| | |
| |
|
| | 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() |
| |
|
