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| import matplotlib
import matplotlib.pyplot as plt
matplotlib.rc("font",family='SimHei')
plt.plot([1,2,3], [100,500,300])
plt.title('matplotlib中文字体测试', fontsize=25)
plt.xlabel('X轴', fontsize=15)
plt.ylabel('Y轴', fontsize=15)
plt.show()
import pandas as pd
log_path = './work_dirs/ZihaoDataset-PSPNet/20230818_210528/vis_data/scalars.json'
with open(log_path, "r") as f:
json_list = f.readlines()
len(json_list)
eval(json_list[4])
for each in json_list[:-1]:
new_row = pd.DataFrame([eval(each)])
if 'aAcc' in each:
df_test = pd.concat([df_test, new_row], ignore_index=True)
else:
df_train = pd.concat([df_train, new_row], ignore_index=True)
df_train
df_test
df_train.to_csv('图表/训练日志-训练集.csv', index=False)
df_test.to_csv('图表/训练日志-测试集.csv', index=False)
from matplotlib import colors as mcolors
import random
random.seed(124)
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan', 'black', 'indianred', 'brown', 'firebrick', 'maroon', 'darkred', 'red', 'sienna', 'chocolate', 'yellow', 'olivedrab', 'yellowgreen', 'darkolivegreen', 'forestgreen', 'limegreen', 'darkgreen', 'green', 'lime', 'seagreen', 'mediumseagreen', 'darkslategray', 'darkslategrey', 'teal', 'darkcyan', 'dodgerblue', 'navy', 'darkblue', 'mediumblue', 'blue', 'slateblue', 'darkslateblue', 'mediumslateblue', 'mediumpurple', 'rebeccapurple', 'blueviolet', 'indigo', 'darkorchid', 'darkviolet', 'mediumorchid', 'purple', 'darkmagenta', 'fuchsia', 'magenta', 'orchid', 'mediumvioletred', 'deeppink', 'hotpink']
markers = [".",",","o","v","^","<",">","1","2","3","4","8","s","p","P","*","h","H","+","x","X","D","d","|","_",0,1,2,3,4,5,6,7,8,9,10,11]
linestyle = ['--', '-.', '-']
def get_line_arg():
'''
随机产生一种绘图线型
'''
line_arg = {}
line_arg['color'] = random.choice(colors)
line_arg['linestyle'] = random.choice(linestyle)
line_arg['linewidth'] = random.randint(1, 4)
return line_arg
metrics = ['loss', 'decode.loss_ce', 'aux.loss_ce']
plt.figure(figsize=(16, 8))
x = df_train['step']
for y in metrics:
try:
plt.plot(x, df_train[y], label=y, **get_line_arg())
except:
pass
plt.tick_params(labelsize=20)
plt.xlabel('step', fontsize=20)
plt.ylabel('Loss', fontsize=20)
plt.title('训练集损失函数', fontsize=25)
plt.legend(fontsize=20)
plt.savefig('图表/训练集损失函数.pdf', dpi=120, bbox_inches='tight')
plt.show()
metrics = ['decode.acc_seg', 'aux.acc_seg']
plt.figure(figsize=(16, 8))
x = df_train['step']
for y in metrics:
try:
plt.plot(x, df_train[y], label=y, **get_line_arg())
except:
pass
plt.tick_params(labelsize=20)
plt.xlabel('step', fontsize=20)
plt.ylabel('Metrics', fontsize=20)
plt.title('训练集准确率', fontsize=25)
plt.legend(fontsize=20)
plt.savefig('图表/训练集准确率.pdf', dpi=120, bbox_inches='tight')
plt.show()
df_test.columns
metrics = ['aAcc', 'mIoU', 'mAcc', 'mDice', 'mFscore', 'mPrecision', 'mRecall']
plt.figure(figsize=(16, 8)
x = df_test['step']
for y in metrics:
try:
plt.plot(x, df_test[y], label=y, **get_line_arg())
except:
pass
plt.tick_params(labelsize=20)
plt.ylim([0, 100])
plt.xlabel('step', fontsize=20)
plt.ylabel('Metrics', fontsize=20)
plt.title('测试集评估指标', fontsize=25)
plt.legend(fontsize=20)
plt.savefig('图表/测试集分类评估指标.pdf', dpi=120, bbox_inches='tight')
plt.show()
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