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104 lines
4.7 KiB
Python
104 lines
4.7 KiB
Python
from typing import Optional
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import logging
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from pathlib import Path
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from wasabi import msg
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import typer
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import srsly
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from .. import util
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from ..training.initialize import init_nlp, convert_vectors
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from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error
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from ._util import import_code, setup_gpu
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@init_cli.command("vectors")
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def init_vectors_cli(
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# fmt: off
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lang: str = Arg(..., help="The language of the nlp object to create"),
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vectors_loc: Path = Arg(..., help="Vectors file in Word2Vec format", exists=True),
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output_dir: Path = Arg(..., help="Pipeline output directory"),
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prune: int = Opt(-1, "--prune", "-p", help="Optional number of vectors to prune to"),
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truncate: int = Opt(0, "--truncate", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
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name: Optional[str] = Opt(None, "--name", "-n", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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# fmt: on
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):
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"""Convert word vectors for use with spaCy. Will export an nlp object that
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you can use in the [initialize.vocab] block of your config to initialize
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a model with vectors.
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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msg.info(f"Creating blank nlp object for language '{lang}'")
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nlp = util.get_lang_class(lang)()
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convert_vectors(nlp, vectors_loc, truncate=truncate, prune=prune, name=name)
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msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors")
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nlp.to_disk(output_dir)
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msg.good(
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"Saved nlp object with vectors to output directory. You can now use the "
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"path to it in your config as the 'vectors' setting in [initialize.vocab].",
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output_dir.resolve(),
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)
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@init_cli.command(
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"nlp",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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hidden=True,
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)
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def init_pipeline_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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output_path: Path = Arg(..., help="Output directory for the prepared data"),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
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# fmt: on
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):
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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setup_gpu(use_gpu)
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides)
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with show_validation_error(hint_fill=False):
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nlp = init_nlp(config, use_gpu=use_gpu)
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nlp.to_disk(output_path)
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msg.good(f"Saved initialized pipeline to {output_path}")
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@init_cli.command(
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"labels",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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)
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def init_labels_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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output_path: Path = Arg(..., help="Output directory for the labels"),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
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# fmt: on
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):
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"""Generate a JSON file for labels in the data. This helps speed up the
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training process, since spaCy won't have to preprocess the data to
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extract the labels."""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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if not output_path.exists():
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output_path.mkdir()
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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setup_gpu(use_gpu)
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides)
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with show_validation_error(hint_fill=False):
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nlp = init_nlp(config, use_gpu=use_gpu)
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for name, component in nlp.pipeline:
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if getattr(component, "label_data", None) is not None:
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output_file = output_path / f"{name}.json"
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srsly.write_json(output_file, component.label_data)
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msg.good(f"Saving {name} labels to {output_file}")
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else:
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msg.info(f"No labels found for {name}")
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