def tile_anchors(grid_height,
grid_width,
scales,
aspect_ratios,
base_anchor_size,
anchor_stride,
anchor_offset):
ratio_sqrts = tf.sqrt(aspect_ratios)
heights = scales / ratio_sqrts * base_anchor_size[0]
widths = scales * ratio_sqrts * base_anchor_size[1]
# Get a grid of box centers
y_centers = tf.cast(tf.range(grid_height), dtype=tf.float32)
y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
x_centers = tf.cast(tf.range(grid_width), dtype=tf.float32)
x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
x_centers, y_centers = ops.meshgrid(x_centers, y_centers)
widths_grid, x_centers_grid = ops.meshgrid(widths, x_centers)
heights_grid, y_centers_grid = ops.meshgrid(heights, y_centers)
bbox_centers = tf.stack([y_centers_grid, x_centers_grid], axis=3)
bbox_sizes = tf.stack([heights_grid, widths_grid], axis=3)
bbox_centers = tf.reshape(bbox_centers, [-1, 2])
bbox_sizes = tf.reshape(bbox_sizes, [-1, 2])
bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
return box_list.BoxList(bbox_corners)
def tile_anchors(grid_height,
grid_width,
scales,
aspect_ratios,
base_anchor_size,
anchor_stride,
anchor_offset):
"""Create a tiled set of anchors strided along a grid in image space.
This op creates a set of anchor boxes by placing a "basis" collection of
boxes with user-specified scales and aspect ratios centered at evenly
distributed points along a grid. The basis collection is specified via the
scale and aspect_ratios arguments. For example, setting scales=[.1, .2, .2]
and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
.1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
and aspect ratio 1/2. Each box is multiplied by "base_anchor_size" before
placing it over its respective center.
Grid points are specified via grid_height, grid_width parameters as well as
the anchor_stride and anchor_offset parameters.
Args:
grid_height: size of the grid in the y direction (int or int scalar tensor)
grid_width: size of the grid in the x direction (int or int scalar tensor)
scales: a 1-d (float) tensor representing the scale of each box in the
basis set.
aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
box in the basis set. The length of the scales and aspect_ratios tensors
must be equal.
base_anchor_size: base anchor size as [height, width]
(float tensor of shape [2])
anchor_stride: difference in centers between base anchors for adjacent grid
positions (float tensor of shape [2])
anchor_offset: center of the anchor with scale and aspect ratio 1 for the
upper left element of the grid, this should be zero for
feature networks with only VALID padding and even receptive
field size, but may need some additional calculation if other
padding is used (float tensor of shape [2])
Returns:
a BoxList holding a collection of N anchor boxes
"""
ratio_sqrts = tf.sqrt(aspect_ratios)
heights = scales / ratio_sqrts * base_anchor_size[0]
widths = scales * ratio_sqrts * base_anchor_size[1]
# Get a grid of box centers
y_centers = tf.to_float(tf.range(grid_height))
y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
x_centers = tf.to_float(tf.range(grid_width))
x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
x_centers, y_centers = ops.meshgrid(x_centers, y_centers)
widths_grid, x_centers_grid = ops.meshgrid(widths, x_centers)
heights_grid, y_centers_grid = ops.meshgrid(heights, y_centers)
bbox_centers = tf.stack([y_centers_grid, x_centers_grid], axis=3)
bbox_sizes = tf.stack([heights_grid, widths_grid], axis=3)
bbox_centers = tf.reshape(bbox_centers, [-1, 2])
bbox_sizes = tf.reshape(bbox_sizes, [-1, 2])
bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
return box_list.BoxList(bbox_corners)
def tile_anchors(grid_height, grid_width, scales, aspect_ratios,
base_anchor_size, anchor_stride, anchor_offset):
"""Create a tiled set of anchors strided along a grid in image space.
This op creates a set of anchor boxes by placing a "basis" collection of
boxes with user-specified scales and aspect ratios centered at evenly
distributed points along a grid. The basis collection is specified via the
scale and aspect_ratios arguments. For example, setting scales=[.1, .2, .2]
and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
.1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
and aspect ratio 1/2. Each box is multiplied by "base_anchor_size" before
placing it over its respective center.
Grid points are specified via grid_height, grid_width parameters as well as
the anchor_stride and anchor_offset parameters.
Args:
grid_height: size of the grid in the y direction (int or int scalar tensor)
grid_width: size of the grid in the x direction (int or int scalar tensor)
scales: a 1-d (float) tensor representing the scale of each box in the
basis set.
aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
box in the basis set. The length of the scales and aspect_ratios tensors
must be equal.
base_anchor_size: base anchor size as [height, width]
(float tensor of shape [2])
anchor_stride: difference in centers between base anchors for adjacent grid
positions (float tensor of shape [2])
anchor_offset: center of the anchor with scale and aspect ratio 1 for the
upper left element of the grid, this should be zero for
feature networks with only VALID padding and even receptive
field size, but may need some additional calculation if other
padding is used (float tensor of shape [2])
Returns:
a BoxList holding a collection of N anchor boxes
"""
ratio_sqrts = tf.sqrt(aspect_ratios)
heights = scales / ratio_sqrts * base_anchor_size[0]
widths = scales * ratio_sqrts * base_anchor_size[1]
# Get a grid of box centers
y_centers = tf.cast(tf.range(grid_height), dtype=tf.float32)
y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
x_centers = tf.cast(tf.range(grid_width), dtype=tf.float32)
x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
x_centers, y_centers = ops.meshgrid(x_centers, y_centers)
widths_grid, x_centers_grid = ops.meshgrid(widths, x_centers)
heights_grid, y_centers_grid = ops.meshgrid(heights, y_centers)
bbox_centers = tf.stack([y_centers_grid, x_centers_grid], axis=3)
bbox_sizes = tf.stack([heights_grid, widths_grid], axis=3)
bbox_centers = tf.reshape(bbox_centers, [-1, 2])
bbox_sizes = tf.reshape(bbox_sizes, [-1, 2])
bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
c = bbox_corners
#print(c)
#with tf.Session() as sess:
# print(sess.run(c))
# sess.close()
return box_list.BoxList(bbox_corners)
def test_meshgrid_multidimensional(self):
np.random.seed(18)
x = np.random.rand(4, 1, 2).astype(np.float32)
y = np.random.rand(2, 3).astype(np.float32)
xgrid, ygrid = ops.meshgrid(x, y)
grid_shape = list(y.shape) + list(x.shape)
self.assertEqual(xgrid.get_shape().as_list(), grid_shape)
self.assertEqual(ygrid.get_shape().as_list(), grid_shape)
with self.test_session() as sess:
xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
# Check the shape of the output grids
self.assertEqual(xgrid_output.shape, tuple(grid_shape))
self.assertEqual(ygrid_output.shape, tuple(grid_shape))
# Check a few elements
test_elements = [((3, 0, 0), (1, 2)),
((2, 0, 1), (0, 0)),
((0, 0, 0), (1, 1))]
for xind, yind in test_elements:
# These are float equality tests, but the meshgrid op should not introduce
# rounding.
self.assertEqual(xgrid_output[yind + xind], x[xind])
self.assertEqual(ygrid_output[yind + xind], y[yind])
def _generate(self, feature_map_shape_list):
"""Generates a collection of bounding boxes to be used as anchors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0)]. For example, setting
feature_map_shape_list=[(8, 8)] asks for anchors that correspond
to an 8x8 layer. For this anchor generator, only lists of length 1 are
allowed.
Returns:
boxes: a BoxList holding a collection of N anchor boxes
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
ValueError: if feature_map_shape_list does not consist of pairs of
integers
"""
if not (isinstance(feature_map_shape_list, list)
and len(feature_map_shape_list) == 1):
raise ValueError(
'feature_map_shape_list must be a list of length 1.')
if not all([
isinstance(list_item, tuple) and len(list_item) == 2
for list_item in feature_map_shape_list
]):
raise ValueError('feature_map_shape_list must be a list of pairs.')
grid_height, grid_width = feature_map_shape_list[0]
scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
return tile_anchors(grid_height, grid_width, scales_grid,
aspect_ratios_grid, self._base_anchor_size,
self._anchor_stride, self._anchor_offset)
def test_meshgrid_multidimensional(self):
np.random.seed(18)
x = np.random.rand(4, 1, 2).astype(np.float32)
y = np.random.rand(2, 3).astype(np.float32)
xgrid, ygrid = ops.meshgrid(x, y)
grid_shape = list(y.shape) + list(x.shape)
self.assertEqual(xgrid.get_shape().as_list(), grid_shape)
self.assertEqual(ygrid.get_shape().as_list(), grid_shape)
with self.test_session() as sess:
xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
# Check the shape of the output grids
self.assertEqual(xgrid_output.shape, tuple(grid_shape))
self.assertEqual(ygrid_output.shape, tuple(grid_shape))
# Check a few elements
test_elements = [((3, 0, 0), (1, 2)),
((2, 0, 1), (0, 0)),
((0, 0, 0), (1, 1))]
for xind, yind in test_elements:
# These are float equality tests, but the meshgrid op should not introduce
# rounding.
self.assertEqual(xgrid_output[yind + xind], x[xind])
self.assertEqual(ygrid_output[yind + xind], y[yind])
def _generate(self, feature_map_shape_list):
"""Generates a collection of bounding boxes to be used as anchors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0)]. For example, setting
feature_map_shape_list=[(8, 8)] asks for anchors that correspond
to an 8x8 layer. For this anchor generator, only lists of length 1 are
allowed.
Returns:
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
ValueError: if feature_map_shape_list does not consist of pairs of
integers
"""
if not (isinstance(feature_map_shape_list, list)
and len(feature_map_shape_list) == 1):
raise ValueError(
'feature_map_shape_list must be a list of length 1.')
if not all([
isinstance(list_item, tuple) and len(list_item) == 2
for list_item in feature_map_shape_list
]):
raise ValueError('feature_map_shape_list must be a list of pairs.')
# Create constants in init_scope so they can be created in tf.functions
# and accessed from outside of the function.
with tf.init_scope():
self._base_anchor_size = tf.cast(tf.convert_to_tensor(
self._base_anchor_size),
dtype=tf.float32)
self._anchor_stride = tf.cast(tf.convert_to_tensor(
self._anchor_stride),
dtype=tf.float32)
self._anchor_offset = tf.cast(tf.convert_to_tensor(
self._anchor_offset),
dtype=tf.float32)
grid_height, grid_width = feature_map_shape_list[0]
scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
anchors = tile_anchors(grid_height, grid_width, scales_grid,
aspect_ratios_grid, self._base_anchor_size,
self._anchor_stride, self._anchor_offset)
num_anchors = anchors.num_boxes_static()
if num_anchors is None:
num_anchors = anchors.num_boxes()
anchor_indices = tf.zeros([num_anchors])
anchors.add_field('feature_map_index', anchor_indices)
#print(anchors)
return [anchors]
def test_meshgrid_numpy_comparison(self):
"""Tests meshgrid op with vectors, for which it should match numpy."""
x = np.arange(4)
y = np.arange(6)
exp_xgrid, exp_ygrid = np.meshgrid(x, y)
xgrid, ygrid = ops.meshgrid(x, y)
with self.test_session() as sess:
xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
self.assertAllEqual(xgrid_output, exp_xgrid)
self.assertAllEqual(ygrid_output, exp_ygrid)
def test_meshgrid_numpy_comparison(self):
"""Tests meshgrid op with vectors, for which it should match numpy."""
x = np.arange(4)
y = np.arange(6)
exp_xgrid, exp_ygrid = np.meshgrid(x, y)
xgrid, ygrid = ops.meshgrid(x, y)
with self.test_session() as sess:
xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
self.assertAllEqual(xgrid_output, exp_xgrid)
self.assertAllEqual(ygrid_output, exp_ygrid)
def _generate(self, feature_map_shape_list):
"""Generates a collection of bounding boxes to be used as anchors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0)]. For example, setting
feature_map_shape_list=[(8, 8)] asks for anchors that correspond
to an 8x8 layer. For this anchor generator, only lists of length 1 are
allowed.
Returns:
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
ValueError: if feature_map_shape_list does not consist of pairs of
integers
"""
if not (isinstance(feature_map_shape_list, list)
and len(feature_map_shape_list) == 1):
raise ValueError('feature_map_shape_list must be a list of length 1.')
if not all([isinstance(list_item, tuple) and len(list_item) == 2
for list_item in feature_map_shape_list]):
raise ValueError('feature_map_shape_list must be a list of pairs.')
self._base_anchor_size = tf.to_float(tf.convert_to_tensor(
self._base_anchor_size))
self._anchor_stride = tf.to_float(tf.convert_to_tensor(
self._anchor_stride))
self._anchor_offset = tf.to_float(tf.convert_to_tensor(
self._anchor_offset))
grid_height, grid_width = feature_map_shape_list[0]
scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
anchors = tile_anchors(grid_height,
grid_width,
scales_grid,
aspect_ratios_grid,
self._base_anchor_size,
self._anchor_stride,
self._anchor_offset)
num_anchors = anchors.num_boxes_static()
if num_anchors is None:
num_anchors = anchors.num_boxes()
anchor_indices = tf.zeros([num_anchors])
anchors.add_field('feature_map_index', anchor_indices)
return [anchors]
def _generate(self, feature_map_shape_list):
"""Generates a collection of bounding boxes to be used as anchors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0)]. For example, setting
feature_map_shape_list=[(8, 8)] asks for anchors that correspond
to an 8x8 layer. For this anchor generator, only lists of length 1 are
allowed.
Returns:
boxes: a BoxList holding a collection of N anchor boxes. Additionally
this BoxList also holds a `feature_map_index` field which is set to 0
for each anchor; this field exists for interchangeability reasons with
the MultipleGridAnchorGenerator (see the docstring for the corresponding
`_generate` function in multiple_grid_anchor_generator.py)
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
ValueError: if feature_map_shape_list does not consist of pairs of
integers
"""
if not (isinstance(feature_map_shape_list, list)
and len(feature_map_shape_list) == 1):
raise ValueError('feature_map_shape_list must be a list of length 1.')
if not all([isinstance(list_item, tuple) and len(list_item) == 2
for list_item in feature_map_shape_list]):
raise ValueError('feature_map_shape_list must be a list of pairs.')
grid_height, grid_width = feature_map_shape_list[0]
scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
anchors = tile_anchors(grid_height,
grid_width,
scales_grid,
aspect_ratios_grid,
self._base_anchor_size,
self._anchor_stride,
self._anchor_offset)
num_anchors = anchors.num_boxes_static()
if num_anchors is None:
num_anchors = anchors.num_boxes()
anchor_indices = tf.zeros([num_anchors])
anchors.add_field('feature_map_index', anchor_indices)
return anchors
def _generate(self, feature_map_shape_list):
if not (isinstance(feature_map_shape_list, list)
and len(feature_map_shape_list) == 1):
raise ValueError('feature_map_shape_list must be a list of length 1.')
if not all([isinstance(list_item, tuple) and len(list_item) == 2
for list_item in feature_map_shape_list]):
raise ValueError('feature_map_shape_list must be a list of pairs.')
# Create constants in init_scope so they can be created in tf.functions
# and accessed from outside of the function.
with tf.init_scope():
self._base_anchor_size = tf.cast(tf.convert_to_tensor(
self._base_anchor_size), dtype=tf.float32)
self._anchor_stride = tf.cast(tf.convert_to_tensor(
self._anchor_stride), dtype=tf.float32)
self._anchor_offset = tf.cast(tf.convert_to_tensor(
self._anchor_offset), dtype=tf.float32)
grid_height, grid_width = feature_map_shape_list[0]
scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
anchors = tile_anchors(grid_height,
grid_width,
scales_grid,
aspect_ratios_grid,
self._base_anchor_size,
self._anchor_stride,
self._anchor_offset)
num_anchors = anchors.num_boxes_static()
if num_anchors is None:
num_anchors = anchors.num_boxes()
anchor_indices = tf.zeros([num_anchors])
anchors.add_field('feature_map_index', anchor_indices)
return [anchors]
def _generate(self, feature_map_shape_list):
"""Generates a collection of bounding boxes to be used as anchors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0)]. For example, setting
feature_map_shape_list=[(8, 8)] asks for anchors that correspond
to an 8x8 layer. For this anchor generator, only lists of length 1 are
allowed.
Returns:
boxes: a BoxList holding a collection of N anchor boxes
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
ValueError: if feature_map_shape_list does not consist of pairs of
integers
"""
if not (isinstance(feature_map_shape_list, list)
and len(feature_map_shape_list) == 1):
raise ValueError('feature_map_shape_list must be a list of length 1.')
if not all([isinstance(list_item, tuple) and len(list_item) == 2
for list_item in feature_map_shape_list]):
raise ValueError('feature_map_shape_list must be a list of pairs.')
grid_height, grid_width = feature_map_shape_list[0]
scales_grid, aspect_ratios_grid = ops.meshgrid(self._scales,
self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
return tile_anchors(grid_height,
grid_width,
scales_grid,
aspect_ratios_grid,
self._base_anchor_size,
self._anchor_stride,
self._anchor_offset)
def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
"""Generates a collection of bounding boxes to be used as anchors.
The number of anchors generated for a single grid with shape MxM where we
place k boxes over each grid center is k*M^2 and thus the total number of
anchors is the sum over all grids.
Args:
feature_map_shape_list: list of a pair of convnet layer resolutions in the
format [(height, width)]. For example,
setting feature_map_shape=[(8, 8)] asks for anchors that
correspond to an 8x8 layer.
im_height: the height of the image to generate the grid for. If both
im_height and im_width are 1, the generated anchors default to
normalized coordinates, otherwise absolute coordinates are used for the
grid.
im_width: the width of the image to generate the grid for. If both
im_height and im_width are 1, the generated anchors default to
normalized coordinates, otherwise absolute coordinates are used for the
grid.
Returns:
boxes: a BoxList holding a collection of N anchor boxes
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
ValueError: if feature_map_shape_list does not consist of pairs of
integers
"""
if len(feature_map_shape_list) != 1 or len(
feature_map_shape_list[0]) != 2:
raise ValueError('feature_map_shape_list must be a list of a pair')
# Yolo has only one feature_map. so [0] mean the only map which is first.
feature_map_shape = feature_map_shape_list[0]
im_height = tf.to_float(im_height)
im_width = tf.to_float(im_width)
if not self._anchor_stride:
anchor_stride = (1.0 / tf.to_float(feature_map_shape[0]),
1.0 / tf.to_float(feature_map_shape[1]))
else:
anchor_stride = (tf.to_float(self._anchor_stride[0]) / im_height,
tf.to_float(self._anchor_stride[1]) / im_width)
if not self._anchor_offset:
anchor_offset = (0.5 * anchor_stride[0], 0.5 * anchor_stride[1])
else:
anchor_offset = (tf.to_float(self._anchor_offset[0]) / im_height,
tf.to_float(self._anchor_offset[1]) / im_width)
if (anchor_stride and len(anchor_stride) != 2):
raise ValueError('anchor_stride must be a pair.')
if (anchor_offset and len(anchor_offset) != 2):
raise ValueError('anchor_offset must be a pair.')
# Anchors are devided into size of feature map to make the size of anchors within (0 ~ 1)
anchor_widths = [
anchor[0] / feature_map_shape[0] for anchor in self._anchors
]
anchor_heights = [
anchor[1] / feature_map_shape[1] for anchor in self._anchors
]
heights = anchor_heights * self._base_anchor_size[0]
widths = anchor_widths * self._base_anchor_size[1]
x_centers = tf.to_float(tf.range(feature_map_shape[0]))
x_centers = x_centers * anchor_stride[0] + anchor_offset[0]
y_centers = tf.to_float(tf.range(feature_map_shape[1]))
y_centers = y_centers * anchor_stride[1] + anchor_offset[1]
x_centers, y_centers = ops.meshgrid(x_centers, y_centers)
widths_grid, x_centers_grid = ops.meshgrid(widths, x_centers)
heights_grid, y_centers_grid = ops.meshgrid(heights, y_centers)
bbox_centers = tf.stack([y_centers_grid, x_centers_grid], axis=3)
bbox_sizes = tf.stack([heights_grid, widths_grid], axis=3)
bbox_centers = tf.reshape(bbox_centers, [-1, 2])
bbox_sizes = tf.reshape(bbox_sizes, [-1, 2])
bbox_corners = grid_anchor_generator._center_size_bbox_to_corners_bbox(
bbox_centers, bbox_sizes)
anchors = box_list.BoxList(bbox_corners)
num_anchors = anchors.num_boxes_static()
if num_anchors is None:
num_anchors = anchors.num_boxes()
stddevs_tensor = 0.01 * tf.ones(
[num_anchors, 4], dtype=tf.float32, name='stddevs')
anchors.add_field('stddev', stddevs_tensor)
return anchors