opencv_zoo / benchmark /generate_table.py
Wanli
beautify benchmark table (#157)
a9286c4
raw
history blame
4.87 kB
import re
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
mpl.use("svg")
# parse a '.md' file and find a table. return table information
def parse_table(filepath):
with open(filepath, "r", encoding="utf-8") as f:
content = f.read()
lines = content.split("\n")
header = []
body = []
found_start = False # if found table start line
parse_done = False # if parse table done
for l in lines:
if found_start and parse_done:
break
l = l.strip()
if not l:
continue
if l.startswith("|") and l.endswith("|"):
if not found_start:
found_start = True
row = [c.strip() for c in l.split("|") if c.strip()]
if not header:
header = row
else:
body.append(row)
elif found_start:
parse_done = True
return header, body
# parse models information
def parse_data(models_info):
min_list = []
max_list = []
colors = []
for model in models_info:
# remove \*
data = [x.replace("\\*", "") for x in model]
# get max data
max_data = -1
max_idx = -1
min_data = 9999999
min_idx = -1
for i in range(len(data)):
try:
d = float(data[i])
if d > max_data:
max_data = d
max_idx = i
if d < min_data:
min_data = d
min_idx = i
except:
pass
min_list.append(min_idx)
max_list.append(max_idx)
# calculate colors
color = []
for t in data:
try:
t = (float(t) - min_data) / (max_data - min_data)
color.append(cmap(t))
except:
color.append('white')
colors.append(color)
return colors, min_list, max_list
if __name__ == '__main__':
hardware_info, models_info = parse_table("./README.md")
cmap = mpl.colormaps.get_cmap("RdYlGn_r")
# remove empty line
models_info.pop(0)
# remove reference
hardware_info = [re.sub(r'\[(.+?)]\(.+?\)', r'\1', r) for r in hardware_info]
models_info = [[re.sub(r'\[(.+?)]\(.+?\)', r'\1', c) for c in r] for r in models_info]
table_colors, min_list, max_list = parse_data(models_info)
table_texts = [hardware_info] + models_info
table_colors = [['white'] * len(hardware_info)] + table_colors
# create a color bar. base width set to 1000, color map height set to 80
fig, axs = plt.subplots(nrows=3, figsize=(10, 0.8))
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
axs[0].imshow(gradient, aspect='auto', cmap=cmap)
axs[0].text(-0.01, 0.5, "Faster", va='center', ha='right', fontsize=11, transform=axs[0].transAxes)
axs[0].text(1.01, 0.5, "Slower", va='center', ha='left', fontsize=11, transform=axs[0].transAxes)
# initialize a table
table = axs[1].table(cellText=table_texts,
cellColours=table_colors,
cellLoc="left",
loc="upper left")
# adjust table position
table_pos = axs[1].get_position()
axs[1].set_position([
table_pos.x0,
table_pos.y0 - table_pos.height,
table_pos.width,
table_pos.height
])
table.set_fontsize(11)
table.auto_set_font_size(False)
table.scale(1, 2)
table.auto_set_column_width(list(range(len(table_texts[0]))))
table.AXESPAD = 0 # cancel padding
# highlight the best number
for i in range(len(min_list)):
cell = table.get_celld()[(i + 1, min_list[i])]
cell.set_text_props(weight='bold', color='white')
table_height = 0
table_width = 0
# calculate table height and width
for i in range(len(table_texts)):
cell = table.get_celld()[(i, 0)]
table_height += cell.get_height()
for i in range(len(table_texts[0])):
cell = table.get_celld()[(0, i)]
table_width += cell.get_width() + 0.1
# add notes for table
axs[2].text(0, -table_height - 0.8, "\*: Models are quantized in per-channel mode, which run slower than per-tensor quantized models on NPU.", va='bottom', ha='left', fontsize=11, transform=axs[1].transAxes)
# turn off labels
for ax in axs:
ax.set_axis_off()
ax.set_xticks([])
ax.set_yticks([])
# adjust color map position to center
cm_pos = axs[0].get_position()
axs[0].set_position([
(table_width - 1) / 2,
cm_pos.y0,
cm_pos.width,
cm_pos.height
])
plt.rcParams['svg.fonttype'] = 'none'
plt.savefig("./color_table.svg", format='svg', bbox_inches="tight", pad_inches=0, metadata={'Date': None, 'Creator': None})