wehub-resource-sync
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import os
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from typing import Dict, List, Optional, Tuple
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Item = Dict[str, float]
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TB_COLOR, TB_COLOR_SMOOTH = '#FFE2D9', '#FF7043'
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def read_tensorboard_file(fpath: str) -> Dict[str, List[Item]]:
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from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
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if not os.path.isfile(fpath):
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raise FileNotFoundError(f'fpath: {fpath}')
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ea = EventAccumulator(fpath)
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ea.Reload()
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res: Dict[str, List[Item]] = {}
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tags = ea.Tags()['scalars']
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for tag in tags:
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values = ea.Scalars(tag)
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r: List[Item] = []
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for v in values:
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r.append({'step': v.step, 'value': v.value})
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res[tag] = r
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return res
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def tensorboard_smoothing(values: List[float], smooth: float = 0.9) -> List[float]:
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norm_factor = 0
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x = 0
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res: List[float] = []
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for i in range(len(values)):
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x = x * smooth + values[i] # Exponential decay
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norm_factor *= smooth
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norm_factor += 1
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res.append(x / norm_factor)
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return res
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def plot_images(images_dir: str,
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tb_dir: str,
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smooth_key: Optional[List[str]] = None,
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smooth_val: float = 0.9,
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figsize: Tuple[int, int] = (8, 5),
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dpi: int = 100) -> None:
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"""Using tensorboard's data content to plot images"""
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import matplotlib.pyplot as plt
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if not os.path.exists(tb_dir):
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return
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smooth_key = smooth_key or []
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os.makedirs(images_dir, exist_ok=True)
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matches = []
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for root, dirs, files in os.walk(tb_dir):
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for f in files:
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if f.startswith('events.out.tfevents.'):
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matches.append(os.path.join(root, f))
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if not matches:
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return
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fname = matches[0]
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tb_path = os.path.join(tb_dir, fname)
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data = read_tensorboard_file(tb_path)
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for k in data.keys():
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_data = data[k]
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steps = [d['step'] for d in _data]
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values = [d['value'] for d in _data]
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if len(values) == 0:
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continue
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_, ax = plt.subplots(1, 1, squeeze=True, figsize=figsize, dpi=dpi)
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ax.set_title(k)
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if len(values) == 1:
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ax.scatter(steps, values, color=TB_COLOR_SMOOTH)
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elif k in smooth_key:
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ax.plot(steps, values, color=TB_COLOR, label='original')
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values_s = tensorboard_smoothing(values, smooth_val)
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ax.plot(steps, values_s, color=TB_COLOR_SMOOTH, label='smoothed')
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ax.legend()
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else:
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ax.plot(steps, values, color=TB_COLOR_SMOOTH)
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fpath = os.path.join(images_dir, k.replace('/', '_').replace('.', '_'))
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plt.savefig(fpath, dpi=dpi, bbox_inches='tight')
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plt.close()
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