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plot_cifar10.py
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plot_cifar10.py
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import numpy as np
import matplotlib.pyplot as plt
import sys, os
from lib.config import cfg
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
def plot_accuracies(data, title='Accuracy Plot', plot_type='Accuracy', x_axis_label='Epochs', save_location=None, mode='Test'):
"""
This helper function can be used to plot(visualize) the accuracies saved using lib.utils.save_accuracies()
:return: None
"""
plt.tick_params(direction='out', length=6, width=2)
if(save_location==None):
save_location = './final_plots/cifar10/'
if not os.path.exists(save_location):
os.makedirs(save_location)
for (i,info) in enumerate(data):
with open(info[0], 'rb') as f:
acc = pickle.load(f)
_plot_indiv_accuracies(acc, color=info[2], label=info[1], plot_type=plot_type)
if(info[1]=='SGD'):
with open(info[3], 'rb') as f:
upper_limit = pickle.load(f)
with open(info[4], 'rb') as f:
lower_limit = pickle.load(f)
x = np.arange(1, len(acc) + 1)
if(plot_type=="Accuracy"):
lower_limit = 100-lower_limit
upper_limit = 100-upper_limit
plt.fill_between(x, lower_limit, upper_limit, color='lightskyblue', alpha=0.000002)
size =15
plt.legend(fontsize=size)
# if title is not None:
# plt.title(title)
plt.xlabel(x_axis_label, fontsize=size)
plt.grid(True, linestyle='--', axis='y')
# plt.xticks(np.arange(0, 101, step=20))
if plot_type == 'Accuracy':
plt.yticks(np.arange(0, 110, step=10))
if(mode=='Test'):
plt.ylabel('Test Error', fontsize=size)
elif(mode=='Train'):
plt.ylabel('Train Error', fontsize=size)
plt.ylim([10,60])
else:
# plt.yticks(np.arange(0, 2, step=0.5))
if (mode == 'Test'):
plt.ylabel('Test Loss', fontsize=size)
elif (mode == 'Train'):
plt.ylabel('Train Loss', fontsize=size)
plt.ylim([0.2,1.6])
plt.savefig(save_location + title.replace(' ', '_').replace('(', '_').replace(')', '_') + '.eps', format='eps')
plt.close()
def _plot_indiv_accuracies(accuracies, color='blue', label='', plot_type=None):
x = np.arange(1, len(accuracies) + 1)
if(plot_type=='Accuracy'):
accuracies = 100 - np.array(accuracies)
plt.plot(x, accuracies, color=color, label=label)
if __name__ == '__main__':
#
# for i in range(1, 61):
# try:
# test_data = [[
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_between_iteration_epoch_' + str(i) + '_accuracy.pkl',
# 'SGD', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_submodcomb_refresh-5_epochs-60_0203_103925/accuracies/test_acc_between_iteration_epoch_' + str(i) + '_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='CIFAR100 Epoch ' + str(i) + ' Test Accuracy', x_axis_label='# of iterations (x10)')
# except Exception as error:
# print ('Exception occured for index {}, {}'.format(i, error))
# CIFAR 10
test_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_acc_mean_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_acc_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL', 'green'],
]
plot_accuracies(test_data, title='CIFAR 10 Test Error(Main)')
train_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/train_acc_round_0_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_acc_mean_accuracy.pkl',
'SGD(averaged)', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/train_acc_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL', 'green'],
]
plot_accuracies(train_data, title='CIFAR 10 Train Error(Main)')
train_loss_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_loss_mean_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/train_loss_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL', 'green'],
]
plot_accuracies(train_loss_data, title='CIFAR 10 Train Loss(Main)', plot_type='Loss')
test_loss_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_loss_mean_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_loss_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL', 'green'],
]
plot_accuracies(test_loss_data, title='CIFAR 10 Test Loss(Main)', plot_type='Loss')
################################## ablations #######################################
# refresh rate
test_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_acc_mean_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_mean_accuracy.pkl',
'SGD', 'blue',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_lower_limit_accuracy.pkl'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet32_Refresh1_0212_123903/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection Refresh Rate-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh10_0212_103211/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh25_0212_103645/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR10_ResNet20_refresh40_0221_223527/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh50_0212_103902/accuracies/test_acc_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black']
]
plot_accuracies(test_data, title='CIFAR 10 Test Error with RF Ablation', save_location='./final_plots/cifar10/Refresh/')
train_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/train_acc_round_0_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_acc_mean_accuracy.pkl',
'SGD', 'blue',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_acc_lower_limit_accuracy.pkl'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet32_Refresh1_0212_123903/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection Refresh Rate-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh10_0212_103211/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh25_0212_103645/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR10_ResNet20_refresh40_0221_223527/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh50_0212_103902/accuracies/train_acc_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black'],
]
plot_accuracies(train_data, title='CIFAR 10 Train Error with RF Ablation', save_location='./final_plots/cifar10/Refresh/')
train_loss_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_loss_mean_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/train_loss_lower_limit_accuracy.pkl'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet32_Refresh1_0212_123903/accuracies/test_loss_round_0_accuracy.pkl',
# 'Submodular Selection Refresh Rate-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh10_0212_103211/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh25_0212_103645/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR10_ResNet20_refresh40_0221_223527/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh50_0212_103902/accuracies/train_loss_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black']
]
plot_accuracies(train_loss_data, title='CIFAR 10 Train Loss with RF Ablation', plot_type='Loss', save_location='./final_plots/cifar10/Refresh/')
test_loss_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_CIFAR10_ResNet20_0215_193022/accuracies/test_loss_mean_accuracy.pkl',
# 'Loss', 'red'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_loss_mean_accuracy.pkl',
'SGD', 'blue',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_loss_lower_limit_accuracy.pkl'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet32_Refresh1_0212_123903/accuracies/test_loss_round_0_accuracy.pkl',
# 'Submodular Selection Refresh Rate-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_0211_154331/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh10_0212_103211/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh25_0212_103645/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR10_ResNet20_refresh40_0221_223527/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_CIFAR_10_ResNet20_Refresh50_0212_103902/accuracies/test_loss_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black']
]
plot_accuracies(test_loss_data, title='CIFAR 10 Test Loss with RF Ablation', plot_type='Loss', save_location='./final_plots/cifar10/Refresh/')