mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-15 06:09:01 +03:00
266 lines
9.5 KiB
Python
266 lines
9.5 KiB
Python
from typing import TYPE_CHECKING, Dict, Any, Tuple, Callable, List, Optional, IO
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from wasabi import Printer
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import tqdm
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import sys
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from ..util import registry
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from .. import util
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from ..errors import Errors
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if TYPE_CHECKING:
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from ..language import Language # noqa: F401
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def setup_table(
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*, cols: List[str], widths: List[int], max_width: int = 13
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) -> Tuple[List[str], List[int], List[str]]:
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final_cols = []
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final_widths = []
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for col, width in zip(cols, widths):
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if len(col) > max_width:
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col = col[: max_width - 3] + "..." # shorten column if too long
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final_cols.append(col.upper())
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final_widths.append(max(len(col), width))
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return final_cols, final_widths, ["r" for _ in final_widths]
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@registry.loggers("spacy.ConsoleLogger.v1")
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def console_logger(progress_bar: bool = False):
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def setup_printer(
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nlp: "Language", stdout: IO = sys.stdout, stderr: IO = sys.stderr
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) -> Tuple[Callable[[Optional[Dict[str, Any]]], None], Callable[[], None]]:
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write = lambda text: print(text, file=stdout, flush=True)
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msg = Printer(no_print=True)
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# ensure that only trainable components are logged
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logged_pipes = [
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name
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for name, proc in nlp.pipeline
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if hasattr(proc, "is_trainable") and proc.is_trainable
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]
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eval_frequency = nlp.config["training"]["eval_frequency"]
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score_weights = nlp.config["training"]["score_weights"]
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score_cols = [col for col, value in score_weights.items() if value is not None]
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loss_cols = [f"Loss {pipe}" for pipe in logged_pipes]
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spacing = 2
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table_header, table_widths, table_aligns = setup_table(
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cols=["E", "#"] + loss_cols + score_cols + ["Score"],
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widths=[3, 6] + [8 for _ in loss_cols] + [6 for _ in score_cols] + [6],
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)
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write(msg.row(table_header, widths=table_widths, spacing=spacing))
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write(msg.row(["-" * width for width in table_widths], spacing=spacing))
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progress = None
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def log_step(info: Optional[Dict[str, Any]]) -> None:
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nonlocal progress
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if info is None:
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# If we don't have a new checkpoint, just return.
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if progress is not None:
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progress.update(1)
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return
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losses = [
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"{0:.2f}".format(float(info["losses"][pipe_name]))
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for pipe_name in logged_pipes
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]
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scores = []
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for col in score_cols:
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score = info["other_scores"].get(col, 0.0)
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try:
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score = float(score)
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except TypeError:
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err = Errors.E916.format(name=col, score_type=type(score))
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raise ValueError(err) from None
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if col != "speed":
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score *= 100
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scores.append("{0:.2f}".format(score))
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data = (
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[info["epoch"], info["step"]]
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+ losses
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+ scores
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+ ["{0:.2f}".format(float(info["score"]))]
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)
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if progress is not None:
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progress.close()
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write(
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msg.row(data, widths=table_widths, aligns=table_aligns, spacing=spacing)
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)
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if progress_bar:
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# Set disable=None, so that it disables on non-TTY
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progress = tqdm.tqdm(
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total=eval_frequency, disable=None, leave=False, file=stderr
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)
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progress.set_description(f"Epoch {info['epoch']+1}")
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def finalize() -> None:
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pass
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return log_step, finalize
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return setup_printer
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@registry.loggers("spacy.WandbLogger.v2")
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def wandb_logger(
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project_name: str,
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remove_config_values: List[str] = [],
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model_log_interval: Optional[int] = None,
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log_dataset_dir: Optional[str] = None,
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):
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try:
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import wandb
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# test that these are available
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from wandb import init, log, join # noqa: F401
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except ImportError:
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raise ImportError(Errors.E880)
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console = console_logger(progress_bar=False)
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def setup_logger(
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nlp: "Language", stdout: IO = sys.stdout, stderr: IO = sys.stderr
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) -> Tuple[Callable[[Dict[str, Any]], None], Callable[[], None]]:
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config = nlp.config.interpolate()
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config_dot = util.dict_to_dot(config)
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for field in remove_config_values:
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del config_dot[field]
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config = util.dot_to_dict(config_dot)
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run = wandb.init(project=project_name, config=config, reinit=True)
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console_log_step, console_finalize = console(nlp, stdout, stderr)
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def log_dir_artifact(
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path: str,
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name: str,
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type: str,
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metadata: Optional[Dict[str, Any]] = {},
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aliases: Optional[List[str]] = [],
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):
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dataset_artifact = wandb.Artifact(name, type=type, metadata=metadata)
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dataset_artifact.add_dir(path, name=name)
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wandb.log_artifact(dataset_artifact, aliases=aliases)
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if log_dataset_dir:
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log_dir_artifact(path=log_dataset_dir, name="dataset", type="dataset")
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def log_step(info: Optional[Dict[str, Any]]):
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console_log_step(info)
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if info is not None:
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score = info["score"]
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other_scores = info["other_scores"]
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losses = info["losses"]
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wandb.log({"score": score})
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if losses:
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wandb.log({f"loss_{k}": v for k, v in losses.items()})
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if isinstance(other_scores, dict):
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wandb.log(other_scores)
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if model_log_interval and info.get("output_path"):
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if info["step"] % model_log_interval == 0 and info["step"] != 0:
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log_dir_artifact(
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path=info["output_path"],
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name="pipeline_" + run.id,
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type="checkpoint",
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metadata=info,
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aliases=[
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f"epoch {info['epoch']} step {info['step']}",
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"latest",
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"best"
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if info["score"] == max(info["checkpoints"])[0]
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else "",
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],
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)
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def finalize() -> None:
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console_finalize()
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wandb.join()
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return log_step, finalize
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return setup_logger
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@registry.loggers("spacy.WandbLogger.v3")
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def wandb_logger(
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project_name: str,
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remove_config_values: List[str] = [],
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model_log_interval: Optional[int] = None,
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log_dataset_dir: Optional[str] = None,
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entity: Optional[str] = None,
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run_name: Optional[str] = None,
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):
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try:
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import wandb
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# test that these are available
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from wandb import init, log, join # noqa: F401
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except ImportError:
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raise ImportError(Errors.E880)
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console = console_logger(progress_bar=False)
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def setup_logger(
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nlp: "Language", stdout: IO = sys.stdout, stderr: IO = sys.stderr
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) -> Tuple[Callable[[Dict[str, Any]], None], Callable[[], None]]:
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config = nlp.config.interpolate()
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config_dot = util.dict_to_dot(config)
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for field in remove_config_values:
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del config_dot[field]
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config = util.dot_to_dict(config_dot)
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run = wandb.init(
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project=project_name, config=config, entity=entity, reinit=True
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)
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if run_name:
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wandb.run.name = run_name
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console_log_step, console_finalize = console(nlp, stdout, stderr)
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def log_dir_artifact(
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path: str,
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name: str,
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type: str,
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metadata: Optional[Dict[str, Any]] = {},
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aliases: Optional[List[str]] = [],
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):
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dataset_artifact = wandb.Artifact(name, type=type, metadata=metadata)
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dataset_artifact.add_dir(path, name=name)
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wandb.log_artifact(dataset_artifact, aliases=aliases)
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if log_dataset_dir:
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log_dir_artifact(path=log_dataset_dir, name="dataset", type="dataset")
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def log_step(info: Optional[Dict[str, Any]]):
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console_log_step(info)
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if info is not None:
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score = info["score"]
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other_scores = info["other_scores"]
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losses = info["losses"]
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wandb.log({"score": score})
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if losses:
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wandb.log({f"loss_{k}": v for k, v in losses.items()})
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if isinstance(other_scores, dict):
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wandb.log(other_scores)
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if model_log_interval and info.get("output_path"):
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if info["step"] % model_log_interval == 0 and info["step"] != 0:
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log_dir_artifact(
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path=info["output_path"],
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name="pipeline_" + run.id,
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type="checkpoint",
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metadata=info,
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aliases=[
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f"epoch {info['epoch']} step {info['step']}",
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"latest",
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"best"
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if info["score"] == max(info["checkpoints"])[0]
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else "",
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],
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)
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def finalize() -> None:
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console_finalize()
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wandb.join()
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return log_step, finalize
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return setup_logger
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