71 lines
1.6 KiB
Python
71 lines
1.6 KiB
Python
# import torch
|
|
# print(torch.__version__)
|
|
# from torch.utils.tensorboard import SummaryWriter
|
|
# %load_ext tensorboard
|
|
|
|
|
|
import os
|
|
import math
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
# %matplotlib inline
|
|
import numpy as np
|
|
import pandas as pd
|
|
sns.set()
|
|
|
|
|
|
# !rm -R ./runs
|
|
# writer = SummaryWriter()
|
|
|
|
# root_dir = "../../workflow_v3/test_case/big_model/{}/output/mse_analysis".format("Facedetection_SSD_without_BN")
|
|
|
|
# writer.close()
|
|
|
|
|
|
|
|
# plt.plot(threshold_lst, mse_lst)
|
|
# plt.title(fn)
|
|
# plt.xlabel("threshold")
|
|
# plt.ylabel("MSE");
|
|
|
|
|
|
def main():
|
|
|
|
# input validation
|
|
if len(sys.argv) != 2:
|
|
print("need mse dump dir")
|
|
return
|
|
|
|
root_dir = sys.argv[1]
|
|
num_fig = len(os.listdir(root_dir))
|
|
nrows=math.ceil(num_fig/2.0)
|
|
ncols= 2
|
|
|
|
fig = plt.figure(figsize=(15, 100))
|
|
fig.subplots_adjust(hspace=0.4, wspace=0.4)
|
|
for idx, fn in enumerate(os.listdir(root_dir)[:]):
|
|
file_path = os.path.join(root_dir, fn)
|
|
file = open(file_path, "r")
|
|
|
|
threshold_lst = []
|
|
mse_lst = []
|
|
for line in file.readlines():
|
|
line = line.rstrip().split(",")
|
|
threshold = float(line[0])
|
|
threshold_lst.append(threshold)
|
|
mse = 10*math.log10(float(line[1]))
|
|
mse_lst.append(mse)
|
|
# writer.add_scalar(fn, mse, walltime=threshold)
|
|
idx+=1
|
|
ax = fig.add_subplot(nrows, ncols, idx)
|
|
ax.plot(threshold_lst, mse_lst)
|
|
ax.set_title(fn)
|
|
|
|
ax.plot(threshold_lst, mse_lst)
|
|
ax.set_xlabel("threshold")
|
|
ax.set_ylabel("MSE")
|
|
plt.savefig("test.pdf",bbox_inches='tight')
|
|
|
|
if __name__ == "__main__":
|
|
main()
|