def main():
log_files = ['/hnn/examples/knees_sagittal_fold1/knees_sagittal_fold1.log']
train_iteration = []
train_loss = []
base_test_iter = 0
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
line_count = 0
for line in f:
line_count += 1
#print('{}: {}'.format(line_count, line))
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(
int(matched.group(1)) + base_train_iter)
elif strstr(line, ', loss'):
matched = match_loss(line)
print(matched.group(1))
train_loss.append(float(matched.group(1)))
log_base = os.path.splitext(os.path.basename(log_files[0]))[0]
result = {'TRAIN': (train_iteration, train_loss)}
pickle.dump(result, open(log_base + '.pkl', "wb"))
print('read {} lines'.format(line_count))
print("TRAIN", train_iteration, train_loss, np.shape(train_iteration),
np.shape(train_loss))
print("Best TRAIN performance at:")
min_train_loss = np.min(train_loss)
best_idx = np.where(train_loss == min_train_loss)[0]
print(best_idx)
print("{}: iteration {}, loss {}".format(log_base,
train_iteration[best_idx],
min_train_loss))
# loss
#plt.plot(train_iteration[0:len(train_loss)], train_loss[0:len(train_loss)], 'k', label='Train loss')
plt.semilogy(train_iteration[0:len(train_loss)],
train_loss[0:len(train_loss)],
'k',
label='Train loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.grid(True)
plt.show()
plt.savefig(log_base + '.png')
def main():
log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
test_iteration = []
test_loss = []
test_accuracy = []
base_test_iter = 0
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(
int(matched.group(1)) + base_train_iter)
elif strstr(line, 'Train net output'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(
int(matched.group(1)) + base_test_iter)
elif strstr(line, 'Test net output'):
matched = match_loss(line)
if matched:
test_loss.append(float(matched.group(1)))
else:
matched = match_accuracy(line)
test_accuracy.append(float(matched.group(1)))
print("TRAIN", train_iteration, train_loss)
print("TEST", test_iteration, test_loss)
print("ACCURACY", test_iteration, test_accuracy)
# loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.plot(test_iteration, test_loss, 'r', label='Test loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
def main():
log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
test_iteration = []
test_loss = []
test_accuracy = []
base_test_iter = 0
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(int(matched.group(1))+base_train_iter)
elif strstr(line, 'Train net output'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(int(matched.group(1))+base_test_iter)
elif strstr(line, 'Test net output'):
matched = match_loss(line)
if matched:
test_loss.append(float(matched.group(1)))
else:
matched = match_accuracy(line)
test_accuracy.append(float(matched.group(1)))
print("TRAIN", train_iteration, train_loss)
print("TEST", test_iteration, test_loss)
print("ACCURACY", test_iteration, test_accuracy)
# loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.plot(test_iteration, test_loss, 'r', label='Test loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
def main():
log_file = process_arguments(sys.argv)
train_iteration = []
train_loss = []
test_iteration = []
test_loss = []
with open(log_file, 'rb') as f:
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(int(matched.group(1)))
elif strstr(line, 'Train net output'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(int(matched.group(1)))
elif strstr(line, 'Test net output'):
matched = match_loss(line)
test_loss.append(float(matched.group(1)))
print("TRAIN", train_iteration, train_loss)
print("TEST", test_iteration, test_loss)
plt.plot(train_iteration, train_loss, 'k')
plt.plot(test_iteration, test_loss, 'r')
plt.ylabel('loss')
plt.xlabel('number of iterations')
plt.savefig('loss.png')
def main():
log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
test_iteration = []
test_loss = []
test_accuracy = []
pixel_accuracy = []
mean_accuracy = []
mean_IU = []
frequency_weighted_IU = []
base_test_iter = 0
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(
int(matched.group(1)) + base_train_iter)
elif strstr(line, 'Iteration') and strstr(line, 'loss'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(
int(matched.group(1)) + base_test_iter)
elif strstr(line, 'Test net output'):
matched = match_loss(line)
if matched:
test_loss.append(float(matched.group(1)))
else:
matched = match_accuracy(line)
test_accuracy.append(float(matched.group(1)))
#train_loss.sort()
print("TRAIN", train_iteration, train_loss)
print("TEST", test_iteration, test_loss)
print("ACCURACY", test_iteration, test_accuracy)
# loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.plot(test_iteration, test_loss, 'r', label='Test loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
# evaluation
plt.clf()
plt.plot(range(len(pixel_accuracy)),
pixel_accuracy,
'k',
label='pixel accuracy')
plt.plot(range(len(mean_accuracy)),
mean_accuracy,
'r',
label='mean accuracy')
plt.plot(range(len(mean_IU)), mean_IU, 'g', label='mean IU')
plt.plot(range(len(frequency_weighted_IU)),
frequency_weighted_IU,
'b',
label='frequency weighted IU')
plt.legend(loc=0)
plt.savefig('evaluation.png')
def main():
log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
lr = []
test_iteration = []
detection_eval = []
base_test_iter = 0
base_train_iter = 0
base_lr = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
base_lr = lr[-1]
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(int(matched.group(1)))
matched = match_lr(line)
lr.append(float(matched.group(1)))
elif strstr(line, 'Train net output'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(int(matched.group(1)))
elif strstr(line, 'Test net output'):
matched = match_evaluation(line)
detection_eval.append(float(matched.group(1)))
# print("TRAIN", train_iteration, train_loss)
# print("TEST", test_iteration, detection_eval)
# print("LEARNING_RATE", train_iteration, lr)
# loss
plt.plot(train_iteration, train_loss, 'b', label='Train loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
# learning rate
plt.plot(train_iteration, lr, 'g', label='Learning rate')
plt.legend()
plt.ylabel('Learning rate')
plt.xlabel('Number of iterations')
plt.savefig('learning_rate.png')
# evaluation
plt.clf()
plt.plot(test_iteration, detection_eval, 'r', label='Detection evaluation')
plt.legend(loc='lower right')
plt.ylabel('Detection_eval')
plt.xlabel('Number of iterations')
plt.savefig('evaluation.png')
# overlays
# 1 - training loss vs. detection evaluation
fig, ax1 = plt.subplots()
ax1.plot(train_iteration, train_loss, 'b', label='Train loss')
ax1.set_xlabel('Number of iterations')
ax1.set_ylabel('Loss', color='b')
ax2 = ax1.twinx()
ax2.plot(test_iteration, detection_eval, 'r', label='Detection evaluation')
ax2.set_ylabel('Detection_eval', color='r')
plt.savefig('loss_eval.png')
# 2 - training loss vs. learning rate
fig, ax1 = plt.subplots()
ax1.plot(train_iteration, lr, 'g', label='Learning rate')
ax1.set_xlabel('Number of iterations')
ax1.set_ylabel('Learning rate', color='g')
ax2 = ax1.twinx()
ax2.plot(train_iteration, train_loss, 'b', label='Train loss')
ax2.set_ylabel('Loss', color='b')
plt.savefig('lr_loss.png')
# 3 - learning rate vs. detection evaluation
fig, ax1 = plt.subplots()
ax1.plot(train_iteration, lr, 'g', label='Learning rate')
ax1.set_xlabel('Number of iterations')
ax1.set_ylabel('Learning rate', color='g')
ax2 = ax1.twinx()
ax2.plot(test_iteration, detection_eval, 'r', label='Detection evaluation')
ax2.set_ylabel('Detection_eval', color='r')
plt.savefig('lr_eval.png')
f, axarr = plt.subplots(3, sharex=True)
axarr[0].plot(train_iteration, train_loss)
axarr[0].set_title('Iters vs. Loss')
axarr[1].plot(train_iteration, lr, 'r')
axarr[1].set_title('Iters vs. Learning Rate')
axarr[2].plot(test_iteration, detection_eval, 'g')
axarr[2].set_title('Iters vs. Detection Evaluation')
plt.savefig('tri.png')
plt.show()
def main():
log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
test_iteration = []
test_loss = []
test_accuracy = []
pixel_accuracy = []
mean_accuracy = []
mean_IU = []
frequency_weighted_IU = []
base_test_iter = 0
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(int(matched.group(1))+base_train_iter)
elif strstr(line, 'Iteration') and strstr(line, 'loss'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(int(matched.group(1))+base_test_iter)
elif strstr(line, 'Test net output'):
matched = match_loss(line)
if matched:
test_loss.append(float(matched.group(1)))
else:
matched = match_accuracy(line)
test_accuracy.append(float(matched.group(1)))
#train_loss.sort()
print("TRAIN", train_iteration, train_loss)
print("TEST", test_iteration, test_loss)
print("ACCURACY", test_iteration, test_accuracy)
# loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.plot(test_iteration, test_loss, 'r', label='Test loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
# evaluation
plt.clf()
plt.plot(range(len(pixel_accuracy)), pixel_accuracy, 'k', label='pixel accuracy')
plt.plot(range(len(mean_accuracy)), mean_accuracy, 'r', label='mean accuracy')
plt.plot(range(len(mean_IU)), mean_IU, 'g', label='mean IU')
plt.plot(range(len(frequency_weighted_IU)), frequency_weighted_IU, 'b', label='frequency weighted IU')
plt.legend(loc=0)
plt.savefig('evaluation.png')
def main():
output_data, log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
train_accuracy0 = []
train_accuracy1 = []
train_accuracy2 = []
train_accuracy3 = []
train_accuracy4 = []
train_accuracy5 = []
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
for line in f:
if strstr(line, 'Iteration') and strstr(line, 'loss'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
matched = match_iteration(line)
train_iteration.append(int(matched.group(1))+base_train_iter)
# strong labels
elif strstr(line, 'Train net output #0: accuracy '):
matched = match_net_accuracy(line)
train_accuracy0.append(float(matched.group(1)))
elif strstr(line, 'Train net output #1: accuracy '):
matched = match_net_accuracy(line)
train_accuracy1.append(float(matched.group(1)))
elif strstr(line, 'Train net output #2: accuracy '):
matched = match_net_accuracy(line)
train_accuracy2.append(float(matched.group(1)))
# weak labels
elif strstr(line, 'Train net output #0: accuracy_bbox'):
matched = match_net_accuracy_bbox(line)
train_accuracy0.append(float(matched.group(1)))
elif strstr(line, 'Train net output #1: accuracy_bbox'):
matched = match_net_accuracy_bbox(line)
train_accuracy1.append(float(matched.group(1)))
elif strstr(line, 'Train net output #2: accuracy_bbox'):
matched = match_net_accuracy_bbox(line)
train_accuracy2.append(float(matched.group(1)))
elif strstr(line, 'Train net output #3: accuracy_strong'):
matched = match_net_accuracy_strong(line)
train_accuracy3.append(float(matched.group(1)))
elif strstr(line, 'Train net output #4: accuracy_strong'):
matched = match_net_accuracy_strong(line)
train_accuracy4.append(float(matched.group(1)))
elif strstr(line, 'Train net output #5: accuracy_strong'):
matched = match_net_accuracy_strong(line)
train_accuracy5.append(float(matched.group(1)))
if output_data == 'loss':
for x in train_loss:
print(x)
if output_data == 'acc1':
for x,y,z in zip(train_accuracy0, train_accuracy1, train_accuracy2):
print(x, y, z)
if output_data == 'acc2':
for x,y,z in zip(train_accuracy3, train_accuracy4, train_accuracy5):
print(x, y, z)
## loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
## evaluation
plt.clf()
if len(train_accuracy3) != 0:
plt.plot(range(len(train_accuracy0)), train_accuracy0, 'k', label='accuracy bbox 0')
plt.plot(range(len(train_accuracy1)), train_accuracy1, 'r', label='accuracy bbox 1')
plt.plot(range(len(train_accuracy2)), train_accuracy2, 'g', label='accuracy bbox 2')
plt.plot(range(len(train_accuracy3)), train_accuracy3, 'b', label='accuracy strong 0')
plt.plot(range(len(train_accuracy4)), train_accuracy4, 'c', label='accuracy strong 1')
plt.plot(range(len(train_accuracy5)), train_accuracy5, 'm', label='accuracy strong 2')
else:
plt.plot(range(len(train_accuracy0)), train_accuracy0, 'k', label='train accuracy 0')
plt.plot(range(len(train_accuracy1)), train_accuracy1, 'r', label='train accuracy 1')
plt.plot(range(len(train_accuracy2)), train_accuracy2, 'g', label='train accuracy 2')
plt.legend(loc=0)
plt.savefig('evaluation.png')
def main():
#log_files = process_arguments(sys.argv)
# get newest log
log_dir = 'log'
log_files = [
max(glob.iglob(os.path.join(log_dir, '*.log')), key=os.path.getctime)
]
print(log_files)
XLIM = []
YLIM = []
#XLIM = (0, 1000)
#YLIM = (0.06, 0.5)
LOGSCALE = False
#LOGSCALE = True
REMOVE_ZERO_LOSS = True
train_iteration = []
train_loss = []
test_iteration = []
test_loss = []
base_test_iter = 0
base_train_iter = 0
curr_train_loss = []
curr_test_loss = []
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
line_count = 0
for line in f:
line_count += 1
#print('{}: {}'.format(line_count, line))
if strstr(line, 'Iteration') and strstr(line, ', loss'):
matched = match_iteration(line)
train_iteration.append(
int(matched.group(1)) + base_train_iter)
TEST_LOSS = False
TRAIN_LOSS = True
elif strstr(line, 'Iteration') and strstr(line, 'loss'):
TEST_LOSS = True
TRAIN_LOSS = False
matched = match_test_iteration(line)
test_iteration.append(
int(matched.group(1)) + base_test_iter)
else:
TEST_LOSS = False
TRAIN_LOSS = False
# TRAIN LOSS
if TRAIN_LOSS:
matched = match_train_loss(line)
if matched:
curr_train_loss.append(float(matched.group(1)))
#print('#{} train loss: {}'.format(k_train,float(matched.group(2))))
train_loss.append(np.sum(curr_train_loss))
print('Iter {}: total train loss: {}'.format(
train_iteration[-1], np.sum(curr_train_loss)))
curr_train_loss = []
# TEST LOSS
if TEST_LOSS:
matched = match_test_loss(line)
if matched:
curr_test_loss.append(float(matched.group(1)))
#print('#{} test loss: {}'.format(k_test,float(matched.group(2))))
test_loss.append(np.sum(curr_test_loss))
print('Iter {}: total test loss: {}'.format(
test_iteration[-1], np.sum(curr_test_loss)))
curr_test_loss = []
log_base = os.path.splitext(os.path.basename(log_files[0]))[0]
result = {'TRAIN': (train_iteration, train_loss)}
pickle.dump(result, open(log_base + '.pkl', "wb"))
print('read {} lines'.format(line_count))
print("TRAIN", np.shape(train_iteration), np.shape(train_loss))
print("TEST", np.shape(test_iteration), np.shape(test_loss))
if REMOVE_ZERO_LOSS:
# convert to numpy
Ntrain0 = len(train_loss)
train_loss = np.array(train_loss)
train_iteration = np.array(train_iteration)
idx = train_loss > 0.0
train_iteration = train_iteration[idx]
train_loss = train_loss[idx]
print('Removed {} zeros: Size changed from {} to {}'.format(
np.sum(idx), Ntrain0, np.size(train_loss)))
if len(train_loss) < len(train_iteration):
Ntrain = len(train_loss)
else:
Ntrain = len(train_iteration)
if len(test_loss) < len(test_iteration):
Ntest = len(test_loss)
else:
Ntest = len(test_iteration)
if Ntrain > 0:
print("Best TRAIN performance at index:")
min_train_loss = np.min(train_loss)
best_idx = np.where(train_loss == min_train_loss)[0][0]
print(best_idx)
print("{}: iteration {}, loss {}".format(log_base,
train_iteration[best_idx],
min_train_loss))
if Ntest > 0:
print("Best TEST performance at index:")
min_test_loss = np.min(test_loss)
best_idx = np.where(test_loss == min_test_loss)[0][0]
print(best_idx)
print("{}: iteration {}, loss {}".format(log_base,
test_iteration[best_idx],
min_test_loss))
# Smoothing
window = int(0.1 * float(Ntrain))
window = -int(0.1 * float(Ntrain)) # median
if abs(window) > 100:
window = 100 * abs(window) / window # keep sign
if window % 2 == 0: # make uneven
window = window + 1
testwindow = int(0.1 * float(Ntest))
testwindow = -int(0.1 * float(Ntest)) # median
if abs(testwindow) > 100:
testwindow = 100 * abs(testwindow) / testwindow # keep sign
if testwindow % 2 == 0: # make uneven
testwindow = testwindow + 1
if Ntrain > 0 and window < Ntrain:
print('smoothing {} data points with window of {}'.format(
Ntrain, window))
if window > 0 and window < Ntrain and window > 3:
train_loss_smooth = savgol_filter(
train_loss, abs(window), 3) # window size, polynomial order
else:
train_loss_smooth = medfilt(train_loss,
abs(window)) # window size 51
else:
train_loss_smooth = []
if Ntest > 0 and window < Ntest:
print('smoothing {} data points with window of {}'.format(
Ntest, testwindow))
if window > 0 and window > 3:
test_loss_smooth = savgol_filter(
test_loss, abs(testwindow), 3) # window size, polynomial order
else:
test_loss_smooth = medfilt(test_loss,
abs(testwindow)) # window size 51
else:
test_loss_smooth = []
##print("Best smoothed TRAIN performance at index:")
##min_train_loss = np.min(train_loss_smooth[window:Ntrain-abs(window/2)])
##best_idx = np.where(train_loss_smooth[window:Ntrain-abs(window/2)]==min_train_loss)[0][0] + -abs(window/2)
##print(best_idx)
##print("{}: iteration {}, loss {}".format(log_base,train_iteration[best_idx],min_train_loss))
##print("Best smoothed TEST performance at index:")
##min_test_loss = np.min(test_loss_smooth[window:Ntest-abs(window/2)])
##best_idx = np.where(test_loss_smooth[window:Ntest-abs(window/2)]==min_test_loss)[0][0] + -abs(window/2)
##print(best_idx)
##print("{}: iteration {}, loss {}".format(log_base,test_iteration[best_idx],min_test_loss))
# Visualization
plot_loss(train_iteration, train_loss, test_iteration, test_loss, LOGSCALE,
XLIM, YLIM, log_base)
plot_loss(train_iteration, train_loss_smooth, test_iteration,
test_loss_smooth, LOGSCALE, XLIM, YLIM, log_base + '_smooth')
def main():
output_data, log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
train_accuracy0 = []
train_accuracy1 = []
train_accuracy2 = []
train_accuracy3 = []
train_accuracy4 = []
train_accuracy5 = []
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
for line in f:
if strstr(line, 'Iteration') and strstr(line, 'loss'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
matched = match_iteration(line)
train_iteration.append(
int(matched.group(1)) + base_train_iter)
# strong labels
elif strstr(line, 'Train net output #0: accuracy '):
matched = match_net_accuracy(line)
train_accuracy0.append(float(matched.group(1)))
elif strstr(line, 'Train net output #1: accuracy '):
matched = match_net_accuracy(line)
train_accuracy1.append(float(matched.group(1)))
elif strstr(line, 'Train net output #2: accuracy '):
matched = match_net_accuracy(line)
train_accuracy2.append(float(matched.group(1)))
# weak labels
elif strstr(line, 'Train net output #0: accuracy_bbox'):
matched = match_net_accuracy_bbox(line)
train_accuracy0.append(float(matched.group(1)))
elif strstr(line, 'Train net output #1: accuracy_bbox'):
matched = match_net_accuracy_bbox(line)
train_accuracy1.append(float(matched.group(1)))
elif strstr(line, 'Train net output #2: accuracy_bbox'):
matched = match_net_accuracy_bbox(line)
train_accuracy2.append(float(matched.group(1)))
elif strstr(line, 'Train net output #3: accuracy_strong'):
matched = match_net_accuracy_strong(line)
train_accuracy3.append(float(matched.group(1)))
elif strstr(line, 'Train net output #4: accuracy_strong'):
matched = match_net_accuracy_strong(line)
train_accuracy4.append(float(matched.group(1)))
elif strstr(line, 'Train net output #5: accuracy_strong'):
matched = match_net_accuracy_strong(line)
train_accuracy5.append(float(matched.group(1)))
if output_data == 'loss':
for x in train_loss:
print(x)
if output_data == 'acc1':
for x, y, z in zip(train_accuracy0, train_accuracy1, train_accuracy2):
print(x, y, z)
if output_data == 'acc2':
for x, y, z in zip(train_accuracy3, train_accuracy4, train_accuracy5):
print(x, y, z)
## loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
## evaluation
plt.clf()
if len(train_accuracy3) != 0:
plt.plot(range(len(train_accuracy0)),
train_accuracy0,
'k',
label='accuracy bbox 0')
plt.plot(range(len(train_accuracy1)),
train_accuracy1,
'r',
label='accuracy bbox 1')
plt.plot(range(len(train_accuracy2)),
train_accuracy2,
'g',
label='accuracy bbox 2')
plt.plot(range(len(train_accuracy3)),
train_accuracy3,
'b',
label='accuracy strong 0')
plt.plot(range(len(train_accuracy4)),
train_accuracy4,
'c',
label='accuracy strong 1')
plt.plot(range(len(train_accuracy5)),
train_accuracy5,
'm',
label='accuracy strong 2')
else:
plt.plot(range(len(train_accuracy0)),
train_accuracy0,
'k',
label='train accuracy 0')
plt.plot(range(len(train_accuracy1)),
train_accuracy1,
'r',
label='train accuracy 1')
plt.plot(range(len(train_accuracy2)),
train_accuracy2,
'g',
label='train accuracy 2')
plt.legend(loc=0)
plt.savefig('evaluation.png')
def main():
log_files = process_arguments(sys.argv)
train_iteration = []
train_loss = []
lr = []
test_iteration = []
test_loss = []
test_accuracy = []
top1_accuracy = []
top5_accuracy = []
base_test_iter = 0
base_train_iter = 0
for log_file in log_files:
with open(log_file, 'rb') as f:
if len(train_iteration) != 0:
base_train_iter = train_iteration[-1]
base_test_iter = test_iteration[-1]
for line in f:
# TRAIN NET
if strstr(line, 'Iteration') and strstr(line, 'lr'):
matched = match_iteration(line)
train_iteration.append(int(matched.group(1)))
matched = match_lr(line)
lr.append(float(matched.group(1)))
elif strstr(line, 'Train net output'):
matched = match_loss(line)
train_loss.append(float(matched.group(1)))
# TEST NET
elif strstr(line, 'Iteration') and strstr(line, 'Testing net'):
matched = match_iteration(line)
test_iteration.append(int(matched.group(1)))
elif strstr(line, 'Test net output #2'):
matched = match_loss(line)
test_loss.append(float(matched.group(1)))
elif strstr(line, 'Test net output #0'):
matched = match_top1(line)
top1_accuracy.append(float(matched.group(1)))
elif strstr(line, 'Test net output #1'):
matched = match_top5(line)
top5_accuracy.append(float(matched.group(1)))
print("TRAIN", train_iteration, train_loss)
print("TEST", test_iteration, test_loss)
print("LEARNING RATE", train_iteration, lr)
print("TOP1_ACCURACY", test_iteration, top1_accuracy)
print("TOP5_ACCURACY", test_iteration, top5_accuracy)
# loss
plt.plot(train_iteration, train_loss, 'k', label='Train loss')
plt.plot(test_iteration, test_loss, 'r', label='Test loss')
plt.legend()
plt.ylabel('Loss')
plt.xlabel('Number of iterations')
plt.savefig('loss.png')
plt.show()
# learning rate
plt.clf()
plt.plot(train_iteration, lr, 'g', label='Learning rate')
plt.legend()
plt.ylabel('Learning rate')
plt.xlabel('Number of iterations')
plt.savefig('lr.png')
plt.show()
# evaluation
plt.clf()
plt.plot(test_iteration, top1_accuracy, 'm', label='Top-1 accuracy')
plt.plot(test_iteration, top5_accuracy, 'c', label='Top-5 accuracy')
plt.legend(loc=0)
plt.savefig('evaluation.png')
plt.show()