|
|
|
|
|
|
|
|
import os |
|
|
from typing import Dict, List, Optional, Tuple |
|
|
|
|
|
import matplotlib.pyplot as plt |
|
|
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator |
|
|
|
|
|
Item = Dict[str, float] |
|
|
TB_COLOR, TB_COLOR_SMOOTH = '#FFE2D9', '#FF7043' |
|
|
|
|
|
|
|
|
def read_tensorboard_file(fpath: str) -> Dict[str, List[Item]]: |
|
|
if not os.path.isfile(fpath): |
|
|
raise FileNotFoundError(f'fpath: {fpath}') |
|
|
ea = EventAccumulator(fpath) |
|
|
ea.Reload() |
|
|
res: Dict[str, List[Item]] = {} |
|
|
tags = ea.Tags()['scalars'] |
|
|
for tag in tags: |
|
|
values = ea.Scalars(tag) |
|
|
r: List[Item] = [] |
|
|
for v in values: |
|
|
r.append({'step': v.step, 'value': v.value}) |
|
|
res[tag] = r |
|
|
return res |
|
|
|
|
|
|
|
|
def tensorboard_smoothing(values: List[float], smooth: float = 0.9) -> List[float]: |
|
|
norm_factor = 0 |
|
|
x = 0 |
|
|
res: List[float] = [] |
|
|
for i in range(len(values)): |
|
|
x = x * smooth + values[i] |
|
|
norm_factor *= smooth |
|
|
norm_factor += 1 |
|
|
res.append(x / norm_factor) |
|
|
return res |
|
|
|
|
|
|
|
|
def plot_images(images_dir: str, |
|
|
tb_dir: str, |
|
|
smooth_key: Optional[List[str]] = None, |
|
|
smooth_val: float = 0.9, |
|
|
figsize: Tuple[int, int] = (8, 5), |
|
|
dpi: int = 100) -> None: |
|
|
"""Using tensorboard's data content to plot images""" |
|
|
smooth_key = smooth_key or [] |
|
|
os.makedirs(images_dir, exist_ok=True) |
|
|
fname = [fname for fname in os.listdir(tb_dir) if os.path.isfile(os.path.join(tb_dir, fname))][0] |
|
|
tb_path = os.path.join(tb_dir, fname) |
|
|
data = read_tensorboard_file(tb_path) |
|
|
|
|
|
for k in data.keys(): |
|
|
_data = data[k] |
|
|
steps = [d['step'] for d in _data] |
|
|
values = [d['value'] for d in _data] |
|
|
if len(values) == 0: |
|
|
continue |
|
|
_, ax = plt.subplots(1, 1, squeeze=True, figsize=figsize, dpi=dpi) |
|
|
ax.set_title(k) |
|
|
if len(values) == 1: |
|
|
ax.scatter(steps, values, color=TB_COLOR_SMOOTH) |
|
|
elif k in smooth_key: |
|
|
ax.plot(steps, values, color=TB_COLOR) |
|
|
values_s = tensorboard_smoothing(values, smooth_val) |
|
|
ax.plot(steps, values_s, color=TB_COLOR_SMOOTH) |
|
|
else: |
|
|
ax.plot(steps, values, color=TB_COLOR_SMOOTH) |
|
|
fpath = os.path.join(images_dir, k.replace('/', '_').replace('.', '_')) |
|
|
plt.savefig(fpath, dpi=dpi, bbox_inches='tight') |
|
|
plt.close() |
|
|
|
