spaCy/spacy/cli/init_pipeline.py

from typing import Optional
import logging
from pathlib import Path
from wasabi import msg
import typer
import srsly

from .. import util
from ..training.initialize import init_nlp, convert_vectors
from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error
from ._util import import_code, setup_gpu


@init_cli.command("vectors")
def init_vectors_cli(
    # fmt: off
    lang: str = Arg(..., help="The language of the nlp object to create"),
    vectors_loc: Path = Arg(..., help="Vectors file in Word2Vec format", exists=True),
    output_dir: Path = Arg(..., help="Pipeline output directory"),
    prune: int = Opt(-1, "--prune", "-p", help="Optional number of vectors to prune to"),
    truncate: int = Opt(0, "--truncate", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
    jsonl_loc: Optional[Path]=Opt(None, "--lexemes-jsonl", "-j", help="Location of JSONL-formatted attributes file"),
    name: Optional[str] = Opt(None, "--name", "-n", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
    verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
    # fmt: on
):
    """Convert word vectors for use with spaCy. Will export an nlp object that
    you can use in the [initialize] block of your config to initialize
    a model with vectors.
    """
    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
    msg.info(f"Creating blank nlp object for language '{lang}'")
    nlp = util.get_lang_class(lang)()
    if jsonl_loc is not None:
        lex_attrs = srsly.read_jsonl(jsonl_loc)
        for attrs in lex_attrs:
            if "settings" in attrs:
                continue
            lexeme = nlp.vocab[attrs["orth"]]
            lexeme.set_attrs(**attrs)
    convert_vectors(nlp, vectors_loc, truncate=truncate, prune=prune, name=name)
    msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors")
    nlp.to_disk(output_dir)
    msg.good(
        "Saved nlp object with vectors to output directory. You can now use the "
        "path to it in your config as the 'vectors' setting in [initialize.vocab].",
        output_dir.resolve(),
    )


@init_cli.command(
    "nlp",
    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
    hidden=True,
)
def init_pipeline_cli(
    # fmt: off
    ctx: typer.Context,  # This is only used to read additional arguments
    config_path: Path = Arg(..., help="Path to config file", exists=True),
    output_path: Path = Arg(..., help="Output directory for the prepared data"),
    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
    use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
    # fmt: on
):
    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
    overrides = parse_config_overrides(ctx.args)
    import_code(code_path)
    setup_gpu(use_gpu)
    with show_validation_error(config_path):
        config = util.load_config(config_path, overrides=overrides)
    with show_validation_error(hint_fill=False):
        nlp = init_nlp(config, use_gpu=use_gpu)
    nlp.to_disk(output_path)
    msg.good(f"Saved initialized pipeline to {output_path}")


@init_cli.command(
    "labels",
    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def init_labels_cli(
    # fmt: off
    ctx: typer.Context,  # This is only used to read additional arguments
    config_path: Path = Arg(..., help="Path to config file", exists=True),
    output_path: Path = Arg(..., help="Output directory for the labels"),
    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
    use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
    # fmt: on
):
    """Generate a JSON file for labels in the data. This helps speed up the
    training process, since spaCy won't have to preprocess the data to
    extract the labels."""
    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
    if not output_path.exists():
        output_path.mkdir()
    overrides = parse_config_overrides(ctx.args)
    import_code(code_path)
    setup_gpu(use_gpu)
    with show_validation_error(config_path):
        config = util.load_config(config_path, overrides=overrides)
    with show_validation_error(hint_fill=False):
        nlp = init_nlp(config, use_gpu=use_gpu)
    for name, component in nlp.pipeline:
        if getattr(component, "label_data", None) is not None:
            output_file = output_path / f"{name}.json"
            srsly.write_json(output_file, component.label_data)
            msg.good(f"Saving {name} labels to {output_file}")
        else:
            msg.info(f"No labels found for {name}")