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318674b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | import matplotlib.pyplot as plt
import numpy as np
from brokenaxes import brokenaxes
import random
random.seed(999)
import matplotlib.font_manager as fm
# Path to a downloaded Times New Roman font file (.ttf)
font_path = '/nfs/ywang29/GmNet/times.ttf'
# Add the font to Matplotlib's font manager
font_manager = fm.FontManager(fm.findSystemFonts())
font_manager.addfont(font_path)
prop = fm.FontProperties(fname=font_path)
plt.rcParams['font.family'] = prop.get_name()
# Randomly generating some fake data for the plot
my_color = (0.98,0.1,0.1)
models = [
#{"name": "EfficientFormerV2-S0", "top1_accuracy": random.uniform(75, 76), "latency": 2.0, "param": random.uniform(100, 200), "color": np.random.rand(3,)},
#{"name": "MobileNetV2", "top1_accuracy": random.uniform(71, 73), "latency": random.uniform(0.6, 0.9), "param": random.uniform(50, 100), "color": np.random.rand(3,)},
#{"name": "EfficientFormer-L1", "top1_accuracy": random.uniform(78, 80), "latency": 12.1, "param": random.uniform(200, 300), "color": np.random.rand(3,)},
#{"name": "ResNet50", "top1_accuracy": random.uniform(76, 78), "latency": random.uniform(2, 2.5), "param": random.uniform(150, 250), "color": np.random.rand(3,)},
#{"name": "MobileViT-XS", "top1_accuracy": random.uniform(73, 74), "latency": random.uniform(5, 6), "param": random.uniform(80, 150), "color": np.random.rand(3,)},
#
# {"name": "", "top1_accuracy":72.5, "latency":0.67, "param":2.4, "color":my_color},
# {"name": "", "top1_accuracy": 71.6, "latency": 0.63, "param": 2.2, "color": my_color, "shift": [0, 0]},
#{"name": "", "top1_accuracy":73.5, "latency":0.716, "param":2.9, "color":my_color, "shift":[0,0]},
{"name": "StarNet-S2", "top1_accuracy":74.8, "latency":1.9, "param":3.7, "color":np.random.rand(3,), "shift":[0,0.2]},
{"name": "StarNet-S3", "top1_accuracy":77.3, "latency":2.4, "param":5.8, "color":np.random.rand(3,), "shift":[0,-0.2]},
{"name": "StarNet-S4", "top1_accuracy":78.4, "latency":3.3, "param":7.5, "color":np.random.rand(3,), "shift":[-0.1,-0.3]},
# others
# {"name": "MobileOne-S0", "top1_accuracy":71.4, "latency":0.67, "param":2.1, "color":np.random.rand(3,),"shift":[0.13,-0.2]},
#{"name": "MobileOne-S1", "top1_accuracy":75.9, "latency":0.854, "param":4.8, "color":np.random.rand(3,), "shift":[0.13,-0.2]},
{"name": "MobileOne-S2", "top1_accuracy":77.2, "latency":1.8, "param":7.8, "color":np.random.rand(3,), "shift":[0.2,0.2]},
{"name": "MobileOne-S4", "top1_accuracy":79.6, "latency":2.9, "param":7.8, "color":np.random.rand(3,), "shift":[0.15,0]},
#{"name": "EfficientNet-B0", "top1_accuracy":77.1, "latency":, "param":5.3, "color":np.random.rand(3,), "shift":[0,0]},
# {"name": "EdgeViT-xxs", "top1_accuracy":74.4, "latency":2.839, "param":4.1, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "EdgeViT-xs", "top1_accuracy":77.5, "latency":3.0, "param":4.1, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "Mobilev2-x1.4", "top1_accuracy":74.7, "latency":1.7, "param":6.9, "color":np.random.rand(3,), "shift":[0,-0.2]},
#{"name": "MobileNetv2-x1.0", "top1_accuracy":72, "latency":1.7, "param":3.4, "color":np.random.rand(3,), "shift":[0,0]},
#{"name": "Mobile v2-x2.0", "top1_accuracy":74.7, "latency":2.0, "param":7.4, "color":np.random.rand(3,), "shift":[0.2,0]},
#{"name": "MobileNetv2-x1.4", "param":6.9, "top1_accuracy":74, "latency":1.7, "color":np.random.rand(3,), "shift":[0,0]},
#{"name": "MobileNetv2-x1.0", "param":3.4, "top1_accuracy":72, "latency":1.7, "color":np.random.rand(3,), "shift":[0,0]},
#{"name": "MobileNetv2-x2.0", "param":7.4, "top1_accuracy":74, "latency":2.0, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "RepViT-M0.9", "param":6.9, "top1_accuracy":77.4, "latency":3.2, "color":np.random.rand(3,), "shift":[0,-0.2]},
# {"name": "RepViT-M1.0", "param":3.4, "top1_accuracy":78.6, "latency":3.6, "color":np.random.rand(3,), "shift":[-0.1,0]},
# {"name": "RepViT-M1.1", "param":7.4, "top1_accuracy":79.4, "latency":5.1, "color":np.random.rand(3,), "shift":[-0.3,0]},
# {"name": "RepViT-M1.5", "param":7.4, "top1_accuracy":81.2, "latency":6.4, "color":np.random.rand(3,), "shift":[-0.3,0]},
#{"name": "GhostNet0.5", "param":6.9, "top1_accuracy":77.4, "latency":3.2, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "GhostNet1.0", "param":3.4, "top1_accuracy":73.9, "latency":2.9, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "GhostNet1.3", "param":7.4, "top1_accuracy":75.7, "latency":3.0, "color":np.random.rand(3,), "shift":[-0,0]},
# {"name": "FastViT-S12", "param":3.4, "top1_accuracy":79.8, "latency":5.3, "color":np.random.rand(3,), "shift":[-0.3,-0.3]},
# {"name": "RepViT-M1.1", "param":7.4, "top1_accuracy":81.2, "latency":5.1, "color":np.random.rand(3,), "shift":[0,0]},
# {"name": "LSNet-B", "param":3.4, "top1_accuracy":80.3, "latency":6.2, "color":np.random.rand(3,), "shift":[-0.3,-0.3]},
{"name": "LSNet-T", "param":3.4, "top1_accuracy":74.9, "latency":2.9, "color":np.random.rand(3,), "shift":[-0,-0]},
{"name": "UniRepLKNet-F", "param":3.4, "top1_accuracy":78.6, "latency":3.1, "color":np.random.rand(3,), "shift":[-0,-0]},
{"name": "EfficientFormerV2-S0", "param":6.9, "top1_accuracy":73.7, "latency":2.0, "color":np.random.rand(3,), "shift":[0,0.3]},
#{"name": "EfficientFormerV2-S1", "param":3.4, "top1_accuracy":74.7, "latency":2.8, "color":np.random.rand(3,), "shift":[0,0.3]},
# {"name": "EfficientFormerV2-S2", "param":7.4, "top1_accuracy":80.4, "latency":5.4, "color":np.random.rand(3,), "shift":[-0.35,0.15]},
{"name": "EfficientMod-xxs", "param":6.9, "top1_accuracy":73.7, "latency":2.3, "color":np.random.rand(3,), "shift":[0,-0]},
{"name": "EfficientMod-xs", "param":3.4, "top1_accuracy":74.7, "latency":2.9, "color":np.random.rand(3,), "shift":[-0,-0.1]},
# {"name": "EfficientMod-s", "param":7.4, "top1_accuracy":80.4, "latency":4.5, "color":np.random.rand(3,), "shift":[0,-0.3]},
# {"name": "FasterNet-T0", "top1_accuracy":71.9, "latency":0.708, "param":3.9, "color":np.random.rand(3,), "shift":[0,0]},
#{"name": "Fasternet-T0", "param":6.9, "top1_accuracy":71.9, "latency":2.3, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "Fasternet-T1", "param":3.4, "top1_accuracy":76.2, "latency":2.6, "color":np.random.rand(3,), "shift":[0,0]},
# {"name": "Fasternet-T2", "param":7.4, "top1_accuracy":78.9, "latency":4.5, "color":np.random.rand(3,), "shift":[0,0]},
{"name": "", "top1_accuracy":75.5, "latency":1.6, "param":7.6, "color":'r', "shift":[0.15,0]},
{"name": "", "top1_accuracy":78.3, "latency":1.8, "param":7.6, "color":'r', "shift":[0.15,0]},
{"name": "", "top1_accuracy":79.3, "latency":2.1, "param":7.8, "color":'r', "shift":[0.15,0]},
{"name": "GmNet", "top1_accuracy":81.5, "latency":2.9, "param":13.0, "color":'r', "shift":[0.1,0]},
# Add more models as needed
]
#GmNet
plot_data_x = [ 1.6, 1.8, 2.1, 2.9]
plot_data_y = [75.5,78.3, 79.3, 81.5]
#StarNet
#plot_data_x1 = [ 2.0,2.3, 3.3]
#plot_data_y1 = [74.8,77.3,78.4]
plot_data_x1 = [ 2.0, 3.3]
plot_data_y1 = [74.8,78.4]
#RepViT
plot_data_x2 = [ 3.2, 3.6, 5.1]
plot_data_y2 = [77.4, 78.6, 81.2]
#EfficientFormerV2
plot_data_x3 = [ 2.0, 2.8, 5.4]
plot_data_y3 = [73.7, 74.7, 80.4]
#EfficientMod
plot_data_x4 = [ 2.3, 2.9, 4.5]
plot_data_y4 = [76, 78.3, 81.0]
#Fasternet
plot_data_x5 = [ 2.3, 2.6, 4.5]
plot_data_y5 = [71.9, 76.2, 78.9]
#Fasternet
plot_data_x5 = [ 2.3, 2.6, 4.5]
plot_data_y5 = [71.9, 76.2, 78.9]
# 71.9,
# 76.2,
# 78.9,
#MobileOne
plot_data_x5 = [ 1.8, 2.9]
plot_data_y5 = [77.4, 79.4]
#GmNet
#plot_data_x = [ 1.6, 2.2, 2.9]
#plot_data_y = [74.6, 79.3, 81.2]
# Set up the figure and axis
fig, ax = plt.subplots(figsize=(6, 6))
ax.grid(True, linestyle='dashed', color=(0.95,0.95,0.95))
# fig = plt.figure(figsize=(4, 3))
# ax = brokenaxes(xlims=((0, 2), ()),
# ylims=((-4.3, -4), (-0.5, 0.2), (0.65, 1.5))) # !! These lims need to be manually adjusted for the best view
# Scatter plot, each model is a point whose size is proportional to the model 'size' and color is unique
ax.plot(plot_data_x, plot_data_y, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x1, plot_data_y1, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x2, plot_data_y2, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x3, plot_data_y3, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x4, plot_data_y4, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x5, plot_data_y5, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
for spine in ax.spines.values():
spine.set_edgecolor((0.5, 0.5, 0.5))
for model in models:
ax.scatter(model['latency'], model['top1_accuracy'], s =40, alpha=0.8, color=model['color'], marker='o')
ax.text(model['latency']+model['shift'][0], model['top1_accuracy']+model['shift'][1], model['name'], fontsize=11, ha='center')
# s=int(model['param']*100),
# Setting the axis labels
ax.set_xlabel('Latency (ms)', fontsize=12)
ax.set_ylabel('Top-1 Accuracy (%)', fontsize=12)
#ax.annotate('(Bubble size indicates model parameter number)', (1.45, 72.5),fontsize=11, horizontalalignment='center', verticalalignment='center', color=(0.4,0.4,0.4))
# Title of the plot
# ax.set_title('Model Performance Comparison')
# Show the plot
plt.show()
out = 'acc_latency3.pdf'
fig.savefig(out, bbox_inches='tight')
out = 'acc_latency3.png'
fig.savefig(out, bbox_inches='tight')
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