| 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') | |