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import os
import time
import pprint
import torch
from sklearn.metrics import confusion_matrix
def set_gpu(x):
os.environ['CUDA_VISIBLE_DEVICES'] = x
print('using gpu:', x)
def check_dir(path):
'''
Create directory if it does not exist.
path: Path of directory.
'''
if not os.path.exists(path):
os.mkdir(path)
def count_accuracy(logits, label):
pred = torch.argmax(logits, dim=1).view(-1)
label = label.view(-1)
acc = [0 for c in range(3)]
for c in range(3):
acc[c] = (pred.eq(label) * label.eq(c)).float() / max((label.eq(c)).sum(), 1)
matrix = confusion_matrix(label.cpu().detach().numpy(), pred.cpu().detach().numpy())
pca = matrix.diagonal()/matrix.sum(axis=1)
accuracy = 100 * pred.eq(label).float().mean()
return accuracy, pca * 100
class Timer():
def __init__(self):
self.o = time.time()
def measure(self, p=1):
x = (time.time() - self.o) / float(p)
x = int(x)
if x >= 3600:
return '{:.1f}h'.format(x / 3600)
if x >= 60:
return '{}m'.format(round(x / 60))
return '{}s'.format(x)
def log(log_file_path, string):
'''
Write one line of log into screen and file.
log_file_path: Path of log file.
string: String to write in log file.
'''
with open(log_file_path, 'a+') as f:
f.write(string + '\n')
f.flush()
print(string) |