def __init__(self,
shared_value,
final_value,
epochs_to_saturation):
assert_is_instance(shared_value,
theano.tensor.sharedvar.SharedVariable)
assert_is_subdtype(shared_value.dtype, numpy.floating)
assert_equal(shared_value.ndim == 0, numpy.isscalar(final_value))
if numpy.isscalar(final_value):
assert_floating(final_value)
else:
assert_is_subdtype(final_value.dtype, numpy.floating)
assert_equal(final_value.shape,
shared_value.get_value().shape)
assert_integer(epochs_to_saturation)
assert_greater(epochs_to_saturation, 0)
self.shared_value = shared_value
cast = numpy.cast[shared_value.dtype]
self._final_value = cast(final_value)
self._epochs_to_saturation = epochs_to_saturation
self._num_epochs_seen = None
self._initial_value = None
def __init__(self, dataset, batch_size):
super(DatasetIterator, self).__init__()
assert_is_instance(dataset, Dataset)
assert_greater(len(dataset.tensors), 0)
assert_greater(batch_size, 0)
assert_integer(batch_size)
self.dataset = dataset
self.batch_size = batch_size
def _getitem_arg_to_slice(arg):
'''
Turns arg into a slice, if it isn't one.
'''
if numpy.isscalar(arg):
assert_integer(arg)
if arg == -1:
return slice(arg, None)
else:
return slice(arg, arg + 1)
else:
assert_is_instance(arg, slice)
return arg
def get_expected_values(start_row, end_row=None):
if end_row is None:
assert_is_instance(start_row, numpy.ndarray)
values = start_row
else:
assert_integer(start_row)
assert_integer(end_row)
values = numpy.arange(start_row, end_row)
values = values % dtype_max
values = values.reshape((values.shape[0], ) +
((1, ) * (len(shape) - 1)))
return numpy.tile(values, shape[1:])
def get_iters(yaml_dict, seed):
assert_is_instance(yaml_dict, dict)
assert_integer(seed)
training_set, validation_set = _get_datasets(yaml_dict)
hyperparams_dict = yaml_dict['hyperparams']
training_iter = training_set.iterator(
'random_vec3',
hyperparams_dict['training_batch_size'],
rng=RandomState(seed))
validation_iter = validation_set.iterator(
'random_vec3',
hyperparams_dict['validation_batch_size'],
rng=RandomState(seed))
return training_iter, validation_iter
def __init__(self,
shared_value,
final_scale,
epochs_to_saturation):
assert_is_instance(shared_value,
theano.tensor.sharedvar.SharedVariable)
assert_floating(final_scale)
assert_greater_equal(final_scale, 0.0)
assert_less_equal(final_scale, 1.0)
assert_integer(epochs_to_saturation)
assert_greater(epochs_to_saturation, 0)
self.shared_value = shared_value
self._final_scale = final_scale
self._epochs_to_saturation = epochs_to_saturation
# initialized in on_start_training()
self._initial_value = None
self._num_epochs_seen = None
def get_example_image(self, object_id, camera_dir):
assert_integer(object_id)
assert_equal(len(camera_dir.shape), 1)
def normalize(vec):
return vec / numpy.sqrt((vec * vec).sum())
camera_dir = normalize(camera_dir)
xy_projection_length = numpy.sqrt((camera_dir[:2] ** 2).sum())
elevation = numpy.arctan2(camera_dir[2], # z
xy_projection_length)
# atan2 args need not be on the unit circle; otherwise the following
# would be wrong.
azimuth = numpy.arctan2(camera_dir[1], # y
camera_dir[0]) # x
if azimuth < 0.0:
azimuth += (2 * numpy.pi)
assert_greater_equal(azimuth, 0.0)
def rad_to_deg(rad):
return rad / numpy.pi * 180.0
elevation = rad_to_deg(elevation)
azimuth = rad_to_deg(azimuth)
def round_to_int(floating):
return int(numpy.round(floating))
elevation_label = round_to_int((elevation - 30.0) / 5.0)
azimuth_label = round_to_int(azimuth / 20.0) * 2
result = self.id_elev_azim_to_example.get((object_id,
elevation_label,
azimuth_label),
None)
return result
def __check_arg_types(input_node,
yaml_dict,
use_dropout,
numpy_rng,
theano_rng,
output_list,
output_size=None):
assert_is_instance(input_node, Node)
assert_is_instance(yaml_dict, dict)
assert_is_instance(use_dropout, bool)
assert_is_instance(numpy_rng, numpy.random.RandomState)
if use_dropout:
assert_is_instance(theano_rng, RandomStreams)
else:
assert_true(theano_rng is None or isinstance(theano_rng, RandomStreams))
assert_is_instance(output_list, list)
assert_all_is_instance(output_list, Node)
if output_size is not None:
assert_integer(output_size)
def __init__(self,
model_id_node,
model_cam_dir_node,
# label_id_node,
# label_cam_dir_node,
iterator_input_nodes,
blank_id):
assert_equal(model_id_node.output_format.axes, ('b', 'f'))
assert_equal(model_id_node.output_symbol.dtype, floatX)
assert_equal(model_cam_dir_node.output_format.axes, ('b', 'f'))
assert_equal(model_cam_dir_node.output_format.shape[1], 3)
assert_equal(model_cam_dir_node.output_symbol.dtype, floatX)
label_id_node = iterator_input_nodes.id
label_cam_dir_node = iterator_input_nodes.camera_dir
assert_equal(label_id_node.output_format.axes, ('b',))
assert_equal(str(label_id_node.output_format.dtype), 'int32')
assert_equal(label_cam_dir_node.output_format.axes, ('b', 'f'))
assert_equal(label_cam_dir_node.output_format.dtype, floatX)
# assert_in('id_layers', model.__dict__)
# assert_in('cam_dir_layers', model.__dict__)
# assert_is_instance(model, IdAndCameraDirModel)
assert_integer(blank_id)
# def assert_formats_equal(fmt_0, fmt_1):
# assert_equal(fmt_0.axes, fmt_1.axes)
# assert_equal(fmt_0.shape, fmt_1.shape)
# assert_equal(fmt_0.dtype, fmt_1.dtype)
# assert_formats_equal(model.input_node.output_format,
# iterator_input_nodes[0].output_format)
assert_equal(blank_id, model_id_node.output_format.shape[1] - 1)
id_loss = CrossEntropy(model_id_node, label_id_node) #model.id_layers[-1], iterator_input_nodes.id)
def to_floatX(arg):
return numpy.cast[floatX](arg)
# This is a bit different from the other scales, in that it
# normalizes the initial expected loss to 1.0, whereas
# the regression scales normalize the initial max loss to 1.0.
# So id loss will initially be ~ 2x the regression losses, which
# suits me fine.
def get_id_scale(model_id_node):
# id_node = model.id_layers[-1]
assert_equal(model_id_node.output_format.axes, ('b', 'f'))
num_ids = model_id_node.output_format.shape[1]
assert_equal(num_ids, 7)
return to_floatX((1.0 / -numpy.log(1.0 / num_ids)) ** 2.0)
id_scale = get_id_scale(model_id_node)
cam_dir_loss = UnitVectorSqNorm(model_cam_dir_node, #model.cam_dir_layers[-1],
label_cam_dir_node) #iterator_input_nodes.camera_dir)
cam_dir_scale = to_floatX((1.0 / 2.0) ** 2.0)
id_loss_symbol = id_loss.output_symbol * id_scale
cam_dir_loss_symbol = cam_dir_loss.output_symbol * cam_dir_scale
id_label_symbol = label_id_node.output_symbol #iterator_input_nodes.id.output_symbol
# A vector along the 'b' axis. Has 0.0 for bkg images and 1.0 elsewhere
self.non_bkg_mask = T.cast(T.neq(id_label_symbol, blank_id), floatX)
total_loss_symbol = (id_loss_symbol +
self.non_bkg_mask * cam_dir_loss_symbol)
# total_loss_symbol = T.switch(T.eq(id_label_symbol, blank_id),
# id_loss_symbol,
# id_loss_symbol + cam_dir_loss_symbol)
# loss_symbol = (id_loss.output_symbol * to_floatX(id_scale) +
# cam_dir_loss.output_symbol * to_floatX(cam_dir_scale))
assert_equal(str(total_loss_symbol.dtype), floatX)
super(IdAndCameraDirLoss, self).__init__(
input_nodes=iterator_input_nodes, # not all of these are used
output_symbol=total_loss_symbol,
output_format=copy.deepcopy(id_loss.output_format))
self.id_loss = id_loss
self.cam_dir_loss = cam_dir_loss
def __init__(self,
model,
foreground_dataset,
video_frame,
smoothing,
input_scale,
output_dir=None):
'''
model: simplelearn.io.SerializeableModel
foreground_dataset: simplelearn.data.Dataset
A Dataset with NORB-style label vectors. Used to compute a mapping
from object ID to example images.
video_frame: numpy.ndarray
A frame from the input video stream. Used to initialize display pixel
buffer's size and dtype.
smoothing: float
A float in the range [0.0, 1.0).
The displayed pose and softmax z_t is computed as:
z_t = k * z_(t-1) + (1.0 - k) * y(t)
Where:
t is the timestep
z is the displayed value
y is the model output value
k is this smoothing argument
input_scale: int
The ratio of the input reticule to the model's actual input size.
output_dir: string or None
The directory to save frame images to. If omitted, no frame images
will be saved.
'''
def get_input_size(model):
image_format = model.input_node.output_format
assert_equal(image_format.axes, ('b', '0', '1', 'c'))
return image_format.shape[1:3]
super(ReticuleDisplay, self).__init__(model,
foreground_dataset,
video_frame,
get_input_size(model),
output_dir)
assert_is_instance(model, IdAndCameraDirModel)
assert_integer(input_scale)
assert_greater_equal(input_scale, 1)
assert_greater_equal(smoothing, 0.0)
assert_less(smoothing, 1.0)
self.smoothing = smoothing
self._displayed_camera_dir = None
self._displayed_softmax = None
input_format = model.input_node.output_format
input_shape = numpy.asarray(
[input_format.shape[input_format.axes.index(a)]
for a in ('0', '1')])
reticule_shape = input_shape * input_scale
self.reticule_min_corner = (numpy.asarray(self.video_pixels.shape[:2])
- reticule_shape) // 2
self.reticule_max_corner = self.reticule_min_corner + reticule_shape
self.input_pixels = self._get_example_window(0, 0)
self.preprocessed_pixels = self._get_example_window(0, 1)
model_output_nodes = [model.id_layers[-1],
model.cam_dir_layers[-1]]
self.model_function = theano.function(
[model.input_node.output_symbol],
[n.output_symbol for n in model_output_nodes])
# outputs bc01
self.preprocess_function = theano.function(
[model.input_node.output_symbol],
_find_lcn_node(model).output_symbol)
num_candidates = 3
self.example_pixels_windows = [self._get_example_window(1, c)
for c in range(num_candidates)]
self.caption_pixels_windows = [self._get_example_window(2, c)
for c in range(num_candidates)]
def make_instance_dataset(norb_name,
a_norb,
b_norb,
test_elevation_stride,
test_azimuth_stride,
objects=None):
'''
Creates instance recognition datasets from category recognition datasets.
Merges two category recognition datasets (with disjoint object instances),
and re-partitions them into instance recognition datasets (with disjoint
camera views).
The instance recognition dataset consists of a train and test set.
All objects not selected by <objects> are ignored.
Of the remaining images, he test set consists of all images that satisfy
both the test_elevation_stride and test_azimuth_stride. The other
images are used for the training set.
If the category datset is in stereo, only the left stereo images are used.
Parameters
----------
norb_name: str
The name of the category recognition dataset (e.g. 'big_norb'). Used to
build the name of the instance recognition dataset. Alphanumeric
characters and '_' only.
a_norb: NORB Dataset
One of the category recognition datasets (i.e. training set).
b_norb: NORB Dataset
The other category recognition dataset (i.e. testing set).
test_elevation_stride: int
Use every M'th elevation as a test image.
test_azimuth_stride: int
Use every N'th azimuth as a test image.
objects: Sequence
[(c0, i0), (c1, i1), ..., (cN, iN)]
Each (cx, ix) pair specifies an object to include, by their
class and instance labels cx and ix.
Returns
-------
rval: str
The path to the newly created .h5 file.
'''
assert_is_instance(norb_name, basestring)
assert_all_true(c.isalnum() or c == '_' for c in norb_name)
assert_is_instance(a_norb, Dataset)
assert_is_instance(b_norb, Dataset)
assert_all_equal(a_norb.names, b_norb.names)
assert_all_equal(a_norb.formats, b_norb.formats)
assert_integer(test_elevation_stride)
assert_greater(test_elevation_stride, 0)
assert_integer(test_azimuth_stride)
assert_greater(test_azimuth_stride, 0)
if objects is not None:
assert_is_instance(objects, Sequence)
for id_pair in objects:
assert_equal(len(id_pair), 2)
assert_all_integer(id_pair)
assert_all_greater_equal(id_pair, 0)
#
# Done sanity-checking args
#
(category_index,
instance_index,
azimuth_index,
elevation_index) = range(4) # no need for lighting_index (= 4)
def get_row_indices(labels,
test_elevation_stride,
test_azimuth_stride,
objects):
'''
Returns row indices or training and testing sets.
'''
logical_and = numpy.logical_and
if objects is not None:
objects = numpy.asarray(objects)
obj_cols = (category_index, instance_index)
object_mask = (labels[:, obj_cols] == objects).all(axis=1)
else:
object_mask = numpy.ones(labels.shape[0], dtype=bool)
test_mask = logical_and(
object_mask,
(labels[:, elevation_index] % test_elevation_stride) == 0)
test_mask = logical_and(
test_mask,
(labels[:, azimuth_index] % (test_azimuth_stride * 2)) == 0)
train_mask = logical_and(object_mask, numpy.logical_not(test_mask))
return tuple(numpy.nonzero(m)[0] for m in (train_mask, test_mask))
a_train_indices, a_test_indices = get_row_indices(a_norb.tensors[1],
test_elevation_stride,
test_azimuth_stride,
objects)
b_train_indices, b_test_indices = get_row_indices(b_norb.tensors[1],
test_elevation_stride,
test_azimuth_stride,
objects)
def create_h5_filepath(norb_name,
test_elevation_stride,
test_azimuth_stride,
objects):
'''
Creates an hdf filepath based on the args.
For which-norb: "big_norb", elevation_stride: 2, azimuth_stride: 1:
<data_dir>/big_norb_instance/e2_a1_all.h5
For same as above, but with objects: [[1, 2], [3, 7], [4, 1]]
<data_dir>/big_norb_instance/e2_a1_1-2_3-7_4-1.h5
'''
output_dir = os.path.join(simplelearn.data.data_path,
'{}_instance'.format(norb_name))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
filename = "e{:02d}_a{:02d}_o_".format(test_elevation_stride,
test_azimuth_stride)
if objects is None:
filename = filename + 'all'
else:
for id_pair in objects:
filename = filename + "%d-%d" % tuple(id_pair)
filename = filename + '.h5'
return os.path.join(output_dir, filename)
h5_path = create_h5_filepath(norb_name,
test_elevation_stride,
test_azimuth_stride,
objects)
def get_mono_format(input_image_format):
axes = input_image_format.axes
shape = input_image_format.shape
if 's' in axes:
s_index = axes.index('s')
axes = list(axes)
del axes[s_index]
shape = list(shape)
del shape[s_index]
return DenseFormat(axes=axes,
shape=shape,
dtype=input_image_format.dtype)
mono_image_format = get_mono_format(a_norb.formats[0])
label_format = a_norb.formats[1]
partition_names = ['train', 'test']
partition_sizes = [len(a_train_indices) + len(b_train_indices),
len(a_test_indices) + len(b_test_indices)]
train_indices = (a_train_indices, b_train_indices)
test_indices = (a_test_indices, b_test_indices)
# Creates a .h5 file and copies repartitioned data into it.
with make_h5_file(h5_path,
partition_names,
partition_sizes,
a_norb.names,
[mono_image_format, label_format]) as h5_file:
partitions = h5_file['partitions']
for partition_name, (a_indices, b_indices) \
in safe_izip(partition_names, [train_indices, test_indices]):
partition = partitions[partition_name]
a_images = a_norb.tensors[0]
b_images = b_norb.tensors[0]
out_images = partition['images']
print("Copying {} partition.".format(partition_name))
if 's' in a_norb.formats[0].axes:
assert_equal(a_norb.formats[0].axes.index('s'), 1)
out_images[:len(a_indices), ...] = a_images[a_indices, 0, ...]
out_images[len(a_indices):, ...] = b_images[b_indices, 0, ...]
else:
out_images[:len(a_indices), ...] = a_images[a_indices, ...]
out_images[len(a_indices):, ...] = b_images[b_indices, ...]
a_labels = a_norb.tensors[1]
b_labels = b_norb.tensors[1]
out_labels = partition['labels']
out_labels[:len(a_indices), :] = a_labels[a_indices, :]
out_labels[len(a_indices):, :] = b_labels[b_indices, :]
# Don't shuffle the data; that's the iterator's job.
return h5_path
def make_batch(self, is_symbolic, batch_size=None, dtype=None, name=None):
'''
Makes a numeric or symbolic batch.
May later split this into two functions: make_numeric_batch and
make_symbolic_batch. Keep it like this for now. If implementations of
_make_batch end up not sharing much logic between is_symbolic=True
and is_symbolic=False, then maybe split it.
Parameters
----------
is_symbolic: bool
if True, return a symbolic batch. Otherwise return a numeric batch.
batch_size: int
Number of examples in numeric batch. Omit if is_symbolic is True or
if there is no batch axis.
dtype: str/numpy.dtype, or NoneType
A numpy/theano dtype. Required if self.dtype is None.
Prohibited otherwise.
name: str, or NoneType
Name of the symbolic batch.
Optional when is_symbolic is True.
Omit if is_symbolic is False.
'''
assert_is_instance(is_symbolic, bool)
# checks is_symbolic vs batch_size
assert_equal(is_symbolic, batch_size is None,
"Must not supply a batch_size when is_symbolic is True."
if is_symbolic
else
"Must supply a batch_size when is_symbolic is False.")
# Sanity-checks batch_size
if not is_symbolic:
assert_integer(batch_size)
assert_greater_equal(batch_size, 0)
# checks is_symbolic vs name
if not is_symbolic and name is not None:
raise ValueError("Can't supply a name when is_symbolic is False.")
# Checks dtype vs self.dtype
if dtype is None:
if self.dtype is None:
raise TypeError("Must supply a dtype argument, because this "
"%s has no dtype of its own." % type(self))
else:
dtype = self.dtype
elif self.dtype is not None:
raise TypeError("Can't supply a dtype argument because this %s's "
"dtype is not None (it's %s)." %
(type(self), self.dtype))
elif dtype == 'floatX':
dtype = theano.config.floatX
else:
dtype = numpy.dtype(dtype)
result = self._make_batch(is_symbolic, batch_size, dtype, name)
self.check(result)
return result
