mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
d9a07a7f6e
v2.1 introduced a regression when deserializing the parser after parser.add_label() had been called. The code around the class mapping is pretty confusing currently, as it was written to accommodate backwards model compatibility. It needs to be revised when the models are next retrained. Closes #3433
669 lines
29 KiB
Cython
669 lines
29 KiB
Cython
# cython: infer_types=True
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# cython: cdivision=True
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# cython: boundscheck=False
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# coding: utf-8
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from __future__ import unicode_literals, print_function
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from collections import OrderedDict
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import numpy
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cimport cython.parallel
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import numpy.random
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cimport numpy as np
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from itertools import islice
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from cpython.ref cimport PyObject, Py_XDECREF
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from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
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from libc.math cimport exp
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from libcpp.vector cimport vector
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from libc.string cimport memset, memcpy
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from libc.stdlib cimport calloc, free
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from cymem.cymem cimport Pool
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from thinc.typedefs cimport weight_t, class_t, hash_t
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from thinc.extra.search cimport Beam
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from thinc.api import chain, clone
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from thinc.v2v import Model, Maxout, Affine
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from thinc.misc import LayerNorm
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from thinc.neural.ops import CupyOps
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from thinc.neural.util import get_array_module
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from thinc.linalg cimport Vec, VecVec
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import srsly
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from ._parser_model cimport resize_activations, predict_states, arg_max_if_valid
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from ._parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
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from ._parser_model cimport get_c_weights, get_c_sizes
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from ._parser_model import ParserModel
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from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
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from .._ml import link_vectors_to_models, create_default_optimizer
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from ..compat import copy_array
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from ..tokens.doc cimport Doc
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from ..gold cimport GoldParse
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from ..errors import Errors, TempErrors
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from .. import util
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from .stateclass cimport StateClass
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from ._state cimport StateC
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from .transition_system cimport Transition
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from . cimport _beam_utils
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from . import _beam_utils
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from . import nonproj
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cdef class Parser:
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"""
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Base class of the DependencyParser and EntityRecognizer.
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"""
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@classmethod
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def Model(cls, nr_class, **cfg):
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depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
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subword_features = util.env_opt('subword_features',
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cfg.get('subword_features', True))
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conv_depth = util.env_opt('conv_depth', cfg.get('conv_depth', 4))
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bilstm_depth = util.env_opt('bilstm_depth', cfg.get('bilstm_depth', 0))
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if depth != 1:
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raise ValueError(TempErrors.T004.format(value=depth))
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parser_maxout_pieces = util.env_opt('parser_maxout_pieces',
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cfg.get('maxout_pieces', 2))
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token_vector_width = util.env_opt('token_vector_width',
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cfg.get('token_vector_width', 96))
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hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 64))
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embed_size = util.env_opt('embed_size', cfg.get('embed_size', 2000))
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pretrained_vectors = cfg.get('pretrained_vectors', None)
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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conv_depth=conv_depth,
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subword_features=subword_features,
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pretrained_vectors=pretrained_vectors,
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bilstm_depth=bilstm_depth)
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tok2vec = chain(tok2vec, flatten)
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tok2vec.nO = token_vector_width
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lower = PrecomputableAffine(hidden_width,
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nF=cls.nr_feature, nI=token_vector_width,
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nP=parser_maxout_pieces)
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lower.nP = parser_maxout_pieces
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with Model.use_device('cpu'):
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upper = Affine(nr_class, hidden_width, drop_factor=0.0)
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upper.W *= 0
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cfg = {
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'nr_class': nr_class,
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'hidden_depth': depth,
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'token_vector_width': token_vector_width,
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'hidden_width': hidden_width,
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'maxout_pieces': parser_maxout_pieces,
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'pretrained_vectors': pretrained_vectors,
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'bilstm_depth': bilstm_depth
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}
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return ParserModel(tok2vec, lower, upper), cfg
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name = 'base_parser'
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def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
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"""Create a Parser.
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vocab (Vocab): The vocabulary object. Must be shared with documents
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to be processed. The value is set to the `.vocab` attribute.
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moves (TransitionSystem): Defines how the parse-state is created,
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updated and evaluated. The value is set to the .moves attribute
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unless True (default), in which case a new instance is created with
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`Parser.Moves()`.
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model (object): Defines how the parse-state is created, updated and
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evaluated. The value is set to the .model attribute. If set to True
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(default), a new instance will be created with `Parser.Model()`
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in parser.begin_training(), parser.from_disk() or parser.from_bytes().
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**cfg: Arbitrary configuration parameters. Set to the `.cfg` attribute
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"""
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self.vocab = vocab
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if moves is True:
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self.moves = self.TransitionSystem(self.vocab.strings)
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else:
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self.moves = moves
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if 'beam_width' not in cfg:
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cfg['beam_width'] = util.env_opt('beam_width', 1)
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if 'beam_density' not in cfg:
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cfg['beam_density'] = util.env_opt('beam_density', 0.0)
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if 'beam_update_prob' not in cfg:
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cfg['beam_update_prob'] = util.env_opt('beam_update_prob', 1.0)
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cfg.setdefault('cnn_maxout_pieces', 3)
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self.cfg = cfg
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self.model = model
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self._multitasks = []
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self._rehearsal_model = None
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def __reduce__(self):
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return (Parser, (self.vocab, self.moves, self.model), None, None)
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@property
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def tok2vec(self):
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return self.model.tok2vec
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@property
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def move_names(self):
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names = []
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for i in range(self.moves.n_moves):
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name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
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names.append(name)
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return names
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nr_feature = 8
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@property
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def labels(self):
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class_names = [self.moves.get_class_name(i) for i in range(self.moves.n_moves)]
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return class_names
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@property
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def tok2vec(self):
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'''Return the embedding and convolutional layer of the model.'''
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return None if self.model in (None, True, False) else self.model.tok2vec
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@property
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def postprocesses(self):
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# Available for subclasses, e.g. to deprojectivize
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return []
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def add_label(self, label):
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resized = False
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for action in self.moves.action_types:
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added = self.moves.add_action(action, label)
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if added:
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resized = True
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if resized and "nr_class" in self.cfg:
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self.cfg["nr_class"] = self.moves.n_moves
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if self.model not in (True, False, None) and resized:
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self.model.resize_output(self.moves.n_moves)
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def add_multitask_objective(self, target):
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# Defined in subclasses, to avoid circular import
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raise NotImplementedError
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def init_multitask_objectives(self, get_gold_tuples, pipeline, **cfg):
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'''Setup models for secondary objectives, to benefit from multi-task
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learning. This method is intended to be overridden by subclasses.
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For instance, the dependency parser can benefit from sharing
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an input representation with a label prediction model. These auxiliary
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models are discarded after training.
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'''
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pass
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def preprocess_gold(self, docs_golds):
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for doc, gold in docs_golds:
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yield doc, gold
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def use_params(self, params):
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# Can't decorate cdef class :(. Workaround.
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with self.model.use_params(params):
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yield
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def __call__(self, Doc doc, beam_width=None):
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"""Apply the parser or entity recognizer, setting the annotations onto
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the `Doc` object.
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doc (Doc): The document to be processed.
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"""
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if beam_width is None:
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beam_width = self.cfg.get('beam_width', 1)
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beam_density = self.cfg.get('beam_density', 0.)
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states = self.predict([doc], beam_width=beam_width,
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beam_density=beam_density)
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self.set_annotations([doc], states, tensors=None)
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return doc
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def pipe(self, docs, int batch_size=256, int n_threads=-1, beam_width=None):
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"""Process a stream of documents.
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stream: The sequence of documents to process.
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batch_size (int): Number of documents to accumulate into a working set.
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YIELDS (Doc): Documents, in order.
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"""
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if beam_width is None:
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beam_width = self.cfg.get('beam_width', 1)
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beam_density = self.cfg.get('beam_density', 0.)
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cdef Doc doc
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for batch in util.minibatch(docs, size=batch_size):
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batch_in_order = list(batch)
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by_length = sorted(batch_in_order, key=lambda doc: len(doc))
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for subbatch in util.minibatch(by_length, size=max(batch_size//4, 2)):
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subbatch = list(subbatch)
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parse_states = self.predict(subbatch, beam_width=beam_width,
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beam_density=beam_density)
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self.set_annotations(subbatch, parse_states, tensors=None)
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for doc in batch_in_order:
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yield doc
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def require_model(self):
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"""Raise an error if the component's model is not initialized."""
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if getattr(self, 'model', None) in (None, True, False):
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raise ValueError(Errors.E109.format(name=self.name))
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def predict(self, docs, beam_width=1, beam_density=0.0, drop=0.):
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self.require_model()
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if isinstance(docs, Doc):
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docs = [docs]
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if not any(len(doc) for doc in docs):
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return self.moves.init_batch(docs)
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if beam_width < 2:
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return self.greedy_parse(docs, drop=drop)
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else:
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return self.beam_parse(docs, beam_width=beam_width,
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beam_density=beam_density, drop=drop)
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def greedy_parse(self, docs, drop=0.):
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cdef vector[StateC*] states
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cdef StateClass state
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batch = self.moves.init_batch(docs)
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# This is pretty dirty, but the NER can resize itself in init_batch,
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# if labels are missing. We therefore have to check whether we need to
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# expand our model output.
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self.model.resize_output(self.moves.n_moves)
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model = self.model(docs)
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weights = get_c_weights(model)
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for state in batch:
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if not state.is_final():
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states.push_back(state.c)
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sizes = get_c_sizes(model, states.size())
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with nogil:
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self._parseC(&states[0],
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weights, sizes)
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return batch
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def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
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cdef Beam beam
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cdef Doc doc
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cdef np.ndarray token_ids
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beams = self.moves.init_beams(docs, beam_width, beam_density=beam_density)
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# This is pretty dirty, but the NER can resize itself in init_batch,
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# if labels are missing. We therefore have to check whether we need to
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# expand our model output.
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self.model.resize_output(self.moves.n_moves)
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model = self.model(docs)
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token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature),
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dtype='i', order='C')
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cdef int* c_ids
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cdef int nr_feature = self.nr_feature
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cdef int n_states
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model = self.model(docs)
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todo = [beam for beam in beams if not beam.is_done]
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while todo:
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token_ids.fill(-1)
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c_ids = <int*>token_ids.data
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n_states = 0
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for beam in todo:
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for i in range(beam.size):
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state = <StateC*>beam.at(i)
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# This way we avoid having to score finalized states
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# We do have to take care to keep indexes aligned, though
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if not state.is_final():
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state.set_context_tokens(c_ids, nr_feature)
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c_ids += nr_feature
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n_states += 1
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if n_states == 0:
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break
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vectors = model.state2vec(token_ids[:n_states])
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scores = model.vec2scores(vectors)
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todo = self.transition_beams(todo, scores)
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return beams
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cdef void _parseC(self, StateC** states,
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WeightsC weights, SizesC sizes) nogil:
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cdef int i, j
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cdef vector[StateC*] unfinished
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cdef ActivationsC activations
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memset(&activations, 0, sizeof(activations))
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while sizes.states >= 1:
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predict_states(&activations,
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states, &weights, sizes)
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# Validate actions, argmax, take action.
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self.c_transition_batch(states,
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activations.scores, sizes.classes, sizes.states)
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for i in range(sizes.states):
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if not states[i].is_final():
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unfinished.push_back(states[i])
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for i in range(unfinished.size()):
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states[i] = unfinished[i]
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sizes.states = unfinished.size()
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unfinished.clear()
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def set_annotations(self, docs, states_or_beams, tensors=None):
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cdef StateClass state
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cdef Beam beam
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cdef Doc doc
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states = []
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beams = []
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for state_or_beam in states_or_beams:
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if isinstance(state_or_beam, StateClass):
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states.append(state_or_beam)
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else:
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beam = state_or_beam
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state = StateClass.borrow(<StateC*>beam.at(0))
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states.append(state)
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beams.append(beam)
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for i, (state, doc) in enumerate(zip(states, docs)):
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self.moves.finalize_state(state.c)
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for j in range(doc.length):
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doc.c[j] = state.c._sent[j]
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self.moves.finalize_doc(doc)
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for hook in self.postprocesses:
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hook(doc)
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for beam in beams:
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_beam_utils.cleanup_beam(beam)
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def transition_states(self, states, float[:, ::1] scores):
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cdef StateClass state
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cdef float* c_scores = &scores[0, 0]
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cdef vector[StateC*] c_states
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for state in states:
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c_states.push_back(state.c)
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self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0])
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return [state for state in states if not state.c.is_final()]
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cdef void c_transition_batch(self, StateC** states, const float* scores,
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int nr_class, int batch_size) nogil:
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is_valid = <int*>calloc(self.moves.n_moves, sizeof(int))
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cdef int i, guess
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cdef Transition action
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for i in range(batch_size):
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self.moves.set_valid(is_valid, states[i])
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guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
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if guess == -1:
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# This shouldn't happen, but it's hard to raise an error here,
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# and we don't want to infinite loop. So, force to end state.
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states[i].force_final()
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else:
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action = self.moves.c[guess]
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action.do(states[i], action.label)
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states[i].push_hist(guess)
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free(is_valid)
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def transition_beams(self, beams, float[:, ::1] scores):
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cdef Beam beam
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cdef float* c_scores = &scores[0, 0]
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for beam in beams:
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for i in range(beam.size):
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state = <StateC*>beam.at(i)
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if not state.is_final():
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self.moves.set_valid(beam.is_valid[i], state)
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memcpy(beam.scores[i], c_scores, scores.shape[1] * sizeof(float))
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c_scores += scores.shape[1]
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beam.advance(_beam_utils.transition_state, _beam_utils.hash_state, <void*>self.moves.c)
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beam.check_done(_beam_utils.check_final_state, NULL)
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return [b for b in beams if not b.is_done]
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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self.require_model()
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if isinstance(docs, Doc) and isinstance(golds, GoldParse):
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docs = [docs]
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golds = [golds]
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if len(docs) != len(golds):
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raise ValueError(Errors.E077.format(value='update', n_docs=len(docs),
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n_golds=len(golds)))
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if losses is None:
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losses = {}
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losses.setdefault(self.name, 0.)
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for multitask in self._multitasks:
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multitask.update(docs, golds, drop=drop, sgd=sgd)
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# The probability we use beam update, instead of falling back to
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# a greedy update
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beam_update_prob = self.cfg.get('beam_update_prob', 0.5)
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if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() < beam_update_prob:
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return self.update_beam(docs, golds, self.cfg.get('beam_width', 1),
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drop=drop, sgd=sgd, losses=losses,
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beam_density=self.cfg.get('beam_density', 0.001))
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# Chop sequences into lengths of this many transitions, to make the
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# batch uniform length.
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cut_gold = numpy.random.choice(range(20, 100))
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states, golds, max_steps = self._init_gold_batch(docs, golds, max_length=cut_gold)
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states_golds = [(s, g) for (s, g) in zip(states, golds)
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if not s.is_final() and g is not None]
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# Prepare the stepwise model, and get the callback for finishing the batch
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model, finish_update = self.model.begin_update(docs, drop=drop)
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for _ in range(max_steps):
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if not states_golds:
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break
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states, golds = zip(*states_golds)
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scores, backprop = model.begin_update(states, drop=drop)
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d_scores = self.get_batch_loss(states, golds, scores, losses)
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backprop(d_scores, sgd=sgd)
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# Follow the predicted action
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self.transition_states(states, scores)
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states_golds = [eg for eg in states_golds if not eg[0].is_final()]
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# Do the backprop
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finish_update(golds, sgd=sgd)
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return losses
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def rehearse(self, docs, sgd=None, losses=None, **cfg):
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"""Perform a "rehearsal" update, to prevent catastrophic forgetting."""
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if isinstance(docs, Doc):
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docs = [docs]
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if losses is None:
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losses = {}
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for multitask in self._multitasks:
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if hasattr(multitask, 'rehearse'):
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multitask.rehearse(docs, losses=losses, sgd=sgd)
|
|
if self._rehearsal_model is None:
|
|
return None
|
|
losses.setdefault(self.name, 0.)
|
|
|
|
states = self.moves.init_batch(docs)
|
|
# This is pretty dirty, but the NER can resize itself in init_batch,
|
|
# if labels are missing. We therefore have to check whether we need to
|
|
# expand our model output.
|
|
self.model.resize_output(self.moves.n_moves)
|
|
self._rehearsal_model.resize_output(self.moves.n_moves)
|
|
# Prepare the stepwise model, and get the callback for finishing the batch
|
|
tutor, _ = self._rehearsal_model.begin_update(docs, drop=0.0)
|
|
model, finish_update = self.model.begin_update(docs, drop=0.0)
|
|
n_scores = 0.
|
|
loss = 0.
|
|
while states:
|
|
targets, _ = tutor.begin_update(states, drop=0.)
|
|
guesses, backprop = model.begin_update(states, drop=0.)
|
|
d_scores = (guesses - targets) / targets.shape[0]
|
|
# If all weights for an output are 0 in the original model, don't
|
|
# supervise that output. This allows us to add classes.
|
|
loss += (d_scores**2).sum()
|
|
backprop(d_scores, sgd=sgd)
|
|
# Follow the predicted action
|
|
self.transition_states(states, guesses)
|
|
states = [state for state in states if not state.is_final()]
|
|
n_scores += d_scores.size
|
|
# Do the backprop
|
|
finish_update(docs, sgd=sgd)
|
|
losses[self.name] += loss / n_scores
|
|
return losses
|
|
|
|
def update_beam(self, docs, golds, width, drop=0., sgd=None, losses=None,
|
|
beam_density=0.0):
|
|
lengths = [len(d) for d in docs]
|
|
states = self.moves.init_batch(docs)
|
|
for gold in golds:
|
|
self.moves.preprocess_gold(gold)
|
|
model, finish_update = self.model.begin_update(docs, drop=drop)
|
|
states_d_scores, backprops, beams = _beam_utils.update_beam(
|
|
self.moves, self.nr_feature, 10000, states, golds, model.state2vec,
|
|
model.vec2scores, width, drop=drop, losses=losses,
|
|
beam_density=beam_density)
|
|
for i, d_scores in enumerate(states_d_scores):
|
|
losses[self.name] += (d_scores**2).mean()
|
|
ids, bp_vectors, bp_scores = backprops[i]
|
|
d_vector = bp_scores(d_scores, sgd=sgd)
|
|
if isinstance(model.ops, CupyOps) \
|
|
and not isinstance(ids, model.state2vec.ops.xp.ndarray):
|
|
model.backprops.append((
|
|
util.get_async(model.cuda_stream, ids),
|
|
util.get_async(model.cuda_stream, d_vector),
|
|
bp_vectors))
|
|
else:
|
|
model.backprops.append((ids, d_vector, bp_vectors))
|
|
model.make_updates(sgd)
|
|
cdef Beam beam
|
|
for beam in beams:
|
|
_beam_utils.cleanup_beam(beam)
|
|
|
|
def _init_gold_batch(self, whole_docs, whole_golds, min_length=5, max_length=500):
|
|
"""Make a square batch, of length equal to the shortest doc. A long
|
|
doc will get multiple states. Let's say we have a doc of length 2*N,
|
|
where N is the shortest doc. We'll make two states, one representing
|
|
long_doc[:N], and another representing long_doc[N:]."""
|
|
cdef:
|
|
StateClass state
|
|
Transition action
|
|
whole_states = self.moves.init_batch(whole_docs)
|
|
max_length = max(min_length, min(max_length, min([len(doc) for doc in whole_docs])))
|
|
max_moves = 0
|
|
states = []
|
|
golds = []
|
|
for doc, state, gold in zip(whole_docs, whole_states, whole_golds):
|
|
gold = self.moves.preprocess_gold(gold)
|
|
if gold is None:
|
|
continue
|
|
oracle_actions = self.moves.get_oracle_sequence(doc, gold)
|
|
start = 0
|
|
while start < len(doc):
|
|
state = state.copy()
|
|
n_moves = 0
|
|
while state.B(0) < start and not state.is_final():
|
|
action = self.moves.c[oracle_actions.pop(0)]
|
|
action.do(state.c, action.label)
|
|
state.c.push_hist(action.clas)
|
|
n_moves += 1
|
|
has_gold = self.moves.has_gold(gold, start=start,
|
|
end=start+max_length)
|
|
if not state.is_final() and has_gold:
|
|
states.append(state)
|
|
golds.append(gold)
|
|
max_moves = max(max_moves, n_moves)
|
|
start += min(max_length, len(doc)-start)
|
|
max_moves = max(max_moves, len(oracle_actions))
|
|
return states, golds, max_moves
|
|
|
|
def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
|
|
cdef StateClass state
|
|
cdef GoldParse gold
|
|
cdef Pool mem = Pool()
|
|
cdef int i
|
|
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
|
|
costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
|
|
cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
|
|
dtype='f', order='C')
|
|
c_d_scores = <float*>d_scores.data
|
|
for i, (state, gold) in enumerate(zip(states, golds)):
|
|
memset(is_valid, 0, self.moves.n_moves * sizeof(int))
|
|
memset(costs, 0, self.moves.n_moves * sizeof(float))
|
|
self.moves.set_costs(is_valid, costs, state, gold)
|
|
for j in range(self.moves.n_moves):
|
|
if costs[j] <= 0.0 and j in self.model.unseen_classes:
|
|
self.model.unseen_classes.remove(j)
|
|
cpu_log_loss(c_d_scores,
|
|
costs, is_valid, &scores[i, 0], d_scores.shape[1])
|
|
c_d_scores += d_scores.shape[1]
|
|
if losses is not None:
|
|
losses.setdefault(self.name, 0.)
|
|
losses[self.name] += (d_scores**2).sum()
|
|
return d_scores
|
|
|
|
def create_optimizer(self):
|
|
return create_default_optimizer(self.model.ops,
|
|
**self.cfg.get('optimizer', {}))
|
|
|
|
def begin_training(self, get_gold_tuples, pipeline=None, sgd=None, **cfg):
|
|
if 'model' in cfg:
|
|
self.model = cfg['model']
|
|
if not hasattr(get_gold_tuples, '__call__'):
|
|
gold_tuples = get_gold_tuples
|
|
get_gold_tuples = lambda: gold_tuples
|
|
cfg.setdefault('min_action_freq', 30)
|
|
actions = self.moves.get_actions(gold_parses=get_gold_tuples(),
|
|
min_freq=cfg.get('min_action_freq', 30))
|
|
for action, labels in self.moves.labels.items():
|
|
actions.setdefault(action, {})
|
|
for label, freq in labels.items():
|
|
if label not in actions[action]:
|
|
actions[action][label] = freq
|
|
self.moves.initialize_actions(actions)
|
|
cfg.setdefault('token_vector_width', 96)
|
|
if self.model is True:
|
|
self.model, cfg = self.Model(self.moves.n_moves, **cfg)
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
doc_sample = []
|
|
gold_sample = []
|
|
for raw_text, annots_brackets in islice(get_gold_tuples(), 1000):
|
|
for annots, brackets in annots_brackets:
|
|
ids, words, tags, heads, deps, ents = annots
|
|
doc_sample.append(Doc(self.vocab, words=words))
|
|
gold_sample.append(GoldParse(doc_sample[-1], words=words, tags=tags,
|
|
heads=heads, deps=deps, ents=ents))
|
|
self.model.begin_training(doc_sample, gold_sample)
|
|
if pipeline is not None:
|
|
self.init_multitask_objectives(get_gold_tuples, pipeline, sgd=sgd, **cfg)
|
|
link_vectors_to_models(self.vocab)
|
|
else:
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
self.model.begin_training([])
|
|
self.cfg.update(cfg)
|
|
return sgd
|
|
|
|
def to_disk(self, path, exclude=tuple(), **kwargs):
|
|
serializers = {
|
|
'model': lambda p: (self.model.to_disk(p) if self.model is not True else True),
|
|
'vocab': lambda p: self.vocab.to_disk(p),
|
|
'moves': lambda p: self.moves.to_disk(p, exclude=["strings"]),
|
|
'cfg': lambda p: srsly.write_json(p, self.cfg)
|
|
}
|
|
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
|
|
util.to_disk(path, serializers, exclude)
|
|
|
|
def from_disk(self, path, exclude=tuple(), **kwargs):
|
|
deserializers = {
|
|
'vocab': lambda p: self.vocab.from_disk(p),
|
|
'moves': lambda p: self.moves.from_disk(p, exclude=["strings"]),
|
|
'cfg': lambda p: self.cfg.update(srsly.read_json(p)),
|
|
'model': lambda p: None
|
|
}
|
|
exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
|
|
util.from_disk(path, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
path = util.ensure_path(path)
|
|
if self.model is True:
|
|
self.model, cfg = self.Model(**self.cfg)
|
|
else:
|
|
cfg = {}
|
|
with (path / 'model').open('rb') as file_:
|
|
bytes_data = file_.read()
|
|
self.model.from_bytes(bytes_data)
|
|
self.cfg.update(cfg)
|
|
return self
|
|
|
|
def to_bytes(self, exclude=tuple(), **kwargs):
|
|
serializers = OrderedDict((
|
|
('model', lambda: (self.model.to_bytes() if self.model is not True else True)),
|
|
('vocab', lambda: self.vocab.to_bytes()),
|
|
('moves', lambda: self.moves.to_bytes(exclude=["strings"])),
|
|
('cfg', lambda: srsly.json_dumps(self.cfg, indent=2, sort_keys=True))
|
|
))
|
|
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
|
|
return util.to_bytes(serializers, exclude)
|
|
|
|
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
|
|
deserializers = OrderedDict((
|
|
('vocab', lambda b: self.vocab.from_bytes(b)),
|
|
('moves', lambda b: self.moves.from_bytes(b, exclude=["strings"])),
|
|
('cfg', lambda b: self.cfg.update(srsly.json_loads(b))),
|
|
('model', lambda b: None)
|
|
))
|
|
exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
|
|
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
# TODO: Remove this once we don't have to handle previous models
|
|
if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
|
|
self.cfg['pretrained_vectors'] = self.vocab.vectors.name
|
|
if self.model is True:
|
|
self.model, cfg = self.Model(**self.cfg)
|
|
else:
|
|
cfg = {}
|
|
if 'model' in msg:
|
|
self.model.from_bytes(msg['model'])
|
|
self.cfg.update(cfg)
|
|
return self
|