def testReshapeDepth(self):
"""Tests that depth can be reshaped to the x dimension."""
fake = tf.placeholder(
tf.float32, shape=(None, None, None, self.depth), name='inputs')
real = _rand(self.batch_size, self.im_height, self.im_width, self.depth)
with self.test_session() as sess:
outputs = shapes.transposing_reshape(
fake, src_dim=3, part_a=4, part_b=-1, dest_dim_a=2, dest_dim_b=3)
res_image = sess.run([outputs], feed_dict={fake: real})
self.assertEqual(
tuple(res_image[0].shape),
(self.batch_size, self.im_height, self.im_width * 4, self.depth / 4))
def AddReShape(self, prev_layer, index):
"""Reshapes the input tensor by moving each (x_scale,y_scale) rectangle to.
the depth dimension. NOTE that the TF convention is that inputs are
[batch, y, x, depth].
Args:
prev_layer: Input tensor.
index: Position in model_str to start parsing
Returns:
Output tensor, end index in model_str.
"""
pattern = re.compile(R'(S)(?:{(\w)})?(\d+)\((\d+)x(\d+)\)(\d+),(\d+)')
m = pattern.match(self.model_str, index)
if m is None:
return None, None
name = self._GetLayerName(m.group(0), index, m.group(2))
src_dim = int(m.group(3))
part_a = int(m.group(4))
part_b = int(m.group(5))
dest_dim_a = int(m.group(6))
dest_dim_b = int(m.group(7))
if part_a == 0:
part_a = -1
if part_b == 0:
part_b = -1
prev_shape = tf.shape(prev_layer)
layer = shapes.transposing_reshape(prev_layer,
src_dim,
part_a,
part_b,
dest_dim_a,
dest_dim_b,
name=name)
# Compute scale factors.
result_shape = tf.shape(layer)
for i in xrange(len(self.reduction_factors)):
if self.reduction_factors[i] is not None:
factor1 = tf.cast(self.reduction_factors[i], tf.float32)
factor2 = tf.cast(prev_shape[i], tf.float32)
divisor = tf.cast(result_shape[i], tf.float32)
self.reduction_factors[i] = tf.div(tf.mul(factor1, factor2),
divisor)
return layer, m.end()
def testTransposingReshape_2_2_3_2_3(self):
"""Case: dest_a == src, dest_b > src: Split with Least sig part going right.
"""
with self.test_session() as sess:
fake = tf.placeholder(
tf.float32, shape=(None, None, None, 2), name='inputs')
outputs = shapes.transposing_reshape(
fake, src_dim=2, part_a=2, part_b=3, dest_dim_a=2, dest_dim_b=3)
# Make real inputs. The tensor looks like this:
# tensor=[[[[0, 1][2, 3][4, 5][6, 7][8, 9][10, 11]]
# [[12, 13][14, 15][16, 17][18, 19][20, 21][22, 23]]
# [[[24, 25]...
real = np.arange(120).reshape((5, 2, 6, 2))
np_array = sess.run([outputs], feed_dict={fake: real})[0]
self.assertEqual(tuple(np_array.shape), (5, 2, 2, 6))
self.assertAllEqual(
np_array[0, :, :, :],
[[[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]],
[[12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]]])
def AddReShape(self, prev_layer, index):
"""Reshapes the input tensor by moving each (x_scale,y_scale) rectangle to.
the depth dimension. NOTE that the TF convention is that inputs are
[batch, y, x, depth].
Args:
prev_layer: Input tensor.
index: Position in model_str to start parsing
Returns:
Output tensor, end index in model_str.
"""
pattern = re.compile(R'(S)(?:{(\w)})?(\d+)\((\d+)x(\d+)\)(\d+),(\d+)')
m = pattern.match(self.model_str, index)
if m is None:
return None, None
name = self._GetLayerName(m.group(0), index, m.group(2))
src_dim = int(m.group(3))
part_a = int(m.group(4))
part_b = int(m.group(5))
dest_dim_a = int(m.group(6))
dest_dim_b = int(m.group(7))
if part_a == 0:
part_a = -1
if part_b == 0:
part_b = -1
prev_shape = tf.shape(prev_layer)
layer = shapes.transposing_reshape(
prev_layer, src_dim, part_a, part_b, dest_dim_a, dest_dim_b, name=name)
# Compute scale factors.
result_shape = tf.shape(layer)
for i in xrange(len(self.reduction_factors)):
if self.reduction_factors[i] is not None:
factor1 = tf.cast(self.reduction_factors[i], tf.float32)
factor2 = tf.cast(prev_shape[i], tf.float32)
divisor = tf.cast(result_shape[i], tf.float32)
self.reduction_factors[i] = tf.div(tf.mul(factor1, factor2), divisor)
return layer, m.end()
