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graph_loss_prog.py
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graph_loss_prog.py
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# -*- coding: utf-8 -*-
# Author: Antoine DELPLACE
# Last update: 18/11/2019
"""
Post-processing program to visualize loss functions and the processing time during training of ProGAN
Parameters
----------
directory_name : name of the folder containing the log of the training program called "output.txt"
(contains regex like (\d+.\d+) - Epoch (\d+), Batch (\d+): d_loss=(-?\d+.\d+(e(\+|-)\d+)?|nan), g_loss=(-?\d+.\d+(e(\+|-)\d+)?|nan))
Return
----------
Plot and save the two graphs "learning_curve.pdf" and "processing_time.pdf" in the folder directory_name
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
import re
if __name__ == "__main__":
# Reading parameters
if len(sys.argv) < 2:
print("Need a directory_name")
sys.exit(1)
directory_name = sys.argv[1]
print(directory_name)
# Reading output text file
with open(os.path.join(directory_name, "output.txt")) as f:
file_lines = f.readlines()
file_lines = [x.strip() for x in file_lines]
# Initialization
i=0
while "Beginning nblock=1 and is_transition=0" not in file_lines[i]:
i += 1
tab_time_val = []
tab_extra_batch_val = []
tab_epoch_val = []
tab_batch_val = []
tab_d_loss_val = []
tab_g_loss_val = []
tab_time_test = []
tab_extra_batch_test = []
tab_epoch_test = []
tab_batch_test = []
tab_d_loss_test = []
tab_g_loss_test = []
tab_nblock_trans = []
# Extracting data
extra_batch = 0
while i < len(file_lines):
if "Beginning nblock=" in file_lines[i]:
regex_group = re.search("Beginning nblock=(\d+) and is_transition=(0|1)", file_lines[i])
#print(i, regex_group.group(1), regex_group.group(2))
tab_nblock_trans.append([regex_group.group(1), regex_group.group(2)])
extra_batch = len(tab_time_val)
elif "Not found: " in file_lines[i]:
#print(i, "Not found")
pass
elif " - Testing: " in file_lines[i]:
regex_group = re.search("(\d+.\d+) - Epoch (\d+), Batch (\d+) - Testing: d_loss=(-?\d+.\d+(e(\+|-)\d+)?|nan), g_loss=(-?\d+.\d+(e(\+|-)\d+)?|nan)", file_lines[i])
#print(i, regex_group.group(1), regex_group.group(2), regex_group.group(3), regex_group.group(4), regex_group.group(7))
tab_time_test.append(regex_group.group(1))
tab_extra_batch_test.append(extra_batch)
tab_epoch_test.append(regex_group.group(2))
tab_batch_test.append(regex_group.group(3))
tab_d_loss_test.append(regex_group.group(4))
tab_g_loss_test.append(regex_group.group(7))
else:
regex_group = re.search("(\d+.\d+) - Epoch (\d+), Batch (\d+): d_loss=(-?\d+.\d+(e(\+|-)\d+)?|nan), g_loss=(-?\d+.\d+(e(\+|-)\d+)?|nan)", file_lines[i])
#print(i, regex_group.group(1), regex_group.group(2), regex_group.group(3), regex_group.group(4), regex_group.group(7))
tab_time_val.append(regex_group.group(1))
tab_extra_batch_val.append(extra_batch)
tab_epoch_val.append(regex_group.group(2))
tab_batch_val.append(regex_group.group(3))
tab_d_loss_val.append(regex_group.group(4))
tab_g_loss_val.append(regex_group.group(7))
i += 1
tab_time_val = np.array(tab_time_val, dtype=float)
tab_epoch_val = np.array(tab_epoch_val, dtype=int)
tab_batch_val = np.array(tab_batch_val, dtype=int)
tab_d_loss_val = np.array(tab_d_loss_val, dtype=float)
tab_g_loss_val = np.array(tab_g_loss_val, dtype=float)
tab_time_val_difference = np.maximum(np.diff(tab_time_val), 0)
tab_batch_glob_val = tab_batch_val+tab_epoch_val*(np.max(tab_batch_val)+1)+tab_extra_batch_val
tab_batch_glob_val_difference = np.diff(tab_batch_glob_val)
tab_time_test = np.array(tab_time_test, dtype=float)
tab_epoch_test = np.array(tab_epoch_test, dtype=int)
tab_batch_test = np.array(tab_batch_test, dtype=int)
tab_d_loss_test = np.array(tab_d_loss_test, dtype=float)
tab_g_loss_test = np.array(tab_g_loss_test, dtype=float)
tab_time_test_difference = np.maximum(np.diff(tab_time_test), 0)
tab_batch_glob_test = tab_batch_test+tab_epoch_test*(np.max(tab_batch_val)+1)+tab_extra_batch_test
tab_batch_glob_test_difference = np.diff(tab_batch_glob_test)
# Plotting learning curve
plt.figure(figsize=(8, 8))
plt.title("Learning curve")
plt.plot(tab_d_loss_val)
plt.plot(tab_g_loss_val)
plt.plot(tab_batch_glob_test, tab_d_loss_test, '.')
plt.plot(tab_batch_glob_test, tab_g_loss_test, '.')
label_new_res = "New Resolution or Stop Transition"
label_epochs = "Epochs"
for i in range(0, len(tab_batch_val)):
if tab_batch_val[i] == 0:
if tab_epoch_val[i] == 0:
plt.axvline(x=tab_batch_glob_val[i], linestyle="--", color="purple", label=label_new_res)
label_new_res = "_nolegend_"
else:
plt.axvline(x=tab_batch_glob_val[i], linestyle="--", color="gray", label=label_epochs)
label_epochs = "_nolegend_"
plt.xlabel("Batchs")
plt.ylabel("Loss")
if np.nanmin(tab_d_loss_test) < 0 or np.nanmin(tab_g_loss_test) < 0 or np.nanmin(tab_d_loss_val) < 0 or np.nanmin(tab_g_loss_val) < 0:
plt.yscale('symlog')
else:
plt.yscale('log')
plt.legend(["Discriminator validation loss", "Generator validation loss", "Discriminator test loss", "Generator test loss", "New Resolution or Stop Transition", "Epochs"])
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.savefig(os.path.join(directory_name, "learning_curve.pdf"), format="pdf")
plt.show()
# Plotting processing time graph
plt.figure(figsize=(8, 8))
plt.title("Average processing time")
plt.plot(np.cumsum(tab_batch_glob_val_difference), np.divide(tab_time_val_difference, tab_batch_glob_val_difference))
plt.plot(np.cumsum(tab_batch_glob_test_difference), np.divide(tab_time_test_difference, tab_batch_glob_test_difference), '.')
label_new_res = "New Resolution or Stop Transition"
label_epochs = "Epochs"
for i in range(0, len(tab_batch_val)):
if tab_batch_val[i] == 0:
if tab_epoch_val[i] == 0:
plt.axvline(x=tab_batch_glob_val[i], linestyle="--", color="purple", label=label_new_res)
label_new_res = "_nolegend_"
else:
plt.axvline(x=tab_batch_glob_val[i], linestyle="--", color="gray", label=label_epochs)
label_epochs = "_nolegend_"
plt.xlabel("Batchs")
plt.ylabel("Time (sec/input)")
plt.yscale('log')
plt.legend(["Validation processing time", "Test processing time", "New Resolution or Stop Transition", "Epochs"])
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.savefig(os.path.join(directory_name, "processing_time.pdf"), format="pdf")
plt.show()