def test_op_forward_pass(self, on_gpu, dtype, tol):
np.random.seed(0)
data_width = 7
data_height = 9
data_channels = 5
warp_width = 4
warp_height = 8
batch_size = 10
warp = _make_warp(batch_size, warp_height, warp_width,
dtype.as_numpy_dtype)
data_shape = (batch_size, data_height, data_width, data_channels)
data = np.random.rand(*data_shape).astype(dtype.as_numpy_dtype)
with self.test_session(use_gpu=on_gpu, force_gpu=False) as sess:
data_ph = tf.placeholder(dtype, shape=(None, ) + data.shape[1:])
warp_ph = tf.placeholder(dtype, shape=(None, ) + warp.shape[1:])
outputs = snt.resampler(data=data_ph, warp=warp_ph)
self.assertEqual(outputs.get_shape().as_list(),
[None, warp_height, warp_width, data_channels])
out = sess.run(outputs, feed_dict={data_ph: data, warp_ph: warp})
# Generate reference output via bilinear interpolation in numpy
reference_output = np.zeros_like(out)
for batch in xrange(batch_size):
for c in xrange(data_channels):
reference_output[batch, :, :, c] = _bilinearly_interpolate(
data[batch, :, :, c], warp[batch, :, :, 0],
warp[batch, :, :, 1])
self.assertAllClose(out, reference_output, rtol=tol, atol=tol)
def test_op_backward_pass(self, on_gpu, dtype, tol):
np.random.seed(13)
data_width = 5
data_height = 4
data_channels = 3
warp_width = 2
warp_height = 6
batch_size = 10
warp = _make_warp(batch_size, warp_height, warp_width,
dtype.as_numpy_dtype)
data_shape = (batch_size, data_height, data_width, data_channels)
data = np.random.rand(*data_shape).astype(dtype.as_numpy_dtype)
with self.test_session(use_gpu=on_gpu, force_gpu=False):
data_tensor = tf.constant(data)
warp_tensor = tf.constant(warp)
output_tensor = snt.resampler(data=data_tensor, warp=warp_tensor)
grads = tf.test.compute_gradient(
[data_tensor, warp_tensor], [
data_tensor.get_shape().as_list(),
warp_tensor.get_shape().as_list()
], output_tensor,
output_tensor.get_shape().as_list(), [data, warp])
if not on_gpu:
# On CPU we perform numerical differentiation at the best available
# precision, and compare against that. This is necessary for test to
# pass for float16.
data_tensor_64 = tf.constant(data, dtype=tf.float64)
warp_tensor_64 = tf.constant(warp, dtype=tf.float64)
output_tensor_64 = snt.resampler(data=data_tensor_64,
warp=warp_tensor_64)
grads_64 = tf.test.compute_gradient(
[data_tensor_64, warp_tensor_64], [
data_tensor.get_shape().as_list(),
warp_tensor.get_shape().as_list()
], output_tensor_64,
output_tensor.get_shape().as_list(), [data, warp])
for g, g_64 in zip(grads, grads_64):
self.assertLess(np.fabs(g[0] - g_64[1]).max(), tol)
else:
for g in grads:
self.assertLess(np.fabs(g[0] - g[1]).max(), tol)
def spatial_transformer(img_tensor, transform_params, crop_size):
"""
:param img_tensor: tf.Tensor of size (batch_size, Height, Width, channels)
:param transform_params: tf.Tensor of size (batch_size, 4), where params are (scale_y, shift_y, scale_x, shift_x)
:param crop_size): tuple of 2 ints, size of the resulting crop
"""
constraints = snt.AffineWarpConstraints.no_shear_2d()
img_size = img_tensor.shape.as_list()[1:]
warper = snt.AffineGridWarper(img_size, crop_size, constraints)
grid_coords = warper(transform_params)
glimpse = snt.resampler(img_tensor[..., tf.newaxis], grid_coords)
return glimpse
def _build(self, img, transform_params):
if len(img.get_shape()) == 3:
img = img[..., tf.newaxis]
grid_coords = self._warper(transform_params)
return snt.resampler(img, grid_coords)
def test_op_errors(self):
data_width = 7
data_height = 9
data_depth = 3
data_channels = 5
warp_width = 4
warp_height = 8
batch_size = 10
# Input data shape is not defined over a 2D grid, i.e. its shape is not like
# (batch_size, data_height, data_width, data_channels).
with self.test_session() as sess:
data_shape = (batch_size, data_height, data_width, data_depth,
data_channels)
data = np.zeros(data_shape)
warp_shape = (batch_size, warp_height, warp_width, 2)
warp = np.zeros(warp_shape)
outputs = snt.resampler(tf.constant(data), tf.constant(warp))
with self.assertRaisesRegexp(
tf.errors.UnimplementedError,
"Only bilinear interpolation is currently "
"supported."):
sess.run(outputs)
# Warp tensor must be at least a matrix, with shape [batch_size, 2].
with self.test_session() as sess:
data_shape = (batch_size, data_height, data_width, data_channels)
data = np.zeros(data_shape)
warp_shape = (batch_size, )
warp = np.zeros(warp_shape)
outputs = snt.resampler(tf.constant(data), tf.constant(warp))
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"warp should be at least a matrix"):
sess.run(outputs)
# The batch size of the data and warp tensors must be the same.
with self.test_session() as sess:
data_shape = (batch_size, data_height, data_width, data_channels)
data = np.zeros(data_shape)
warp_shape = (batch_size + 1, warp_height, warp_width, 2)
warp = np.zeros(warp_shape)
outputs = snt.resampler(tf.constant(data), tf.constant(warp))
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"Batch size of data and warp tensor"):
sess.run(outputs)
# The warp tensor must contain 2D coordinates, i.e. its shape last dimension
# must be 2.
with self.test_session() as sess:
data_shape = (batch_size, data_height, data_width, data_channels)
data = np.zeros(data_shape)
warp_shape = (batch_size, warp_height, warp_width, 3)
warp = np.zeros(warp_shape)
outputs = snt.resampler(tf.constant(data), tf.constant(warp))
with self.assertRaisesRegexp(
tf.errors.UnimplementedError,
"Only bilinear interpolation is supported, "
"warping"):
sess.run(outputs)
