def __init__(self,
n_components,
opu=None,
ndims=1,
n_2d_features=None,
packed=False,
simulated=False,
max_n_features=None,
verbose_level=-1,
linear=False):
if verbose_level >= 0:
lightonml.set_verbose_level(verbose_level)
self.verbose_level = lightonml.get_verbose_level()
super(OPUMap, self).__init__()
if opu is None:
if simulated:
simulated_opu = SimulatedOpuDevice()
if max_n_features is None:
raise ValueError(
"When using simulated=True, you need to provide max_n_features."
)
self.opu = OPU(opu_device=simulated_opu,
max_n_features=max_n_features,
n_components=n_components)
else:
self.opu = OPU(n_components=n_components)
else:
self.opu = opu
self.opu.n_components = n_components
if simulated and not isinstance(opu.device, SimulatedOpuDevice):
warnings.warn(
"You provided a real OPU object but set simulated=True."
" Will use the real OPU.")
if isinstance(opu.device, SimulatedOpuDevice) and not simulated:
warnings.warn(
"You provided a simulated OPU object but set simulated=False. "
"Will use simulated OPU.")
self.n_components = self.opu.n_components
if ndims not in [1, 2]:
raise ValueError("Number of input dimensions must be 1 or 2")
self.ndims = ndims
self.n_2d_features = n_2d_features
self.packed = packed
self.simulated = simulated
self.linear = linear
self.max_n_features = max_n_features
self.fitted = False
self.online = False
if lightonml.get_verbose_level() >= 1:
print("OPU output is detached from the computational graph.")
def __init__(self,
n_components,
opu=None,
ndims=1,
n_2d_features=None,
packed=False,
simulated=False,
max_n_features=None,
verbose_level=-1,
linear=False):
# verbose_level shouldn't be used anymore, but put it as attributes
# in order to comply with sklearn estimator
if verbose_level >= 0:
lightonml.set_verbose_level(verbose_level)
self.verbose_level = lightonml.get_verbose_level()
if opu is None:
if simulated:
simulated_opu_device = SimulatedOpuDevice()
if max_n_features is None:
raise ValueError(
"When using simulated=True, you need to provide max_n_features."
)
self.opu = OPU(opu_device=simulated_opu_device,
max_n_features=max_n_features,
n_components=n_components)
else:
self.opu = OPU(n_components=n_components)
else:
self.opu = opu
self.opu.n_components = n_components
if simulated and not isinstance(opu.device, SimulatedOpuDevice):
warnings.warn(
"You provided a real OPU object but set simulated=True."
" Will use the real OPU.")
if isinstance(opu.device, SimulatedOpuDevice) and not simulated:
warnings.warn(
"You provided a simulated OPU object but set simulated=False."
" Will use simulated OPU.")
if ndims not in [1, 2]:
raise ValueError("Number of input dimensions must be 1 or 2")
self.ndims = ndims
self.n_2d_features = n_2d_features
self.packed = packed
self.simulated = simulated
self.linear = linear
self.max_n_features = max_n_features
self.fitted = False
def __init__(self,
n_components: int = 200000,
opu_device: Optional[Union["OpuDevice",
SimulatedOpuDevice]] = None,
max_n_features: int = 1000,
config_file: str = "",
config_override: dict = None,
verbose_level: int = -1,
input_roi_strategy: types.InputRoiStrategy = types.
InputRoiStrategy.full,
open_at_init: bool = None,
disable_pbar=False,
simulated=False,
rescale: Union[OutputRescaling,
str] = OutputRescaling.variance):
self.__opu_config = None
self.__config_file = config_file
self.__config_override = config_override
self._max_n_features = max_n_features
self.disable_pbar = disable_pbar
self.rescale = rescale
# Get trace and print functions
if verbose_level != -1:
warnings.warn(
"Verbose level arg will removed in 1.3, "
"Use lightonml.set_verbose_level instead", DeprecationWarning)
lightonml.set_verbose_level(verbose_level)
else:
verbose_level = lightonml.get_verbose_level()
self._debug = lightonml.get_debug_fn()
self._trace = lightonml.get_trace_fn()
self._print = lightonml.get_print_fn()
no_config_msg = "No configuration files for the OPU was found on this machine.\n" \
"You may want to run the OPU in a simulated manner, by passing the " \
"simulated argument to True at init.\n" \
"See https://docs.lighton.ai/notes/get_started.html#Simulating-an-OPU " \
"for more details.\n" \
"See also https://lighton.ai/products for getting access to our technology."
if simulated and opu_device is not None:
raise ValueError(
"simulated and opu_device arguments are conflicting")
# Device init, or take the one passed as input
if opu_device:
if type(opu_device).__name__ not in [
"SimulatedOpuDevice", "OpuDevice"
]:
raise TypeError(
"opu_device must be of type SimulatedOpuDevice or OpuDevice"
)
self.device = opu_device
elif simulated:
self.device = SimulatedOpuDevice()
else:
# Instantiate device directly
from lightonopu.internal.device import OpuDevice
if not self.__config_file and not config.host_has_opu_config():
# Looks like there's no OPU on this host as we didn't find configuration files
raise RuntimeError(no_config_msg)
opu_type = self.config["type"]
frametime_us = self.config["input"]["frametime_us"]
exposure_us = self.config["output"]["exposure_us"]
seq_nb_prelim = self.config.get("sequence_nb_prelim", 0)
name = self.config["name"]
self.device = OpuDevice(opu_type, frametime_us, exposure_us,
seq_nb_prelim, None, verbose_level, name)
self._base_frametime_us = self.device.frametime_us
self._base_exposure_us = self.device.exposure_us
if self._s.simulated:
# build the random matrix if not done already
self._resize_rnd_matrix(max_n_features, n_components)
else:
# Make sure lightonopu is at 1.4.1 or later, needed for linear_reconstruction
pkg_resources.require("lightonopu>=1.4.1")
# initialize linear_reconstruction library
from lightonopu import linear_reconstruction
linear_reconstruction.init(np.prod(self.device.input_shape))
self._output_roi = output_roi.OutputRoi(
self.device.output_shape_max, self.device.output_roi_strategy,
self._s.allowed_roi, self._s.min_n_components)
# This also sets the output ROI
self.n_components = n_components
self.input_roi_strategy = input_roi_strategy
# Runner initialized when entering fit
self._runner = None # type: Optional[TransformRunner]
# ExitStack for device acquisition, initialized when entering fit
self._acq_stack = ExitStack()
self._trace("OPU initialized")
# Open at init, unless relevant host.json option is False
if open_at_init is None:
open_at_init = get_host_option("lightonml_open_at_init", True)
if open_at_init:
self.open()
def __try_or_giveup(self, func, *args, **kwargs):
# in verbose mode, print the exception
print_exc = get_verbose_level() >= 2
return utils.try_or_giveup(func, self.s.n_tries, print_exc, *args,
**kwargs)
def __init__(self, total, description, disable):
self.total = total
self.disable_pbar = get_verbose_level() < 1 or disable
self.pbar = None
self.description = description
def __init__(self, n_components: int = 200000,
opu_device: Optional[Union[OpuDevice, SimulatedOpuDevice]] = None,
max_n_features: int = 1000, config_file: str = "",
config_override: dict = None, verbose_level: int = -1,
input_roi_strategy: types.InputRoiStrategy = types.InputRoiStrategy.auto,
open_at_init: bool = None, disable_pbar=False):
self.__opu_config = None
self.__config_file = config_file
self.__config_override = config_override
self._max_n_features = max_n_features
self.disable_pbar = disable_pbar
# Get trace and print functions
if verbose_level != -1:
warnings.warn("Verbose level arg will removed in 1.3, "
"Use lightonml.set_verbose_level instead",
DeprecationWarning)
lightonml.set_verbose_level(verbose_level)
else:
verbose_level = lightonml.get_verbose_level()
self._debug = lightonml.get_debug_fn()
self._trace = lightonml.get_trace_fn()
self._print = lightonml.get_print_fn()
# Device init, or take the one passed as input
if not opu_device:
opu_type = self.config["type"]
self._base_frametime_us = self.config["input"]["frametime_us"]
self._base_exposure_us = self.config["output"]["exposure_us"]
seq_nb_prelim = self.config.get("sequence_nb_prelim", 0)
name = self.config["name"]
self.device = OpuDevice(opu_type, self._base_frametime_us,
self._base_exposure_us, seq_nb_prelim,
None, verbose_level, name)
else:
if not isinstance(opu_device, (SimulatedOpuDevice, OpuDevice)):
raise TypeError("opu_device must be of type {} or {}"
.format(SimulatedOpuDevice.__qualname__,
OpuDevice.__qualname__))
self.device = opu_device
self._base_frametime_us = self.device.frametime_us
self._base_exposure_us = self.device.exposure_us
if self._s.simulated:
# build the random matrix if not done already
self._resize_rnd_matrix(max_n_features, n_components)
self._output_roi = output_roi.OutputRoi(self.device.output_shape_max,
self.device.output_roi_strategy,
self._s.allowed_roi, self._s.min_n_components)
# This also sets the output ROI
self.n_components = n_components
self.input_roi_strategy = input_roi_strategy
# Runner initialized when entering fit
self._runner = None # type: Optional[TransformRunner]
# ExitStack for device acquisition, initialized when entering fit
self._acq_stack = ExitStack()
self._trace("OPU initialized")
# Open at init, unless relevant host.json option is False
if open_at_init is None:
open_at_init = get_host_option("lightonml_open_at_init", True)
if open_at_init:
self.open()
