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###########################################################################
# Computer vision - Binary neural networks demo software by HyperbeeAI. #
# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. hello@hyperbee.ai #
###########################################################################
import torch, matplotlib, os, sys, argparse
sys.path.append('..')
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
matplotlib.use('Agg')
from datetime import date
# bizden
from generate_leaderboard import get_checkpoints_except_reproduce, get_checkpoint_top1s_sizes
def main():
cp_full_paths, cp_name_list = get_checkpoints_except_reproduce()
print('')
print('Found checkpoints (except reproduce checkpoints) at these locations:')
for cp_path in cp_full_paths:
print(cp_path)
print('')
print('Gathering hardware-mode top-1 accuracy and size info from each checkpoint')
cp_best_top1s, cp_sizes_in_bytes, cp_sizes_in_bytes_max78000, cp_sizes_antipodal = get_checkpoint_top1s_sizes(cp_full_paths, cp_name_list)
print('')
print('Generating results graph under documentation, with timestamp')
###############################################
### Hardcoded
fig, ax = plt.subplots(figsize=(9, 5))
ax.set_xlim((80,400))
ax.set_ylim((53,68.0))
ax.grid(True)
ax.set_axisbelow(True)
ax.set_xlabel('Size [KBytes]', fontsize=15)
ax.set_ylabel('Validation set accuracy [%]', fontsize=15)
###############################################
color_maxim = np.asarray([30,30,255])/256
color_shallow = np.asarray([255,30,30])/256
for i, name in enumerate(cp_name_list):
if('maxim' in name):
color = color_maxim
annot = 'm'+name[5:8]
elif('shallow' in name):
color = color_shallow
annot = 's'+name[7:10]
else:
print('')
print('whose model is this?! ->', name)
print('exiting')
print('')
sys.exit()
if(cp_sizes_antipodal[i]):
ax.scatter(cp_sizes_in_bytes[i]/1000.0, cp_best_top1s[i], color = color, s = 70, linestyle='None', alpha=0.2)
ax.scatter(cp_sizes_in_bytes_max78000[i]/1000.0, cp_best_top1s[i], color = color, s = 70, linestyle='None', alpha=0.8)
ax.plot([cp_sizes_in_bytes[i]/1000.0, cp_sizes_in_bytes_max78000[i]/1000.0], [cp_best_top1s[i], cp_best_top1s[i]], color = color, linestyle='dashed')
else:
ax.scatter(cp_sizes_in_bytes[i]/1000.0, cp_best_top1s[i], color = color, s = 70, linestyle='None', alpha=0.8)
#annot_position_x = cp_sizes_in_bytes[i]/1000.0-10
#annot_position_y = cp_best_top1s[i]+0.6
#ax.text(annot_position_x, annot_position_y, annot, fontsize=11, color=color)
custom_lines = [Line2D([0], [0], color=color_maxim, lw=4),
Line2D([0], [0], color=color_shallow, lw=4)]
ax.legend(custom_lines, ['maxim', 'shallow'], loc='upper left', fontsize=12)
plt.title('Models for CIFAR-100', fontsize=15)
today = date.today()
dd = today.strftime("%Y-%m-%d")
graph_path = 'documentation/'+dd+'-results-graph.png'
plt.savefig(graph_path)
print('')
print('Saved graph under', graph_path)
print('')
if __name__ == '__main__':
main()
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