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plot.py
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plot.py
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import numpy as np
import matplotlib.pyplot as plt
import sys
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'):
"""
This helper function can be used to plot(visualize) the accuracies saved using lib.utils.save_accuracies()
:return: None
"""
save_location = './output/'
for info in data:
with open(info[0], 'rb') as f:
acc = pickle.load(f)
_plot_indiv_accuracies(acc, color=info[2], label=info[1])
plt.legend()
if title is not None:
plt.title(title)
plt.xlabel(x_axis_label)
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))
plt.ylabel('Accuracy')
else:
# plt.yticks(np.arange(0, 2, step=0.5))
plt.ylabel('Loss')
plt.savefig(save_location + title.replace(' ', '_').replace('(', '_').replace(')', '_') + '.png')
plt.close()
def _plot_indiv_accuracies(accuracies, color='blue', label=''):
x = np.arange(1, len(accuracies) + 1)
plt.plot(x, accuracies, color=color, label=label)
if __name__ == '__main__':
#
# for i in range(1, 41):
# try:
# test_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_random_full_0119_214903/accuracies/test_acc_between_iteration_epoch_' + str(i) + '_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_latest_0131_010429/accuracies/test_acc_between_iteration_epoch_' + str(i) + '_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='SVHN 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/output/cifar10_resnet20_random_full_0119_214639/accuracies/test_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar10_resnet20_submod_comb_0128_211805/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='CIFAR 10 (Early Random Stopping) Test Accuracy')
#
# train_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/cifar10_resnet20_random_full_0119_214639/accuracies/train_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar10_resnet20_submod_comb_0128_211805/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(train_data, title='CIFAR 10 (with warm-up) Train Accuracy')
#
# loss_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/cifar10_resnet20_random_full_0119_214639/accuracies/loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar10_resnet20_submod_comb_0128_211805/accuracies/loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='CIFAR 10 (with warm-up) Loss', plot_type='Loss')
# CIFAR - 100
#-------------------
# test_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_random_full_0119_214225/accuracies/test_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_submod_new_0125_220927/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='CIFAR 100 Test Accuracy')
#
# train_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_random_full_0119_214225/accuracies/train_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_submod_new_0125_220927/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(train_data, title='CIFAR 100 Train Accuracy')
#
# loss_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_random_full_0119_214225/accuracies/loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_submod_new_0125_220927/accuracies/loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='CIFAR 100 Loss', plot_type='Loss')
# SVHN
# test_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_random_full_0119_214903/accuracies/test_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_latest_0131_010429/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='SVHN Test Accuracy')
#
# train_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_random_full_0119_214903/accuracies/train_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_latest_0131_010429/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(train_data, title='SVHN Train Accuracy')
#
# loss_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_random_full_0119_214903/accuracies/loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_latest_0131_010429/accuracies/loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='SVHN Loss', plot_type='Loss')
# F-MNIST
# test_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/fmnist_simplenet_random_full_0119_220306/accuracies/test_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/fmnist_simplenet_submod_new_0125_221439/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='F-MNIST Test Accuracy')
#
# train_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/fmnist_simplenet_random_full_0119_220306/accuracies/train_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/fmnist_simplenet_submod_new_0125_221439/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(train_data, title='F-MNIST Train Accuracy')
#
# loss_data = [[
# '/home/vamshi/PycharmProjects/SMDL/output/fmnist_simplenet_random_full_0119_220306/accuracies/loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/fmnist_simplenet_submod_new_0125_221439/accuracies/loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='F-MNIST Loss', plot_type='Loss')
# ImageNet- New server
# test_data = [[
# '/home/joseph/workspace/SMDL/output/imagenet_sgd_0419_173214/accuracies/test_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/joseph/workspace/SMDL/output/imagenet_smdl_0419_173502/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='ImageNet Test Accuracy')
#
# train_data = [[
# '/home/joseph/workspace/SMDL/output/imagenet_sgd_0419_173214/accuracies/train_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/joseph/workspace/SMDL/output/imagenet_smdl_0419_173502/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(train_data, title='ImageNet Train Accuracy')
#
# loss_data = [[
# '/home/joseph/workspace/SMDL/output/imagenet_sgd_0419_173214/accuracies/train_loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/joseph/workspace/SMDL/output/imagenet_smdl_0419_173502/accuracies/train_loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='ImageNet Train Loss', plot_type='Loss')
#
# loss_data = [[
# '/home/joseph/workspace/SMDL/output/imagenet_sgd_0419_173214/accuracies/test_loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/home/joseph/workspace/SMDL/output/imagenet_smdl_0419_173502/accuracies/test_loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='ImageNet Test Loss', plot_type='Loss')
# ImageNet DGX
# test_data = [[
# '/home/joseph/omml/SMDL/output/imagenet_sgd_cfr_0419_124227/accuracies/test_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/test_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='ImageNet Test Accuracy')
#
# train_data = [[
# '/home/joseph/omml/SMDL/output/imagenet_sgd_cfr_0419_124227/accuracies/train_acc_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/train_acc_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(train_data, title='ImageNet Train Accuracy')
#
# loss_data = [[
# '/home/joseph/omml/SMDL/output/imagenet_sgd_cfr_0419_124227/accuracies/train_loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/train_loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='ImageNet Train Loss', plot_type='Loss')
#
# loss_data = [[
# '/home/joseph/omml/SMDL/output/imagenet_sgd_cfr_0419_124227/accuracies/test_loss_round_0_accuracy.pkl',
# 'Random Selection', 'blue'],
# [
# '/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/test_loss_round_0_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(loss_data, title='ImageNet Test Loss', plot_type='Loss')
test_data = [[
'/raid/joseph/workspace/SMDL/output/imagenet_sgd_0419_141859/accuracies/test_acc_round_0_accuracy.pkl',
'Random Selection', 'blue'],
[
'/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/test_acc_round_0_accuracy.pkl',
'Submodular Selection', 'green']
]
plot_accuracies(test_data, title='ImageNet Test Accuracy')
train_data = [[
'/raid/joseph/workspace/SMDL/output/imagenet_sgd_0419_141859/accuracies/train_acc_round_0_accuracy.pkl',
'Random Selection', 'blue'],
[
'/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/train_acc_round_0_accuracy.pkl',
'Submodular Selection', 'green']
]
plot_accuracies(train_data, title='ImageNet Train Accuracy')
loss_data = [[
'/raid/joseph/workspace/SMDL/output/imagenet_sgd_0419_141859/accuracies/train_loss_round_0_accuracy.pkl',
'Random Selection', 'blue'],
[
'/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/train_loss_round_0_accuracy.pkl',
'Submodular Selection', 'green']
]
plot_accuracies(loss_data, title='ImageNet Train Loss', plot_type='Loss')
loss_data = [[
'/raid/joseph/workspace/SMDL/output/imagenet_sgd_0419_141859/accuracies/test_loss_round_0_accuracy.pkl',
'Random Selection', 'blue'],
[
'/raid/joseph/workspace/SMDL/output/imagenet_SMDL_0419_165953/accuracies/test_loss_round_0_accuracy.pkl',
'Submodular Selection', 'green']
]
plot_accuracies(loss_data, title='ImageNet Test Loss', plot_type='Loss')