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lr_test.py
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lr_test.py
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# import matplotlib.pyplot as plt
# import numpy as np
import pickle as pkl
# gamma_list = [0.9]
# print('ExponentialLR:')
# for gamma in gamma_list:
# a = 1e-2
# lr = [a]
# for i in range(200):
# a *= gamma
# lr.append(a)
# # plt.plot(lr)
# print('gamma:', gamma)
# m = 10
# n = 60
# print(f'LR of epoch {m}:', lr[m-1])
# print(f'LR of epoch {n}:', lr[n-1])
# path = '/home/sda1/Jinge/Attention_analysis/logs/CASIA_WebFace_20000_0.15/[resnet50]_[0.01]_[0.5]_[train_mouth0.15_crop]_[test_mouth0.15_crop]_[baseModel]/lr_dict.pkl'
# with open(path, 'rb') as f:
# a = pkl.load(f)
# a = list(a.values())
# # plt.plot(a[:150])
# print('\nReduceLROnPlateau:')
# print('LR of epoch 15:', a[14])
# print('LR of epoch 80:', a[79])
# import os
# path1 = './data/CASIA_WebFace_20000/test_crop'
# path2 = './data/CASIA_WebFace_20000/train_crop'
# res1 = []
# for r, d, f in os.walk(path1):
# for file in f:
# if file.endswith('.jpg'):
# res1.append(os.path.join(r, file))
# res2 = []
# for r, d, f in os.walk(path2):
# for file in f:
# if file.endswith('.jpg'):
# res2.append(os.path.join(r, file))
# len(res1) + len(res2)
import numpy as np
import matplotlib.pyplot as plt
def learning_rate_decay(init_lr, gamma, epoch):
return init_lr * (gamma ** epoch)
init_lr = 1e-2
gamma = 0.9
total_epochs = 150
learning_rates = [learning_rate_decay(init_lr, gamma, epoch) for epoch in range(total_epochs)]
plt.plot(learning_rates)
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.title('ExponentialLR Decay Curve')
# plt.grid(True)
plt.show()
def learning_rate_decay(init_lr, gamma, epoch, decay_interval):
return init_lr * (gamma ** (epoch // decay_interval))
init_lr = 1e-2
gamma = 0.5
total_epochs = 150
decay_interval = 30
learning_rates = [learning_rate_decay(init_lr, gamma, epoch, decay_interval) for epoch in range(total_epochs)]
plt.plot(learning_rates)
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.title('StepLR Decay Curve')
# plt.grid(True)
plt.show()
path = '/home/sda1/Jinge/Critical_Period_Analysis/logs/CASIA_WebFace_20000_0.15_final/[resnet50]_[0.01]_[0.5]_[32]_[train_crop]_[test_crop]_[baseModel]/lr_dict.pkl'
with open(path, 'rb') as f:
lr_dict = pkl.load(f)
plt.plot(list(lr_dict.values()))
plt.xlabel('Epoch')
plt.ylabel('Learning Rate')
plt.title('Adaptive Decay Curve')