__init__.py

 
"""
Generate PXD background samples for background overlay on the fly.
"""
import functools
import hashlib
import os.path
import pathlib
from itertools import product
from typing import Callable, Union
 
import basf2
from ROOT.Belle2 import DataStore, PyStoreArray, PyStoreObj
from ROOT.Belle2 import DBAccessorBase, DBStoreEntry
from ROOT.Belle2 import VxdID, PXDDigit
 
from .models import MODELS
from .models import _get_model_cls, _get_generate_func
 
 
VXDID_ARGS = tuple(
    tuple(product([1], [ladder + 1 for ladder in range(8)], [1, 2]))
    + tuple(product([2], [ladder + 1 for ladder in range(12)], [1, 2]))
)
 
 
 
def _verify_model(model: str) -> str:
    if not isinstance(model, str):
        raise TypeError("expecting type str `model`")
    elif model not in MODELS:
        options = ", ".join(map(repr, MODELS))
        raise ValueError(f"invalid `model`: {model!r} (options: {options}")
    return model
 
 
 
def _verify_checkpoint(checkpoint: Union[None, str, pathlib.Path]) -> str:
    if not isinstance(checkpoint, (type(None), str, pathlib.Path)):
        raise TypeError("expecting None or type str `checkpoint`")
    if checkpoint is None:
        return checkpoint
    checkpoint = os.path.expandvars(str(checkpoint))
    if not (os.path.exists(checkpoint) and os.path.isfile(checkpoint)):
        raise ValueError(f"invalid `checkpoint`: {checkpoint!r}")
    return checkpoint
 
 
 
def _verify_seed(seed: Union[None, int]) -> Union[None, int]:
    if not isinstance(seed, (type(None), int)):
        raise TypeError("expecting None or type int `seed`")
    if seed is None:
        return seed
    if not -(2 ** 63) <= seed < 2 ** 63:
        raise ValueError(f"expecting -2^63 <= `seed` < 2^63 (got: {seed})")
    return seed
 
 
 
def _verify_nintra(nintra: int) -> int:
    if not isinstance(nintra, int):
        raise TypeError("expecting type int `nintra`")
    elif not nintra > 0:
        raise ValueError(f"expecting `nintra` > 0 (got: {nintra}")
    return nintra
 
 
 
def _verify_ninter(ninter: int) -> int:
    if not isinstance(ninter, int):
        raise TypeError("expecting type int `ninter`")
    elif not ninter > 0:
        raise ValueError(f"expecting `ninter` > 0 (got: {ninter}")
    return ninter
 
 
 
def _verify_globaltag(globaltag: str) -> str:
    if not isinstance(globaltag, str):
        raise TypeError("expecting type str `globaltag`")
    return globaltag
 
 
 
class PXDBackgroundGenerator(basf2.Module):
    """Generates PXD background samples for background overlay on the fly.
 
    :param model: Name of the generator model to use - either "convnet" or "resnet",
        defaults to "convnet" (optional)
    :type model: str
 
    :param checkpoint: Path to the checkpoint file with weights for the selected model,
        defaults to None - use the default checkpoint from the conditions database (optional)
    :type checkpoint: str
 
    :param seed: Integer number in the interval :math:`[-2^{63}, 2^{63} - 1]`
        used internally as the initial seed,
        defaults to None - derive a deterministic seed from the
        value returned by :py:func:`basf2.get_random_seed` (optional)
    :type seed: int
 
    :param nintra: Number of intra-op threads to be utilized for the generation,
        defaults to 1 (optional)
    :type nintra: int
 
    :param ninter: Number of inter-op threads to be utilized for the generation,
        defaults to 1 (optional)
    :type ninter: int
 
    :param globaltag: Global tag of the conditions database
        providing the default checkpoints stored as payloads,
        defaults to "PXDBackgroundGenerator" (optional)
    :type globaltag: str
    """
 
    
    def __init__(
        self,
        model: str = "convnet",
        checkpoint: Union[None, str, pathlib.Path] = None,
        seed: Union[None, int] = None,
        nintra: int = 1,
        ninter: int = 1,
        globaltag: str = "PXDBackgroundGenerator",
    ):
        super().__init__()
        # process `model`
        self.model = _verify_model(model)
 
        # process `checkpoint`
        self.checkpoint = _verify_checkpoint(checkpoint)
 
        # process `seed`
        self.seed = _verify_seed(seed)
 
        # process `nintra`
        self.nintra = _verify_nintra(nintra)
 
        # process `ninter`
        self.ninter = _verify_ninter(ninter)
 
        # process `globaltag`
        self.globaltag = _verify_globaltag(globaltag)
 
        # enable the specified global tag
        basf2.conditions.append_globaltag(self.globaltag)
 
    
    def initialize(self):
        try:
            import torch
        except ImportError as exc:
            exc.msg = "please install PyTorch: `pip3 install torch==1.4.0`"
            raise
 
        # set the number of inter-op CPU threads
        torch.set_num_interop_threads(self.ninter)
 
        # set the number of intra-op CPU threads
        torch.set_num_threads(self.nintra)
 
        # get the generation function for the selected model
        self._generate_func = _get_generate_func(self.model)
 
        # instantiate the generator model
        self._generator = _get_model_cls(self.model)()
 
        # initialize the model weights
        checkpoint = self.checkpoint
        if self.checkpoint is None:
            # use the default checkpoint from the conditions database
            payload = f"PXDBackgroundGenerator_{self.model}"
            accessor = DBAccessorBase(DBStoreEntry.c_RawFile, payload, True)
            checkpoint = accessor.getFilename()
        self._generator.load_state_dict(torch.load(checkpoint, map_location="cpu"))
 
        # set mode of operation to inference
        self._generator.eval()
 
        # disable the computation of gradients
        for param in self._generator.parameters():
            param.requires_grad = False
 
        # initialize the seed
        seed = self.seed
        if seed is None:
            # derive from the basf2 seed value
            obj = basf2.get_random_seed()
            func = hashlib.sha512()
            func.update(bytes(str(obj), "utf-8"))
            digest = func.digest()[:8]
            seed = int.from_bytes(digest, "big", signed=True)
        basf2.B2INFO(f"PXD background generator seed initialized to {seed}")
        torch.manual_seed(seed)
 
        # instantiate objects for specifying distinct PXD modules
        self._vxdids = tuple(VxdID(*arg) for arg in VXDID_ARGS)
 
        # get the name of the extension used by BGOverlayInput for background collections
        bkginfo = PyStoreObj("BackgroundInfo", DataStore.c_Persistent)
        if not bkginfo.isValid():
            # no information about background overlay input available
            basf2.B2ERROR("path must contain the BGOverlayInput module")
        extension = bkginfo.getExtensionName()
 
        # register the PXD background digit collection - array - in the data store
        self._pystorearray = PyStoreArray("PXDDigits", DataStore.c_DontWriteOut)
        self._pystorearray.registerInDataStore(f"PXDDigits{extension}")
 
    
    def event(self):
        # get the low-level array accessor
        digit_array = self._pystorearray.getPtr()
 
        # clear digits stored by BGOverlayInput
        digit_array.Clear()
 
        # generate a batch of images - one image for each PXD module
        x = self._generate_func(self._generator)
 
        # locate indices of pixels with non-zero values - pixel hits
        nonzero = x.nonzero(as_tuple=True)
        args = [indices.tolist() for indices in nonzero]
        vals = x[nonzero].tolist()
 
        # store indices and pixel values into the data store as background digits
        for n, (idx, ucell, vcell, charge) in enumerate(zip(*args, vals)):
            # append a new default digit to expand the array
            self._pystorearray.appendNew()
            # modify the array to point to the correct digit
            digit_array[n] = PXDDigit(self._vxdids[idx], ucell, vcell, charge)
 
        # delete references to release memory
        del x, nonzero, args, vals
 
    
 
    
 
    
 
    
 
    
 
    
 
    
 
    
 
    
 
    
 
 
 
def inject_simulation(module: Union[None, basf2.Module] = None) -> Callable:
    """Incorporate a module instance
    into :py:func:`.add_simulation` after `!BGOverlayInput`.
 
    :param module: Module instance to be incorporated,
        defaults to None - return unmodified function
    :type module: :py:class:`basf2.Module`, optional
 
    :returns: Drop-in replacement function for :py:func:`.add_simulation`
    """
    from simulation import add_simulation
 
    if module is None:
        # no modifications necessary
        return add_simulation
    elif not isinstance(module, basf2.Module):
        raise TypeError("expecting None or type basf2.Module `module`")
 
    @functools.wraps(add_simulation)
    def injected_simulation(path, *args, **kwargs):
        # create a separate path with simulation modules
        simulation_path = basf2.Path()
        add_simulation(simulation_path, *args, **kwargs)
 
        # manually add the simulation modules to the given path
        for simulation_module in simulation_path.modules():
            # append the next module from the simulation path
            path.add_module(simulation_module)
 
            if simulation_module.name() == "BGOverlayInput":
                # incorporate the given module
                path.add_module(module)
 
    return injected_simulation