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plot_curve.py
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plot_curve.py
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import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import re
import argparse
res = [re.compile('Iteration (\d+)/(\d+)'),
re.compile('Train Loss: ([.\d]+) Acc: ([.\d]+)'),
re.compile('Validation Loss: ([.\d]+) Acc: ([.\d]+)')]
def plot_acc(log_name):
data = {}
with open(log_name) as f:
lines = f.readlines()
for l in lines:
i = 0
for r in res:
m = r.match(l)
if m is not None:
break
i += 1
if m is None:
continue
if i == 0:
iteration = int(m.groups()[0])
total_iter = int(m.groups()[1])
if iteration not in data:
data[iteration] = [0] * 4
else:
loss = float(m.groups()[0])
acc = float(m.groups()[1])
data[iteration][(i-1)*2] = loss
data[iteration][(i-1)*2 +1] = acc
train_acc = []
train_loss = []
val_acc = []
val_loss = []
for k, v in data.items():
train_loss.append(v[0])
train_acc.append(v[1])
val_loss.append(v[2])
val_acc.append(v[3])
iter_list = [int(x) + 1 for x in data.keys()]
x_train = iter_list
x_val = iter_list
# x_train = np.arange(len(train_acc))
# x_val = np.arange(len(val_acc))
plt.subplot(1, 2, 1)
plt.plot(x_train, train_acc, '-', linestyle='-', color='r', linewidth=2,
label='train_top1')
plt.plot(x_val, val_acc, '-', linestyle='-', color='b', linewidth=2,
label='val_top1')
plt.legend(loc="best")
plt.xticks(np.arange(0, iter_list[-1], iter_list[-1]//10))
plt.yticks(np.arange(0.1, 1, 0.05))
plt.xlim([0, iter_list[-1]])
min_y = min([min(train_acc), min(val_acc)]) - 0.05
max_y = max([max(train_acc), max(val_acc)]) + 0.05
if max_y - min_y < 0.1:
min_y = max(0, min_y - 0.05)
max_y = min(1, max_y + 0.05)
plt.ylim(min_y, max_y)
# plt.ylim([min([min(train_acc), min(val_acc)]),
# max([max(train_acc), max(val_acc)])])
plt.grid(True)
plt.subplot(1, 2, 2)
plt.semilogy(x_train, train_loss, '-', linestyle='-', color='r', linewidth=2,
label='train_loss')
plt.semilogy(x_val, val_loss, '-', linestyle='-', color='b', linewidth=2,
label='val_loss')
plt.legend(loc="best")
plt.xticks(np.arange(0, iter_list[-1], iter_list[-1]//10))
plt.yticks(np.arange(0.1, 1, 0.05))
plt.xlim([0, iter_list[-1]])
# plt.ylim([min([min(train_loss), min(val_loss)]),
# max([max(train_loss), max(val_loss)])])
plt.yscale('log')
plt.grid(True)
return max(val_acc)
def plot_log(log_path, save_path, close_fig=True):
plt.figure(figsize=(14, 8))
plt.xlabel("Iterations")
plt.ylabel("Accuracy")
max_acc = plot_acc(log_path)
plt.grid(True)
plt.savefig(save_path)
if close_fig:
plt.close()
return max_acc
def main(args):
_ = plot_log(os.path.join('../exp', args.exp_dir, args.logs),
os.path.join('../exp', args.exp_dir, args.output_filename))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--logs', type=str, default='train_history.txt')
parser.add_argument('--exp_dir', type=str, default='exp')
parser.add_argument('--output_filename', type=str,
default='train_curve.png')
args = parser.parse_args()
main(args)