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import json
import math
import os
from typing import List, Optional
from transformers.trainer import TRAINER_STATE_NAME
from .logging import get_logger
from .packages import is_matplotlib_available
if is_matplotlib_available():
import matplotlib.pyplot as plt
logger = get_logger(__name__)
def smooth(scalars: List[float]) -> List[float]:
r"""
EMA implementation according to TensorBoard.
"""
last = scalars[0]
smoothed = list()
weight = 1.8 * (1 / (1 + math.exp(-0.05 * len(scalars))) - 0.5) # a sigmoid function
for next_val in scalars:
smoothed_val = last * weight + (1 - weight) * next_val
smoothed.append(smoothed_val)
last = smoothed_val
return smoothed
def plot_loss(save_dictionary: os.PathLike, keys: Optional[List[str]] = ["loss"]) -> None:
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f:
data = json.load(f)
for key in keys:
steps, metrics = [], []
for i in range(len(data["log_history"])):
if key in data["log_history"][i]:
steps.append(data["log_history"][i]["step"])
metrics.append(data["log_history"][i][key])
if len(metrics) == 0:
logger.warning(f"No metric {key} to plot.")
continue
plt.figure()
plt.plot(steps, metrics, alpha=0.4, label="original")
plt.plot(steps, smooth(metrics), label="smoothed")
plt.title("training {} of {}".format(key, save_dictionary))
plt.xlabel("step")
plt.ylabel(key)
plt.legend()
plt.savefig(os.path.join(save_dictionary, "training_{}.png".format(key)), format="png", dpi=100)
print("Figure saved:", os.path.join(save_dictionary, "training_{}.png".format(key)))
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