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plot_dice_curve.py
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plot_dice_curve.py
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import matplotlib.pyplot as plt
import os
import csv
from scipy.ndimage.filters import uniform_filter1d
# import pixiedust
class Hist:
def __init__(self, file_name, log_dir, task_name, skip_number, new_arch=True, net_name=None, average_N=100):
...
...
self.file_name = os.path.join(log_dir, task_name, file_name)
self.skip_number = skip_number
self.task_name = task_name
if task_name == 'vessel':
self.out_chn = 2
if task_name == 'airway':
self.out_chn = 2
if task_name == 'lobe':
self.out_chn = 6
if task_name == 'no_label':
self.out_chn = 1
# print(self.out_chn)
self.train_color = 'blue'
self.average_N = average_N
self.new_arch = new_arch
if not self.new_arch:
self.val_out = 'val_out_' + self.task_name + '_segmentation_dice_coef_mean'
self.train_out = 'out_' + self.task_name + '_segmentation_dice_coef_mean'
else:
self.val_out = 'val_' + self.task_name + '_out_segmentation_dice_coef_mean'
self.train_out = self.task_name + '_out_segmentation_dice_coef_mean'
self.valid_color = 'red'
self.super_title = net_name
def _plot(self, x, y_valid, y_train=None):
fig = plt.figure(facecolor='w', figsize=(6, 6), dpi=300)
# fig.suptitle(self.super_title, fontsize=16)
ax = fig.add_subplot(111)
ax.scatter(x, y_valid, s=5, c=self.valid_color, marker='o',
label='valid')
y_valid = uniform_filter1d(y_valid, size=self.average_N)
ax.plot(x, y_valid, c=self.valid_color, label='valid_average')
if y_train is not None:
# # ax2 = fig.add_axes([0,0,1,1])
ax.scatter(x, y_train, s=5, c=self.train_color, marker='o',
label='train')
y_train = uniform_filter1d(y_train, size=self.average_N)
ax.plot(x, y_train, c=self.train_color, label='train_average')
# ax2.legend()
ax.legend()
ax.set_ylim((0, 1))
ax.set_xlim(left=0)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 1])
ax.set_ylabel('dice')
ax.set_xlabel('steps')
# plt.xlim(left=0)
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.plot([0, 90000], [0.95, 0.95], 'k-', lw=1, dashes=[2, 2])
# ax.plot([0,90000], [0.97, 0.97], 'k-', lw=1, dashes=[2,2])
plt.rc('font', size=18)
# plt.show()
frame = plt.gca()
frame.axes.get_yaxis().set_visible(True)
frame.axes.get_xaxis().set_visible(True)
plt.savefig('all_train_valid_dice.png')
plt.close()
def _get_data(self, y_name):
with open(self.file_name, encoding='utf-8') as f:
reader = csv.DictReader((l.replace('\0', '') for l in f)) # avoid error: 'line contains null'
index = 0
x = []
val_ave_dice = []
for row in reader:
# print(row/)
if row[self.val_out] is not None and row[self.train_out] is not None:
x.append(index)
val_ave_dice.append(float(row[y_name]))
index += 1
y = val_ave_dice
return (x, y)
def plot_val_dice(self):
# # self.super_title = self.super_title + 'valid_dice_mean'
x, y = self._get_data(self.val_out)
self._plot(x, y)
def plot_train_dice(self):
# self.super_title = self.task_name + 'train_dice_mean'
x, y = self._get_data(self.train_out)
self._plot(x, y)
def plot_train_val_dice(self):
x, y_valid = self._get_data(self.val_out)
_, y_train = self._get_data(self.train_out)
self._plot(x, y_valid, y_train=y_train)
def plot_all_val_dice(self):
fig = plt.figure(facecolor='w', figsize=(6, 6), dpi=300)
ax = fig.add_subplot(111)
for i in range(1, self.out_chn):
if self.new_arch:
y_name = 'val_' + self.task_name + '_out_segmentation_dice_' + str(i)
else:
y_name = 'val_out_' + self.task_name + '_segmentation_dice_' + str(i)
x, y = self._get_data(y_name)
y = uniform_filter1d(y, size=self.average_N)
ax.plot(x, y, label='val_dice_' + str(i))
ax.legend()
ax.set_ylim((0, 1))
ax.set_xlim(left=0)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 1])
ax.set_ylabel('dice')
ax.set_xlabel('steps')
# plt.xlim(left=0)
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.plot([0, 90000], [0.95, 0.95], 'k-', lw=1, dashes=[2, 2])
# ax.plot([0,90000], [0.97, 0.97], 'k-', lw=1, dashes=[2,2])
plt.rc('font', size=18)
frame = plt.gca()
frame.axes.get_yaxis().set_visible(True)
frame.axes.get_xaxis().set_visible(True)
# plt.show()
plt.savefig('all_valid_dice.png')
plt.close()
def plot_all_train_dice(self):
fig = plt.figure(facecolor='w', figsize=(6, 6), dpi=300)
ax = fig.add_subplot(111)
for i in range(1, self.out_chn):
if self.new_arch:
y_name = self.task_name + '_out_segmentation_dice_' + str(i)
else:
y_name = 'out_' + self.task_name + '_segmentation_dice_' + str(i)
x, y = self._get_data(y_name)
x, y = x[::skip_number], y[::skip_number]
y = uniform_filter1d(y, size=self.average_N)
ax.plot(x, y, label='train_dice_' + str(i))
ax.legend()
ax.set_ylim((0, 1))
ax.set_xlim(left=0)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 1])
ax.set_ylabel('dice')
ax.set_xlabel('steps')
# plt.xlim(left=0)
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.plot([0, 90000], [0.95, 0.95], 'k-', lw=1, dashes=[2, 2])
# ax.plot([0,90000], [0.97, 0.97], 'k-', lw=1, dashes=[2,2])
plt.rc('font', size=18)
# plt.show()
frame = plt.gca()
frame.axes.get_yaxis().set_visible(True)
frame.axes.get_xaxis().set_visible(True)
plt.savefig('all_train_dice.png')
plt.close()
class Hist_:
def __init__(self, file_name, task_name, skip_number, new_arch=True, net_name=None, average_N=100):
...
...
self.skip_number = skip_number
self.task_name = task_name
if task_name == 'vessel':
self.out_chn = 2
if task_name == 'airway':
self.out_chn = 2
if task_name == 'lobe':
self.out_chn = 6
if task_name == 'no_label':
self.out_chn = 1
# print(self.out_chn)
self.train_color = 'blue'
self.average_N = average_N
self.new_arch = new_arch
self.val_out = 'ave_total'
self.train_out = 'ave_total'
self.valid_color = 'red'
self.super_title = net_name
def _plot(self, x, y_valid, y_train=None):
fig = plt.figure(facecolor='w', figsize=(6, 6), dpi=300)
# fig.suptitle(self.super_title, fontsize=16)
ax = fig.add_subplot(111)
ax.scatter(x, y_valid, s=5, c=self.valid_color, marker='o',
label='valid')
y_valid = uniform_filter1d(y_valid, size=self.average_N)
ax.plot(x, y_valid, c=self.valid_color, label='valid_average')
if y_train is not None:
# # ax2 = fig.add_axes([0,0,1,1])
ax.scatter(x, y_train, s=5, c=self.train_color, marker='o',
label='train')
y_train = uniform_filter1d(y_train, size=self.average_N)
ax.plot(x, y_train, c=self.train_color, label='train_average')
# ax2.legend()
ax.legend()
ax.set_ylim((0, 1))
ax.set_xlim(left=0)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 1])
ax.set_ylabel('dice')
ax.set_xlabel('epochs')
# plt.xlim(left=0)
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.plot([0, 90000], [0.95, 0.95], 'k-', lw=1, dashes=[2, 2])
# ax.plot([0,90000], [0.97, 0.97], 'k-', lw=1, dashes=[2,2])
plt.rc('font', size=18)
# plt.show()
frame = plt.gca()
frame.axes.get_yaxis().set_visible(True)
frame.axes.get_xaxis().set_visible(True)
plt.savefig('all_train_valid_dice.png')
plt.close()
def _get_data(self, y_name):
with open(self.file_name, encoding='utf-8') as f:
reader = csv.DictReader((l.replace('\0', '') for l in f)) # avoid error: 'line contains null'
index = 0
x = []
val_ave_dice = []
for row in reader:
# print(row/)
x.append(index)
val_ave_dice.append(float(row[y_name]))
index += 1
y = val_ave_dice
return (x, y)
def plot_val_dice(self):
# # self.super_title = self.super_title + 'valid_dice_mean'
x, y = self._get_data(self.val_out)
self._plot(x, y)
def plot_train_dice(self):
# self.super_title = self.task_name + 'train_dice_mean'
x, y = self._get_data(self.train_out)
self._plot(x, y)
def plot_train_val_dice(self):
self.log_dir = 'results/' + task_name + '/valid/pred/GLUCOLD/' + number + '/'
self.file_name = os.path.join(self.log_dir, file_name)
x, y_valid = self._get_data(self.val_out)
self.log_dir = 'results/' + task_name + '/train/pred/GLUCOLD/' + number + '/'
self.file_name = os.path.join(self.log_dir, file_name)
_, y_train = self._get_data(self.train_out)
self._plot(x, y_valid, y_train=y_train)
def plot_all_val_dice(self):
self.log_dir = 'results/' + task_name + '/valid/pred/GLUCOLD/' + number + '/'
self.file_name = os.path.join(self.log_dir, file_name)
fig = plt.figure(facecolor='w', figsize=(6, 6), dpi=300)
ax = fig.add_subplot(111)
for i in range(1, self.out_chn):
y_name = 'ave_dice_class_'+str(i)
x, y = self._get_data(y_name)
y = uniform_filter1d(y, size=self.average_N)
ax.plot(x, y, label='val_dice_' + str(i))
ax.legend()
ax.set_ylim((0, 1))
ax.set_xlim(left=0)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 1])
ax.set_ylabel('dice')
ax.set_xlabel('epochs')
# plt.xlim(left=0)
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.plot([0, 90000], [0.95, 0.95], 'k-', lw=1, dashes=[2, 2])
# ax.plot([0,90000], [0.97, 0.97], 'k-', lw=1, dashes=[2,2])
plt.rc('font', size=18)
frame = plt.gca()
frame.axes.get_yaxis().set_visible(True)
frame.axes.get_xaxis().set_visible(True)
# plt.show()
plt.savefig('all_valid_dice.png')
plt.close()
def plot_all_train_dice(self):
self.log_dir = 'results/' + task_name + '/train/pred/GLUCOLD/' + number + '/'
self.file_name = os.path.join(self.log_dir, file_name)
fig = plt.figure(facecolor='w', figsize=(6, 6), dpi=300)
ax = fig.add_subplot(111)
for i in range(1, self.out_chn):
y_name = 'ave_dice_class_'+str(i)
x, y = self._get_data(y_name)
y = uniform_filter1d(y, size=self.average_N)
ax.plot(x, y, label='train_dice_' + str(i))
ax.legend()
ax.set_ylim((0, 1))
ax.set_xlim(left=0)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 0.9, 0.95, 1])
ax.set_ylabel('dice')
ax.set_xlabel('epochs')
# plt.xlim(left=0)
# plt.gca().yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.plot([0, 90000], [0.95, 0.95], 'k-', lw=1, dashes=[2, 2])
# ax.plot([0,90000], [0.97, 0.97], 'k-', lw=1, dashes=[2,2])
plt.rc('font', size=18)
frame = plt.gca()
frame.axes.get_yaxis().set_visible(True)
frame.axes.get_xaxis().set_visible(True)
# plt.show()
plt.savefig('all_train_dice.png')
plt.close()
task_name = 'vessel'
number = '15850005'
file_name = '1585000573.7211952_0.00011a_o_0ds2dr1bn1fs16ptsz144ptzsz64.log'
log_dir = 'logs/'
skip_number = 1 # patches per epoch
hist = Hist(file_name, log_dir, task_name, skip_number, new_arch=True, average_N=10)
hist.plot_all_val_dice()
hist.plot_all_train_dice()
hist.plot_train_val_dice()
# 'net_only_lobe'
# task_name = 'lobe'
# number = '15847902'
# file_name = 'dices.csv'
# skip_number = 10 # patches per epoch
#
# hist = Hist_(file_name, task_name, skip_number, new_arch=True, average_N=10)
#
#
# hist.plot_all_val_dice()
#
# hist.plot_all_train_dice()
#
# hist.plot_train_val_dice()