tools/plot_linear.py

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# 设置数据
resolution = [336.00, 448.00, 560.00, 672.00, 784.00, 896.00, 1024.00]

# 数据：RoI Thing
roi_thing = {
    "Resolution": resolution,
    "CLIPself": [65.90, 69.50, 70.90, 71.70, 72.00, 72.60, 72.30],
    "RegionCLIP": [66.40, 70.00, 71.20, 72.10, 72.10, 72.00, 71.90],
    "DeCLIP": [68.90, 72.10, 74.30, 75.20, 75.50, 75.70, 75.40]
}

# 数据：Mask Thing
mask_thing = {
    "Resolution": resolution,
    "CLIPself": [60.10, 66.00, 69.50, 71.40, 72.70, 74.00, 74.60],
    "RegionCLIP": [60.60, 66.90, 69.70, 72.50, 73.50, 73.90, 74.70],
    "DeCLIP": [62.00, 68.40, 72.20, 74.50, 75.80, 76.80, 77.20]
}

# 数据：Mask Stuff
mask_stuff = {
    "Resolution": resolution,
    "CLIPself": [40.80, 43.10, 44.60, 45.40, 45.90, 46.50, 46.90],
    "RegionCLIP": [28.20, 30.10, 30.30, 31.10, 32.50, 35.00, 34.80],
    "DeCLIP": [45.50, 49.10, 50.40, 51.20, 51.30, 52.70, 52.50]
}

# 将数据转换为 pandas DataFrame
df_roi_thing = pd.DataFrame(roi_thing)
df_mask_thing = pd.DataFrame(mask_thing)
df_mask_stuff = pd.DataFrame(mask_stuff)

# 设置绘图风格和颜色
sns.set_style("whitegrid")

# 定义新的颜色、线条样式和标记符号
color_palette = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']  # 参考图的颜色
line_styles = ['--', '-.', ':']
markers = ['o', 's', '^']

# 设置全局字体和线宽
plt.rcParams.update({
    'font.size': 10,  # 字体大小
    'axes.labelsize': 10,
    'lines.linewidth': 2.0,  # 线条宽度
    'legend.fontsize': 8,  # 图例字体大小
    'xtick.labelsize': 8,  # x轴刻度字体大小
    'ytick.labelsize': 8,  # y轴刻度字体大小
    'grid.color': 'gray',  # 网格线颜色
    'grid.alpha': 0.3,  # 调整网格线透明度
})

# 绘制 RoI Thing 折线图并保存
plt.figure(figsize=(4, 3))  # 调整图的大小
plt.plot(df_roi_thing['Resolution'], df_roi_thing['DeCLIP'], linestyle=line_styles[2], marker=markers[2], color=color_palette[2], label='DeCLIP')
plt.plot(df_roi_thing['Resolution'], df_roi_thing['CLIPself'], linestyle=line_styles[0], marker=markers[0], color=color_palette[0], label='CLIPself')
plt.plot(df_roi_thing['Resolution'], df_roi_thing['RegionCLIP'], linestyle=line_styles[1], marker=markers[1], color=color_palette[1], label='RegionCLIP')
plt.xlabel('Resolution')
plt.ylabel(' RoI Align (Thing) mAcc')  # 统一纵坐标标签
plt.xticks(resolution)
plt.grid(True, linestyle='--', color='gray', alpha=0.3)  # 更淡的网格
# plt.legend(loc='lower right')
plt.legend(loc='best',fontsize=10)
plt.tight_layout()
plt.savefig('roi_thing.pdf', dpi=300)  # 保存为PDF
plt.close()

# 绘制 Mask Thing 折线图并保存
plt.figure(figsize=(4, 3))
plt.plot(df_mask_thing['Resolution'], df_mask_thing['DeCLIP'], linestyle=line_styles[2], marker=markers[2], color=color_palette[2], label='DeCLIP')
plt.plot(df_mask_thing['Resolution'], df_mask_thing['CLIPself'], linestyle=line_styles[0], marker=markers[0], color=color_palette[0], label='CLIPself')
plt.plot(df_mask_thing['Resolution'], df_mask_thing['RegionCLIP'], linestyle=line_styles[1], marker=markers[1], color=color_palette[1], label='RegionCLIP')
plt.xlabel('Resolution')
plt.ylabel(' Mask Pooling (Thing) mAcc')  # 统一纵坐标标签
plt.xticks(resolution)
plt.grid(True, linestyle='--', color='gray', alpha=0.3)  # 更淡的网格
# plt.legend(loc='lower right')
plt.legend(loc='best',fontsize=10)
plt.tight_layout()
plt.savefig('mask_thing.pdf', dpi=300)  # 保存为PDF
plt.close()

# 绘制 Mask Stuff 折线图并保存
plt.figure(figsize=(4, 3))
plt.plot(df_mask_stuff['Resolution'], df_mask_stuff['DeCLIP'], linestyle=line_styles[2], marker=markers[2], color=color_palette[2], label='DeCLIP')
plt.plot(df_mask_stuff['Resolution'], df_mask_stuff['CLIPself'], linestyle=line_styles[0], marker=markers[0], color=color_palette[0], label='CLIPself')
plt.plot(df_mask_stuff['Resolution'], df_mask_stuff['RegionCLIP'], linestyle=line_styles[1], marker=markers[1], color=color_palette[1], label='RegionCLIP')
plt.xlabel('Resolution')
plt.ylabel('Mask Pooling (Stuff) mAcc')  # 统一纵坐标标签
plt.xticks(resolution)
plt.grid(True, linestyle='--', color='gray', alpha=0.3)  # 更淡的网格
# plt.legend(loc='lower right')
plt.legend(loc='best', fontsize=10)
plt.tight_layout()
plt.savefig('mask_stuff.pdf', dpi=300)  # 保存为PDF
plt.close()