spaCy/spacy/training/loggers.py
Elia Robyn Lake (Robyn Speer) 5b0b0ca809
Move WandB loggers into spacy-loggers (#9223)
* factor out the WandB logger into spacy-loggers

Signed-off-by: Elia Robyn Speer <gh@arborelia.net>

* depend on spacy-loggers so they are available

Signed-off-by: Elia Robyn Speer <gh@arborelia.net>

* remove docs of spacy.WandbLogger.v2 (moved to spacy-loggers)

Signed-off-by: Elia Robyn Speer <elia@explosion.ai>

* Version number suggestions from code review

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

* update references to WandbLogger

Signed-off-by: Elia Robyn Speer <elia@explosion.ai>

* make order of deps more consistent

Signed-off-by: Elia Robyn Speer <elia@explosion.ai>

Co-authored-by: Elia Robyn Speer <elia@explosion.ai>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
2021-09-29 11:12:50 +02:00

102 lines
3.7 KiB
Python

from typing import TYPE_CHECKING, Dict, Any, Tuple, Callable, List, Optional, IO
from wasabi import Printer
import tqdm
import sys
from ..util import registry
from ..errors import Errors
if TYPE_CHECKING:
from ..language import Language # noqa: F401
def setup_table(
*, cols: List[str], widths: List[int], max_width: int = 13
) -> Tuple[List[str], List[int], List[str]]:
final_cols = []
final_widths = []
for col, width in zip(cols, widths):
if len(col) > max_width:
col = col[: max_width - 3] + "..." # shorten column if too long
final_cols.append(col.upper())
final_widths.append(max(len(col), width))
return final_cols, final_widths, ["r" for _ in final_widths]
@registry.loggers("spacy.ConsoleLogger.v1")
def console_logger(progress_bar: bool = False):
def setup_printer(
nlp: "Language", stdout: IO = sys.stdout, stderr: IO = sys.stderr
) -> Tuple[Callable[[Optional[Dict[str, Any]]], None], Callable[[], None]]:
write = lambda text: print(text, file=stdout, flush=True)
msg = Printer(no_print=True)
# ensure that only trainable components are logged
logged_pipes = [
name
for name, proc in nlp.pipeline
if hasattr(proc, "is_trainable") and proc.is_trainable
]
eval_frequency = nlp.config["training"]["eval_frequency"]
score_weights = nlp.config["training"]["score_weights"]
score_cols = [col for col, value in score_weights.items() if value is not None]
loss_cols = [f"Loss {pipe}" for pipe in logged_pipes]
spacing = 2
table_header, table_widths, table_aligns = setup_table(
cols=["E", "#"] + loss_cols + score_cols + ["Score"],
widths=[3, 6] + [8 for _ in loss_cols] + [6 for _ in score_cols] + [6],
)
write(msg.row(table_header, widths=table_widths, spacing=spacing))
write(msg.row(["-" * width for width in table_widths], spacing=spacing))
progress = None
def log_step(info: Optional[Dict[str, Any]]) -> None:
nonlocal progress
if info is None:
# If we don't have a new checkpoint, just return.
if progress is not None:
progress.update(1)
return
losses = [
"{0:.2f}".format(float(info["losses"][pipe_name]))
for pipe_name in logged_pipes
]
scores = []
for col in score_cols:
score = info["other_scores"].get(col, 0.0)
try:
score = float(score)
except TypeError:
err = Errors.E916.format(name=col, score_type=type(score))
raise ValueError(err) from None
if col != "speed":
score *= 100
scores.append("{0:.2f}".format(score))
data = (
[info["epoch"], info["step"]]
+ losses
+ scores
+ ["{0:.2f}".format(float(info["score"]))]
)
if progress is not None:
progress.close()
write(
msg.row(data, widths=table_widths, aligns=table_aligns, spacing=spacing)
)
if progress_bar:
# Set disable=None, so that it disables on non-TTY
progress = tqdm.tqdm(
total=eval_frequency, disable=None, leave=False, file=stderr
)
progress.set_description(f"Epoch {info['epoch']+1}")
def finalize() -> None:
pass
return log_step, finalize
return setup_printer