def testGlimpseNonNormalizedNonCentered(self):
img = constant_op.constant(np.arange(25).reshape((1, 5, 5, 1)),
dtype=dtypes.float32)
with self.test_session():
result1 = image_ops.extract_glimpse(img, [3, 3], [[0, 0]],
centered=False, normalized=False)
result2 = image_ops.extract_glimpse(img, [3, 3], [[1, 0]],
centered=False, normalized=False)
self.assertAllEqual(np.asarray([[0, 1, 2], [5, 6, 7], [10, 11, 12]]),
result1.eval()[0, :, :, 0])
self.assertAllEqual(np.asarray([[5, 6, 7], [10, 11, 12], [15, 16, 17]]),
result2.eval()[0, :, :, 0])
def testEmptyTensor(self):
empty_image = np.zeros((0, 4, 3, 0))
offsets = np.zeros((0, 2))
with self.cached_session():
result = image_ops.extract_glimpse(empty_image, [1, 1], offsets)
self.assertAllEqual(np.zeros((0, 1, 1, 0), dtype=np.float32),
self.evaluate(result))
def testEmptyTensor(self):
empty_image = np.zeros((0, 4, 3, 0))
offsets = np.zeros((0, 2))
with self.cached_session():
result = image_ops.extract_glimpse(empty_image, [1, 1], offsets)
self.assertAllEqual(
np.zeros((0, 1, 1, 0), dtype=np.float32), self.evaluate(result))
def testGlimpseNonNormalizedNonCentered(self):
img = constant_op.constant(np.arange(25).reshape((1, 5, 5, 1)),
dtype=dtypes.float32)
with self.test_session():
result1 = image_ops.extract_glimpse(img, [3, 3], [[0, 0]],
centered=False,
normalized=False)
result2 = image_ops.extract_glimpse(img, [3, 3], [[1, 0]],
centered=False,
normalized=False)
self.assertAllEqual(
np.asarray([[0, 1, 2], [5, 6, 7], [10, 11, 12]]),
result1.eval()[0, :, :, 0])
self.assertAllEqual(
np.asarray([[5, 6, 7], [10, 11, 12], [15, 16, 17]]),
result2.eval()[0, :, :, 0])
def _VerifyValues(self, tensor_in_sizes, glimpse_sizes, offsets,
expected_rows, expected_cols):
"""Verifies the output values of the glimpse extraction kernel.
Args:
tensor_in_sizes: Input tensor dimensions in [input_rows, input_cols].
glimpse_sizes: Dimensions of the glimpse in [glimpse_rows, glimpse_cols].
offsets: Relative location of the center of the glimpse in the input
image expressed as [row_offset, col_offset].
expected_rows: A list containing the expected row numbers (None for
out of bound entries that are expected to be replaced by uniform
random entries in [0,1) ).
expected_cols: Same as expected_rows, but for column numbers.
"""
rows = tensor_in_sizes[0]
cols = tensor_in_sizes[1]
# Row Tensor with entries by row.
# [[ 1 1 1 ... ]
# [ 2 2 2 ... ]
# [ 3 3 3 ... ]
# [ ...
# ]
t_rows = array_ops.tile([[1.0 * r] for r in range(1, rows + 1)],
[1, cols],
name='tile_rows')
# Shuffle to switch to a convention of (batch_size, height, width, depth).
t_rows_4d = array_ops.transpose(
array_ops.expand_dims(array_ops.expand_dims(t_rows, 0), 3),
[0, 2, 1, 3])
# Column Tensor with entries by column.
# [[ 1 2 3 4 ... ]
# [ 1 2 3 4 ... ]
# [ 1 2 3 4 ... ]
# [ ... ]
# ]
t_cols = array_ops.tile([[1.0 * r for r in range(1, cols + 1)]],
[rows, 1],
name='tile_cols')
# Shuffle to switch to a convention of (batch_size, height, width, depth).
t_cols_4d = array_ops.transpose(
array_ops.expand_dims(array_ops.expand_dims(t_cols, 0), 3),
[0, 2, 1, 3])
# extract_glimpses from Row and Column Tensor, respectively.
# Switch order for glimpse_sizes and offsets to switch from (row, col)
# convention to tensorflows (height, width) convention.
t1 = constant_op.constant([glimpse_sizes[1], glimpse_sizes[0]],
shape=[2])
t2 = constant_op.constant([offsets[1], offsets[0]], shape=[1, 2])
glimpse_rows = (array_ops.transpose(
image_ops.extract_glimpse(t_rows_4d, t1, t2), [0, 2, 1, 3]))
glimpse_cols = (array_ops.transpose(
image_ops.extract_glimpse(t_cols_4d, t1, t2), [0, 2, 1, 3]))
# Evaluate the TensorFlow Graph.
with self.cached_session() as sess:
value_rows, value_cols = self.evaluate(
[glimpse_rows, glimpse_cols])
# Check dimensions of returned glimpse.
self.assertEqual(value_rows.shape[1], glimpse_sizes[0])
self.assertEqual(value_rows.shape[2], glimpse_sizes[1])
self.assertEqual(value_cols.shape[1], glimpse_sizes[0])
self.assertEqual(value_cols.shape[2], glimpse_sizes[1])
# Check entries.
min_random_val = 0
max_random_val = max(rows, cols)
for i in range(glimpse_sizes[0]):
for j in range(glimpse_sizes[1]):
if expected_rows[i] is None or expected_cols[j] is None:
self.assertGreaterEqual(value_rows[0][i][j][0],
min_random_val)
self.assertLessEqual(value_rows[0][i][j][0],
max_random_val)
self.assertGreaterEqual(value_cols[0][i][j][0],
min_random_val)
self.assertLessEqual(value_cols[0][i][j][0],
max_random_val)
else:
self.assertEqual(value_rows[0][i][j][0], expected_rows[i])
self.assertEqual(value_cols[0][i][j][0], expected_cols[j])
def _VerifyValues(self, tensor_in_sizes, glimpse_sizes, offsets,
expected_rows, expected_cols):
"""Verifies the output values of the glimpse extraction kernel.
Args:
tensor_in_sizes: Input tensor dimensions in [input_rows, input_cols].
glimpse_sizes: Dimensions of the glimpse in [glimpse_rows, glimpse_cols].
offsets: Relative location of the center of the glimpse in the input
image expressed as [row_offset, col_offset].
expected_rows: A list containing the expected row numbers (None for
out of bound entries that are expected to be replaced by uniform
random entries in [0,1) ).
expected_cols: Same as expected_rows, but for column numbers.
"""
rows = tensor_in_sizes[0]
cols = tensor_in_sizes[1]
# Row Tensor with entries by row.
# [[ 1 1 1 ... ]
# [ 2 2 2 ... ]
# [ 3 3 3 ... ]
# [ ...
# ]
t_rows = array_ops.tile(
[[1.0 * r] for r in range(1, rows + 1)], [1, cols], name='tile_rows')
# Shuffle to switch to a convention of (batch_size, height, width, depth).
t_rows_4d = array_ops.transpose(
array_ops.expand_dims(array_ops.expand_dims(t_rows, 0), 3),
[0, 2, 1, 3])
# Column Tensor with entries by column.
# [[ 1 2 3 4 ... ]
# [ 1 2 3 4 ... ]
# [ 1 2 3 4 ... ]
# [ ... ]
# ]
t_cols = array_ops.tile(
[[1.0 * r for r in range(1, cols + 1)]], [rows, 1], name='tile_cols')
# Shuffle to switch to a convention of (batch_size, height, width, depth).
t_cols_4d = array_ops.transpose(
array_ops.expand_dims(array_ops.expand_dims(t_cols, 0), 3),
[0, 2, 1, 3])
# extract_glimpses from Row and Column Tensor, respectively.
# Switch order for glimpse_sizes and offsets to switch from (row, col)
# convention to tensorflows (height, width) convention.
t1 = constant_op.constant([glimpse_sizes[1], glimpse_sizes[0]], shape=[2])
t2 = constant_op.constant([offsets[1], offsets[0]], shape=[1, 2])
glimpse_rows = (array_ops.transpose(
image_ops.extract_glimpse(t_rows_4d, t1, t2), [0, 2, 1, 3]))
glimpse_cols = (array_ops.transpose(
image_ops.extract_glimpse(t_cols_4d, t1, t2), [0, 2, 1, 3]))
# Evaluate the TensorFlow Graph.
with self.test_session() as sess:
value_rows, value_cols = sess.run([glimpse_rows, glimpse_cols])
# Check dimensions of returned glimpse.
self.assertEqual(value_rows.shape[1], glimpse_sizes[0])
self.assertEqual(value_rows.shape[2], glimpse_sizes[1])
self.assertEqual(value_cols.shape[1], glimpse_sizes[0])
self.assertEqual(value_cols.shape[2], glimpse_sizes[1])
# Check entries.
min_random_val = 0
max_random_val = max(rows, cols)
for i in range(glimpse_sizes[0]):
for j in range(glimpse_sizes[1]):
if expected_rows[i] is None or expected_cols[j] is None:
self.assertGreaterEqual(value_rows[0][i][j][0], min_random_val)
self.assertLessEqual(value_rows[0][i][j][0], max_random_val)
self.assertGreaterEqual(value_cols[0][i][j][0], min_random_val)
self.assertLessEqual(value_cols[0][i][j][0], max_random_val)
else:
self.assertEqual(value_rows[0][i][j][0], expected_rows[i])
self.assertEqual(value_cols[0][i][j][0], expected_cols[j])
