86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
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"""
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def tabulate_events(dpath):
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files = os.listdir(dpath)[0]
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summary_iterators = [EventAccumulator(os.path.join(dpath, files)).Reload()]
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tags = summary_iterators[0].Tags()['scalars']
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for it in summary_iterators:
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assert it.Tags()['scalars'] == tags
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out = {t: [] for t in tags}
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steps = []
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for tag in tags:
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steps = [e.step for e in summary_iterators[0].Scalars(tag)]
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for events in zip(*[acc.Scalars(tag) for acc in summary_iterators]):
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assert len(set(e.step for e in events)) == 1
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out[tag].append([e.value for e in events])
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return out, steps
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events, steps = tabulate_events('/home/vedant/pytorch_sac_ae/log/runs')
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data = []
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for tag, values in events.items():
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for run_idx, run_values in enumerate(values):
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for step_idx, value in enumerate(run_values):
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data.append({
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'tag': tag,
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'run': run_idx,
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'step': steps[step_idx],
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'value': value,
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})
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df = pd.DataFrame(data)
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print(df.head())
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exit()
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plt.figure(figsize=(10,6))
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sns.lineplot(data=df, x='step', y='value', hue='tag', ci='sd')
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plt.show()
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"""
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from tensorboard.backend.event_processing import event_accumulator
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def data_from_tb(files):
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all_steps, all_rewards = [], []
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for file in files:
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ea = event_accumulator.EventAccumulator(file, size_guidance={'scalars': 0})
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ea.Reload()
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episode_rewards = ea.Scalars('train/episode_reward')
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steps = [event.step for event in episode_rewards][:990000]
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rewards = [event.value for event in episode_rewards][:990000]
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all_steps.append(steps)
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all_rewards.append(rewards)
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return all_steps, all_rewards
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files = ['/home/vedant/pytorch_sac_ae/log/runs/tb_21_05_2023-13_19_36/events.out.tfevents.1684667976.cpswkstn6-nvidia4090.1749060.0',
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'/home/vedant/pytorch_sac_ae/log/runs/tb_22_05_2023-09_56_30/events.out.tfevents.1684742190.cpswkstn6-nvidia4090.1976229.0']
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all_steps, all_rewards = data_from_tb(files)
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mean_rewards = np.mean(all_rewards, axis=0)
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std_rewards = np.std(all_rewards, axis=0)
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mean_steps = np.mean(all_steps, axis=0)
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df = pd.DataFrame({'Steps': mean_steps,'Rewards': mean_rewards,'Standard Deviation': std_rewards})
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sns.relplot(x='Steps', y='Rewards', kind='line', data=df, ci="sd")
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plt.fill_between(df['Steps'], df['Rewards'] - df['Standard Deviation'], df['Rewards'] + df['Standard Deviation'], color='b', alpha=.1)
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plt.title("Mean Rewards vs Steps with Standard Deviation")
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plt.show() |