Tensor) [batch_size, grid_h, grid_w, depth, n_outputs]
grid to slice from.
guide: (Tensor) [batch_size, h, w ] guide map to slice along.
input_image: (Tensor) [batch_size, h, w, n_input] input data onto which to
apply the affine transform.
name: (string) name for the operation.
Returns:
sliced: (Tensor) [batch_size, h, w, n_outputs] sliced output.
"""
with tf.name_scope(name):
gridshape = grid.get_shape().as_list()
if len(gridshape) == 6:
gs = tf.shape(grid)
_, _, _, _, n_out, n_in = gridshape
grid = tf.reshape(grid, tf.stack([gs[0], gs[1], gs[2], gs[3], gs[4]*gs[5]]))
# grid = tf.concat(tf.unstack(grid, None, axis=5), 4)
# grid:[-1,16,16,8,12] [batch_size, grid_h, grid_w, depth, n_outputs]
# guide:[-1,512,512]
# input_image:[-1,512,512,3]
sliced = hdrnet_ops.bilateral_slice_apply(grid, guide, input_image, has_offset=has_offset)
return sliced
def test_guide_gradient(self):
#TODO: this does not work yet, differentiable 'max' in the tent: max(1-abs(x), 0)?
for dev in ['/gpu:0']:
batch_size = 1
h = 6
w = 15
gh = 3
gw = 9
d = 7
i_chans = 1
o_chans = 1
grid_shape = [batch_size, gh, gw, d, (i_chans+1)*o_chans]
guide_shape = [batch_size, h, w]
input_shape = [batch_size, h, w, i_chans]
output_shape = [batch_size, h, w, o_chans]
grid_data = np.random.rand(*grid_shape).astype(np.float32)
guide_data = np.random.rand(*guide_shape).astype(np.float32)
input_data = np.random.rand(*input_shape).astype(np.float32)
with tf.device(dev):
grid_tensor = tf.convert_to_tensor(grid_data,
name='data',
dtype=tf.float32)
guide_tensor = tf.convert_to_tensor(guide_data,
name='guide',
dtype=tf.float32)
input_tensor = tf.convert_to_tensor(input_data,
name='input',
dtype=tf.float32)
output_tensor = ops.bilateral_slice_apply(grid_tensor, guide_tensor, input_tensor, has_offset=True)
with self.test_session():
num, th = tf.test.compute_gradient(
guide_tensor,
guide_shape,
output_tensor,
output_shape)
margin = 1e-2
idx = np.where(np.abs(th-num) >= margin)
for i in range(len(idx[0])):
guide_idx = np.unravel_index(idx[0][i], guide_shape)
output_idx = np.unravel_index(idx[1][i], output_shape)
err = tf.test.compute_gradient_error(
guide_tensor,
guide_shape,
output_tensor,
output_shape)
self.assertLess(err, margin)
def test_guide_gradient(self):
#TODO: this does not work yet, differentiable 'max' in the tent: max(1-abs(x), 0)?
for dev in ['/gpu:0']:
batch_size = 1
h = 6
w = 15
gh = 3
gw = 9
d = 7
i_chans = 1
o_chans = 1
grid_shape = [batch_size, gh, gw, d, (i_chans + 1) * o_chans]
guide_shape = [batch_size, h, w]
input_shape = [batch_size, h, w, i_chans]
output_shape = [batch_size, h, w, o_chans]
grid_data = np.random.rand(*grid_shape).astype(np.float32)
guide_data = np.random.rand(*guide_shape).astype(np.float32)
input_data = np.random.rand(*input_shape).astype(np.float32)
with tf.device(dev):
grid_tensor = tf.convert_to_tensor(grid_data,
name='data',
dtype=tf.float32)
guide_tensor = tf.convert_to_tensor(guide_data,
name='guide',
dtype=tf.float32)
input_tensor = tf.convert_to_tensor(input_data,
name='input',
dtype=tf.float32)
output_tensor = ops.bilateral_slice_apply(grid_tensor,
guide_tensor,
input_tensor,
has_offset=True)
with self.test_session():
num, th = tf.test.compute_gradient(guide_tensor, guide_shape,
output_tensor, output_shape)
margin = 1e-2
idx = np.where(np.abs(th - num) >= margin)
for i in range(len(idx[0])):
guide_idx = np.unravel_index(idx[0][i], guide_shape)
output_idx = np.unravel_index(idx[1][i], output_shape)
err = tf.test.compute_gradient_error(guide_tensor, guide_shape,
output_tensor,
output_shape)
self.assertLess(err, margin)
def run_bilateral_slice_apply(self, dev, grid_data, guide_data, input_data, has_offset=False):
with tf.device(dev):
grid_tensor = tf.convert_to_tensor(
grid_data, name='grid', dtype=tf.float32)
guide_tensor = tf.convert_to_tensor(
guide_data, name='guide', dtype=tf.float32)
input_tensor = tf.convert_to_tensor(
input_data, name='input', dtype=tf.float32)
output_tensor = ops.bilateral_slice_apply(grid_tensor, guide_tensor, input_tensor, has_offset=has_offset)
with self.test_session() as sess:
output_data = sess.run(output_tensor)
return output_data
def test_grid_gradient(self):
for dev in ['/gpu:0']:
batch_size = 3
h = 8
w = 5
gh = 6
gw = 3
d = 7
i_chans = 3
o_chans = 3
grid_shape = [batch_size, gh, gw, d, (1 + i_chans) * o_chans]
guide_shape = [batch_size, h, w]
input_shape = [batch_size, h, w, i_chans]
output_shape = [batch_size, h, w, o_chans]
grid_data = np.random.rand(*grid_shape).astype(np.float32)
guide_data = 0.8 * np.random.rand(*guide_shape).astype(
np.float32) + 0.1
input_data = np.random.rand(*input_shape).astype(np.float32)
with tf.device(dev):
grid_tensor = tf.convert_to_tensor(grid_data,
name='data',
dtype=tf.float32)
guide_tensor = tf.convert_to_tensor(guide_data,
name='guide',
dtype=tf.float32)
input_tensor = tf.convert_to_tensor(input_data,
name='input',
dtype=tf.float32)
output_tensor = ops.bilateral_slice_apply(grid_tensor,
guide_tensor,
input_tensor,
has_offset=True)
with self.test_session():
err = tf.test.compute_gradient_error(grid_tensor, grid_shape,
output_tensor,
output_shape)
self.assertLess(err, 3e-4)
def test_input_gradient(self):
for dev in ['/gpu:0']:
batch_size = 1
h = 8
w = 5
gh = 6
gw = 3
d = 7
i_chans = 3
o_chans = 3
grid_shape = [batch_size, gh, gw, d, (1+i_chans)*o_chans]
guide_shape = [batch_size, h, w]
input_shape = [batch_size, h, w, i_chans]
output_shape = [batch_size, h, w, o_chans]
grid_data = np.random.rand(*grid_shape).astype(np.float32)
guide_data = np.random.rand(*guide_shape).astype(np.float32)
input_data = np.random.rand(*input_shape).astype(np.float32)
with tf.device(dev):
grid_tensor = tf.convert_to_tensor(grid_data,
name='data',
dtype=tf.float32)
guide_tensor = tf.convert_to_tensor(guide_data,
name='guide',
dtype=tf.float32)
input_tensor = tf.convert_to_tensor(input_data,
name='input',
dtype=tf.float32)
output_tensor = ops.bilateral_slice_apply(grid_tensor, guide_tensor, input_tensor, has_offset=True)
with self.test_session():
err = tf.test.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape)
self.assertLess(err, 3e-4)
def bilateral_slice_apply(grid, guide, input_image, has_offset=True, name=None):
"""Slices into a bilateral grid using the guide map.
Args:
grid: (Tensor) [batch_size, grid_h, grid_w, depth, n_outputs]
grid to slice from.
guide: (Tensor) [batch_size, h, w ] guide map to slice along.
input_image: (Tensor) [batch_size, h, w, n_input] input data onto which to
apply the affine transform.
name: (string) name for the operation.
Returns:
sliced: (Tensor) [batch_size, h, w, n_outputs] sliced output.
"""
with tf.name_scope(name):
gridshape = grid.get_shape().as_list()
if len(gridshape) == 6:
gs = tf.shape(grid)
_, _, _, _, n_out, n_in = gridshape
grid = tf.reshape(grid, tf.stack([gs[0], gs[1], gs[2], gs[3], gs[4]*gs[5]]))
# grid = tf.concat(tf.unstack(grid, None, axis=5), 4)
sliced = hdrnet_ops.bilateral_slice_apply(grid, guide, input_image, has_offset=has_offset)
return sliced
